With the rapid development of the Internet of Things, massive amounts of data will be generated at the network edge, resulting in increased latency of the traditional cloud computing model. Cloud service providers deploy edge servers near the users to improve the quality of service. Due to the large scale of networks, the inefficiency of edge server deployment and task scheduling will result in excessive latency and severe workload imbalance among edge servers. In this paper, we propose a Server Deployment and Task Scheduling (SDTS) framework in multi-edge collaborative computing, which can effectively reduce latency and ensure workload balancing. First, a metropolitan area network (MAN) is divided into dense candidate regions (DCRs) based on the density characteristics of mobile end devices, and then servers are deployed independently in each sub-region. Then, a two-step clustering algorithm is designed to deploy servers as close as possible to the mobile devices. Finally, a task scheduling algorithm is presented based on an edge scheduling layer in DCRs, which can leverage collaboration between edge servers to improve the performance of the entire edge computing system. The experimental results based on the Cellular Base Station (CBS) dataset of Shanghai Telecom indicate that the proposed approach outperforms several representative strategies in terms of latency and workload balancing.

The Internet of Things (IoT) generates a large volume of data whenever devices are interconnected and exchange data across a network. As a result, there is a range of services with varying needs, for example, capacity requirements, data quality, and latency demands. These services operate on fog computing devices, which are limited in power and bandwidth compared to the cloud. The main challenge is deciding where to implement services on the fog, the cloud, or the hybrid. This paper proposes an efficient allocation technique that pushes processing closer to the network's fog side. It investigates which devices and services may best allocate while preserving resource usage in the IoT architecture. It also examines the importance of allocating services to devices and optimizing resource use in fog computing. In IoT settings, there is a wide range of services and devices; thus, it is critical to effectively assign the services to the devices. We propose Priority-based Service allocation (PSA) and Sort-based Service Allocation (SSA) techniques, which are used to enable an optimum order to employ devices to perform the various services. The experimental results indicate that our proposed technique minimize the data communication over the network by 82% by allocating most of the services locally in fog. We have maximized the number of distributed services to fog devices by 90% while minimizing the wastage of fog resources.

Cloud computing has revolutionized how industries store, process, and access data. However, the increasing adoption of cloud technology has also raised concerns regarding data security. Machine learning (ML) is a promising technique to enhance cloud computing security. This paper focuses on utilizing ML techniques (Support Vector Machine, XGBoost, and Artificial Neural Networks) to progress cloud computing security in the industry. The selection of 11 important features for the ML study satisfies the study's objectives. This study focused on identifying gaps in utilizing ML techniques in cloud cyber security. Moreover. this study aims at developing a practical strategy for predicting the employment of machine learning in an Industrial Cloud environment regarding trust and privacy issues. The efficiency of the employed models is assessed by applying validation matrices of Precision, Accuracy, Recall values, F1 score, R.O.C. curves, and Confusion matrix. The results demonstrated that the X.G.B. model outperformed in terms of all the matrices with an Accuracy of 97.50 %, 97.60 % Precision, 97.60 % Recall values, and 97.50 % F1 score. This research highlights the potential of ML algorithms in enhancing cloud computing security for industries. It emphasizes the need for continued research and development to create more advanced and efficient security solutions for cloud computing.

A full connected world is expected to gain in the 6th generation mobile network (6G). As a typi-cal fully connected scenario, the internet of vehicle (IoV) enables intelligent vehicle operations via artificial intelligence (AI) and edge computing technologies. In the future of vehicular net-works, wide variety of services need powerful computing resources and higher quality of ser-vice (QoS). Existing resources are insufficient to match these requirements. Aim to this problem, An intelligent service offloading framework is provided. Based on the framework, an Algorithm of Improved Gradient Descent (AIGD) is created to accelerate the speed of iteration. So, the con-vergence of convolutional neural network (CNN) based on AIGD is able to be accelerated too. Then, an Algorithm of convolutional long short-term memory (CN_LSTM) Based Traffic Predic-tion (ACLBTP) is designed to gain the predicted number of vehicles belonged to the edge node. At last, an Algorithm of Service Offloading Based on CN_LSTM (ASOBCL) is conducted to of-fload these services to the vehicles belonged to the edge node. In ASOBCL, sorting technique is adopted to speed up the offloading work. Simulation results demonstrate the fact that the pre-diction strategy designed in this paper has high accuracy. The low offloading time and main-taining stable load balance is gained via running ASOBCL. Low offloading time means short response time. And, the QoS is guaranteed. So, these strategies designed in this paper are effec-tive and valuable.

Multimodal user interfaces promise a natural and intuitive human machine interactions. But is the extra effort for the development of a complex multi-sensor system justified, or can users also be satisfied with one input modality already? This study investigates interactions in an industrial weld inspection workstation. Three unimodal interfaces, including spatial interaction with buttons augmented on a workpiece or a worktable, and speech commands, were tested individually and in a multimodal combination. Within the unimodal conditions, users preferred the augmented worktable, but overall, the interindividual usage of all input technologies in the multimodal condition was ranked best. Our findings indicate that the implementation and use of multiple input modalities is valuable, and that it is difficult to predict the usability of individual input modalities for complex systems.

This paper presents some aspects of command and control of the new generation of machines-tools with numerical control. A problem faced by numerical control programmers is related to the length (relatively long) of the programs, with implications in the costs and duration of the manufacturing preparation. A method of reducing the main program and other redundant expenses is the introduction and use of processing cycles and subroutines for automatic calculation of the cutting tool trajectory, a fact that also allows the optimization and simplification of numerical control programming on flexible order manufacturing systems numeric. The method involves the use of predefined cycles and appropriate subroutines in order to improve the efficiency of the programming activity in a balanced way. It is a new and rapid method for optimize the structure of program using the numerical subroutine for command the movement of tool, in our case, with application for turning and milling.

The rapid development of quantum computing devices promises powerful machines with capabilities that solve a wide range of problems that traditional computers cannot. Therefore, quantum computers generate new threats at unprecedented speed and scale and specifically pose an enormous threat to encryption. Lattice-based cryptography is considered to be the rival to a quantum computer attack and the future of post-quantum cryptography. So, cryptographic protocols based on lattices have a variety of benefits, like security, efficiency, lower energy consumption, and speed. In this work, we study the most well-known lattice-based cryptosystems while a systematic evaluation and comparison is presented also, and focuses on their strengths and weaknesses.

The drug discovery process is a rigorous and time-consuming endeavor, typically requiring several years of extensive research and development. Although classical machine learning (ML) has proven successful in this field, its computational demands in terms of speed and resources are significant. In recent years, researchers have sought to explore the potential benefits of quantum computing (QC) in the context of ML, leading to the emergence of Quantum Machine Learning (QML) as a distinct research field. The objective of the current study is twofold: first, to present a review of the proposed QML algorithms for application in the drug discovery pipeline, and second, to compare QML algorithms with their classical and hybrid counterparts in terms of their efficiency. A query-based search of various databases took place, and five different categories of algorithms were identified in which QML was implemented. The majority of QML applications in drug discovery are primarily focused on the initial stages of the drug discovery pipeline, particularly with regard to the identification of novel drug-like molecules. Comparison results revealed that QML algorithms are strong rivals against the classical ones and a hybrid solution is the recommended approach at present.

With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP

In order to enable patients with severe disabilities who have lost their limbs to accurately control their wheelchair movement and optimize the power loss of smart wearable tongue control devices, a non-invasive intraoral stand-alone tongue control system based on RSIC-V edge computing is proposed in this paper. Patients can detect the pressure signal of the tongue muscles’ touching the palate by placing the flexible carbon nanotube-PDMS pressure sensor array in the mouth, and the erroneous movement signal can be avoided by setting the pressure threshold, thereby improving the reliability of the device and the user's experience. The system converts the pressure data into specific control instructions on the edge of the RSIC-V chip and transmits them to the receiver, thus reducing the transmission of data. In this experiment, the speed response time test, the center click task and the power consumption experiment are carried out, whose results show that the adaptive fuzzy PID control algorithm has good robustness in the system: when the DC motor with a given speed of 750r/min reaches the steady state, its rise time is 0.108s and the adjustment time is 0.59s. The dynamic power consumption of the non-intrusive intraoral stand-alone tongue control system proposed in this paper is tested to be 3.745 MW, which is 11.5% lower than the total power consumption of the sTDs system.

Large-scale land cover plays a crucial role in global resource monitoring and management, as well as research on sustainable development. However, the complexity of the mapping process, coupled with significant computational and data storage requirements, often leads to delays between data processing and product publication, creating challenges for dynamic monitoring of large-scale land cover. Therefore, improving the efficiency of each stage in large-scale land cover mapping and automating the mapping process is currently an urgent and critical issue that needs to be addressed. We propose a high-performance automated large-scale land cover mapping framework(HALF) that introduces high-performance computing technology to the field of land cover production. HALF optimizes key processes, such as automated sample point extraction, sample-remote sensing image matching, and large-scale classification result mosaic and update. We selected several 10°×10° regions globally and the research makes several significant contributions:(1)We design HALF for land cover mapping based on docker and CWL-Airflow, which solves the heterogeneity of models between complex processes in land cover mapping and simplifies the model deployment process. By introducing workflow organization, this method achieves a high degree of decoupling between the production models of each stage and the overall process, enhancing the scalability of the framework. (2)HALF propose an automatic sample points method that generates a large number of samples by overlaying and analyzing multiple prior products, thus saving the cost of manual sample selection. Using high-performance computing technology improved the computational efficiency of sample-image matching and feature extraction phase, with 10 times faster than traditional matching methods.(3)HALF propose a high-performance classification result mosaic method based on the idea of grid division. By quickly establishing the spatial relationship between the image and the product and performing parallel computing, the efficiency of the mosaicking in large areas is significantly improved. The average processing time for a single image is around 6.5 seconds.

We find at least 4 quantum properties that are indeed universal in some number systems; and we can use them for quantum consciousness. Using the sets B={0,1}, N, Z, or Q has the +4 quantum properties that one can trust, as archetypes of easy communication to friends and foes, to ignoranti or cognoscenti, and for quantum computing. This paper proposes the use of Bayesian geometry to reduce the space of possible results, reducing or increasing initial beliefs by successive experimentation. This is exemplified in biology and mathematics, and can be applied to physics and other sciences. The conclusion is that an experimental fact can be absolutely verified. Any mathematical choice (as verified by computers) militating against R=Q, is under an absolutely low chance to succeed. Infinitesimals are denied based on 3^16 independent factors. This is not a probabilistic result, but based on reduction of the Bayes geometry for updating beliefs. This work applies to non-probabilistic and unknown phenomena, such as those found in oscillations (neutrino, health), evolution, and quantum computing, denies Gödel's uncertainty, and solves the liar's paradox by reflection. This connects different scientific fields with one another, in an internet, i.e., in a network of connections of multiple open networks, using the set Q, resembling a hologram, and includes entanglement.

With the proliferating of video surveillance system deployment and related applications, real-time video analysis is very critical to achieve intelligent monitoring, autonomous driving, etc. It is non-trivial to achieve high accuracy and low latency video stream analysis through the traditional cloud computing. In this paper, we propose a non-orthogonal multiple access (NOMA) based edge real-time video analysis framework with one edge server (ES) and multiple user equipments (UEs). A cost minimization problem composed of delay, energy and accuracy is formulated to improve the QoE of UEs. In order to efficiently solve this problem, we propose the joint video frame resolution scaling, task offloading, and resource allocation algorithm based on the Deep Q-Learning Network (JVFRS-TO-RA-DQN), which effectively overcomes the sparsity of the single-layer reward function and accelerates the training convergence speed. JVFRS-TO-RA-DQN consists of two DQN networks to reduce the curse of dimension, which respectively select the offloading and resource allocation action, the resolution scaling action. Experimental results show that JVFRS-TO-RA-DQN can effectively reduce the cost of transmission and computation, and have better performance in convergence compared to other baseline.

The goal of finding a commutative group of gates from the set of unitary matrices is to simplify the process of quantum computing. If a set of gates is commutative, then we can apply the gates in any order without affecting the final outcome. This is important because quantum systems are very sensitive to errors and decoherence, and any disruption to the system can cause errors in the computation.By having a commutative group of gates, we can simplify the process of designing quantum algorithms and reduce the risk of errors. This is particularly important for solving complex computational problems that cannot be solved by classical computers.

We present a new vision on the double-slit experiment (DSE), that does not use any waves or the so-called wave-particle duality. It is based on particles, viewed as localized vibrations. Rather than being illogical, or mysterious, the approach reproduces logically the interference patterns observed externally, using the well-known ``amplification of stimulated emission'' (ASE). The conservation of particle number is not observed. Different particles can present ASE with in-phase or counter-phase behavior, making the patterns observed for electrons, other particles, even atoms. The Law of the Excluded Middle (LEM) is not broken illogically in two-states macroscopically, but broken naturally in three or more states, microscopically, using ASE. Any particle supporting ASE, in-phase or counter-phase, can enter a DSE, and provide an external interference pattern. Frequency plays a role, while amplitude is not considered. This work is important in quantum computing, possibly saving cost, time, and welcoming more participants.

The article presents a new scalable iterative method for linear programming called the “apex method”. The key feature of this method is constructing a path close to optimal on the surface of the feasible region from a certain starting point to the exact solution of linear programming problem. The optimal path refers to a path of minimum length according to the Euclidean metric. The apex method is based on the predictor-corrector framework and proceeds in two stages: quest (predictor) and target (corrector). The quest stage calculates a rough initial approximation of linear programming problem. The target stage refines the initial approximation with a given precision. The main operation used in the apex method is an operation that calculates the pseudoprojection, which is a generalization of the metric projection to a convex closed set. This operation is used both in the quest stage and in the target stage. A parallel algorithm using a Fejér mapping to compute the pseudoprojection is presented. An analytical estimation of the parallelism degree of this algorithm is obtained. Also, an algorithm implementing the target stage is given. The convergence of this algorithm is proven. An experimental study of the scalability of the apex method on a cluster computing system is described. The results of applying the apex method to solve problems from the Netlib-LP repository are presented.

Condition monitoring is a primary principle for predictive maintenance. In this regard, several techniques have been employed, each of which has its strengths and weaknesses. In this work, the relationships between the electrical (ε', ε", tan δ) and chemical (Fe, Pb, Cu, Cr, Al, Si, Zn) properties of engine lubricants were investigated using soft computing models. The models' performance was evaluated considering some criteria, including RMSE and MAPE. The RBF model was determined as the best one to predict chemical properties (Fe, Pb, Cu, Cr, Al, Si, Zn) through electrical indexes (ε', ε", tan δ). The RBF’s parameters (e.g., hidden size and training algorithm) were then optimized for optimum performance. For instance, in the RBF model implemented to predict Al, the best hidden size and training algorithmic were 15 and 'trainlm', respectively. Finally, by sensitivity analysis it was observed that eliminating any of the inputs does not improve the performance of the RBF model performance. In general, the results of the current research showed that the electrical properties (e.g., ε', ε", tan δ) have the capability of estimating the contaminants in the lubricants which can improve the existing monitoring methods.

This study describes the development of an image-based insect trap diverging from the plug-in camera insect trap paradigm. In short, a) it does not require manual annotation of images to learn how to count targeted pests and, b) it self-disposes the captured insects, and therefore is suitable for long-term deployment. The device consists of an imaging sensor integrated with Raspberry Pi microcontroller units with embedded deep learning algorithms that count agricultural pests inside a pheromone-based funnel trap. The device also receives commands from the server which configures its operation while an embedded servomotor can automatically rotate the detached bottom of the bucket to dispose of hydrated insects as they begin to pile up. Therefore, it completely overcomes a major limitation of camera-based insect traps: the inevitable overlap and occlusion caused by the decay and layering of insects during long-term operation, thus extending the autonomous operational capability. We study cases that are underrepresented in literature such as counting in situations of congestion and significant debris using crowd counting algorithms encountered in human surveillance. Finally, we perform comparative analysis of the results from different deep-learning approaches (YOLO7/8, crowd counting, deep learning regression) and we open-source the code and a large database of Lepidopteran plant pests.

Whether it’s cellphones, personal computers, or gaming consoles, technology is a part of everyone’s everyday lives, and storage is frequently a problem. One of the solutions we have for this difficulty is the on-demand accessibility of computer framework assets, which enables cloud storage and makes it accessible in any format and from any device. It is a major benefit of cloud computing. Amazon Web Administrations (or basically AWS) may be a secure cloud services stage advertising about everything businesses ought to construct advanced applications with adaptability, versatility, and unwavering quality. Another advantage is that it is significantly less expensive than purchasing items with comparable functionality, such as an SSD storage device. For many enterprises, it is also preferable to host their servers in the cloud using services like Google Cloud and Oracle Cloud. Our main topic of the paper is to compare three different major cloud computing services—AWS, Google, and Azure. Since there are different types of cloud computing services available, we would compare them to determine which is best for usage by individuals or organizations. We can also look at the services’ shared features and unique aspects that they provide to consumers.

Revealing the time structure of physical or biological objects is usually performed thanks to the tools of signal processing like the fast Fourier transform, Ramanujan sum signal processing and many other techniques. For space-time topological objects in physics and biology, we propose a type of algebraic processing based on schemes in which the discrimination of singularities within objects is based on the space-time-spin group SL(2,C). Such topological objects possess an homotopy structure encoded in their fundamental group and the related SL(2,C) multivariate polynomial character variety contains a plethora of singularities somehow analogous to the frequency spectrum in time structures. Our approach is applied to an Akbulut cork in exotic R4, to an hyperbolic model of topological quantum computing based on algebraic surfaces and to microRNAs in genetics. Such diverse topics reveal the manifold of possibilities of using the concept of a scheme spectrum.

In recent years, Cloud Computing and Big Data have been considered the most attractive areas that are revolutionizing the IT world. Cloud Computing paradigm has recently appeared that allows running proprietary or difficult portable applications outside their original software environment on one or more virtual hardware platforms. Therefore, we are to developing such techniques which make it possible to secure communication between the communicating Cloud entities. These techniques must take into account several factors due to the data transmitted in this type of environment is proprietary and of significant size. Conventional data security techniques are not suitable for today's cloud usage. Hence, the main research of this thesis is to define an adaptable architecture with the aim to propose a scalable system that supports cloud services. We will define feasible security solutions dedicated to the Cloud computing context in order to robustly protect data stored in the Cloud. We are more precisely looking for working on NoSQL databases. We also intend to propose a secure solution based on the blockchain that has powerful features like decentralization, autonomy, security, reliability, and transparency.

Recently, a layer-stacked ESN model named deep echo state Network (DeepESN) has been established. As an interactional model of recurrent neural network and deep neural network, investigations of DeepESN are of significant importance in both areas. Optimizing the structure of neural networks remains a common task in artificial neural networks, and the question of how many neurons should be used in each layer of DeepESN must be stressed. In this paper, our aim is to solve the problem of choosing the optimized size of DeepESN. Inspired by the sensitive iterative pruning algorithm, a neuronal similarity-based iterative pruning merging algorithm (NS-IPMA) is proposed to iteratively prune or merge the most similar neurons in DeepESN. Two chaotic time series prediction tasks are applied to demonstrate the effectiveness of NS-IPMA. The results show that the DeepESN pruned by NS-IPMA outperforms unpruned DeepESN with the same network size, and NS-IPMA is a feasible and superior approach to improving the generalization performance of DeepESN.

This paper aims at computing feasible control strategies and the corresponding feasible state trajectories to drive an autonomous rear-axle bicycle robot from a given initial state to a final state such that the total running cost is minimized. Pontryagin’s Minimum Principle is applied and derives the optimality conditions from which the feasible control functions, expressed as functions of state and costate variables, are substituted into the combined state-costate system to obtain a new free-control state-costate nonlinear system of ordinary differential equations. A computer program was written in Scilab to solve the combined state-costate system and obtain the feasible state functions, the feasible costate functions and the feasible control functions. Associated Computational Simulations were provided to show the effectiveness and the reliability of the approach.

Advanced state-of-the-art technologies, mainly computational intelligence including Machine/Deep Learning and Fuzzy Computing, through applied research, can provide added value to modern science and, in general, to entrepreneurship and the economy. Artificial intelligence is the field of computer science that attempts to model concepts such as learning, adaptability and perception to synthesize intelligent behavior in solving complex problems, utilizing elements of adaptation to the environment and philosophical reasoning. About the science of civil engineering and, in general, the construction industry, which is one of the most important in economic entrepreneurship, both in terms of the size of the workforce employed and the amount of capital invested, the penetration of artificial intelligence is possible to change industry business models, eliminate costly mistakes, reduce Jobsite injuries and generally make large-scale engineering projects more efficient. The purpose of the paper is to present the recent research on artificial intelligence methods (machine-deep learning, computer vision, natural language processing, fuzzy systems, robotics, etc.) and the corresponding related technologies related to it (extensive data analysis, blockchain, cloud computing, internet of things, augmented reality), in the fields of application of civil engineering science, including structural engineering, geotechnical engineering, hydraulics and water resources management, marine and coastal technology, transport and transportation infrastructure, planning and technical project management, critical infrastructure security and disaster mitigation..

Rescaling of rainfall requires measurements of rainfall rates over many dimensions. This paper develops one approach using 10 m vertical spatial observations of the Doppler spectra of falling rain every 10 seconds over intervals varying from 15 up to 41minutes in two different locations and in two different years using two different Micro-Rain Radars (MRR). The transformation of the temporal domain into spatial observations uses the Taylor ‘frozen’ turbulence hypothesis to estimate an average advection speed over an entire observation interval. Thus, when no other advection estimates are possible, this paper offers a new approach for estimating the appropriate frozen turbulence advection speed by minimizing power spectral differences between the ensemble of purely spatial radial power spectra observed at all times in the vertical and those using the ensemble of temporal spectra at all heights to yield statistically reliable scaling relations. Thus, it is likely that, MRR and other vertically pointing Doppler radars may often help to obviate the need for expensive and immobile large networks of instruments in order to determine such scaling relations, but not the need of those radars for surveillance.

This paper introduces a novel 3D NoC router that combines buffered and bufferless routing with approximate priority comparison when deflecting flits. Our proposal is a modification of an asymmetrical router that is buffered in the z dimension ports and bufferless in the x and y dimension ports. Flits that request output ports in the x and y dimensions are granted or deflected based on approximate, instead of accurate, priority comparison. Experimental results show that the proposed router, besides effectively combining the advantages of both buffered and bufferless routers, it achieves additional performance and area gains due to the reduced logic required for approximate priority comparison in flit deflections. Experimental results using synthetic and realistic traffic show that the proposed router begins to saturate at a sifnificantly higher injection rate than a bufferless router, but at a slightly lower injection rate than when using accurate priority comparison. Furthermore, the proposed router achieves higher clock frequencies and reduced area compared to the simpler permutation network.

Optical microcombs represent a new paradigm for generating laser frequency combs based on compact chip-scale devices, which have underpinned many modern technological advances for both fundamental science and industrial applications. Along with the surge in activity related to optical micro-combs in the past decade, their applications have also experienced rapid progress ‒ not only in traditional fields such as frequency synthesis, signal processing, and optical communications, but also in new interdisciplinary fields spanning the frontiers of light detection and ranging (LiDAR), astronomical detection, neuromorphic computing, and quantum optics. This paper reviews the applications of optical microcombs. First, an overview of the devices and methods for generating optical microcombs is provided, which are categorized into material platforms, device architectures, soliton classes, and driving mechanisms. Second, the broad applications of optical microcombs are systematically reviewed, which are categorized into microwave photonics, optical communications, precision measurements, neuromorphic computing, and quantum optics. Finally, the current challenges and future perspectives are discussed.

Having access to a unique quantum circuit that one wants to protect against use by others is a very likely scenario. However currently users rely on classical computer security schemes, which have known shortcomings. In this chapter we introduce a novel protection scheme along with a survey of other known methods to protect quantum information. In particular we review physically unclonable functions (PUFs), obfuscation, and introduce quantum logic locking.

In this discussion paper, I give an account for non-experts of, arguably, quantum dynamics in the brain, underlying the modeling of affective behavior of humanoid robots in the making. Outreach to the larger audience inevitably leads to abbreviations and simplifications; nonetheless, I try to offer the backgrounds of why it is important to study the virtual aspects of ‘people’ we meet online, what dimensions play a role in assessing such creatures, what humanities, psychology, communication, and computer science provide to help us understand how we become attached to non-existent others. As its capstone for the time being, an approach derived from physics is discussed for a robot to handle emotional ambiguity and vagueness of its user. Two computational models, Silicon and Quantum Coppélia, are discussed for their potential and limitations in explaining human affective behavior while dealing with mediated characters.

Why do neurons communicate through spikes? By definition, spikes are all-or-none neural events which occur at continuous times. In other words, spikes are on one side binary, existing or not without further details, and on the other can occur at any asynchronous time, without the need for a centralized clock. This stands in stark contrast to the analog representation of values and the discretized timing classically used in digital processing and at the base of modern-day neural networks. As neural systems almost systematically use this so-called event-based representation in the living world, a better understanding of this phenomenon remains a fundamental challenge in neurobiology in order to better interpret the profusion of recorded data. With the growing need for intelligent embedded systems, it also emerges as a new computing paradigm to enable the efficient operation of a new class of sensors and event-based computers, called neuromorphic, which could enable significant gains in computation time and energy consumption — a major societal issue in the era of the digital economy and global warming. In this review paper, we provide evidence from biology, theory and engineering that the precise timing of spikes plays a crucial role in our understanding of the efficiency of neural networks.

With the rapid development of the Internet of Things (IoT), there are dramatic increasing number of devices in the network, which causes the challenge that only using infrastructure such as based station cannot provide service with all devices with high quality. Therefore, due to their flexibility and economy, unmanned aerial vehicles (UAV) are widely used to increase the performance of IoT networks. UAVs can not only provide communication services for IoT devices in the absence of a network, but they can also perform video surveillance, cargo transportation, pesticide spraying, and other specialized tasks. However, due to the complexity of the scenario and the need for real-time decision making, it is challenging to schedule UAVs in the network using traditional optimization methods, and growing attention has focused on using AI to optimize UAVs in the network. In this paper, we focus on the AI-enabled UAV optimization method in IoT networks and give a comprehensive scope on what and how to use AI-enabled methods to increase the performance of UAV-assisted IoT networks. Moreover, a brief analysis of the challenges of using AI methods in IoT networks and some potential research directions are given.

Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

Good estimators are characterized as robust, unbiased, efficient, and consistent. However, the commonly used estimators are weak or lack one or more of these properties. In this article, eight (8) estimators for statistical and geometrical estimations of symmetry/asymmetry, similarity/dissimilarity, identity/unidentity, and feature transformation were proposed following Kabirian-based optinalysis and other operations. The proposed estimators are characterized as invariant (robust) under scaling, location shift, and rotation or reflection. A computing code was written in python language for each of the proposed estimators so that peers can have working codes for application and performance evaluation.

1) We shall discuss what modern interpretations say about the Heisenberg's uncertainties. These interpretations explain that a quantity begins to 'lose' meaning when a conjugate property begins to 'acquire' definite meaning. We know that a quantity losing meaning means that it has no fixed value and has an uncertainty . In this paper we look deeper into this interpretation and the outcome reveals more evidence of the quantity losing meaning. Newer insights appear. 2) We consider two extreme cases of hypothetical processes nature undergoes, without interference by a measurement: One, a system collapses to an energy eigenstate under the influence of a Hamiltonian instantaneously at a time $t$. This is thus what would happen if we would measure the system's energy. Next, when a particle becomes localised to a point at a time $t_0$ under the influence of a Hamiltonian. This is thus what would happen if we would measure the system's position. We shall prove that both these situations cannot arise under ordinary circumstances and thus measurement processes cannot be modelled by physical Hamiltonians.

In this paper we present a successful implementation of the Hindmarsh–Rose model within a SC environment. The merits of the proposed approach are design simplicity, due to stochastic computing, and the ease of implementation. Simulation results showed that the approximation achieved is equivalent to introducing a noise source into the original model. A study for the level of noise introduced, according to the number of bits in the stochastic sequence, has been performed. Additionally, we demonstrate that such an approach, even though it is noisy, it reproduces the behaviour of biological systems, which are intrinsically noisy. It is also demonstrated that a speedup of x2 compared to biological systems is easily achievable, with a very small number of gates, thus paving the road for the in silico implementation of large neuron networks.

The conceptual and theoretical backbones of symmetry/asymmetry detections or similarity/dissimilarity, and identity/unidentity study are automorphism or isomorphism respectively. However, the development of equations and methods for symmetry/asymmetry detections, similarity/dissimilarity, and identity/unidentity measures deviates from these backbones. In this article, an equation was proposed for symmetry/asymmetry detections, similarity/dissimilarity, and identity/unidentity measures, and proved that its isoreflective pairs-points are functionally bijective and inverse. The proposal, called Kabirian-based optinalysis, is based on the conceptual and theoretical frameworks of automorphism and isomorphism. The Kabirian-based optinalysis is also proven and characterized as invariant (robust) under translation (i.e., scaling and location shift), and rotation or reflection. Computing codes were written in python language for Kabirian-based optinalysis to serve as working codes for application and verification.

Music is capable of conveying many emotions. The level and type of emotion of the music perceived by a listener, however, is highly subjective. In this study, we present the Music Emotion Recognition with Profile information dataset (MERP). This database was collected through Amazon Mechanical Turk (MTurk) and features dynamical valence and arousal ratings of 54 selected full-length songs. The dataset contains music features, as well as user profile information of the annotators. The songs were selected from the Free Music Archive using an innovative method (a Triple Neural Network with the OpenSmile toolkit) to identify 50 songs with the most distinctive emotions. Specifically, the songs were chosen to fully cover the four quadrants of the valence arousal space. Four additional songs were selected from DEAM to act as a benchmark in this study and filter out low quality ratings. A total of 277 participants participated in annotating the dataset, and their demographic information, listening preferences, and musical background were recorded. We offer an extensive analysis of the resulting dataset, together with a baseline emotion prediction model based on a fully connected model and an LSTM model, for our newly proposed MERP dataset.

We recently proposed that topological quantum computing might be based on $SL(2,\mathbb{C})$ representations of the fundamental group $\pi_1(S^3\setminus K)$ for the complement of a link $K$ in the $3$-sphere. The restriction to links whose associated $SL(2,\mathbb{C})$ character variety $\mathcal{V}$ contains a Fricke surface $\kappa_d=xyz -x^2-y^2-z^2+d$ is desirable due to the connection of Fricke spaces to elementary topology. Taking $K$ as the Hopf link $L2a1$, one of the three arithmetic two-bridge links [the Whitehead link $5_1^2$, the Berge link $6_2^2$, the double-eight link $6_3^2$] or the link $7_3^2$, the $\mathcal{V}$ for those links contains the reducible component $\kappa_4$, the so-called Cayley cubic. In addition, the $\mathcal{V}$ for the later two links contains the irreducible component $\kappa_3$, or $\kappa_2$, respectively. Taking $\rho$ to be a representation with character $\kappa_d$ ($d<4$), with $|x|,|y|,|z| \le 2$, then $\rho(\pi_1)$ fixes a unique point in the hyperbolic space $\mathcal{H}_3$ and is conjugate to a $SU(2)$ representation (a qubit). Even though details on the physical implementation remain open, more generally, we show that topological quantum computing may be developed from the point of view of three-bridge links, the topology of the $4$-punctured sphere and Painlev\'e VI equation. The $0$-surgery on the $3$ circles of the Borromean rings L6a4 is taken as an example.

The development of quantum computers seeks software developers' attention regarding security in the era of Information Technology, software security is the primary goal for our quantitative assessment of software security in the development cycle of software. Security assessment of software is identifying the key security factors of the software. A security elective provides ex-tensive strategies and calculations to ensure product safety. The security assessment is the key factor in surveying, administering, and controlling security to further enhance the nature of safety. It should be acknowledged that assessing security early on in the development process is beneficial in identifying worms, hazards, flaws, and threats. The definition and portrayal of Quantum Computing (QC) in software security will be discussed in this study. Researchers use cryptography calculations to secure our financial institutions, medical devices, military weaponry, planes, ships, vehicles, and pilots. Here authors of this study use the Fuzzy Technique for Order Preference by Similarity to Ideal Situation (FTOPSIS) to quantitatively assess the weight/rank of the quantum enable security alternatives like (Diffie-Hellman key-exchange algorithm, Quantum key distribution algorithm, Deutsch-Jozsa Algorithm, Special Deutsch-Jozsa Algorithm, Grover’s Algorithm and Quantum key distribution algorithm in GHZ state) with security factors like (Confidentiality, Integrity, Authenti-cation, Privacy, Reliability, Maintainability, Authorization, Integrity, Possessions, and Availability). Additionally, they critically analyze and select the six alternatives of quantum-based security algorithms. The nature of safety infers the ability to execute a thing on time in this exploration study, specifically 'software security'.

The evolutionary systems (ES) include software applications that solve problems using heuristic methods instead of the deterministic ones. The classical computing used for ES development involves random methods to improve different kinds of genomes. The mappings of these genomes lead to individuals that correspond to the searched solutions. The individual evaluations by simulations serve for the improvement of their genotypes. Quantum computations, unlike the classical computations, can describe and simulate a large set of individuals simultaneously. This feature is used to diminish the time for finding the solutions. Quantum Petri Nets (QPNs) can model dynamical systems with probabilistic features that make them appropriate for the development of ES. Some examples of ES applications using the QPNs are given to show the benefits of the current approach.

This paper aims at computing feasible control strategies and the corresponding feasible state trajectories to drive an autonomous rear-axle bicycle robot from a given initial state to a final state such that the total running cost is minimized. Pontryagin’s Minimum Principle is applied and derives the optimality conditions from which the feasible control functions, expressed as functions of state and costate variables, are substituted into the combined state-costate system to obtain a new free-control state-costate nonlinear system of ordinary differential equations. A computer program was written in Scilab to solve the combined state-costate system and obtain the feasible state functions, the feasible costate functions and the feasible control functions. Associated Computational Simulations were provided to show the effectiveness and the reliability of the approach.

This paper develops feasible control strategies and associated system responses to bring an autonomous front-axle bicycle robot from specified initial conditions to final conditions such that a specific performance index is minimized. To solve the problem, the following approach is used: The feasible controls derived from the normal equations of optimality are substituted into the state and the costate systems and form a combined control-free state-costate system which is vectorized to enable and ease the application of a numerical method. A computer program written in Matlab computer programming language, codes a fourth-order Runge-Kutta numerical method and then solve the combined state-costate system of ordinary differential equations. The obtained results are the feasible bicycle robot trajectory, the feasible state functions, the feasible costate functions and the feasible control functions. Associated Computational Simulations are designed and provided to persuade on the effectiveness and the reliability of the approach.

The simultaneous advances in artificial neural networks and photonic integration technologies have spurred extensive research in optical computing and optical neural networks (ONNs). The potential to simultaneously exploit multiple physical dimensions of time, wavelength and space give ONNs the ability to achieve computing operations with high parallelism and large-data throughput. Different photonic multiplexing techniques based on these multiple degrees of freedom have enabled ONNs with large-scale interconnectivity and linear computing functions. Here, we review the recent advances of ONNs based on different approaches to photonic multiplexing, and present our outlook on key technologies needed to further advance these photonic multiplexing/hybrid-multiplexing techniques of ONNs.

Background: Cloud-native software systems often have a much more decentralized structure and many independently deployable and (horizontally) scalable components, making it more complicated to create a shared and consolidated picture of the overall decentralized system state. Today, observability is often understood as a triad of collecting and processing metrics, distributed tracing data, and logging. The result is often a complex observability system composed of three stovepipes whose data is difficult to correlate. Objective: This study analyzes whether these three historically emerged observability stovepipes of logs, metrics and distributed traces could be handled more integrated and with a more straightforward instrumentation approach. Method: This study applied an action research methodology used mainly in industry-academia collaboration and common in software engineering. The research design utilized iterative action research cycles, including one long-term use case. Results: This study presents a unified logging library for Python and a unified logging architecture that uses the structured logging approach. The evaluation shows that several thousand events per minute are easily processable. Conclusion: The results indicate that a unification of the current observability triad is possible without the necessity to develop utterly new toolchains.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

The Internet of Things (IoT) is experiencing widespread adoption across industry sectors ranging from supply chain management to smart cities, buildings, and health monitoring. However, most software architectures for IoT deployment rely on centralized cloud computing infrastructures to provide storage and computing power, as cloud providers have high economic incentives to organize their infrastructure into clusters. Despite these incentives, there has been a recent shift from centralized to decentralized architecture that harnesses the potential of edge devices, reduces network latency, and lowers infrastructure cost to support IoT applications. This shift has resulted in new edge computing architectures, but many still rely on centralized solutions for managing applications. A truly decentralized approach would offer interesting properties required for IoT use cases. To address these concerns, we introduce a decentralized architecture tailored for large scale deployments of peer-to-peer IoT sensor networks and capable of run-time application migration. The solution combines a blockchain consensus algorithm and verifiable random functions to ensure scalability, fault tolerance, transparency, and no single point of failure. We build on our previously presented theoretical simulations with many protocol improvements and an implementation tested in a use case related to monitoring a Slovenian cultural heritage building located in Bled, Slovenia.

Heart rate (HR) and respiratory rate (RR) are two vital parameters of the body medically used for diagnosing short/long term illness. Out-of-the-body, non-contact HR/RR measurement remains a challenge due to imprecise readings. “Invisible” wearables integrated into day-to-day garments has the potential to produce precise readings with comfortable user experience. Sleep studies and patient monitoring benefit from “Invisibles” due to longer wearability without significant discomfort. This paper suggests a novel method to reduce the footprint of sleep monitoring devices. We use a single silver-coated nylon fabric band integrated into a substrate of standard cotton/nylon garment as a resistive elastomer sensor to measure air and blood volume change across the chest. We introduce a novel event-based architecture to process data at the edge device and describe two algorithms to calculate real-time HR/RR on ARM Cortex-M3 and Cortex-M4F microcontrollers. RR estimations show a sensitivity of 99.03% and a precision of 99.03% for identifying individual respiratory peaks. The two algorithms used for HR calculation show a mean absolute error of 0.81±0.97 and 0.86±0.61 beats/minute compared to a gold standard ECG-based HR. The event-based algorithm converts the respiratory/pulse waveform into instantaneous events, therefore, reducing the data size by 40-140 times and requires 33% less power to process and transfer data. Further, we show that events hold enough information to reconstruct the original waveform, retaining pulse, and respiratory activity. We suggest fabric sensors and event-based algorithms would drastically reduce the device footprint and increase the performance for HR/RR estimations during sleep studies providing better user experience.

Building a widely distributed hotspot network is a very tedious task due to its complexity. Providing security, fully distributed network services, and cost-conscious impact are the major challenges behind this goal. To overcome these issues, we have presented a novel distributed hotspot network architecture with five layers that can provide large-scale hotspot coverage as an assimilated result. Our contributions to this new architecture highlight important aspects. First, scalability can be increased by including many Internet-of-Things (IoT) devices with sensors and Wi-Fi and/or LoraWAN connectivity modules. Second, hotspot owners can rent out their hotspots to create a distributed hotspot network in which the hotspots can act as an ordinary data gateway, a full-fledged hotspot miner, and a lightweight hotspot miner to earn crypto tokens as rewards for certain activities. Third, the advantages of Wi-Fi and LoraWAN can be seamlessly leveraged to achieve optimal coverage, higher network security, and suitable data transmission rate for transferring sensor data from IoT devices to remote application servers and users. Fourth, blockchain is used to enhance the decentralized behavior of the architecture presented here by providing immutability and independence from a centralized regulator and making the network architecture more reliable and transparent. The main feature of our paper is the use of the tau-computing paradigm along with hotspots to improve availability, Internet backhaul-agnostic network coverage, and synchronous update capability, and tau-aware leasing to strengthen and improve coverage. We also discuss the key challenges and future roadmap that require further investment and deployment.

Data augmentation techniques recently gained more adoption in speech processing, including speech emotion recognition. Although more data tends to be more effective, there may be a trade-off in which more data will not provide a better model. This paper reports experiments on investigating the effects of data augmentation in speech emotion recognition. The investigation aims at finding the most useful type of data augmentation and the number of data augmentations for speech emotion recognition. The experiments are conducted on the Japanese Twitter-based emotional speech corpus. The results show that for speaker-independent data, two data augmentations with glottal source extraction and silence removal exhibited the best performance among others, even with more data augmentation techniques. For the text-independent data (including speaker and text-independent), more data augmentations tend to improve speech emotion recognition performances. The results highlight the trade-off between the number of data augmentation and the performance of speech emotion recognition showing the necessity to choose a proper data augmentation technique for a specific application.

Nowadays the practice of developing algorithms to maintain the confidentiality of data shows that there is a lack of some features, such as velocity, predictability, etc. Generating pseudorandom numbers is one such problem that lies in the basement of many algorithms, even in hardware microprograms. An unreliable generator can cause cyberattacks on it, despite the security in the upper layers. At the same time, the algorithm should be fast enough to provide uninterrupted circuit work for the entire system. The paper presents a new algorithm generating pseudorandom numbers on cellular automata, which is not only fast and easy-repeating, but unpredictable enough and can be used in cryptographic systems. Using the NIST statistical test suite for random and pseudorandom number generators (PRNG), it is shown that the presented algorithm is more than three times superior to the state-of-the-art methods and algorithms in terms of ? − ?????. A high level of the presented algorithm’s parallelization allows for implementation it effectively on calculators with parallel structure. CPU-based architecture, FPGA-based architecture, CUDA- based architecture of PRNG and different PRNG implementations are presented to confirm high performance of the proposed solution.

The popularity of cloud computing is growing owing to its large data storage capacity and high computation power. It provides online, on-demand, scalable application solution, removes hardware and software barriers for non-specialist, rapidly integrates and deploys desired and necessary facilities, supports quick upgrading and addition of features. Users get benefitted with the selection of the appropriate cloud computing platform for their projects. Here, our paper provides a comprehensive overview of the services provided to the users by the most common cloud computing service providers. This paper could be used as a reference while selecting the best service provider based on the requirements of the projects.

The Industrial Internet of Things (IIoT) is bringing evolution with remote monitoring, intelligent analytics, and control of industrial processes. A reference architecture provides the general layout information for the flexible integration of IIoT systems; however, as the industrial world is currently in its initial stage of adopting the full-stack development solutions with IIoT, some challenges need to be addressed. To cope with the rising challenges and provide the blueprint guidelines to develop and implement IIoT in real-time, researchers around the globe have proposed IIoT architectures based on different architectural layers and emerging technologies. In this paper, we first review and compare some widely accepted IIoT reference architectures and present a state-of-the-art review of conceptual and experimental IIoT architectures in literature. We highlight scalability, interoperability, security, privacy, reliability, and low latency as the main IIoT architectural requirements and compare how the current architectures address these challenges. We also highlight the role of emerging technologies in current IIoT architectures to address these requirements and present the literature gap for future research work to address the challenges.

Within the field of movement sensing and sound interaction research, multi-user systems have gradually gained interest as a means to facilitate an expressive non-verbal dialogue. When tied with studies grounded in psychology and choreographic theory, we consider the qualities of interaction that foster an elevated sense of social connectedness, non-contingent to occupying one’s personal space. In reflection of the newly adopted social distancing concept, we orchestrate a technological intervention, starting with interpersonal distance and sound at the core of interaction. Materialised as a set of sensory face-masks, a novel wearable system was developed and tested in the context of a live public performance from which we obtain the user’s individual perspectives and correlate this with patterns identified in the recorded data. We identify and discuss traits of the user’s behaviour that were accredited to the system’s influence and construct 4 fundamental design considerations for physically distanced sound interaction. The study concludes with essential technical reflections, accompanied by an adaptation for a pervasive sensory intervention that’s finally deployed in an open public space.

Facial expressions play a key role in interpersonal communication when it comes to negotiating our emotions and intentions, as well as interpreting those of others. Research has shown that we can connect to other people better when we exhibit signs of empathy and facial mimicry. However, the relationship between empathy and facial mimicry is still debated. Among the factors contributing to the difference in results across existing studies is the use of different instruments for measuring both empathy and facial mimicry, as well as often ignoring the differences across various demographic groups. This study first looks at the differences in empathetic abilities of people across different demographic groups based on gender, ethnicity and age. The empathetic ability is measured based on the Empathy Quotient capturing a balanced representation of both emotional and cognitive empathy. Using statistical and machine learning methods, the study then investigates the correlation between the empathetic ability and facial mimicry of subjects in response to images portraying different emotions displayed on a computer screen. Unlike the existing studies measuring facial mimicry using electromyography, this study employs a technology detecting facial expressions based on video capture and deep learning. This choice was made in the context of increased online communication during and post the COVID-19 pandemic. The results of this study confirm the previously reported difference in the empathetic ability between females and males. However, no significant difference in the empathetic ability was found across different age and ethnic groups. Furthermore, no strong correlation was found between empathy and facial reactions to faces portraying different emotions shown on a computer screen. Overall, the results of this study can be used to inform the design of online communication technologies and tools for training empathy team leaders, educators, social, and health care providers.

It has been shown that the Micro-Rain Radar (MRR) can be used to derive rainfall rates every 10 m over a depth of 1.28 km using the mean vertical air velocity corrected Doppler raindrop fall speed spectra. Furthermore, it has been shown that by assuming a reasonable advection velocity for the rain, these data can be analyzed to produce spatial radial power spectra often readily fit using a power function. Previous work has shown, however, that each spectrum applies only to each particular set of data and usually lacks the statistical qualifications necessary to be considered generally applicable. However, this limitation does not preclude the potential existence of other generalizations that can be used to explore the rainfall formation processes. The intent of this study, then, is to perform an initial look for such possible behaviors using time-height profiles of the rainfall rate. It is found that once the rainfall rate, R, exceeds about 20 mm h-1, there is, apparently, an associated flattening of the spectra with increasing R so that the smaller scales play an ever increasingly important role in such rain near the ground perhaps reflecting the increasing importance of such scales in the formation of pockets of more intense convective rain. The true generality of this finding needs additional scrutiny using more data particularly from two spatially separated MRR as is currently under preparation.

The practice of quantified-self sleep tracking is increasingly common nowadays among healthy individuals as well as patients with sleep problems. However, existing sleep-tracking technologies only support simple data collection and visualization, and are incapable of providing actionable recommendations that are tailored to users' physical, behavioral and environmental context. Here we coined the term context-aware sleep health recommender system (CASHRS) as an emerging multidisciplinary research field that bridges ubiquitous sleep computing and context-aware recommender systems. In this paper, we presented a narrative review to analyze the type of contextual information, the recommendation algorithms, the context filtering techniques, the behavior change techniques, the system evaluation, and the challenges in peer-reviewed publications that meet the characteristics of CASHRS. Analysis results identified current research trends, the knowledge gap, and future research opportunities in CASHRS.

It is shown that the representation theory of some finitely presented groups thanks to their $SL_2(\mathbb{C})$ character variety is related to algebraic surfaces. We make use of the Enriques-Kodaira classification of algebraic surfaces and the related topological tools to make such surfaces explicit. We study the connection of $SL_2(\mathbb{C})$ character varieties to topological quantum computing (TQC) as an alternative to the concept of anyons. The Hopf link $H$, whose character variety is a Del Pezzo surface $f_H$ (the trace of the commutator), is the kernel of our view of TQC. Qutrit and two-qubit magic state computing, derived from the trefoil knot in our previous work, may be seen as TQC from the Hopf link. The character variety of some two-generator Bianchi groups as well as that of the fundamental group for the singular fibers $\tilde{E}_6$ and $\tilde{D}_4$ contain $f_H$. A surface birationally equivalent to a $K_3$ surface is another compound of their character varieties.

A realistic approach for gathering high-resolution observation of the rainfall rate, R, in the vertical plane is to use data from vertical pointing Doppler radars. After accounting for the vertical air velocity and attenuation, it is possible to determine the fine, spatially resolved drop size spectra and to calculate R for further statistical analyses. The first such results in a vertical plane are reported here. Specifically, we present results using MRR-Pro Doppler radar observations at resolutions of ten meters in height over the lowest 1.28 km as well as ten seconds in time over four sets of observations using two different radars at different locations. Both correlation functions and power spectra are useful for translating observations and numerical model outputs of R from on one scale down to other scales that may be more appropriate to particular applications such as flood warnings and soil erosion, for example. However, it was found in all cases that while locally applicable radial power spectra could be calculated, because of statistical heterogeneity, most of the power spectra lost all generality and proper correlation functions could not be computed in general except for one 17 minute interval. Nevertheless, these results are still useful since they could be combined to develop catalogs of power spectra over different meteorological conditions and in different climatological settings and locations. Furthermore, even within the limitations of these data, this approach is being used to gain a deeper understanding of rainfall to be reported in a forthcoming paper.

The non-contact patient monitoring paradigm moves patient care into their homes and enables long-term patient studies. The challenge, however, is to make the system non-intrusive, privacy-preserving, and low-cost. To this end, we describe an open-source edge computing and ambient data capture system, developed using low-cost and readily available hardware. We describe five applications of our ambient data capture system. Namely: (a) Estimating occupancy and human activity phenotyping; (b) Medical equipment alarm classification; (c) Geolocation of humans in a built environment; (d) Ambient light logging; and (e) Ambient temperature and humidity logging. We obtained an accuracy of 94% for estimating occupancy from video. We stress-tested the alarm note classification in the absence and presence of speech and obtained micro averaged F1 scores of 0.98 and 0.93, respectively. The geolocation tracking provided a room-level accuracy of 98.7%. The root mean square error in the temperature sensor validation task was 0.3°C and for the humidity sensor, it was 1% Relative Humidity. The low-cost edge computing system presented here demonstrated the ability to capture and analyze a wide range of activities in a privacy-preserving manner in clinical and home environments and is able to provide key insights into the healthcare practices and patient behaviors.

In magneto-photoluminescence (magneto-PL) spectra of quasi-two dimensional islands (quantum dots) having seven electrons and Wigner-Seitz radius rs~1.5, we revealed a suppression of magnetic field (B) dispersion, paramagnetic shifts and jumps of the energy of the emission components for filling factors &gt;1 (B&lt;10 T). Additionally, we observed B-hysteresis of the jumps and a dependence of all these anomalous features on rs. Using a theoretical description of the magneto-PL spectra and an analysis of the electronic structure of these dots based on the single-particle Fock-Darwin spectrum and many-particle configuration-interaction calculations, we show that these observations can be described by the rs-dependent formation of the anyon (magneto-electron) composites (ACs) involving single-particle states having non-zero angular momentum and that the anyon states observed involve Majorana modes (MMs), including zero-B modes having equal number of vortexes and anti-vortexes, which can be considered as Majorana anyons. We show that the paramagnetic shift corresponds to a destruction of the equilibrium self-formed ~5/2 AC by the external magnetic field and that the jumps and their hysteresis can be described in terms of Majorana qubit states controlled by B and rs. Our results show a critical role of quantum confinement in the formation of magneto-electrons and implies the liquid-crystal nature of fractional quantum Hall effect states, Majorana anyon origin of the states having even , i.e., composite fermions, which provide new opportunities for topological quantum computing.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

Artificial Intelligence (AI) is a leading technology of the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR), with the capability of incorporating human behavior and intelligence into machines or systems. Thus AI-based modeling is the key to building automated, intelligent, and smart systems according to today's needs. To solve real-world issues various types of AI such as analytical, functional, interactive, textual, and visual AI can be applied to enhance the intelligence and capabilities of an application. However, developing an effective AI model is a challenging task due to the dynamic nature and variation in real-world problems and data. In this paper, we present a comprehensive view on "AI-based Modeling" with the principles and capabilities of potential AI techniques that can play an important role in developing intelligent and smart systems in various real-world application areas including business, finance, healthcare, agriculture, smart cities, cybersecurity and many more. We also emphasize and highlight the research issues within the scope of our study. Overall, the goal of this paper is to provide a broad overview of AI-based modeling that can be used as a reference guide by academics and industry people as well as decision-makers in various real-world scenarios and application domains.

Rapid growth of IoT applications and their interference in our daily lives led to many different IoT devices which generates enormous data. The IoT devices’ resources are very limited, so storing and processing IoT data in the devices is very inefficient. Several resources of cloud-computing are efficiently used to handle some IoT resources issues. While using resources in the cloud centers cause some other issues, like latency in the IoT applications, which are time-critical. Thus, the technology of edge cloud has evolved recently. This technology permits storage and data processing at the network edge. This paper studies edge computing in-depth for timeless sensitive devices in IoT. In-depth, cutting-edge IoT computing systems (ECAs-IoT) are evaluated and characterized in this paper according to numerous criteria, such as information placement, improvisation facilities, reliability, and data visualization. Moreover, according to distinctive properties, the paper aims at comparing each structure in detail. The paper also highlights the significant limitations of the new ECAs-IoT and recommends solutions to them. The studies also introduce and propose solutions to some of the most important restrictions of the current ECAs-IoT. Consequently, in the edge computing domain, this survey outlines the IoT implementations. Lastly, with the use of IoT implementations for ECAs-IoT, the paper suggests four distinct scenarios.

We communicate the theoretical results in the study of utilizing the quantum phenomena in optical current for quantum computation in the context of high precision voltage measurements. These results can be used around computation by quantum sampling and quantum communication as the basis for further research and physical implementations. We propose a Main Optical Setup (MOS) for such computation which allows to make a superstructure to implement specific computations and algorithms. To create MOS we used the nonlinear units (e.g., beta- barium borate crystal), arranged in series, powered with pulsed laser pump, and ended with the beam splitter to generate the output state of a number of entangled photon pairs. The computation is made by propagation of entanglement with beam splitters applied crossword and adjustable phase shifters that are tools for parameter steering. We show how to implement the series of cosine-based components on the example of two-component case. The results opens a broad area for future research in the area of building quantum optimizer using the quantum sampling methods and in the area of high precision temporal measurement of voltage, which is the important process for building high-fidelity devices.

Computational neuroscience combines mathematics, computer science models, and neurosciences for theorizing, investigating, and simulating neural systems involved in the development, structure, physiology, and cognitive abilities of the brain. Computational models constitute a major stake in translational neuroscience: the analytical understanding of these models seems fundamental to consider a translation towards clinical applications. Method: We propose a minimal typology of computational models, which allows distinguishing between more realistic models (e.g., mechanistic models) and pragmatic models (e.g., phenomenological models). Result: Understanding the translational aspects of computational models goes far beyond the intrinsic characteristics of models. First, we assume that a computational model is rarely uniquely mechanistic or phenomenological. Idealization seems necessary because of i) the researcher’s perspectives on the phenomena and the purposes of the study (i.e., by the relativity of the model); ii) The complexity of reality across different levels and therefore the nature and number of dimensions required to consider a phenomenon. Especially, the use of models goes far beyond their function, and requires considering external characteristics rooted in path dependence, interdisciplinarity, and pluralism in neurosciences. Conclusion: The unreasonable use of computational models, which are highly complex and subject to a shift in their initial function, could be limited by bringing to light such factors.

This study analyses some of the leading technologies for the construction and configuration of IT infrastructures to provide services to users. For modern applications, guaranteeing service continuity even in very high computational load or network problems is essential. Our configuration has among the main objectives of being highly available (HA) and horizontally scalable, that is, able to increase the computational resources that can be delivered when needed and reduce them when they are no longer necessary. Various architectural possibilities are analysed, and the central schemes used to tackle problems of this type are also described in terms of disaster recovery. The benefits offered by virtualisation technologies are highlighted and are bought with modern techniques for managing Docker containers that will be used to build the back-end of a sample infrastructure related to a use-case we have developed. In addition to this, an in-depth analysis is reported on the central autoscaling policies that can help manage high loads of requests from users to the services provided by the infrastructure. The results we have presented show an average response time of 21.7 milliseconds with a standard deviation of 76.3 milliseconds showing excellent responsiveness. Some peaks are associated with high-stress events for the infrastructure, but the response time does not exceed 2 seconds even in this case. The results of the considered use case studied for nine months are presented and discussed. In the study period, we improved the back-end configuration and defined the main metrics to deploy the web application efficiently.

The shortage of quantum computers, and their current state of development, constraints research in many fields that could benefit from quantum computing. Although the work of a quantum computer can be simulated with classical computing, personal computers take so long to run quantum experiments that they are not very useful for the progress of research. This manuscript presents an open quantum computing simulation platform that enables quantum computing researchers to have access to high performance simulations. This platform, called QUTE, relies on a supercomputer powerful enough to simulate general purpose quantum circuits of up to 38 qubits, and even more under particular simulations. This manuscript describes in-depth the characteristics of the QUTE platform and the results achieved in certain classical experiments in this field, which would give readers an accurate idea of the system capabilities.

We propose a new binary formulation of the Travelling Salesman Problem (TSP), with which we overcame the best formulation of the Vehicle Routing Problem (VRP) in terms of the minimum number of necessary variables. Furthermore, we present a detailed study of the constraints used and compare our model (GPS) with other frequent formulations (MTZ and native formulation). Finally, we have carried out a coherence and efficiency check of the proposed formulation by running it on a quantum annealing computer, D-Wave\_2000Q6.

In this paper we try to demonstrate that the classical model of certainty factos for dealing with innacurate knowledge can be efficiently implemented in a quantum environment. For this, we assume that certainty factors are strongly correlated with the quantum probability. We first explore the certainty factors approach for inexact reasoning from a classical point of view. Next, we introduce some basic aspects of quantum computing, and we pay special attention to quantum rule-based systems. We then build a use case: an inferential network to be implemented in both, the classical approach and the corresponding quantum circuit. Both implementations have been used to compare the behavior of the classical and the quantum approaches when confronted with the same hypothetical case. We analyze three different situations: (1) Only Imprecision (which refers to inaccuracy in declarative knowledge or facts) is present in the use case, (2) Only Uncertainty (which refers to inaccuracy in procedural knowledge or rules) is present in the use case, and (3) Both Imprecision and Uncertainty are present in the use case. Finally, we analyze the results to reach a conclusion about the eventually intrinsic probabilistic nature of the certainty factors model and to pave the way for future quantum implementations of this method for handling inaccurate knowledge.

As V2X technology develops, acute problems related to reliable and secure information exchange between network objects in real time appear. The article aims to solve the scientific problem of building a network architecture for reliable delivery of correct and uncompromised data within the V2X concept to improve the safety of road users, using blockchain technology and mobile edge computing (MEC). The authors present a formalized mathematical model of the system, taking into account the interconnection of objects and V2X information channels, and an energy-efficient algorithm of traffic offloading to the MEC server. The paper presents the results of application of blockchain technologies and mobile edge computing in the developed system, their description, evaluation of advantages and disadvantages of the implementation.

Due to the increased use of memristors, and its many applications, the use of emulators has grown in parallel to avoid some of the difficulties presented by real devices such as variability and reliability. In this paper, we present a memristive emulator designed using a Switched Capacitor (SC), this is, an analog component/ block and a control part or block implemented using stochastic computing (SCo) and therefore fully digital. Our design is thus a mixed signal circuit. Memristor equations are implemented using stochastic computing to generate the control signals necessary to work with the controllable resistor implemented as switched capacitor.

The US pacific northwest recorded its highest temperature in late June 2021. The three-day stretch of scorching heat had a devastating effect on not only the residents of the state, but also on the crops thus impacting the food supply-chain. It is forecasted that streaks of 100-degree temperatures will become common. Farmers will have to adapt to the changing landscape to preserve their crop yield and profitability. A research collaborative consisting of researchers and academicians in Eastern Washington led by a pioneering startup has setup a 16.9-acre Honeycrisp Apple Smart Orchard in Grandview, WA as a laboratory to study the environmental and plant growth factors in real-time using modern computational tools and techniques like IoT (Internet of Things), Edge and Cloud Computing, and Drone and LiDAR (Light Detection and Ranging) imaging. The computational analysis is used to develop guidelines for precision agriculture for orchard blocks to address plant growth issues scientifically and in a timely fashion. The analysis also helps in creating risk-mitigation strategies for severe weather events while helping prepare farmers to maximize crop yield and profitability per acre. I was fortunate to gain access to the terabytes of farm data related to the weather, soil, water, tree, and canopy health, to analyze and formulate recommendations for the farmers that can be adopted nationwide for different crops and weather conditions. This paper discusses the different streams of farm data that were analyzed (ex. soil moisture, soil water potential, and sap flow) and the development of the framework to use data to convert insights into actionable steps. For example, the use of sensors can inform a farmer that their level of soil water potential is below threshold in a specific patch of the orchard, prompting them to turn on irrigation for the patch instead of the whole orchard. I estimate that using an IoT-sensor-based decision framework discussed in this paper, growers can save up to 55% of their water costs for the season. Using these insights, farmers can better manage their irrigation resources and labor, thus maximizing their crop yield and profits.

Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

Identifying and anticipating potential failures in the cloud is an effective method for increasing cloud reliability and proactive failure management. Many studies have been conducted to predict potential failure, but none have combined SMART (Self-Monitoring, Analysis, and Reporting Technology) hard drive metrics with other system metrics such as CPU utilisation. Therefore, we propose a combined metrics approach for failure prediction based on Artificial Intelligence to improve reliability. We tested over 100 cloud servers’ data and four AI algorithms: Random Forest, Gradient Boosting, Long-Short-Term Memory, and Gated Recurrent Unit. Our experimental result shows the benefits of combining metrics, outperforming state-of-the-art.

Making computing machines mimic living organisms has captured the imagination of many since the dawn of digital computers. However, today’s artificial intelligence technologies fall short in replicating even the basic autopoietic and cognitive behaviors found in primitive biological systems. According Charles Darwin, the difference in mind between humans and the higher animals, great as it is, certainly is one of degree and not of kind. Autopoiesis refers to the behavior of a system that replicates itself and maintains its own identity and stability while facing fluctuations caused by external influences. Cognitive behaviors model the system’s state, sense internal and external changes, analyze, predict and take action to mitigate any risk to its functional fulfilment. How did intelligence evolve? what is the relationship between the mind and body? Answers to these questions should guide us to infuse autopoietic and cognitive behaviors into digital machines. In this paper we use recent advances in our understanding of general theory of information, and the role of structures in managing the transformations between information and knowledge to pave the path to infuse autopoietic and cognitive functions into digital computing and build a new class of intelligent machines going beyond the current state of the art.

The holy grail of Artificial Intelligence (AI) has been to mimic human intelligence using computing machines. Autopoiesis which refers to a system with well-defined identity and is capable of re-producing and maintaining itself and cognition which is the ability to process information, apply knowledge, and change the circumstance are associated with resilience and intelligence. While classical computer science (CCS) with symbolic and sub-symbolic computing has given us tools to decipher the mysteries of physical, chemical and biological systems in nature and allowed us to model, analyze various observations and use information to optimize our interactions with each other and with our environment, it falls short in reproducing even the basic behaviors of living organisms. We present the foundational shortcomings of CCS and discuss the science of infor-mation processing structures (SIPS) that allows us to fill the gaps. SIPS allows us to model su-per-symbolic computations and infuse autopoietic and cognitive behaviors into digital machines. They use common knowledge representation from the information gained using both symbolic and sub-symbolic computations in the form of system-wide knowledge networks consisting of knowledge nodes and information sharing channels with other knowledge nodes. The knowledge nodes wired together fire together to exhibit autopoietic and cognitive behaviors.

Reservoir computing is a machine learning method that uses the response of a dynamical system to a certain input in order to solve a task. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.

This article presents a systematic mapping study dedicated to conduct a literature review on machine learning and IoT applied in the identification of diseases through heart sounds. This research was conducted between January 2010 and July 2021, considering IEEE Xplore, PubMed Central, ACM Digital Library, JMIR- Journal of Medical Internet Research, Springer Library, and Science Direct. The initial search resulted in 4,372 papers, and after applying the inclusion and exclusion criteria, 58 papers were selected for full reading to answer the research questions. The main results are: of the 58 articles selected, 46 (79.31%) mention heart rate observation methods with wearable sensors and digital stethoscopes, and 34 (58.62%) mention care with machine learning algorithms. The analysis of the studies based on the bibliometric network generated by the VOSviewer showed in 13 studies (22.41%) a trend related to the use of intelligent services in the prediction of diagnoses related to cardiovascular disorders.

Cloud computing has become a prominent technology due to its important utility service; this service concentrates on outsourcing data to organizations and individual consumers. Cloud computing has considerably changed the manner in which individuals or organizations store, retrieve, and organize their personal information. Despite the manifest development in cloud computing, there are still some concerns regarding the level of security and issues related to adopting cloud computing that prevent users from fully trusting this useful technology. Hence, for the sake of reinforcing the trust between Cloud Clients (CC) and Cloud Service Providers (CSP), as well as safeguarding the CC’s data in the cloud, several security paradigms of cloud computing based on a Third-Party Auditor (TPA) have been introduced. The TPA, as a trusted party, is responsible for checking the integrity of the CC’s data and all the critical information associated with it. However, the TPA could become an adversary and could aim to deteriorate the privacy of the CC’s data by playing a malicious role. In this paper, we present the state-of-art of cloud computing’s privacy-preserving models (PPM) based on a TPA. Three TPA factors of paramount significance have been discussed: TPA involvement, security requirements, and security threats caused by vulnerabilities. Moreover, TPA’s privacy preserving models have been comprehensively analyzed and categorized into different classes with an emphasis on their dynamicity. Finally, we discuss the limitations of the models and present our recommendations for their improvement.

The search algorithm based on symbiotic organisms’ interactions is a relatively recent bio-inspired algorithm of the swarm intelligence field for solving numerical optimization problems. It is meant to optimize applications based on the simulation of the symbiotic relationship among the distinct species in the ecosystem. The modified SOS algorithm is developed to solve independent task scheduling problems. This paper proposes a modified symbiotic organisms search based scheduling algorithm for efficient mapping of heterogeneous tasks to access cloud resources of different capacities. The significant contribution of this technique is the simplified representation of the algorithm's mutualism process, which uses equity as a measure of relationship characteristics or efficiency of species in the current ecosystem to move to the next generation. These relational characteristics are achieved by replacing the original mutual vector, which uses an arithmetic mean to measure the mutual characteristics with a geometric mean that enhances the survival advantage of two distinct species. The modified symbiotic organisms search algorithm (G_SOS) aimed to minimize the task execution time (Makespan), response, degree of imbalance and cost and improve the convergence speed for an optimal solution in an IaaS cloud. The performances of the proposed technique have been evaluated using a Cladism toolkit simulator, and the solutions are found to be better than the existing standard (SOS) technique and PSO.

Market turbulence with fiscal investment influences has altered IT infrastructure performance as business pursue extravagant new technology adoption. Yet, few studies have examined how shareware solution goes beyond Medium Size Enterprise that pushes efficiency and sustainability. This PLS-SEM integrated with dual primary compilation approach lessens shallow perceptions coupled with outlooks that streamline each phenomenal activity that is worthy of the necessity for competitive innovation. This unified model was applied to sampling respondents and analyzed using an ordinal regression relationship that generates a robust association that triggers the hypothesis acceptance. The adopting of BOINC shareware mesh network towards unified processing designs that were employed to build the yield by promising financial possibility using coordinated interworks hence improved group accomplishment and establishing greater esteem. This paper showcases a flexible inner IT infrastructure alongside the economic uncertainty with the framework advancement for Exostructure as a Service. Associated theoretical and practical implications were discussed.

The efficiency of data sorting algorithms is the key aspect which determines the speed of data processing and searching. The best known efficiency of sorting algorithm has been Log (N) if there are N terms. All of the well-known sorting algorithms use various techniques to sort data. The basis for most of these are comparing the data terms with each other. In this manuscript, we are introducing a new approach for sorting data. This method is postulated to have the highest efficiency ever achieved by any of the sorting algorithms. We achieve this by sorting data without comparing the data terms. Or achieving results of data comparison without comparing the terms explicitly.

The article presents a method of generating Key Performance Indicators related to electric motor energy efficiency on the basis of Big Data gathered and processed in frequency converter. The authors proved that using the proposed solution it is possible to specify the relation between the control mode of an electric drive and the control quality-energy consumption ratio in the start-up phase as well as in the steady operation with various mechanical loads. The tests were carried out on a stand equipped with two electric motors (one driving, the other used to apply the load by adjusting the parameters of the built-in brake). The measurements were made in two load cases, for motor control modes available in industrially applied frequency converters (scalar V/f, vector Voltage Flux Control without encoder, vector Voltage Flux Control with encoder, vector Current Flux Control and Vector Current Flux Control with torque control). During the experiments values of current intensities (active and output), the actual frequency value, IxT utilization factor, relative torque and the current rotational speed were measured and processed. Based on the data the level of the energy efficiency was determined for various control modes.

The Industrial Internet of things (IIoT) enabled smart system has entered into a golden era of rapid technology growth. IIoT is a concept to make every system interrelated such that they are able to collect and transfer data over a wireless network without human intervention. In this paper, we discuss the development of an IoT enabled system to monitor the vibration signature of equipment as part of prognosis and availability management system (P&AM) that serves to prevent unplanned operation downtime and catastrophic failure of a whole system. In order to simply the complexity of processing video content and performing inference, the Intel OpenVINO platform was selected because of it’s simplicity, portability across Intel AI processors, performance and comprehensiveness of it’s analytical and diagnostics capabilities that can be tested in Intel’s DevCloud. The IIoT system consists of a High Performance Computing (HPC) platform based on Intel’s Xeon processors and Movidius AI accelerator, Intel’s OpenVINO toolkit for AI, a Regul high performance programmable controller capturing vibration data through sensors and a low-latency network connection. Notifications of anomalies are sent to a smartphone. This paper reveals an approach for the features extraction and selection, known as feature engineering, of the equipment component we want to protect. Feature engineering is the first step for the P&AM of these components and extends to the whole system. The broader aim of this paper is to help technical leaders at the exploring or experimenting stages of their AI framework to learn the concepts of implementing algorithms using datasets that have real value to their companies. Datasets generated and referred to in this paper were generated by simulation under various material failure scenarios.

Moore’s law will cease to be relevant once transistors are the size of electrons. Once this occurs, we will no longer be able to use silicon for technological advancement. However, breakthroughs will continue in the form of a sixth paradigm: a 3D molecular computing model such as quantum comput- ing. Some of the promising areas of quantum computing researched in this study are (i) algorithmic advancements - such as Shor’s, Grover’s, error correction, fault tolerance and other new algorithms; (ii) hardware - such as the quantum system architecture, circuit models, entanglement, superposition and no-cloning qubits; (iii) market segments - such as finance, healthcare, and chemistry; (iv) the supremacy established over classical computing; and (v) areas of applicability. Significant progress has been made in these areas in recent decades, and they have garnered immense interest, attention and the funding required to make good progress.

A large number of smart devices (things) are being deployed with the swift development of Inter- net of Things (IOT). These devices, owned by different organizations, have a wide variety of services to offer over the web. During a natural disaster or emergency (i.e., a situation), for example, relevant IOT services can be found and put to use. However, appropriate service matching methods are required to find the relevant services. Organizations that manage situation responses and organizations that provide IOT services are likely to be independent of each other, and therefore it is difficult for them to adopt a common ontological model to facilitate the service matching. Moreover, there exists a large conceptual gap between the domain of discourse for situations and the domain of discourse for services, which cannot be adequately bridged by existing techniques. In this paper, we address these issues and propose a new method, WikiServe, to identify IOT services that are functionally relevant to a given situation. Using concepts (terms) from situation and service descriptions, WikiServe employs Wikipedia as a knowledge source to bridge the conceptual gap between situation and service descriptions and match functionally relevant IOT services for a situation. It uses situation terms to retrieve situation related articles from Wikipedia. Then it creates a ranked list of services for the situation using the weighted occurrences of service terms in weighted situation articles. WikiServe performs better than a commonly used baseline method in terms of Precision, Recall and F measure for service matching.

The colossal depths of the deep neural network sometimes suffer from ineffective backpropagation of the gradients through all its depths. Whereas, The strong performance of shallower multilayer neural structures prove their ability to increase the gradient signals in the early stages of training which easily gets backpropagated for global loss corrections. Shallow neural structures are always a good starting point for encouraging the sturdy feature characteristics of the input. In this research, a shallow, deep neural structure called PrimeNet is proposed. PrimeNet is aimed to dynamically identify and encourage the quality visual indicators from the input to be used by the subsequent deep network layers and increase the gradient signals in the lower stages of the training pipeline. In addition to this, the layerwise training is performed with the help of locally generated errors which means the gradient is not backpropagated to previous layers, and the hidden layer weights are updated during the forward pass, making this structure a backpropagation free variant. PrimeNet has obtained state-of-the-art results on various image datasets, attaining the dual objective of (1) compact dynamic deep neural structure, which (2) eliminates the problem of backwards-locking. The PrimeNet unit is proposed as an alternative to traditional convolution and dense blocks for faster and memory-efficient training, outperforming previously reported results aimed at adaptive methods for parallel and multilayer deep neural systems.

A foundational model has been developed based on trends built from empirical data of space exploration and computing power through the first six plus decades of the Space Age which projects earliest possible launch dates for human-crewed missions from cis-lunar space to selected Solar System and interstellar destinations. The model uses computational power, expressed as transistors per microprocessor, as a key broadly limiting factor for deep space missions’ reach and complexity. The goal of this analysis is to provide a projected timeframe for humanity to become a multi-world species through off-world colonization, and in so doing all but guarantees the long-term survival of the human race from natural and human-caused calamities that could befall life on Earth. Be-ginning with the development and deployment of the first nuclear weapons near the end of World War II, humanity entered a ‘Window of Peril’ which will not be safely closed until robust off-world colonies become a reality. Our findings suggest the first human-crewed missions to land on Mars, selected Asteroid Belt objects, and selected moons of Jupiter and Saturn can occur before the end of the 21^st century. Launches of human-crewed interstellar missions to exoplanet destinations within roughly 40 lightyears of the Solar System are seen as possible during the 23^rd century and launch of intragalactic missions by the end of the 24^th century. An aggressive and sustained space exploration program, which includes colonization, is thus seen as critical to the long-term survival of the human race.

Language learners often face communication problems when they need to express themselves and do not have this ability. On the other hand, continuous advances in technology create new opportunities to improve second language (L2) acquisition through context-aware ubiquitous learning (CAUL) technology. Since vocabulary is the foundation of all language acquisition, this article presents the ULearnEnglish, an open-source system to allow ubiquitous English learning focused on incidental vocabulary acquisition. To evaluate the proposal, 15 learners used the system developed, and 10 answered a survey based on the Technology Acceptance Model (TAM). Results indicate a favorable response to the use of the learner context to assist them in their learning. The ULearnEnglish achieved an acceptance of 78.66% for the perception of the utility, 96% for the perception of ease of use, 86% for user context assessment, and 88% for ubiquity. This study presented a positive response in using the location of users to assist their learning. Among the main contributions, this study demonstrates an opportunity for ubiquity use in future research in language learning. Also, furthers studies can use the source available to evolve the model and system.

The reversible computation paradigm aims to provide a new foundation for general classical digital computing that is capable of circumventing the thermodynamic limits to the energy efficiency of the conventional, non-reversible paradigm. However, to date, the essential rationale for and analysis of classical reversible computing (RC) has not yet been expressed in terms that leverage the modern formal methods of non-equilibrium quantum thermodynamics (NEQT). In this paper, we begin developing an NEQT-based foundation for the physics of reversible computing. We use the framework of Gorini-Kossakowski-Sudarshan-Lindblad dynamics (a.k.a. Lindbladians) with multiple asymptotic states, incorporating recent results from resource theory, full counting statistics, and stochastic thermodynamics. Important conclusions include that, as expected: (1) Landauer's Principle indeed sets a strict lower bound on entropy generation in traditional non-reversible architectures for deterministic computing machines when we account for the loss of correlations; and (2) implementations of the alternative reversible computation paradigm can potentially avoid such losses, and thereby circumvent the Landauer limit, potentially allowing the efficiency of future digital computing technologies to continue improving indefinitely. We also outline a research plan for identifying the fundamental minimum energy dissipation of reversible computing machines as a function of speed.

In this ‘New Normal' post-COVID-19 modern world, people's health is becoming increasingly important to track. An advanced technology that uses sensory instruments to track and record critical parameters and communicates with others is the need of the time. It's difficult to keep track of all of the medical parameters and post-operative data of people with non-communicable diseases like diabetes and heart disease on a continuous basis. The system discussed here to be designed for patients who are confined to their homes, particularly when going out and being exposed to the outer world is prohibited. This paper proposed a groundbreaking health management system based on the Internet of Things (IoT) for accessing patient medical parameters in both local and remote areas. When a person's wellbeing becomes urgent, this initiative seeks to send an emergency alert to family members or loved ones. A cloud server records data from the patient's temperature sensor and pulse sensor; the data is analyzed using support vector machine algorithms to identify irregular conditions, and an emergency message is sent to the rest of the family via a mobile application, as well as a warning message to the nearest hospital.

In the study of materials and macromolecules by first-principle methods, the bond order is a useful tool to represent molecules, bulk materials and interfaces in terms of simple chemical concepts. Despite the availability of several methods to compute the bond order, most applications have been limited to small systems because a high spatial resolution of the wave function and an all-electron representation of the electron density are typically required. Both limitations are critical for large-scale atomistic calculations, even within approximate density-functional theory (DFT) approaches. In this work we describe our methodology to quickly compute delocalization indices for all atomic pairs, while keeping the same representation of the wave function used in most compute-intensive DFT calculations on high-performance computing equipments. We describe our implementation into a post-processing tool, designed to work with Quantum ESPRESSO, a popular open-source DFT package. In this way we recover a description in terms of covalent bonds from a representation of wave function containing no explicit information about atomic types and positions.

Cryptography has been used from time immemorial for preserving the confidentiality of data/information in storage or in transit. Thus, cryptography research has also been evolving from the classical Caesar cipher to the modern cryptosystems based on modular arithmetic to the contemporary cryptosystems based on quantum computing. The emergence of quantum computing imposes a major threat on the modern cryptosystems based on modular arithmetic whereby, even the computationally hard problems which constitute for the strength of the modular arithmetic ciphers could be solved in deterministic time. This threat triggered post-quantum cryptography research in order to design and develop post-quantum algorithms that can withstand quantum computing attacks. This paper provides a review of the various post-quantum cryptography and, in specific, code-based cryptography research dimensions. The research directions that are yet to be explored in code-based cryptography research is another key contribution of this paper.

The genetic development of commercial broiler led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broiler 40 d-old were placed to walk on a specially built runway, and their locomotion was recorded. An image segmentation algorithm was developed, and the coordinates of the bird's center of mass were extracted from the segmented images for each frame analyzed, and the Unrest Index (UI) was applied. We calculated the center of mass's movement of the broiler walking's lateral images, therefore, capturing the bird's displacement speed in the onward direction. Results indicated that broiler speed on the runway tends to decrease with the increase of the gait score. The locomotion did not differ between males or females. The proposed algorithm was efficient if predicting the broiler gait score based on their displacement speed.

The digital world has a wealth of data, such as Internet of Things (IoT) data, business data, health data, mobile data, urban data, security data, and many more, in the current age of the Fourth Industrial Revolution (Industry 4.0 or 4IR). Extracting knowledge or useful insights from these data can be used for smart decision-making in various applications domains. In the area of data science, advanced analytics methods including machine learning modeling can provide actionable insights or deeper knowledge about data, which makes the computing process automatic and smart. In this paper, we present a comprehensive view on "Data Science'' including various types of advanced analytics methods that can be applied to enhance the intelligence and capabilities of an application through smart decision-making in different scenarios. We also discuss and summarize ten potential real-world application domains including business, healthcare, cybersecurity, urban and rural data science, and so on by taking into account data-driven smart computing and decision making. Based on this, we finally highlight the challenges and potential research directions within the scope of our study. Overall, this paper aims to serve as a reference point on data science and advanced analytics to the researchers and decision-makers as well as application developers, particularly from the data-driven solution point of view for real-world problems.

Recent advancement of deep learning has been elevated the multifaceted nature in various applications of this field. Artificial neural networks are now turning into a genuinely old procedure in the vast area of computer science; the principal thoughts and models are more than fifty years of age. However, in this modern computing era, 3rd generation intelligent models are introduced by scientists. In the biological neuron, actual film channels control the progression of particles over the layer by opening and shutting in light of voltage changes because of inborn current flows and remotely led to signals. A comprehensive 3rd generation, Spiking Neural Network (SNN) is diminishing the distance between deep learning, machine learning, and neuroscience in a biologically-inspired manner. It also connects neuroscience and machine learning to establish high-level efficient computing. Spiking Neural Networks initiate utilizing spikes, which are discrete functions that happen at focuses as expected, as opposed to constant values. This paper is a review of the biological-inspired spiking neural network and its applications in different areas. The author aims to present a brief introduction to SNN, which incorporates the mathematical structure, applications, and implementation of SNN. This paper also represents an overview of machine learning, deep learning, and reinforcement learning. This review paper can help advanced artificial intelligence researchers to get a compact brief intuition of spiking neural networks.

The wide adoption of the recently coined fog and edge computing paradigms alongside conventional cloud computing creates a novel scenario, known as the cloud continuum, where services may benefit from the overall set of resources to optimize their execution. To operate successfully, such a cloud continuum scenario demands for novel management strategies, enabling a coordinated and efficient management of the entire set of resources, from the edge up to the cloud, designed in particular to address key edge characteristics, such as mobility, heterogeneity and volatility. The design of such a management framework poses many research challenges and has already promoted many initiatives worldwide at different levels. In this paper we present the results of one of these experiences driven by an EU H2020 project, focusing on the lessons learnt from a real deployment of the proposed management solution in three different industrial scenarios. We think that such a description may help understand the benefits brought in by a holistic cloud continuum management and also may help other initiatives in their design and development processes.

Social media has become the most influential battleground for political and societal debate. The reach, the speed, and the fact that everybody can participate make this medium very popular but also highly manipulative, subject to biases and extremely precious to control. Misinformation spreads in an unforeseeable way. Sometimes it just follows usual non-malicious social interaction patterns, but often a targeted and designed-for-purpose manner. In this paper, we propose a novel method to identify the most effective opportunities to moderate the social media debate at a large scale using the properties of social signed graphs, such as structural balance. After building the topic universe for a particular political debate in a given time frame, we extract the most active and influential users and mine their position regarding the topic based on their interactions with other users. The result is a signed graph where each edge represents either support or challenge between a pair of users in the context of the topic. This graph can be modelled as an Ising model, with the goal of minimizing its energy state by using the advantages of Adiabatic Quantum Computing. Applying this principle, we use a Quantum Annealing implementation to solve the problem of finding the edges violating the structural balance of this graph, which in classic computing is known to be an NP-hard problem. We then provide a non-intrusive moderation technique for those violations to mitigate or increase the structural imbalance, depending on the desired strategy. The methodology is shown in the context of the COVID-19 skeptics’ debate in Twitter and implemented using a D-Wave 2000Q-6 quantum annealer.

Information is commonly considered as a mathematical quantity that forms the basis of computing. In mathematics, information can propagate instantly, so its transfer speed is not the subject of information science. In all kinds of implementations of computing, whether technological or biological, some material carrier for the information exists, so the information’s propagation speed cannot exceed the speed of the carrier. Because of this limitation, for any implementation, one must consider the transfer time between computing units. We need a different mathematical method to take this limitation into account: classic mathematics can only describe infinitely fast and infinitely small computing system implementations. The difference between the mathematical handling methods leads to different descriptions of the behavior of the systems. The correct handling also explains why biological implementations can have lifelong learning and technological ones cannot. The conclusion about learning evidences matches others’ experimental evidence, both in technological and biological computing.

Advances in communication technologies have made the interaction of small devices, such as smartphones, wearables, and sensors, scattered on the Internet, bringing a whole new set of complex applications with ever greater task processing needs. These Internet of Things (IoT) devices run on batteries with strict energy restrictions. They tend to offload task processing to remote servers, usually to Cloud Computing (CC) in datacenters geographically located away from the IoT device. In such a context, this work proposes a dynamic cost model to minimize energy consumption and task processing time for IoT scenarios in Mobile Edge Computing environments. Our approach allows for a detailed cost model, with an algorithm called TEMS that considers energy, time consumed during processing, the cost of data transmission, and energy in idle devices. The task scheduling chooses among Cloud or Mobile Edge Computing (MEC) server or local IoT devices to better execution time with lower cost. The simulated environment evaluation saved up to 51.6% energy consumption and improved task completion time up to 86.6%.