BRIEF REPORT | doi:10.20944/preprints202307.0366.v1
Subject: Computer Science And Mathematics, Analysis Keywords: honeypot; botnet; blockchain; security; IoT; auto-encoder
Online: 6 July 2023 (07:30:33 CEST)
Botnet Attacks are one of the many types of ways to attack users in their day to day browsing, often of different trends and techniques to take your information without your control. Most firewalls can amply protect you against these trends and techniques, but scarily enough, recent articles and research have shown that they are starting to bypass said firewalls through layers of attacks, confusing your best line of defense. In this Report, we will be looking into exactly what are Botnets and the different subsets, what we can do to combat Botnet Attacks, where do they come from, their origin, the analysis of their kit and what we can put together from information collected. Through this we will be able to reach a conclusion of what we can use to possibly mitigate the effect they have on the internet and stop them from enacting their purpose.
ARTICLE | doi:10.20944/preprints202103.0215.v1
Subject: Engineering, Automotive Engineering Keywords: Indoor Localization; Wi-Fi Fingerprint; Denoising Auto-encoder; JLGBMLoc
Online: 8 March 2021 (12:23:36 CET)
Wi-Fi based localization has become one of the most practical methods for mobile users in location-based services. However, due to the interference of multipath and high-dimensional sparseness of fingerprint data, the localization system based on received signal strength (RSS) is hard to obtain high accuracy. In this paper, we propose a novel indoor positioning method, named JLGBMLoc (Joint denoising auto-encoder with LightGBM Localization). Firstly, because the noise and outliers may influence the dimensionality reduction on high-dimensional sparseness fingerprint data, we propose a novel feature extraction algorithm, named joint denoising auto-encoder (JDAE), which reconstructs the sparseness fingerprint data for a better feature representation and restores the fingerprint data. Then, the LightGBM is introduced to the Wi-Fi localization by scattering the processed fingerprint data to histogram, and dividing the decision tree under leaf-wise algorithm with depth limitation. At last, we evaluated the proposed JLGBMLoc on UJIIndoorLoc dataset and Tampere dataset, experimental results show that the proposed model increases the positioning accuracy dramatically comparing with other existing methods.
ARTICLE | doi:10.20944/preprints201911.0019.v1
Subject: Engineering, Control And Systems Engineering Keywords: community detection; social network; convolutional neural network; auto-encoder
Online: 3 November 2019 (15:51:34 CET)
With the fast development of the mobile Internet, the online platforms of social networks have rapidly been developing for the purpose of making friends, sharing information, etc. In these online platforms, users being related to each other forms social networks. Literature reviews have shown that social networks have community structure. Through the studies of community structure, the characteristics and functions of networks structure and the dynamical evolution mechanism of networks can be used for predicting user behaviours and controlling information dissemination. Therefore, this study proposes a deep community detection method which includes (1) matrix reconstruction method, (2) spatial feature extraction method and (3) community detection method. The original adjacency matrix in social network is reconstructed based on the opinion leader and nearer neighbors for obtaining spatial proximity matrix. The spatial eigenvector of reconstructed adjacency matrix can be extracted by an auto-encoder based on convolution neural network for the improvement of modularity. In experiments, four open datasets of practical social networks were selected to evaluate the proposed method, and the experimental results show that the proposed deep community detection method obtained higher modularity than other methods. Therefore, the proposed deep community detection method can effectively detect high quality communities in social networks.
ARTICLE | doi:10.20944/preprints202210.0355.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: Auto encoder; surface defects; abnormal defects; visual inspection; unsupervised defect
Online: 24 October 2022 (07:56:04 CEST)
Currently, most deep learning methods cannot solve the problem of scarcity of industrial product defect samples and significant differences in characteristics. This paper proposes an unsupervised defect detection algorithm based on a reconstruction network, which is realized using only a large number of easily obtained defect-free sample data. The network includes two parts: image reconstruction and surface defect area detection. The reconstruction network is designed through a fully convolutional autoencoder with a lightweight structure. Only a small number of normal samples are used for training so that the reconstruction network can be A defect-free reconstructed image is generated. A function combining structural loss and L1 loss is proposed as the loss function of the reconstruction network to solve the problem of poor detection of irregular texture surface defects. Further, the residual of the reconstructed image and the image to be tested is used as the possible region of the defect, and conventional image operations can realize the location of the fault. The unsupervised defect detection algorithm of the proposed reconstruction network is used on multiple defect image sample sets. Compared with other similar algorithms, the results show that the unsupervised defect detection algorithm of the reconstructed network has strong robustness and accuracy.
ARTICLE | doi:10.20944/preprints202208.0201.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: auto-encoder; high sparse binary data; feature extraction; SNV integration
Online: 10 August 2022 (10:27:32 CEST)
Genomics involving tens of thousands of genes is a complex system determining phenotype. An interesting and vital issue is that how to integrate highly sparse genetic genomics data with a mass of minor effects into prediction model for improving prediction power. We find that deep learning method can work well to extract features by transforming highly sparse dichotomous data to lower dimensional continuous data in a non-linear way. This idea may provide benefits in risk prediction based on genome-wide data associated e.g. integrating most of the information in the genotype data. Hence, we developed a multi-stage strategy to extract information from highly sparse binary genotype data and applied it for risk prediction. Specifically, we first reduced the number of biomarkers via a univariable regression model to a moderate size. Then a trainable auto-encoder was used to extract compact representations from the reduced data. Next, we performed a LASSO problem process over a grid of tuning parameter values to select the optimal combination of extracted features. Finally, we applied such feature combination to two prognostic models, and evaluated predictive effect of the models. The results of simulation studies and real data applying indicated that these highly compressed transformation features could better improve predictive performance and did not easily lead to over-fitting.
ARTICLE | doi:10.20944/preprints202103.0408.v1
Subject: Engineering, Automotive Engineering Keywords: Auto encoder; IoT; Image encryption; Artificial Neural Network; Machine Learning
Online: 16 March 2021 (09:32:11 CET)
Machine Learning has completely transformed health care system, which transmits medical data through IOT sensors. So it is very important to encrypt them to protect patient data. encrypting medical images from a performance perspective consumes time; hence the use of an auto encoder is essential. An auto encoder is used in this work to compress the image as a vector prior to the encryption process. The digital image passes across description function and a decoder to get back the image in the proposed work; various experiments are carried out on hyper parameters to achieve the highest outcome of the classification. The findings demonstrate that the combination of Mean Square Logarithmic Error as the loss function, ADA grad as an optimizer, two layers for the encoder, and another reverse for the decoder, RELU as the activation function generates the best auto encoder results. The combination of Mean square error (lose function), RMS prop (optimizer), three layers for the encoder and another reverse for the decoder, and RELU (activation function) has the best classification result. All the experiments with different hyper parameter has run almost very close to each other even when changing the number of layers. The running time is between 9 and 16 second for each epoch.
ARTICLE | doi:10.20944/preprints202312.0113.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Passive Entry Passive Start; Smart Watch; Electrocardiogram; Long Short-Term Memory; Auto Encoder; Collective Decision
Online: 4 December 2023 (04:26:06 CET)
With the development of sensor and communication technologies, Internet of Things (IoT) subsystem is gradually becoming a crucial part in vehicles. It can effectively enhance functionalities of vehicles. However, new attack types are also emerged. For example, a driver with the smart key in the pocket can push the start button to start a car when. At the same time, security issues in the push-to-start scenario are alerted, such as smart key forgery. In this paper, we propose a vehicle Passive Entry Passive Start (PEPS) system that uses deep learning algorithms to recognize the driver by the Electrocardiogram (ECG) signals measured by his or her smart watch. ECG signals are used for verification. Smart watches, as a new smart key of PEPS system, can replace traditional smart keys to improve security. Experiment results show that Long Short-Term Memory (LSTM) models have achieved the best accuracy score for identity recognition (91%) when single ECG cycle is used. However, it takes at least 30 minutes for training. The training time of Auto Encoder successfully reduces to 5 minutes. When 15 continuous ECG cycles are used, it can achieve 100% identity accuracy.
ARTICLE | doi:10.20944/preprints202307.0326.v1
Subject: Engineering, Mechanical Engineering Keywords: Contact Fatigue; Feature Extraction; Health Index; Degradation Prediction; Temporal Convolutional Network; Convolutional Auto-Encoder Network
Online: 5 July 2023 (14:04:06 CEST)
In order to realize the performance degradation trend prediction accurately, a prediction method based on multi-domain features and temporal convolutional network (TCN) is proposed. Firstly, construct a high-dimensional feature set in the multi-domain of vibration signals, and use comprehensive evaluation indicators to preliminarily screen performance degradation indexes with good sensitivity and strong trend. Secondly, the kernel principal component analysis (KPCA) method is adopted to eliminate redundant information between multi-domain features, and construct a health index (HI) based on convolutional auto-encoder (CAE) network. Thirdly, a TCN-based performance degradation trend prediction model is constructed, and direct multi-step prediction is used to predict the performance degradation trend of the monitored object. On this basis, the validity of the proposed method is verified using the bearing public data, and it is successfully applied to performance degradation trend prediction of rolling contact fatigue specimen. The results show that the feature set can be reduced from 14 dimensions to 4 dimensions by using KPCA, while 98.33% of the information of the original feature set is retained. Furthermore, the method of constructing HI based on CAE network is effective. The change process of the HI constructed truly reflects the performance degradation process of the rolling contact fatigue specimen. Compared with the two commonly used HI construction methods, auto-encoding (AE) network and gaussian mixture model (GMM), this method has obvious advantages. At the same time, the prediction model based on TCN can accurately predict the performance degradation of the rolling contact fatigue specimen with the root mean square error 0.0146 and the mean absolute error 0.0105, which has better performance and higher prediction accuracy than the prediction model based on the long short-term memory (LSTM) network and the gated recurrent unit (GRU). This method has general significance and may be extended to the performance degradation prediction of other mechanical equipment/parts.
ARTICLE | doi:10.20944/preprints202007.0209.v1
Subject: Engineering, Control And Systems Engineering Keywords: Deep learning; Head Related Transfer Function (HRTF); Restoration; Ambisonics; Spatial Audio; Spherical harmonic; Audio signal processing; Denoising; Auto-Encoder; Neural Network
Online: 10 July 2020 (08:58:11 CEST)
Spherical harmonic (SH) interpolation is a commonly used method to spatially up-sample sparse Head Related Transfer Function (HRTF) datasets to denser HRTF datasets. However, depending on the number of sparse HRTF measurements and SH order, this process can introduce distortions in high frequency representation of the HRTFs. This paper investigates whether it is possible to restore some of the distorted high frequency HRTF components using machine learning algorithms. A combination of Convolutional Auto-Encoder (CAE) and Denoising Auto-Encoder (DAE) models is proposed to restore the high frequency distortion in SH interpolated HRTFs. Results are evaluated using both Perceptual Spectral Difference (PSD) and localisation prediction models, both of which demonstrate significant improvement after the restoration process.
ARTICLE | doi:10.20944/preprints202301.0093.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Variational Auto-Encoder; topological machine learning; nonlinear dimensionality reduction; Topological Data Analysis; data visualization; representation learning; Betti number; persistence homology; simplicial complex; simplicial regularization
Online: 5 January 2023 (02:47:49 CET)
Variational Auto-Encoders (VAEs) are deep generative models used for unsupervised learning, however their standard version is not topology-aware in practice since the data topology may not be taken into consideration. In this paper, we propose two different approaches with the aim to preserve the topological structure between the input space and the latent representation of a VAE. Firstly, we introduce InvMap-VAE as a way to turn any dimensionality reduction technique, given an embedding it produces, into a generative model within a VAE framework providing an inverse mapping into original space. Secondly, we propose the Witness Simplicial VAE as an extension of the Simplicial Auto-Encoder to the variational setup using a witness complex for computing the simplicial regularization, and we motivate this method theoretically using tools from algebraic topology. The Witness Simplicial VAE is independent of any dimensionality reduction technique and together with its extension, Isolandmarks Witness Simplicial VAE, preserves the persistent Betti numbers of a data set better than a standard VAE.
ARTICLE | doi:10.20944/preprints202303.0070.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: encoder; decoder; seq2seq; LSTM; RNN; chatbots
Online: 3 March 2023 (10:08:32 CET)
Chatbots are extensively needed in customer services to handle customer inquiries, such as tracking orders or providing information about products and services. One of the most reliable implementations of chatbots is using the common architectures of LSTM networks named Seq2Seq networks. The networks are using an encoder and a decoder. Seq2Seq chatbot is a type of chat system that is professional enough to pass the Turing test. The Turing test is a way of deciding the accuracy of the machine by examining its response, it should appear like a human response. In this research, we will introduce a novel architecture that can pass the Turing test. The seq2seq Accuracy is improved by making incremental training to the chatbot. The new proposal provides higher accuracy and high similarity to human chat responses.
ARTICLE | doi:10.20944/preprints202304.1268.v1
Subject: Public Health And Healthcare, Physical Therapy, Sports Therapy And Rehabilitation Keywords: Keywords: OpenPose (OP); MediaPipe (MP); Rehabilitation; Tree Structure Skeleton Image (TSSI); Tree Structure Skeleton Color Image (TSSCI); Variational Auto Encoder (VAE); Siamese twins Neural Network; Simulator; Human body movements
Online: 30 April 2023 (07:10:50 CEST)
In this article, we introduce a new approach to human movement by defining the movement as a static object or a super object in one two-dimensional image. This method can allow researchers to label and describe the total movement as an object isolated from a reference video. This ap-proach allows us to perform various tasks, including finding similar movements in a video, measuring, and comparing movements, generating new similar movements, and defining chore-ography by controlling specific parameters in the human body skeleton. As a result of the pre-sented approach, we can eliminate the need to label images manually, disregard the problem of finding the beginning and the end of a movement, overcome synchronization issues between movements, and perform any deep learning network-based operation that processes super objects in images in general. As part of this article, we will demonstrate two application use cases: one il-lustrates how to verify and score a requested movement. In contrast, the other illustrates how to generate similar movements in the human skeleton space by addressing the challenge of supply-ing sufficient training data for deep learning applications (DL). A Variational Auto Encoder (VAE) simulator and an EfficientNet-B7 classifier architecture embedded within a Siamese twin neural network are presented in this paper in order to demonstrate two use cases. These use cases demonstrated the versatility of our innovative concept in measuring, categorizing, inferring hu-man behavior, and generating gestures for other researchers.
ARTICLE | doi:10.20944/preprints202307.2003.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Artificial Neural Networks; Machine Learning; Optical Encoder
Online: 28 July 2023 (13:32:04 CEST)
Artificial neural networks are a powerful tool for managing data that is difficult to process and interpret. This paper presents the study of artificial neural networks for information processing generated by an optical encoder based on the polarization of light. A machine learning technique is proposed to train the neural networks, such that the system can predict with remarkable accuracy the angular position in which the rotating element of the neuro-encoder is located, based on information provided by light’s phase shifting arrangements. The proposed neural designs show excellent performance in small angular intervals, and a methodology is proposed to avoid losing this remarkable characteristic in measurements from 0 to 180o or even to 360o. The neuro-encoder is implemented in simulation stage to obtain performance results. This study can be useful to improve capabilities of resolvers or other polyphasic sensors.
ARTICLE | doi:10.20944/preprints202203.0206.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: Ratemaking; Machine Learning; Explainability; Auto Insurance
Online: 15 March 2022 (10:59:32 CET)
This paper explores the tuning and results of two-part models on rich datasets provided through the Casualty Actuarial Society (CAS). These data sets include BI (bodily injury), PD (property damage) and COLL (collision) coverage, each documenting policy characteristics and claims across a four year period. The datasets are explored, including summaries of all variables, then the methods for modeling are set forth. Models are tuned and the tuning results are displayed, after which we train the final models and seek to explain select predictions. All of the code will be made available on GitHub. Data was provided by a private insurance carrier to the CAS after anonymizing the data set. This data is available to actuarial researchers for well-defined research projects that have universal benefit to the insurance industry and the public. Our hope is that the methods demonstrated here can be a good foundation for future ratemaking models to be developed and tested more efficiently.
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Amharic script; Attention mechanism; OCR; Encoder-decoder; Text-image
Online: 15 October 2020 (13:42:28 CEST)
In the present, the growth of digitization and worldwide communications make OCR systems of exotic languages a very important task. In this paper, we attempt to develop an OCR system for one of these exotic languages with a unique script, Amharic. Motivated by the recent success of the Attention mechanism in Neural Machine Translation (NMT), we extend the attention mechanism for Amharic text-image recognition. The proposed model consists of CNNs and attention embedded recurrent encoder-decoder networks that are integrated following the configuration of the seq2seq framework. The attention network parameters are trained in an end-to-end fashion and the context vector is injected, with the previously predicted output, at each time steps of decoding. Unlike the existing OCR model that minimizes the CTC objective function, the new model minimizes the categorical cross-entropy loss. The performance of the proposed attention-based model is evaluated against the test dataset from the ADOCR database which consists of both printed and synthetically generated Amharic text-line images and achieved promising results with a CER of 1.54% and 1.17% respectively.
ARTICLE | doi:10.20944/preprints202302.0438.v1
Subject: Engineering, Automotive Engineering Keywords: Auto-labeled; LiDAR, Point of View, Deep Learning
Online: 27 February 2023 (04:12:52 CET)
Auto-labeling is one of the main challenges in 3D vehicle detection. Auto-labeled datasets can be used to identify objects in LiDAR data, which is a challenging task due to the large size of the dataset. In this work, we propose a novel methodology to generate new 3D based auto-labeling datasets with a different point of view setup than the one used in the most recognized datasets (KITTI, WAYMO, etc.). The performance of the methodology has been further demonstrated with the development of our own dataset with the auto-generated labels and tested under boundary conditions on a bridge in a fixed position. The proposed methodology is based on the YOLO model trained with the KITTI dataset. From a camera-LiDAR sensory fusion, it is intended to auto-label new datasets while maintaining the consistency of the Ground Truth. The main contribution of this work is a novel methodology to auto-label autonomous driving datasets using YOLO as the main labelling system. The performance of this approach is measured retraining the contrast models of the KITTI benchmark.
ARTICLE | doi:10.20944/preprints201804.0258.v2
Subject: Computer Science And Mathematics, Information Systems Keywords: audio classification; multi-resolution analysis; LSTM; auto-ml
Online: 19 July 2018 (05:53:20 CEST)
We describe a multi-resolution approach for audio classification and illustrate its application to the open data set for environmental sound classification. The proposed approach utilizes a multi-resolution based ensemble consisting of targeted feature extraction of approximation (coarse scale) and detail (fine scale) portions of the signal under the action of multiple transforms. This is paired with an automatic machine learning engine for algorithm and parameter selection and the LSTM algorithm, capable of mapping several sequences of features to a predicted class membership probability distribution. Initial results show an improvement in multi-class classification accuracy.
ARTICLE | doi:10.20944/preprints202306.0152.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Fine-grained Image Recognition; Yolov5; Transformer Encoder Block; Attention Mechanism
Online: 2 June 2023 (08:09:14 CEST)
Fine-grained image classification remains an ongoing challenge in the computer vision field, which is particularly intended to identify objects within sub-categories. It is a difficult task since there is a minimal and substantial intra-class variance. The current methods address the issue by first locating selective regions with Region Proposal Networks (RPN), object localization, or part localization, followed by implementing a CNN Network or SVM classifier to those selective regions. This approach, however, makes the process simple by implementing a single-stage end-to-end feature encoding with a localization method, which leads to improved feature representations of individual tokens/regions by integrating the transformer encoder blocks into the Yolov5 backbone structure. These Transformer Encoder Blocks, with their self-attention mechanism, effectively captured the global dependencies and enabled the model to learn relationships between distant regions. This improved the model ability to understand context and captured long-range spatial relationships in the image. We also replaced the Yolov5 detection heads with three transformer heads at the output for object recognition using the discriminative and informative features maps from transformer encoder blocks. We established the potential of the single stage detector for the fine-grained image recognition task, by achieving state of the art 93.4% accuracy, as well as outperforming the existing Yolov5 model. The effectiveness of our approach is assessed using the Stanford car dataset, which includes 16,185 images of 196 different classes of vehicles with significantly identical visual appearances.
ARTICLE | doi:10.20944/preprints202301.0402.v1
Subject: Computer Science And Mathematics, Mathematics Keywords: Sequence Encoder; Autoregressive Sequence; Separated Model; Statistical Test; Neural Network
Online: 23 January 2023 (08:30:48 CET)
While the language model using the stop sign as an independent token has been widely used to decide when the model should stop, it may lead to the growth of vocabulary dimensions and further problems. Similarly, present research on game algorithms usually estimate stopping point related problems based on the evaluation of the winning rate. However, information redundancy may also exist in such models, thus increasing the training difficulty. Above two types of tasks (and similar autoregressive tasks) show a common problem of stopping point prediction. In this paper, we describe a design of separated model, trying to separate the complexity of stopping point prediction from the main task model, so that the information used for estimating stopping point can be reduced. On this basis, in order to verify the rationality of using separated model, we propose a model-free test method. It judges the separability of transformed data based on point difference and sequence difference metrics. In this way, it can predict the credibility of the separated model inference.
ARTICLE | doi:10.20944/preprints201805.0414.v1
Subject: Engineering, Mechanical Engineering Keywords: auto-alignment; intelligent stereo camera; stereo film; three-dimensional
Online: 28 May 2018 (15:57:25 CEST)
This study presents an instant preview and analysis system implementation of intelligent stereo cameras (ISCs). A parameter optimization prototype adopted the instant preview and analysis system of the ISCs has been achieved the automatic alignment function, and obtained optimal stereo films with the automatic alignment function by adjusting gap and angle between dual cameras. The instant preview and analysis system of the ISCs with parameter optimization can enhance the quality of stereo films effectively and reduce filmed errors and save retouched cost and time in harsh filmed environment.
ARTICLE | doi:10.20944/preprints202212.0532.v1
Subject: Computer Science And Mathematics, Computer Science Keywords: lung nodule segmentation; 3D segmentation; dual-encoder-based CNN; hard attention
Online: 28 December 2022 (09:03:49 CET)
Measuring pulmonary nodules accurately can help with early diagnosis of lung cancer, which can improve a patient’s chances of survival. Many methods for segmenting nodules have been developed, but they all rely on input from radiologists in the form of a 3D volume of interest (VOI) or use a constant region of interest (ROI) and only consider the presence of nodule within the given VOI. These approaches limit the networks’ ability to detect nodules outside the given VOI and can also include unnecessary structures in the VOI, leading to potentially inaccurate segmentation. In this work, we propose a novel approach for 3D lung nodule segmentation by using 2D region of interest (ROI) inputted from radiologist or computer-aided detection (CADe) system. Particularly, we design a dual-encoder-based hard attention network (DEHA-Net) which incorporates the full slice of thoracic computed tomography scan along with the ROI mask to produce an accurate segmentation mask of lung nodule in the given slice. The proposed architecture exploits the adaptive region of interest (A-ROI) algorithm to automatically investigates the penetration of lung nodule into surrounding slices while eliminating the need to drawing separate ROIs in each slice. Further, the framework performs the multi-view analysis, i.e., in sagittal and coronal views, to improve the segmentation performance. The proposed scheme has been rigorously evaluated on the lung image database consortium and image database resource initiative (LIDC/IDRI) dataset and an extensive analysis of results have been performed. The quantitative analysis shows that the proposed method not only improves the existing state-of-the-art in term of dice score but also, significantly robust against the different types, shape and dimensions of lung nodules.
ARTICLE | doi:10.20944/preprints202211.0090.v1
Subject: Engineering, Industrial And Manufacturing Engineering Keywords: domain generalization; contrastive learning; classification; deep learning; encoder; Zero-Shot Learning
Online: 4 November 2022 (07:29:50 CET)
A common challenge in real-world classification scenarios with sequentially appending target domain data is insufficient training datasets during the training phase. Therefore, conventional deep learning and transfer learning classifiers are not applicable especially when individual classes are not represented or are severely underrepresented at the outset. Domain Generalization approaches reach their limits when domain shifts become too large, making them occasionally unsuitable as well. In many (technical) domains, however, it is only the defect/ worn/ reject classes that are insufficiently represented, while the non-defect class is often available from the beginning. The proposed classification approach addresses such conditions and is based on a CNN encoder. Following a contrastive learning approach, it is trained with a modified triplet loss function using two datasets: Besides the non-defective target domain class (= 1st dataset), a state-of-the-art labeled source domain dataset that contains highly related classes (e.g., a related manufacturing error or wear defect) but originates from a (highly) different domain (e.g., different product, material, or appearance) (= 2nd dataset) is utilized. The approach learns the classification features from the source domain dataset while at the same time learning the differences between the source and the target domain in a single training step, aiming to transfer the relevant features to the target domain. The classifier becomes sensitive to the classification features and – by architecture – robust against the highly domain-specific context. The approach is benchmarked in a technical and a non-technical domain and shows convincing classification results. In particular, it is shown that the domain generalization capabilities and classification results are improved by the proposed architecture, allowing for larger domain shifts between source and target domains.
ARTICLE | doi:10.20944/preprints202006.0343.v1
Subject: Engineering, Control And Systems Engineering Keywords: Microphone; Nonlinear auto regressive moving average-L2; Model predictive control
Online: 28 June 2020 (19:38:35 CEST)
In this paper, a capacitor microphone system is presented to improve the conversion of mechanical energy to electrical energy using a nonlinear auto regressive moving average-L2 (NARMA-L2) and model predictive control (MPC) controllers for the analysis of the open loop and closed loop system. The open loop system response shows that the output voltage signal need to be improved. The comparison of the closed loop system with the proposed controllers have been analyzed and a promising result have been obtained using Matlab/Simulink.
ARTICLE | doi:10.20944/preprints202104.0630.v1
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Paraphrase Identification; Paraphrase Generation; Natural Language Generation; Language Model; Encoder Decoder; Transformer
Online: 23 April 2021 (10:35:20 CEST)
Paraphrase Generation is one of the most important and challenging tasks in the field of Natural Language Generation. The paraphrasing techniques help to identify or to extract/generate phrases/sentences conveying the similar meaning. The paraphrasing task can be bifurcated into two sub-tasks namely, Paraphrase Identification (PI) and Paraphrase Generation (PG). Most of the existing proposed state-of-the-art systems have the potential to solve only one problem at a time. This paper proposes a light-weight unified model that can simultaneously classify whether given pair of sentences are paraphrases of each other and the model can also generate multiple paraphrases given an input sentence. Paraphrase Generation module aims to generate fluent and semantically similar paraphrases and the Paraphrase Identification systemaims to classify whether sentences pair are paraphrases of each other or not. The proposed approach uses an amalgamation of data sampling or data variety with a granular fine-tuned Text-To-Text Transfer Transformer (T5) model. This paper proposes a unified approach which aims to solve the problems of Paraphrase Identification and generation by using carefully selected data-points and a fine-tuned T5 model. The highlight of this study is that the same light-weight model trained by keeping the objective of Paraphrase Generation can also be used for solving the Paraphrase Identification task. Hence, the proposed system is light-weight in terms of the model’s size along with the data used to train the model which facilitates the quick learning of the model without having to compromise with the results. The proposed system is then evaluated against the popular evaluation metrics like BLEU (BiLingual Evaluation Understudy):, ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR, WER (Word Error Rate), and GLEU (Google-BLEU) for Paraphrase Generation and classification metrics like accuracy, precision, recall and F1-score for Paraphrase Identification system. The proposed model achieves state-of-the-art results on both the tasks of Paraphrase Identification and paraphrase Generation.
ARTICLE | doi:10.20944/preprints202007.0474.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Convolutional Neural Network; Encoder-Decoder Architecture; Semantic Segmentation; Feature Silencing; Crack Detection
Online: 21 July 2020 (13:54:13 CEST)
An autonomous concrete crack inspection system is necessary for preventing hazardous incidents arising from deteriorated concrete surfaces. In this paper, we represent a concrete crack detection framework to aid the process of automated inspection. The proposed approach employs a deep convolutional neural network architecture for crack segmentation from concrete image. The proposed network alleviates the effect of gradient vanishing problem present in deep neural network architectures. A feature silencing module is incorporated in the crack detection framework, for eliminating unnecessary feature maps from the network. The overall performance of the network significantly improves as a result. Experimental results support the benefit of incorporating feature silencing within a convolutional neural network architecture for improving the network’s robustness, sensitivity, and specificity. An added benefit of the proposed architecture is its ability to accommodate for the trade-off between specificity (positive class detection accuracy) and sensitivity (negative class detection accuracy) with respect to the target application. Furthermore, the proposed framework achieves a high precision rate and processing time than crack detection architectures present in literature.
ARTICLE | doi:10.20944/preprints202201.0392.v1
Subject: Medicine And Pharmacology, Ophthalmology Keywords: Pterygium; Pterygium surgery; Amniotic membrane; Conjunctival auto-graft; Polish Caucasian population
Online: 26 January 2022 (11:54:17 CET)
This study compares the efficacy of the two most commonly used surgical methods for pterygium in the Polish population, conjunctival autograft versus amniotic membrane transplantation, and to evaluate the postopera-tive recurrence rate. We retrospectively analysed the medical records of 65 patients who underwent surgery for primary or recurrent pterygium at an ophthalmology clinic in Bialystok, Poland between 2016 and 2020. Surgical success (no regrowth) occurred in almost half of the amniotic membrane patients (44%) and in most of the conjunctival autograft patients (79%); this was a significant relationship. The odds of successful surgery were 79% lower for subjects with amniotic membranes than for those with conjunc-tival autografts (OR with 95% CI = 0.21 (0.05; 0.94]; p = 0.045). Our study confirms that in Polish Caucasian population the success rate of the pro-cedure using conjunctival autograft versus the use of amniotic membrane, is in favoured for the procedure with conjunctival graft.
ARTICLE | doi:10.20944/preprints202005.0288.v1
Subject: Computer Science And Mathematics, Probability And Statistics Keywords: COVID-19; Accelerated Failure Time; Proportional Hazard Model; Bayesian; Auto-Regression
Online: 17 May 2020 (08:50:22 CEST)
The constant news about the corona virus is scary. It is not possible to separate treatment for Cancer due to COVID-19. An effective treatment comparison strategy is needed. We need to have a handy tool to understand cancer progression in this unprecedented scenario. Linking different events of cancer progression is the need of the hour. It is a methodological challenge. We provide the solutions to overcome the issue with interval between two consecutive events in motivating head and neck cancer (HNC) data.
ARTICLE | doi:10.20944/preprints201808.0251.v1
Subject: Social Sciences, Behavior Sciences Keywords: potts network; attractor neural networks; auto-associative memory; cortex; semantic memory
Online: 14 August 2018 (12:33:52 CEST)
A statistical analysis of semantic memory should reflect the complex, multifactorial structure of the relations among its items. Still, a dominant paradigm in the study of semantic memory has been the idea that the mental representation of concepts is structured along a simple branching tree spanned by superordinate and subordinate categories. We propose a generative model of item representation with correlations that overcomes the limitations of a tree structure. The items are generated through "factors" that represent semantic features or real-world attributes. The correlation between items has its source in the extent to which items share such factors and the strength of such factors: if many factors are balanced, correlations are overall low; whereas if a few factors dominate, they become strong. Our model allows for correlations that are neither trivial nor hierarchical, but may reproduce the general spectrum of correlations present in a data-set of nouns. We provide an estimate of the number of concepts that can be stored and retrieved by a large-scale cortical network, the Potts network, which is perhaps approximately 107 with human cortical parameters. When this storage capacity is exceeded, however, retrieval fails completely only for balanced factors; above a critical degree of imbalance, a phase transition leads to a regime where the network still extracts considerable information about the cued item, even if not recovering its detailed representation: partial categorization seems to emerge spontaneously as a consequence of the dominance of particular factors, rather than being imposed ad hoc. We argue this to be a relevant model of semantic memory resilience in Tulving’s remember/know paradigms.
ARTICLE | doi:10.20944/preprints201806.0247.v1
Subject: Computer Science And Mathematics, Analysis Keywords: data mining; association rule learning; policyholder lapse; auto insurance; market inefficiency
Online: 15 June 2018 (09:01:03 CEST)
For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.
ARTICLE | doi:10.20944/preprints202308.2087.v1
Subject: Computer Science And Mathematics, Computer Vision And Graphics Keywords: Colony-Forming Unit; Deep Learning; Segmentation; U-Net; Encoder-Decoder; Loss Function; Localization
Online: 30 August 2023 (12:06:16 CEST)
The Colony-Forming Unit (CFU) counting problem remains a complex issue without a universal solution in biomedical and food safety domains. A multitude of sophisticated heuristics and segmentation-driven approaches have been proposed by numerous researchers. Among those, U-Net is the most frequently cited and popular Deep Learning method. The latter approach provides a segmentation output map and requires an additional counting procedure which accounts for unique segmented regions and detected microbial colonies. However, because of pixel-based targets it tends to generate irrelevant artifacts or errant pixels, leading to inaccurate and mixed post-processing results. In response to these challenges, we propose a novel hybrid counting approach, incorporating a multi-loss U-Net reformulation and a post-processing Petri dish localization algorithm. First of all, our unique innovation lies in the multi-loss U-Net reformulation. We introduce an additional loss term at the bottleneck U-Net layer, focusing on delivering an auxiliary signal indicating where to look for distinct CFUs. Second, our novel localization algorithm accurately incorporates an agar plate and its bezel into the CFU counting routines. Finally, our proposition is further enhanced by the integration of a fully automated solution. This comprises a specially designed uniform Petri dish illumination system and a counting web application. The latter application is capable of directly receiving images from the camera, which are subsequently processed, and the segmentation results are sent back to the user. This feature provides an opportunity to correct the CFU counts, offering a feedback loop that contributes to the continued development of the Deep Learning model. Through extensive experimentation, we have found that all probed multi-loss U-Net architectures incorporated in our hybrid approach consistently outperform their single-loss counterparts which utilize exclusively the combination of Tversky and Cross-Entropy training losses at the output U-Net layer. We report further significant improvements by the means of our novel localization algorithm. This reaffirms the effectiveness of our proposed hybrid solution in addressing contemporary challenges of the precise in-vitro CFU counting.
ARTICLE | doi:10.20944/preprints202307.0072.v1
Subject: Engineering, Marine Engineering Keywords: Strong wind gust; early warning; ensemble forecasts; severe convection; auto-response system
Online: 4 July 2023 (03:17:16 CEST)
Ship pilots and maritime safety administration have an urgent need for more accurate and earlier warnings on strong wind gusts. This study firstly investigates the “Oriental Star” cruise ship cap-sizing event in 2015, one of the deadliest shipwreck events in recent years, and explores all related hydro-meteorological components in a global mesoscale model. It is found that, rather than the missing signal in raw surface wind prediction, the cumulus precipitation variable (CP) increases dramatically during the accident occurrence, which significantly corresponds to the sub-grid strong wind gust. The effective lead-time can be extended from 24hr (deterministic model) to 48hr (en-semble model). This finding is then verified in another two recent deadly cruise boat accidents. The introduction of the new variable is bearing the hope of improvement to the current maritime safeguard system, on predicting sub-grid strong wind gusts for small-size cruise boats in offshore and inland rivers. Finally, an automatic-response system is developed to provide economical con-vection prediction via Inmarsat email communication, aiming to explore operational severe con-vective gust early-warning and appropriate numerical mesoscale model application.
REVIEW | doi:10.20944/preprints202306.1714.v1
Subject: Chemistry And Materials Science, Materials Science And Technology Keywords: Charge transfer; Interparticle boundary; Metastable states; Molecular dispersion; Auto-liquefaction; Nanoglassy states:
Online: 25 June 2023 (05:02:22 CEST)
Mechanochemical technology is developing rapidly, judging by the scientific information in both basic and applied studies. However, many issues and points of view remain to be discussed. This review presents some new key issues for the optimization of mechanochemical processes in theoretical and practical aspects. Emphasis is placed on powder technology aspects, which are not always discussed compared to functional or microscopic viewpoints. The transfer of chemical species across the interparticle interface between organic and inorganic species during the mechanosynthesis of nanocomposite oxides offers many new possibilities, which are discussed here in detail. Since all material transport is preceded by charge transfer, its driving force has been searched for using terminology beyond the well-established electrochemical ones. The role of organic compounds during the whole process is emphasized, regardless of their survival in the final product. The similarity with the pharmaceutical phenomena is pointed out, although its mentality is very different from that of the synthesis of nanocomposites. Not only for the rational amorphization of the active pharmaceutical ingredient, but also for the stabilization of molecular dispersion states with the participation of excipients are discussed. The effects of liquids, either added or formed by mechanochemical auto-liquefaction, are presented with reference to the comparison between wet and dry grinding. Finally, mechanisms of apparent stabilization of mechanically activated states of products are elucidated to investigate the practical applicability of mechanochemically synthesized products
ARTICLE | doi:10.20944/preprints202201.0331.v1
Subject: Environmental And Earth Sciences, Geochemistry And Petrology Keywords: Concentration field; Spatial auto-correlation; Association rules; Apriori algorithm; Element co-occurrence
Online: 21 January 2022 (13:42:44 CET)
The spatial distribution of elements can be regarded as a numerical field of concentration values with a continuous spatial coverage. An active area of research is to discover geologically meaningful relationships among elements from their spatial distribution. To solve this problem, we propose an association rule mining method based on clustered events of spatial auto-correlation and applied it to the polymetallic deposits of the Chahanwusu River area, Qinghai Province, China. The elemental data for stream sediments were first clustered into HH (high-high), LL (low-low), HL (high-low), and LH (low-high) groups by using local Moran’s I clustering map (LMIC). Then the Apriori algorithm was used to mine the association rules among different elements in these clusters. More than 86% of the mined rule points are located within 1000 m of faults and near known ore occurrences, and occur in the upper reaches of the stream and catchment areas. In addition, we found that the Indosinian granodiorite is enriched in sulfophile elements, e.g., Zn, Ag and Cd, and the Variscan granite quartz diorite (P1γδο) coexists with Cu and associated elements. Therefore, the proposed algorithm is an effective method for mining co-existence patterns of elements and provides an insight into their enrichment mechanisms.
ARTICLE | doi:10.20944/preprints201804.0057.v1
Subject: Arts And Humanities, Archaeology Keywords: auto-extraction; remote sensing archaeology; tuntian; LATTICs; GF-1; Silk Road; Miran
Online: 4 April 2018 (11:56:47 CEST)
This paper describes the use of the Chinese Gaofen-1 (GF-1) satellite imagery to automatically extract tertiary Linear Archaeological Traces of Tuntian Irrigation Canals (LATTICs) located in the Miran site. The site is adjacent to the ancient Loulan Kingdom at the eastern margin of the Taklimakan Desert in western China. GF-1 data was processed following atmospheric and geometric correction, and spectral analyses were carried out for multispectral data. The low values produced by SSI indicate that it is difficult to distinguish buried tertiary LATTICs from similar backgrounds using spectral signatures. Thus, based on the textual characteristics of high-resolutionGF-1 panchromatic data, this paper proposes an automatic approach that combines joint morphological bottom and hat transformation with a Canny edge operator. The operator was improved by adding stages of geometric filtering and gradient vector direction analysis. Finally, the detected edges of tertiary LATTICs were extracted using the GIS-based draw tool and converted into shapefiles for archaeological mapping within a GIS environment. The proposed automatic approach was verified with an average accuracy of 95.76% for 754 tertiary LATTICs in the entire Miran site and compared with previous manual interpretation results. The results indicate that GF-1 VHR PAN imagery can successfully uncover the ancient tuntian agricultural landscape. Moreover, the proposed method can be generalized and applied to extract linear archaeological traces such as soil and crop marks in other geographic locations.
HYPOTHESIS | doi:10.20944/preprints202303.0441.v1
Subject: Medicine And Pharmacology, Pulmonary And Respiratory Medicine Keywords: IgG4 antibodies; mRNA vaccines; immuno-tolerance; auto-immunity; SARS-CoV-2; COVID-19.
Online: 27 March 2023 (03:56:26 CEST)
Due to the health crisis caused by SARS-CoV-2, the creation of a new vaccine platform based on mRNA was implemented. Globally, around 13.32 billion COVID-19 vaccine doses of diverse platforms have been given, and up to this date, 69.7% of the total population received at least one injection of a COVID-19 vaccine. Although these vaccines prevent hospitalization and severe forms of the disease, increasing evidence has shown they do not produce sterilizing immunity, allowing people to suffer frequent re-infections. Recent research has also raised concerns that mRNA vaccines could induce immune tolerance, which, added to that caused by the virus itself, could complicate the clinical course of a COVID-19 infection. Furthermore, recent investigations have found high IgG4 levels in people who were administered two or more injections of mRNA vaccines. It has been suggested that an increase in IgG4 levels could have a protecting role by preventing immune over-activation, similar to that occurring during successful allergen-specific immunotherapy by inhibiting IgE-induced effects. Altogether, evidence suggests that the reported increase in the IgG4 levels detected after repeated vaccination with the mRNA vaccines is not a protective mechanism; rather, it may be a part of the immune tolerance mechanism to the spike protein that could promote unopposed SARS-CoV2 infection and replication by suppressing natural antiviral responses. IgG4-induced suppression of the immune system due to repeated vaccination can also cause autoimmune diseases, promotes cancer growth, and autoimmune myocarditis in susceptible individuals.
ARTICLE | doi:10.20944/preprints201708.0019.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Bioimpedance Spectroscopy; Field Programmable Gate Array; Digital Auto Balance Bridge; Multichannel data acquisition;
Online: 4 August 2017 (16:05:29 CEST)
This paper presents the design and implementation of a multichannel bio-impedance spectroscopy system on field programmable gate arrays (FPGA). The proposed system is capable of acquiring multiple signals from multiple bio-impedance sensors, process the data on the FPGA and store the final data in the on-board Memory. The system employs the Digital Automatic Balance Bridge (DABB) method to acquire data from biosensors. The DABB measures initial data of a known impedance to extrapolate the value of the impedance for the device under test. This method offers a simpler design because the balancing of the circuit is done digitally in the FPGA rather than using an external circuit. Calculations of the impedance values for the device under test were done in the processor. The final data is sent to an onboard Flash Memory to be stored for later access. The control unit handles the interfacing and the scheduling between these different modules (Processor, Flash Memory) as well as interfacing to multiple Balance Bridge and multiple biosensors. The system has been simulated successfully and has comparable performance to other FPGA based solutions. The system has a robust design that is capable of handling and interfacing input from multiple biosensors. Data processing and storage is also performed with minimal resources on the FPGA.
ARTICLE | doi:10.20944/preprints202002.0273.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: linguistic knowledge; neural machine translation model; machine translation tasks; knowledge-based encoder; model representation ability
Online: 19 February 2020 (10:51:41 CET)
Incorporating source-side linguistic knowledge into the neural machine translation (NMT) model has recently achieved impressive performance on machine translation tasks. One popular method is to generalize the word embedding layer of the encoder to encode each word and its linguistic features. The other method is to change the architecture of the encoder to encode syntactic information. However, the former cannot explicitly balance the contribution from the word and its linguistic features. The latter cannot flexibly utilize various types of linguistic information. Focusing on the above issues, this paper proposes a novel NMT approach that models the words in parallel to the linguistic knowledge by using two separate encoders. Compared with the single encoder based NMT model, the proposed approach additionally employs the knowledge-based encoder to specially encode linguistic features. Moreover, it shares parameters across encoders to enhance the model representation ability of the source-side language. Extensive experiments show that the approach achieves significant improvements of up to 2.4 and 1.1 BLEU points on Turkish→English and English→Turkish machine translation tasks, respectively, which indicates that it is capable of better utilizing the external linguistic knowledge and effective improving the machine translation quality.
ARTICLE | doi:10.20944/preprints202306.0215.v1
Subject: Engineering, Automotive Engineering Keywords: Auto vehicle; Braking system efficiency; Brake pads and disc wear; Road safety; Efficiency parameters
Online: 2 June 2023 (14:53:30 CEST)
The vehicles number continuously growing lead to increasingly intense and congested traffic and it will additionally demand the braking system, and drivers behave more aggressively and as result is required that the braking system to be durable and efficient. For this is necessary the study the braking system behavior in conditions of intense and moderate traffic to increase the safety of traffic participants, respectively to demonstrate the need for more frequent replacement of some braking system elements. Thus, on a vehicle were performed a series of successive tests, through which the degree of wear of the brake pads and discs was monitored periodically and as a result the efficiency evolution of the braking system. The tests were carried out both in laboratory (on dynamometer) and in traffic to establish the efficiency of the braking system according to some parameters considered essential. The experimental tests showed that the recommendations regarding the frequency of replacement of brake pads and disks are inconsistent with their actual wear. Therefore, the aim of this paper is the establishment of the braking system efficiency of an auto-vehicle, subject to testing depending on the auto-vehicle mass, travel speed, distance driven, and braking time, based on experimental on stand and in-traffic tests, according the road safety regulations.
ARTICLE | doi:10.20944/preprints201608.0168.v1
Subject: Business, Economics And Management, Economics Keywords: natural capital; human capital; economic growth; small economies; Vector Auto regression; natural resource curse
Online: 18 August 2016 (05:13:21 CEST)
The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.
ARTICLE | doi:10.20944/preprints202306.0085.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Machine Learning; Groundwater Level; Auto-Regressive Integrated Moving Average; Artificial Neural Network; Data-Driven Models
Online: 1 June 2023 (12:13:24 CEST)
Variations in groundwater levels that are noticed in different parts of the world are typically the primary focus of research carried out in hydrology. The fluctuating levels of groundwater can be ascribed to several different factors, some of which include an increase in the demand for water, inappropriate irrigation practices, improper management of soil, and unregulated extraction from aquifers. It is necessary to effectively manage groundwater resources to have a trustworthy method of measuring and forecasting groundwater levels. The dynamics of groundwater are inherently uneven and difficult to understand. As a result, applying methods driven by data could potentially yield significant gains in hydrology. In this study, two data-driven models were utilized to estimate groundwater levels at a total of 4 monitoring wells located in Colorado state, USA. These models included ARIMA and ANN. The comparative analysis of the algorithms made use of a data set that had one month's worth of information from each of the years 1980 to 2019. The models were put through their paces, and the results of those tests were statistically and visually assessed. To evaluate the relative accuracy and precision of the models, the mean absolute error (MAE), mean square error (MSE), and root-mean-square error (RMSE) were used. When compared to ARIMA, the analysis demonstrates that ANN can produce the most accurate forecasts of groundwater levels in Colorado state USA.
ARTICLE | doi:10.20944/preprints202305.1238.v1
Subject: Computer Science And Mathematics, Information Systems Keywords: low-code; no-code; machine learning; auto ML; ML platform; data scientist scarcity; projects overruns
Online: 17 May 2023 (10:46:06 CEST)
In the context of developing machine learning models, until and unless we have the required data engineering and machine learning development competencies as well as the time to train and test different machine learning models and tune their hyperparameters, it is worth trying out the automatic machine learning features provided by several cloud-based and cloud-agnostic platforms. This paper explores the possibility of generating automatic machine learning models with low-code experience. We have developed criteria to compare different machine learning platforms for generating automatic machine learning models and presenting their results. Thereafter, lessons learned by developing automatic machine learning models from a sample dataset across four different machine learning platforms were elucidated. We have also interviewed machine learning experts to conceptualize their domain-specific problems that automatic machine learning platforms could address. Results showed that automatic machine learning platforms could provide a fast track for organizations seeking digitalization of their businesses. Automatic machine learning platforms help produce results, especially for time-constrained projects where resources are lacking. The contribution of this paper is in the form of a lab experiment in which we demonstrate how low-code platforms could provide a viable option to many business cases and henceforth provide a lane that is faster than the usual hiring and training of already scarce data scientists and to analytics projects that suffer from overruns.
ARTICLE | doi:10.20944/preprints201912.0207.v1
Subject: Engineering, Mechanical Engineering Keywords: press bending; orbital auto welding; steel-tube correction; STKN540B; high-strength steel tube; manufacturing process
Online: 16 December 2019 (06:20:58 CET)
The purpose of this study is to propose a consecutive manufacturing process system to secure the productivity of excellent STKN540B steel tube in the respect of economy and safety as the supporting material for mega structures such as building, bridge and ship. The components of consecutive manufacturing are press-bending, orbital auto welding and steel tube correction. By using STKN540B a high-strength steel material with low yield point that requires a special manufacturing process unlike other steel materials, an actual tube manufacturing is carried out at each stage in this experimental study. With this, the quality of steel tube and the efficiency of the manufacturing process are analyzed to draw out some points to improve in the future.
ARTICLE | doi:10.20944/preprints202306.2253.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Epilepsy; Seizure Prediction; Preictal; Federated Learning (FL); Spiking Encoder; Graph Convolutional Neural Network (GCNN); Patient-specific Personalization
Online: 30 June 2023 (14:48:14 CEST)
Electroencephalography (EEG) signals are the primary source for discriminating the preictal from the interictal stage, enabling early warnings before the seizure onset. The epileptic seizure prediction models face significant challenges due to data scarcity, diversity, and privacy. This paper proposes a three-tier architecture for epileptic seizure prediction associated with the Federated Learning (FL) model that enhances the capability by utilizing the significant amount of seizure patterns from globally distributed patients with data privacy. The determination of the preictal state is influenced by global and local model-assisted decision-making by modeling the two-level edge layer. Integrating the Spiking Encoder (SE) with Graph Convolutional Neural Network (Spiking-GCNN) works as the local model trained using the bi-timescale approach. Each local model utilizes the aggregated seizure knowledge from the different medical centers through FL and determines the preictal probability in the coarse-grained personalization. The Adaptive Neuro-Fuzzy Inference System (ANFIS) is utilized in fine-grained personalization to recognize epileptic seizure patients by examining the outcomes of the FL model, heart rate variability features, and patient-specific clinical features. The proposed seizure prediction is evaluated using benchmark datasets by comparing them with the existing works to demonstrate the potential results.
ARTICLE | doi:10.20944/preprints202309.1720.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Transformer; Hyper-Parameter Selection; Planning Target Volume; Auto-segmentation; Prostate Cancer; VT U-Net v.2
Online: 26 September 2023 (07:05:42 CEST)
U-Net, based on a deep convolutional neural network (CNN), has been clinically used to au-to-segment normal organs and potentially target volumes. However, CNNs with local geometric dependencies may limit the accuracy of segmentation. Additionally, the performance of CNNs can vary depending on the selection of network hyper-parameters, which was mitigated by the proposition of nnU-Net. We chose a vision transformer architecture called VT U-Net, which features a self-attention excluding the convolution layer, to overcome the limitations of CNNs by utilizing global geometric information of images. The VT U-Net v.2 became more powerful thanks to the adaptive hyper-parameter optimizer embedded in nnU-Net. However, despite leveraging the benefits of nnU-Net, VT U-Net v.2 still had additional network hyper-parameters that needed to be optimally chosen. Accordingly, among various hyper-parameters, this study attempted to find the optimal combination of the patch size and the embedded dimension regarding the transformer. From the 4-fold cross-validation, the modified VT U-Net v.2 showed the highest average performance for planning target volume (PTV) segmentation among the investigated networks. Though nnU-Net was based on convolution layers, the adaptive hyper-parameter optimizers turned out to enhance the performance. It was also confirmed that network hyper-parameters affected the segmentation accuracy of vision transformers.
ARTICLE | doi:10.20944/preprints202201.0209.v1
Subject: Business, Economics And Management, Economics Keywords: Economic Growth; Gross Fixed Capital Formation; Government Expenditure; Government Deficit; Vector Auto-Regression and South Africa
Online: 14 January 2022 (11:36:07 CET)
The study uses annual time series data from the South Africa Reverse Bank (SARB) from 1980 to 2020 to examine the effectiveness of fiscal policy on economic growth in South Africa. The Augmented Dickey-Fuller (ADF) and Phillips-Perron (PP) unit root tests, as well as the Johansen Co-integration test, Granger causality test, and Vector Auto-Regression (VAR) method, were used in the study. Real GDP per capita (RGDP) is used as proxy of economic growth and gross fixed capital formation (GFCF), government expenditure (GEXP) and government deficit (GOVD) as the proxies of fiscal policy. The ADF test results show that all variables are stationary at the first difference, with the exception of GFCF and GEXP, which are stationary at I(0), whereas the PP test results show that all variables are stationary at I(1), with the exception of GEXP, which is stationary at I(0). At Maximum Eigenvalue, the four variables are not cointegrated. The findings of the Granger causality test demonstrated a unidirectional causation from GOVD to RGDP, as well as a bidirectional causality from RGDP to GFCF and GEXP. Error Correction Model Estimated using VAR shows that GFCF, GEXP have positive effect on RGDP whereas GOVD has a negative effect on RGDP in the short run. The findings also presented that the VAR's residuals are homoscedastic, which means they are normally distributed and have no serial correlation.
ARTICLE | doi:10.20944/preprints202109.0112.v1
Subject: Engineering, Marine Engineering Keywords: 3D point Cloud Classification, 3D point Cloud Shape Completion,Auto-Encoders, Contrastive Learning, Self-Supervised Learning
Online: 6 September 2021 (18:00:28 CEST)
In this paper, we present the idea of Self Supervised learning on the Shape Completion and Classification of point clouds. Most 3D shape completion pipelines utilize autoencoders to extract features from point clouds used in downstream tasks such as Classification, Segmentation, Detection, and other related applications. Our idea is to add Contrastive Learning into Auto-Encoders to learn both global and local feature representations of point clouds. We use a combination of Triplet Loss and Chamfer distance to learn global and local feature representations. To evaluate the performance of embeddings for Classification, we utilize the PointNet classifier. We also extend the number of classes to evaluate our model from 4 to 10 to show the generalization ability of learned features. Based on our results, embedding generated from the Contrastive autoencoder enhances Shape Completion and Classification performance from 84.2% to 84.9% of point clouds achieving the state-of-the-art results with 10 classes.
ARTICLE | doi:10.20944/preprints202305.0194.v1
Subject: Engineering, Mechanical Engineering Keywords: self-adaptive weighted particle swarm optimization algorithm; stacked denoising automatic encoder; kernel extreme learning machine; gearbox; fault diagnosis
Online: 4 May 2023 (05:16:18 CEST)
In a complex working environment, the fault signal of the gearbox is greatly affected by the outside world, and fault feature recognition is difficult, so the fault diagnosis accuracy is difficult to meet the expected requirements. To solve this problem, this paper proposes a gearbox fault diagnosis method based on an optimized stacked denoising auto encoder (SDAE) and kernel extreme learning machine (KELM). Firstly, the Particle Swarm Optimization algorithm in Adaptive Weight (SAPSO) was adopted to optimize the SDAE network structure, and the number of hidden layer nodes, learning rate, noise addition ratio and iteration times were adaptively obtained to make SDAE obtain the best network structure. Then, the best SDAE network structure was used to extract the deep feature information of weak faults in the original signal. Finally, the extracted fault features are fed into KELM for fault classification. Experimental results show that, compared with existing commonly used methods of fault diagnosis, the fault diagnosis model proposed in this paper can reduce the influence of noise in the original signal can better learn the deep-level features in the original signal and has higher diagnosis accuracy, faster diagnosis speed and good generalization in fault diagnosis.
ARTICLE | doi:10.20944/preprints202111.0430.v1
Subject: Chemistry And Materials Science, Analytical Chemistry Keywords: Ab initio DFT/B3LYP/6-311G/Auto calculation; hydrazine; Hirshfeld surface; hydrogen interactions; single-crystal X-ray study
Online: 23 November 2021 (15:03:30 CET)
The crystals, C11H4BrF5N2S, (I), 1-((4-bromothiophen-2-yl)methylene)-2-(perfluorophenyl)hydrazine and C12H6BrF5N2S, (II), 1-((4-bromo-5-methylthiophen-2-yl)methylene)-2-(perfluorophenyl)hydrazine are molecules with two rings and hydrazone part like a centre of the molecule. The compounds have been synthesized and characterized by elemental, spectroscopic (1H-NMR) analysis. The crystal structures of the solid phase were determined by single crystal X-ray diffraction method. They crystallize in the monoclinic space group with Z = 4 and Z = 2 molecules per unit-cell. The compound (I) crystallizes as a racemate in the centrosymmetric space group and the compound (II) crystallizes as a non-racemate in the non-centrosymmetric space group. The “absolute configuration and conformation for bond values” were derived from the anomalous dispersion (ad) for (II). The crystal structures are revealed diverse non-covalent interactions such as intra- and interhydrogen bonding, π-ring···π-ring, C-H···π-ring and they were investigated. The expected stereochemistry of hydrazones atoms C7, N2 and N1 were confirmed for (I) and (II). The hole molecule of the (I), and (II) possesses “a boat conformation” like a 6-membered ring. The results of the single crystal studies are reproduced with the help of Hirshfeld surface study and Gaussian software.
ARTICLE | doi:10.20944/preprints202309.0940.v3
Subject: Engineering, Control And Systems Engineering Keywords: auto-regressive; control and optimization; energy management; recurrent neural network; long short-term memory; microgrid; switched model predictive control
Online: 9 October 2023 (09:41:16 CEST)
Switched model predictive control (S-MPC) and recurrent neural network with long short-term memory (RNN-LSTM) are powerful control methods that have been extensively studied for energy management in microgrids (MGs). These methods are complementary in terms of constraint satisfaction, computational demand, adaptability, and comprehensibility, but typically one method is chosen over the other. The S-MPC method selects optimal models and control strategies dynamically based on the system’s operating mode and performance objectives. On the other hand, integration of auto-regressive (AR) with these powerful control methods improves prediction accuracy and system conditions’ adaptability. This paper compares the two approaches to control and proposes a novel algorithm called Switched Auto-regressive Neural Control (S-ANC) that combines their respective strengths. Using a control formulation equivalent to S-MPC and the same controller model for learning, the results indicate that pure RNN-LSTM cannot provide constraint satisfaction. The novel S-ANC algorithm can satisfy constraints and deliver comparable performance to MPC while enabling continuous learning. Results indicate that S-MPC optimization increases power flows within the MG, resulting in efficient utilization of energy resources. By merging the AR and LSTM, the model’s computational time decreased by nearly 47.2%. Also, this study evaluated our predictive model’s accuracy: (i) R-squared error is 0.951, indicating strong predictive ability, and (ii) mean absolute error (MAE) and mean square error (MSE) values of 0.571 indicate accurate predictions with minimal deviations from actual values.
REVIEW | doi:10.20944/preprints202010.0179.v1
Subject: Biology And Life Sciences, Biochemistry And Molecular Biology Keywords: Non Michaelis-Menten Kinetics; High-throughput screening; allostery; cooperativity; processive kinetics; distributive kinetics; single-molecule; auto-catalytic; drug discovery
Online: 8 October 2020 (13:34:16 CEST)
Biological systems are highly regulated. They are also highly resistant to sudden perturbations enabling them to maintain the dynamic equilibrium essential for sustenance of life. This robustness is conferred by regulatory mechanisms that influence the activity of enzymes/proteins within their cellular context, to adapt to changing environmental conditions. However, the initial rules governing the study of enzyme kinetics were tested and implemented for mostly cytosolic enzyme systems that were easy to isolate and/or recombinantly express. Moreover, these enzymes lacked complex regulatory modalities. Now, with academic labs and pharmaceutical companies turning their attention to more complex systems (for instance, multi-protein complexes, oligomeric assemblies, membrane proteins and post-translationally modified proteins), the initial axioms defined by Michaelis-Menten (MM) kinetics are rendered inadequate and the development of a new kind of kinetic analysis to study these systems is required. The current review strives to present an overview of enzyme kinetic mechanisms that are atypical and, oftentimes, do not conform to the classical MM kinetics. Further, it presents initial ideas on the design and analysis of experiments in early drug-discovery for such systems, to enable effective screening and characterisation of small-molecule inhibitors with desirable physiological outcomes.
ARTICLE | doi:10.20944/preprints202009.0647.v1
Subject: Computer Science And Mathematics, Algebra And Number Theory Keywords: Lung condition; COVID-19; Machine learning; Custom Vision; Core ML; Auto ML; AI; Pneumonia; Smartphone application; Real-time diagnosis
Online: 26 September 2020 (16:14:39 CEST)
AI is leveraging all aspects of life. Medical services are not untouched. Especially in the field of medical image processing and diagnosis. Big IT and Biotechnology companies are investing millions of dollars in medical and AI research. The recent outbreak of SARS COV-2 gave us a unique opportunity to study for a non interventional and sustainable AI solution. Lung disease remains a major healthcare challenge with high morbidity and mortality worldwide. The predominant lung disease was lung cancer. Until recently, the world has witnessed the global pandemic of COVID19, the Novel coronavirus outbreak. We have experienced how viral infection of lung and heart claimed thousands of lives worldwide. With the unprecedented advancement of Artificial Intelligence in recent years, Machine learning can be used to easily detect and classify medical imagery. It is much faster and most of the time more accurate than human radiologists. Once implemented, it is more cost-effective and time-saving. In our study, we evaluated the efficacy of Microsoft Cognitive Service to detect and classify COVID19 induced pneumonia from other Viral/Bacterial pneumonia based on X-Ray and CT images. We wanted to assess the implication and accuracy of the Automated ML-based Rapid Application Development (RAD) environment in the field of Medical Image diagnosis. This study will better equip us to respond with an ML-based diagnostic Decision Support System(DSS) for a Pandemic situation like COVID19. After optimization, the trained network achieved 96.8% Average Precision which was implemented as a Web Application for consumption. However, the same trained network did not perform like Web Application when ported to Smartphone for Real-time inference, which was our main interest of study. The authors believe, there is scope for further study on this issue. One of the main goals of this study was to develop and evaluate the performance of AI-powered Smartphone-based Real-time Applications. Facilitating primary diagnostic services in less equipped and understaffed rural healthcare centers of the world with unreliable internet service.
ARTICLE | doi:10.20944/preprints202303.0034.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: unmanned aerial vehicle (UAV); synthetic aperture radar (SAR); automatic target recognition (ATR); deep neural network (DNN); adversarial example; transferability; encoder-decoder; real-time attack
Online: 2 March 2023 (04:43:20 CET)
In recent years, the unmanned aerial vehicle (UAV) synthetic aperture radar (SAR) has become a highly sought-after topic for its wide applications in the field of target recognition, detection, and tracking. However, SAR automatic target recognition (ATR) models based on deep neural networks (DNN) are suffering from adversarial examples. Generally, non-cooperators rarely disclose any information about SAR-ATR models, making adversarial attacks challenging. In this situation, we propose Transferable Adversarial Network (TAN) to attack these models with highly transferable adversarial examples. The proposed method improves the transferability via a two-player game, in which we simultaneously train two encoder-decoder models: a generator that crafts malicious samples through a one-step forward mapping from original data, and an attenuator that weakens the effectiveness of malicious samples by capturing the most harmful deformations. In particular, compared to traditional iterative methods, our approach is able to one-step map original samples to adversarial examples, thus enabling real-time attacks. Experimental results indicate that the proposed approach achieves state-of-the-art transferability with acceptable adversarial perturbations and minimum time costs compared to existing attack methods, i.e., it excellently realizes real-time transferable adversarial attacks.
ARTICLE | doi:10.20944/preprints202305.0975.v1
Subject: Engineering, Electrical And Electronic Engineering Keywords: Load Forecasting; Long Short Term Memory; Temporal Convolution Networks; Multilayer Perceptron; Convolutional Neural Networks; CNN-LSTM; Convolutional LSTM Encoder- Decoder; Evaluation Metrics; Power Sector; Data Analysis
Online: 15 May 2023 (04:39:19 CEST)
Nowadays, power sector is an area that gather great scientific interest, due to events such as the increase in electricity prices in the wholesale energy market and new investments due to technological development in various sectors. These new challenges have in turn created new needs, such as the accurate prediction of the electrical load of the end users. On the occasion of the new challenges, Artificial Neural Networks approaches have become increasingly popular due to their ability to adopt efficiently to time-series predictions. In this paper, it is presented the development of a model which, through an automated process, will provide an accurate prediction of electrical load for the island of Thira in Greece. Through an automated application, deep learning load forecasting models have been created, such as Multilayer Perceptron, Long Short-Term Memory (LSTM), Convolutional Neural Network One Dimensional (CNN-1D), CNN-LSTM, Temporal Convolutional Network (TCN) and a proposed hybrid model called Convolutional LSTM Encoder-Decoder. The results in terms of prediction accuracy show satisfactory performances for all models, with the proposed hybrid model achieving the best accuracy.
ARTICLE | doi:10.20944/preprints202302.0066.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Smart Tourism; Sustainable Tourism; Natural language Processing (NLP); Big Data Analytics; Deep Learning; Machine Learning; Unsupervised Learning; Bidirectional Encoder Representations from Transformers (BERT); Literature Review; Smart Societies
Online: 3 February 2023 (09:47:55 CET)
The Global natural and manmade events are exposing the fragility of the tourism industry and its impact on the global economy. Prior to the COVID-19 pandemic, tourism contributed 10.3% to the global GDP and employed 333 million people but saw a significant decline due to the pandemic. Sustainable and smart tourism requires collaboration from all stakeholders and a comprehensive understanding of global and local issues to drive responsible and innovative growth in the sector. This paper presents an approach for leveraging big data and deep learning to dis-cover holistic, multi-perspective (e.g., local, cultural, national, and international) and objective information on a subject. Specifically, we develop a machine learning pipeline to extract parameters from academic literature and public opinions on Twitter, providing a unique and comprehensive view of the industry from both academic and public perspectives. The academic-view dataset was created from the Scopus database and contains 156,759 research articles from 2000 to 2022, which were modelled to identify 33 distinct parameters in 4 categories: Tourism Types, Planning, Challenges, and Media & Technologies. A Twitter dataset of 485,813 tweets was collected over 18 months starting March 2021 to August 2022 to showcase public perception of tourism in Saudi Arabia, which was modelled to reveal 13 parameters categorized into two broader sets: Tourist Attractions and Tourism Services. Discovering system parameters are re-quired to embed autonomous capabilities in systems and for decision-making and problem-solving during system design and operations. The proposed approach improves AI-based information discovery by extending the use of scientific literature, Twitter, and other sources for autonomous, dynamic optimizations of systems, promoting novel research in the tourism sector and contributing to the development of smart and sustainable societies. The paper also presents a comprehensive knowledge structure and literature review of the tourism sector based on over 250 research articles.
ARTICLE | doi:10.20944/preprints202208.0233.v1
Subject: Computer Science And Mathematics, Artificial Intelligence And Machine Learning Keywords: Smart Families; Smart Homes; Sustainable Societies; Smart Cities; Deep Learning; Natural Language Processing (NLP); Social Sustainability; Environmental Sustainability; Economic Sustainability; Bidirectional Encoder Representations from Transformers (BERT); Triple Bottom Line (TBL); Internet of Things (IoT)
Online: 12 August 2022 (10:22:17 CEST)
Technological advancements and innovations have profoundly changed the lives of people giving rise to smart environments, cities, and societies. As homes are the building block of cities and societies, smart homes are critical to establishing smart living and are expected to play a key role in enabling smart cities and societies. The current academic literature and commercial advancements on smart homes have mainly focused on developing and providing smart functions for homes to provide security management and facilitate the residents in their various activities such as ambiance management. Homes are much more than physical structures, buildings, appliances, operational machines, and systems. Homes are composed of families and are inherently complex phenomena underlined by humans and their relationships with each other, subject to individual, intragroup, intergroup, and intercommunity goals. There is a clear need to understand, define, consolidate existing research, and actualize the overarching roles of smart homes, the roles of smart homes that would serve the needs of future smart cities and societies. This paper introduces our data-driven parameter discovery methodology and uses it to provide, for the first time, an extensive, rather fairly comprehensive, analysis of the families and homes landscape seen through the eyes of academics and the public using over a hundred thousand research papers and nearly a million tweets. We develop a methodology using deep learning, natural language processing (NLP), and big data analytics methods and apply it to automatically discover parameters that capture a comprehensive knowledge and design space of smart families and homes comprising social, political, economic, environmental, and other dimensions. The 66 discovered parameters and the knowledge space comprising 100s of dimensions are explained by reviewing and referencing over 300 articles from the academic literature and tweets. The knowledge and parameters discovered in this paper can be used to develop a holistic understanding of matters related to families and homes facilitating the development of better, community-specific, policies, technologies, solutions, and industries for families and homes, leading to strengthening families and homes, and in turn, empowering sustainable societies across the globe.
HYPOTHESIS | doi:10.20944/preprints202109.0372.v1
Subject: Biology And Life Sciences, Immunology And Microbiology Keywords: Endometriosis; microchimerism; maternal microchimerism; reproduction; gynaecology; etiology; auto-immune; immune response; hormonal; vascular; genetic; hereditary; male; fetal; fetus; stem cells; pregnancy; Műllerianosis; embryology; ROS; apoptosis; disease; endometrium; basalis; menstruation; post-menopausal; neurogenesis
Online: 22 September 2021 (10:27:52 CEST)
Endometriosis is an oestrogen-dependant reproductive disease, with genetic, vascular, neural, inflammatory and auto-immune characteristics. There are many theories about the etiology of endometriosis, however, all of these theories have limitations and do not explain all the locations that endometriosis is found or types of patients with endometriosis. The objective of this paper is to postulate the hypothesis that endometriosis is caused by Maternal Microchimerism, the presence of maternal cells in the fetus. A literature review was conducted, analysing the characteristics, current etiological theories of endometriosis, theory limitations and relationship of maternal microchimerism and endometriosis. At time of writing, there was no literature on maternal microchimerism and endometriosis. These results suggest that Maternal Microchimerism could be a cause of endometriosis. This could account for the genetic and auto-immune characteristics seen in people with endometriosis, inducing a micro-environment for vascular, neural and epigenetic changes. This could also account for account for endometriosis seen in non-menstruating patients, such as men, fetuses and post-menopausal women and endometriosis found in non-peritoneal locations. If the hypothesis of Maternal Microchimerism is correct, endometriosis could be considered a pregnancy-related disease that could affect all humans, changing the accepted demographics of patients and potentially new diagnostic techniques and treatment options for patients with endometriosis. Further studies are needed to test this hypothesis.