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Preprint ARTICLE | doi:10.20944/preprints202306.0323.v1

Utilizing Text Mining for Labeling Training Models from Futures Corpus in Generative AI

Hsien-Ming Chou, Tsai-Lun Cho

Subject: Computer Science And Mathematics, Information Systems Keywords: text mining; semantic analysis; labeling bull-bear words; futures corpus; generative AI

Online: 5 June 2023 (13:18:24 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202306.0107.v1

A New RSA Variant Based on Elliptic Curves

Maher Boudabra, Abderrahmane Nitaj

Subject: Computer Science And Mathematics, Information Systems Keywords: Public key Cryptography; RSA; KMOV; Demytko’s scheme; Elliptic curves; Continued fractions; Coppersmith’s method.

Online: 1 June 2023 (15:37:57 CEST)

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Preprint REVIEW | doi:10.20944/preprints202306.0002.v1

Unleashing the Power of IoT: A Comprehensive Review of IoT Applications, Advancements, and Future Prospects in Healthcare, Agriculture, Smart Homes, Smart Cities, and Industry 4.0

Robin Chataut, Alex Phoummalayvane

Subject: Computer Science And Mathematics, Information Systems Keywords: IoT; Smart Cities; Internet-of-Medical-Things; Sensors; Security

Online: 1 June 2023 (02:27:23 CEST)

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The Internet of Things (IoT) technology and devices constitute an exciting field in computer science that is rapidly emerging worldwide. IoT devices function by connecting real-world objects to the internet, resulting in a higher number of interconnected devices than ever witnessed in history. Through internet connectivity, these devices can be utilized in various ways, such as monitoring and tracking. Their prevalence is increasing exponentially, coinciding with advancements in wireless networking technologies. The internet’s enhanced connectivity has played a vital role in fostering the proliferation of IoT devices. Presently, almost any everyday object can be network-connected. The demand for automation and efficiency has also been a contributing factor to the advancements in this technology. This paper aims to review the emergence of IoT devices, analyze their common applications, and explore the future prospects in this promising field of computer science. The examined applications encompass healthcare, agriculture, and smart cities. Although IoT technology exhibits similar deployment trends, this paper will explore different fields to discern the subtle nuances that exist among them. IoT technology can be applied to nearly any domain, and each use case has unique requirements. To comprehend the future of IoT, it is essential to comprehend the driving forces behind its advancements in various industries. By gaining a better understanding of the emergence of IoT devices, readers will develop insights into the factors that have propelled their growth and the conditions that led to technological advancements. Moreover, a comprehensive understanding of the prevalent methodologies will enable readers to distinguish between current practices and future methods. Given the rapid rate at which IoT technology is advancing, this paper aims to provide researchers with an understanding of the factors that have brought us to this point and the ongoing efforts to shape the future of IoT.

Preprint ARTICLE | doi:10.20944/preprints202305.2029.v1

Efficient Distortion Mitigation and Partition Reduction in Mapping Earth's Geospatial Data: Dual Orthogonal Equidistant Cylindrical Projection Approach

Aleksandar Miroslav Dimitrijević, Aleksandar Milosavljević, Dejan Rančić

Subject: Computer Science And Mathematics, Information Systems Keywords: discrete global grid system; equidistant cylindrical projection; yin-yang; distortion

Online: 30 May 2023 (03:56:22 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0660.v2

Signal Design, Detection, and Identification Technology for Ground-Penetrating Communication Based on Ground Electrode Current Field

Jingang He, Zhong Su, Zhan Xu, Yaxing Zha, Xiaowen Wen, Xin Zhou

Subject: Computer Science And Mathematics, Information Systems Keywords: current field; ground-penetrating communication; signal design; detection and identification

Online: 25 May 2023 (05:08:02 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202305.1668.v1

IoT LoRaWAN-Based Tracking System in Egypt

Abdelrahman Ahmed, Mahmoud Elaasar, Ghada Abdelhady

Subject: Computer Science And Mathematics, Information Systems Keywords: Asset; Chirp Spread Spectrum (CSS); IoT; LoraWAN; Low-power wide area networks; Real-time; Tracking.

Online: 24 May 2023 (02:21:29 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1636.v1

Hierarchical Network-Based Tracklets Data Association for Multiple Extended Targets Tracking With Intermittent Measurements

Kaiyi Jiang, Yiguo Li, TianLi Ma, Lin Li

Subject: Computer Science And Mathematics, Information Systems Keywords: Multiple extended targets; Data association; Tracklets; Min-cost network flow; Intermittent measurements

Online: 23 May 2023 (10:17:47 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1555.v1

Drivers of Mobile Payments Acceptance: The Impact of Networks Externalities

QASIM AJAO, Lanre Gbenga Sadeeq, Adam Casady, Makhluk Hossain Prio

Subject: Computer Science And Mathematics, Information Systems Keywords: Nigeria; Smartphones; Network externalities; Mobile payment acceptance; Emerging technology; UTAUT; Empirical study; Africa

Online: 23 May 2023 (04:07:36 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.1238.v1

Low-Code Machine Learning Platforms: A Fastlane to Digitalization

Krishna Raj Raghavendran, Ahmed Elragal

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)

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Preprint ARTICLE | doi:10.20944/preprints202305.0919.v1

Mixed cultural visits or what COVID 19 taught us

Angeliki Antoniou

Subject: Computer Science And Mathematics, Information Systems Keywords: mixed museum visits; hybrid cultural visits contextual model

Online: 12 May 2023 (10:07:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0660.v1

Signal Design, Detection, and Identification Technology for Ground-Penetrating Communication Based on Ground Electrode Current Field

Jingang He, Zhong Su, Zhan Xu, Yaxing Zha, Xiaowen Wen, Xin Zhou

Subject: Computer Science And Mathematics, Information Systems Keywords: current field; ground-penetrating communication; signal design; detection and identification

Online: 9 May 2023 (12:55:51 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202305.0135.v1

An Informed Decision Support Framework From Strategic Perspective in Health Sector

Mohammed Alojail, Mohammad Alturki, Surbhi Bhatia

Subject: Computer Science And Mathematics, Information Systems Keywords: decision support; framework; stakeholders; information systems; analysis

Online: 3 May 2023 (10:43:58 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.1206.v1

A Conceptual Design of AI-enabled Decision Support System for Analysing Donor Behaviour in Non-profit Organisations

Idrees Alsolbi, Renu Agarwal, Bhuvan Unhelkar, Faezeh Karimi, Tareq Al-Jabri, Mahendra Samarawickrama, Siamak Tafavogh, Mukesh Prasad

Subject: Computer Science And Mathematics, Information Systems Keywords: decision support systems; design science research; donor behaviour; non-profit organisations; data analysis; artificial intelligence; machine learning

Online: 29 April 2023 (04:24:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202304.0541.v1

Efficient Dissemination of Safety Messages in Vehicle Ad-hoc Network Environments

Jongtae Lim, Dowoong Pyun, Dojin Choi, Kyoungsoo Bok, Jaesoo Yoo

Subject: Computer Science And Mathematics, Information Systems Keywords: Social Internet of Things; Social Internet of Vehicle; Vehicle-to-Vehicle; Vehicle-to-Infrastructure; Vehicle Ad-hoc Network; Social Networks

Online: 19 April 2023 (04:27:27 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202302.0018.v2

Towards a Simulation Model Complexity Measure

J. Scott Thompson, Douglas D. Hodson, Michael R. Grimaila, Nicholas Hanlon, Richard Dill

Subject: Computer Science And Mathematics, Information Systems Keywords: simulation model; complexity; Kolmogorov complexity; normalized compression distance; resolution; scope; model families

Online: 20 March 2023 (02:47:28 CET)

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Preprint ARTICLE | doi:10.20944/preprints202303.0233.v1

Post Covid19 Mobile NFC Payment

Poh Soon JosephNg, J. H. Wong, Koo Yuen Phan

Subject: Computer Science And Mathematics, Information Systems Keywords: NFC; non-cash; mobile payment; mobile marketing; mobile consumer

Online: 13 March 2023 (13:57:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202303.0135.v1

Evaluation of a Cyber Risk Assessment Approach for Cyber-Physical Systems: Maritime and Energy Use Cases

Ahmed Amro, Vasileios Gkioulos

Subject: Computer Science And Mathematics, Information Systems Keywords: Cyber Risk Assessment; Evaluation; cyber-physical systems; ATT&CK; FMECA; maritime; energy; autonomous passenger ship; digital substation

Online: 7 March 2023 (14:57:52 CET)

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Preprint ARTICLE | doi:10.20944/preprints202303.0120.v1

Crime Detection and Suspect Identification System

Abdulaziz BinDarwish, Salim Alhammadi, ABDULLAH SALEHI

Subject: Computer Science And Mathematics, Information Systems Keywords: Crime Detection; Suspect Identification; ATM; Faces; Protection

Online: 7 March 2023 (02:24:03 CET)

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Preprint REVIEW | doi:10.20944/preprints202303.0073.v1

Comprehensive Study of The Cloud Computing Service Providers

Khalifa Alhosani, Osama Hosameldeen

Subject: Computer Science And Mathematics, Information Systems Keywords: Cloud computing; Cloud services; Virtualization

Online: 6 March 2023 (12:53:32 CET)

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Preprint REVIEW | doi:10.20944/preprints202303.0088.v1

IoT-based Technologies for Addressing the Unique Healthcare Needs of the Elderly Population

Syed Adnan Ali, Rehan Khan

Subject: Computer Science And Mathematics, Information Systems Keywords: IoT; geriatric healthcare; IoMT; biomedical devices; telemedicine; remote monitoring; aging population

Online: 6 March 2023 (04:20:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202303.0070.v1

Improving the Accuracy of Customer Service Seq2Seq Chatbots Through Dataset Pruning

Osama Hosameldeen, Rasha Abousamra, Hussain Al-Aqrabi, Ossama Embarak, Usman Durrani

Subject: Computer Science And Mathematics, Information Systems Keywords: encoder; decoder; seq2seq; LSTM; RNN; chatbots

Online: 3 March 2023 (10:08:32 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0465.v1

CGA-MGAN: Metric GAN based on Convolution-augmented Gated Attention for Speech Enhancement

Haozhe Chen, Xiaojuan Zhang

Subject: Computer Science And Mathematics, Information Systems Keywords: CGA-MGAN; Gated Attention Unit; Speech Enhancement

Online: 27 February 2023 (09:24:31 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0412.v1

An Adaptive Layered Clustering Framework with Improved Genetic Algorithm for Solving Large-Scale Traveling Salesman Problems

Hai-yang Xu, Heng-You Lan

Subject: Computer Science And Mathematics, Information Systems Keywords: Computational complexity analysis; High parallelizability; Improved genetic algorithm; Adaptive layered clustering framework; Large-scale traveling salesman problem

Online: 24 February 2023 (01:20:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0394.v1

Efficiency in B2B Communications: The Benefits of VBA Macro Implementation for Repetitive Tasks

Constantin Torcãtoru, Dan Sãvescu

Subject: Computer Science And Mathematics, Information Systems Keywords: business relationships; B2B collaboration; time management; VBA automation; macro programming; job satisfaction; performance; sustainable human resources management

Online: 23 February 2023 (02:23:30 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0379.v1

Influence of Parameters in SDMs Application to Citrus Presence in Mediterranean Area

Giuseppe Antonio Catalano, Provvidenza Rita D'Urso, Federico Maci, Claudia Arcidiacono

Subject: Computer Science And Mathematics, Information Systems Keywords: Vistrails-SAHM software; citrus; spatial distribution; probability of presence; Mediterranean climate; predictor layers

Online: 22 February 2023 (07:32:03 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0309.v1

Ontology-Driven Knowledge Sharing in Alzheimer's Disease Research

Sophia Lazarova, Dessislava Petrova-Antonova, Todor Kunchev

Subject: Computer Science And Mathematics, Information Systems Keywords: Ontology; Alzheimer’s disease; Basic Formal Ontology; Interdisciplinary Research; Knowledge Sharing

Online: 17 February 2023 (09:40:53 CET)

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Preprint REVIEW | doi:10.20944/preprints202302.0305.v1

National and Ethnic Mutation Frequency Databases: Cross Comparison and Future Direction

Shumaila Khan, Mehmood Alam, Shahnaz Khan, Wahab Khan, Ihsan Rabbi, Iqbal Qasim

Subject: Computer Science And Mathematics, Information Systems Keywords: genetic databases; mutation disorders; ethnic specific databases

Online: 17 February 2023 (07:59:08 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0303.v1

Towards an Integrated Methodology and Toolchain for Machine Learning based Intrusion Detection in Urban IoT Networks and Platforms

Denis Rangelov, Philipp Lämmel, Lisa Brunzel, Stephan Borgert, Paul Darius, Nikolay Tcholtchev, Michell Boerger

Subject: Computer Science And Mathematics, Information Systems Keywords: IoT; Smart City; Open Urban Platform; Machine Learning; cybersecurity; methodology; intrusion detection; toolchain

Online: 17 February 2023 (07:35:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0220.v1

Intrusion Detection using Tripwire in UNIX Operating System

Muhammad Saqib Javed

Subject: Computer Science And Mathematics, Information Systems Keywords: Message Digest; Index Node; Secure Hash Algorithm; Sandboxing

Online: 13 February 2023 (14:54:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0196.v1

SFRRG: A Graph Neural Network Recommendation Model based on Feature and Structure Information

Devon Patterson, John Wilson, Michael Forrow, Tereza Wald

Subject: Computer Science And Mathematics, Information Systems Keywords: Recommendation; GNN; Information; Feature; Structure

Online: 13 February 2023 (03:28:02 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0178.v1

Blockchain-Based Patient-Driven Inter-Operable Model for Healthcare 5.0 System

Kaushal Shah, Lalit Garg, Divam Kachoria, Vaidehi Shah, Shristi Darbar, Andrea Visconti, Chintan Bhatt, Sudeep Tanwar

Subject: Computer Science And Mathematics, Information Systems Keywords: blockchain technology; trusted execution environment; attribute-based encryption; patient-driven inter- operability; Ethereum

Online: 10 February 2023 (03:48:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202302.0078.v1

Real-Time Epidemiology and Acute Care Need Monitoring and Forecasting for COVID-19 via Bayesian Sequential Monte Carlo-leveraged Transmission Models

Xiaoyan Li, Vyom Patel, Lujie Duan, Jalen Mikuliak, Jenny Basran, Nathaniel Osgood

Subject: Computer Science And Mathematics, Information Systems Keywords: COVID-19; Particle Filtering; Machine Learning; Epidemiologic Modeling; Compartmental Model; Projection and Intervention

Online: 6 February 2023 (02:55:38 CET)

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COVID-19 transmission models have conferred great value in informing public health understanding, planning, and response. However, the pandemic also demonstrated the infeasibility of basing public health decision-making on transmission models with pre-set assumptions. No matter how favourably evidenced when built, a model with fixed assumptions is challenged by numerous factors that are difficult to predict. Ongoing planning associated with rolling back and re-instituting measures, initiating surge planning, and issuing public health advisories can benefit from approaches that allow state estimates for transmission models to be continuously updated in light of unfolding time series. A model being continuously regrounded by empirical data in this way can provide a consistent, integrated depiction of the evolving underlying epidemiology and acute care demand, offer the ability to project forward such a depiction in a fashion suitable for triggering the deployment of acute care surge capacity or public health measures, support quantative evaluation of tradeoffs associated with prospective interventions in light of the latest estimates of the underlying epidemiology. We describe here the design, implementation and multi-year daily use for public health and clinical support decision-making of a particle filtered COVID-19 compartmental model, which served Canadian federal and provincial governments via regular reporting starting in June 2020. The use of the Bayesian Sequential Monte Carlo algorithm of Particle Filtering allows the model to be re-grounded daily and adapt to new trends within daily incoming data – including test volumes and positivity rates, endogenous and travel-related cases, hospital census and admissions flows, daily counts dose-specific vaccinations administered, measured concentration of SARS-CoV-2 in wastewater, and mortality. Important model outputs include estimates (via sampling) of the count of undiagnosed infectives, the count of individuals at different stages of the natural history of frankly and pauci-symptomatic infection, the current force of infection, effective reproductive number, and current and cumulative infection prevalence. Following a brief description of model design, we describe how the machine learning algorithm of particle filtering is used to continually reground estimates of dynamic model state, support probabilistic model projection of epidemiology and health system capacity utilization and service demand and probabilistically evaluate trade-offs between potential intervention scenarios. We further note aspects of model use in practice as an effective reporting tool in a manner that is parameterized by jurisdiction, including support of a scripting pipeline that permits a fully automated reporting pipeline other than security-restricted new data retrieval, including automated model deployment, data validity checks, and automatic post-scenario scripting and reporting. As demonstrated by this multi-year deployment of Bayesian machine learning algorithm of particle filtering to provide industrial-strength reporting to inform public health decision making across Canada, such methods offer strong support for evidence-based public health decision making informed by ever-current articulated transmission models whose probabilistic state and parameter estimates are continually regrounded by diverse data streams.

Preprint ARTICLE | doi:10.20944/preprints202302.0032.v1

How to find Orchestrated Trolls?A Case Study on Identifying Polarized Twitter Echo Chambers

Nane Kratzke

Subject: Computer Science And Mathematics, Information Systems Keywords: Social network; Twitter; Structural analysis; Echo chamber; Detection; Case study; German language; Disinformation

Online: 2 February 2023 (06:54:35 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0561.v2

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

Lawrence Mdegela, Yorick De Bock, Esteban Municio, Edith Luhanga, Judith Leo, Steven Latré

Subject: Computer Science And Mathematics, Information Systems Keywords: Sensors; data-set; Machine learning; river floods; river level

Online: 1 February 2023 (03:54:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0580.v1

Spyware Integrated with Prediction Models for Monitoring Corporate Computers

Darlan Noetzold, Anubis Graciela De Moraes Rossetto, Valderi Reis Quietinho Leithardt

Subject: Computer Science And Mathematics, Information Systems Keywords: Electronic monitoring; hate speech; data leakage; prediction.

Online: 31 January 2023 (08:59:39 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0561.v1

Sensor-Based River Monitoring System: A Case for Kikuletwa River Floods in Tanzania

Lawrence Mdegela, Yorick De Bock, Esteban Municio, Edith Luhanga, Judith Leo, Steven Latré

Subject: Computer Science And Mathematics, Information Systems Keywords: Sensors; data-set; Machine learning; river floods; river level

Online: 31 January 2023 (02:13:16 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202301.0335.v2

Safe Control of Autonomous Cloud Entities in Distributed Systems

Mostefa Kara

Subject: Computer Science And Mathematics, Information Systems Keywords: Cloud Computing; Data Protection; Secure Communication; Middleware; Protocols

Online: 30 January 2023 (09:24:01 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0372.v1

Traceability, Data Privacy, and Accountability in IoT using Blockchain

Abid Sultan, Yao Lin

Subject: Computer Science And Mathematics, Information Systems Keywords: Blockchian; IoT; CoAP; MQTT; REST: Traceability

Online: 20 January 2023 (10:29:38 CET)

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Preprint COMMUNICATION | doi:10.20944/preprints202301.0335.v1

Secure Management of Autonomous Cloud Entities in Distributed Systems

Mostefa Kara

Subject: Computer Science And Mathematics, Information Systems Keywords: Cloud Computing; Data Protection; Secure Communication; Middleware; Protocols

Online: 18 January 2023 (10:24:34 CET)

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Preprint REVIEW | doi:10.20944/preprints202301.0291.v1

Towards Sustainable Virtual Reality: Gathering Design Guidelines for Intuitive Authoring Tools

Iolanda Lima Chamusca, Cristiano Vasconcellos Ferreira, Thiago Barros Murari, Antonio Lopes Apolinário Jr., Ingrid Winkler

Subject: Computer Science And Mathematics, Information Systems Keywords: virtual reality; authoring tools; intuitiveness; digitization; sustainability; user-centered design; human-computer interaction; metaverse

Online: 17 January 2023 (01:41:25 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0190.v1

Reframed Design Thinking and Feasibility Analysis of UX Journey: Integrating User Experience and User Requirement for Solo Software Development

Wahyu Andhyka Kusuma, Azrul Hazri Jantan, Novia Indriaty Admodisastro, Noris Mohd Norowi

Subject: Computer Science And Mathematics, Information Systems Keywords: User experience; user requirement; developer productivity; developer self-efficacy; solo software development; UX Journey

Online: 11 January 2023 (02:37:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202301.0092.v1

Federated Learning for Education Data Analytics

Christian Fachola, Agustin Tornaria, Paola Bermolen, Germán Capdehourat, Lorena Etcheverry, Maria Ines Fariello

Subject: Computer Science And Mathematics, Information Systems Keywords: Federated Learning; Learning Analytics

Online: 5 January 2023 (02:39:04 CET)

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Preprint ARTICLE | doi:10.20944/preprints202209.0202.v3

Dynamically Updated Alive Publication Date

Mikhail Gorbunov-Posadov

Subject: Computer Science And Mathematics, Information Systems Keywords: alive publication; dynamic component of bibliographic reference; latest revision date; Crossref; arXiv.org; Preprints.org

Online: 4 January 2023 (12:36:51 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0533.v1

Identification of Actual Bibliometric/Scientometric Issues Based on 2018-2022 Data from the Lens Platform by Building Key Term Co-occurrence Network

Boris Chigarev

Subject: Computer Science And Mathematics, Information Systems Keywords: bibliometrics, scientometrics, the Lens, key terms, VOSviewer, Scimago Graphica

Online: 28 December 2022 (09:07:13 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0490.v1

Vendor-Agnostic Reconfiguration of Kubernetes Clusters in Cloud Federations

Eddy Truyen, Hongjie Xie, Wouter Joosen

Subject: Computer Science And Mathematics, Information Systems Keywords: Kubernetes; Cluster federation; Feature-oriented configuration management; Vendor lock-in

Online: 26 December 2022 (12:06:42 CET)

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Kubernetes (K8s) defines standardized APIs for container-based cluster orchestration so it becomes possible for application managers to deploy their applications in a unified manner across different cloud providers. A practical problem is however feature incompatibility between different K8s vendors, who offer commercial K8s products based on the open-source K8s distribution. A large number of documented features in this open-source distribution are optional features that are turned off by default, but can be activated by setting specific combinations of parameters and plug-in components in configuration manifests for the K8s control plane and worker node agents. However, none of these configuration manifests are standardized, giving K8s vendors the freedom to hide the manifests behind a single, more restricted, and proprietary customization interface. Therefore some optional K8s features cannot be activated consistently across K8s vendors and applications that require these features cannot be run on those vendors. In this paper we present a unified, vendor-agnostic feature management approach that bypasses the proprietary customization interface of K8s vendors in order to consistently activate optional K8s features across a federation of clusters hosted by different Kubernetes vendors. We describe vendor-agnostic reconfiguration tactics that are already applied in industry and cover a wide range of optional K8s features. Based on these tactics, we design and implement an autonomic controller for declarative feature compatibility management across a cluster federation. We found that the features configured through our vendor-agnostic approach have no impact on application performance when compared with a cluster where the features are configured using the configuration manifests of the open-source K8s distribution. Moreover, the maximum time to complete reconfiguration of a single feature is within 100 seconds, which is 6 times faster than using proprietary customization interfaces of mainstream K8s vendors such as Google Kubernetes Engine. However, there is a non-negligible disruption to running applications when performing the reconfiguration; this disruption impact does not appear using the proprietary customization methods of the K8s vendors. Therefore, our approach is best applied in the following three use cases: (i) when starting up new K8s clusters, (ii) when optional K8s features of existing clusters must be activated as quickly as possibly and temporary disruption to running applications can be tolerated or (iii) when proprietary customization interfaces do not allow to activate the desired optional feature.

Preprint ARTICLE | doi:10.20944/preprints202212.0478.v1

Implementing Computer Vision Techniques to Recognize American Sign Language (ASL) Hand Signals

Tauheed Khan Mohd, Alvaro Martin Grande, Rodrigo E. Ayala, Stuart Isteefano

Subject: Computer Science And Mathematics, Information Systems Keywords: Datasets, Neural Networks, Hand Detection, Text Tagging

Online: 26 December 2022 (07:30:24 CET)

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Preprint REVIEW | doi:10.20944/preprints202212.0006.v2

Reconfigurable Intelligent Surfaces for 6G: Applications, Challenges and Solutions

Yajun Zhao, Xin Lv

Subject: Computer Science And Mathematics, Information Systems Keywords: Reconfigurable intelligent surfaces; 6G; Cascade Channel Decoupling; RIS Regulatory Constraint; RIS System Architecture; True Time Delay.

Online: 26 December 2022 (01:45:18 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0423.v1

Application of the Model of Spots for Inverse Problems

Nikolai Anatolievich Simonov

Subject: Computer Science And Mathematics, Information Systems Keywords: inverse problems; image reconstruction; vague figures; mental images; Artificial Intelligence

Online: 22 December 2022 (07:14:16 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0352.v1

Locating Partial Discharges in Power Transformers with Convolutional Iterative Filtering

Jonathan Wang, Kesheng Wu, Alex Sim, Seongwook Hwangbo

Subject: Computer Science And Mathematics, Information Systems Keywords: Partial discharges; Source location; UHF measurements; Time of arrival estimation; Waveform analysis; FDTD methods; Nonlinear wave propagation

Online: 20 December 2022 (03:36:47 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0312.v1

Janus: Hierarchical Multi-blockchain Based Access Control (HMBAC) for Multi-Authority & Multi-Domain Environments

Vangelis Malamas, George Palaiologos, Panayiotis Kotzanikolaou, Mike Burmester, Dimitris Glynos

Subject: Computer Science And Mathematics, Information Systems Keywords: Access control; Blockchain; Multi-Blockchain; Multi-Authority; Multi-Domain; Attribute-Based Encryption

Online: 19 December 2022 (03:19:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0310.v1

User and Professional Aspects for Sustainable Computing based on the Internet of Things in Europe

Vera Pospelova, Inés López-Baldominos, Luis Fernández-Sanz, Ana Castillo-Martínez, Sanjay Misra

Subject: Computer Science And Mathematics, Information Systems Keywords: IoT; implementation; human factors; Europe; survey

Online: 19 December 2022 (01:23:50 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0270.v1

Coupling Quantum Random Walks with Long and Short Term Memory for High Pixel Image Encryption Schemes

Junqing Liang, Zhaoyang Song, Zhongwei Sun, Mou Lv, Hongyang Ma

Subject: Computer Science And Mathematics, Information Systems Keywords: image encryption; high pixel density; neural networks; quantum random walk

Online: 15 December 2022 (06:50:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0132.v1

3DSSCN: A Sentiment Analysis Model for Short Video Based on 3D-Dense Network

Michele silva, Heitor Kruger, Muhammad Vieira, Carlos Stellato

Subject: Computer Science And Mathematics, Information Systems Keywords: Short video; Sentiment Analysis; Feature; 3D Dense Net; 3D Residual Network

Online: 7 December 2022 (11:57:32 CET)

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In recent years, with the development of social media, people are more and more inclined to upload text, pictures and videos on the platform to express their personal emotions, thus the number of short videos is increasing and becoming the first choice for people to socialize. Unlike the traditional way, people can convey their personal emotions and opinions through media other than words, such as video images, etc. for external information. Therefore, the expression and analysis of emotions is not only through text, but also through the analysis of emotional needs in images and videos, and the research scholars have customized products for individual users. Compared with pure text content, video information can more intuitively express users' happiness, anger and sorrow, thus short video-related applications have gained more and more popularity among Internet users in recent years. However, not all short videos on social networking sites can accurately express users' emotions, and related text information can more accurately assist sentiment analysis and thus improve accuracy. However, short video sentiment analysis based on video frame images is inaccurate in some scenarios, such as when expressing tears of joy, the sentiment expressed by the user's facial expression and voice are different, which will cause errors in the analysis of sentiment. As a result, researchers began to consider multimodal sentiment analysis to reduce the impact of the above scenarios on short video sentiment analysis. This paper focuses on proposing a sentiment analysis method for short videos. We first propose a residual attention model to make full use of the information in audio to classify the emotions contained in them. Then the text information in the dataset is classified by feature extraction. The key to extract features from text information is not only to retain the semantic information of the text, but also to explore the potential emotional information in the text, so as to ensure the integrity of the text information features. The experiments show that the sentiment analysis model proposed in this paper is more superior than the baselines.

Preprint DATA DESCRIPTOR | doi:10.20944/preprints202212.0118.v1

Visual Lip Reading Dataset in Turkish

Ali Berkol, Talya Tümer-Sivri, Melike Colak, Nergis Pervan-Akman, Hamit Erdem

Subject: Computer Science And Mathematics, Information Systems Keywords: Lip reading; Visual speech recognition; Turkish dataset; Face parts detection

Online: 7 December 2022 (06:50:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202212.0040.v1

Design and Application of Deep Hash Embedding Algorithm with Fusion Entity Attribute Information(FADH)

Xiao Li Huang, Hai Bo Chen, Zheng Zhang

Subject: Computer Science And Mathematics, Information Systems Keywords: hash embedding; deep learning; attribute information; entity coding

Online: 2 December 2022 (04:23:37 CET)

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Preprint REVIEW | doi:10.20944/preprints202212.0006.v1

Reconfigurable Intelligent Surfaces for 6G: Applications, Challenges and Solutions

Yajun Zhao, Xin Lv

Subject: Computer Science And Mathematics, Information Systems Keywords: Reconfigurable intelligent surfaces; 6G; Cascade Channel Decoupling; RIS Regulatory Constraint; RIS System Architecture; True Time Delay.

Online: 1 December 2022 (02:40:33 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0534.v1

GPRE: Multi-Path Guided Recommender System Based on Graph Neural Networks

Evan webster, Julie Hill, Tyson Shimada, Braedon Smyth

Subject: Computer Science And Mathematics, Information Systems Keywords: Recommendation; GNN, Feature; Path; Embedding

Online: 29 November 2022 (04:19:09 CET)

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Recommender systems as an effective information filtering system can be used to obtain information through the user's explicit or implicit behavior. On the one hand finding items that may be of interest to the user. On the other hand, the recommendation facilitates the interaction between the user and the item to increase the revenue. Recommender systems have been widely used in various fields, such as e-commerce, travel recommendation, online books and movies, social networks, etc, which can satisfy the intrinsic implicit needs of users through personalized services. In recent years, the development of deep learning has further improved the performance of recommendation systems. Although these methods improve the performance of the recommendation system, when the number of users and products increases, the recommendation system may face sparsity and cold start problems, and thus cannot achieve personalized recommendations. Knowledge graphs, which are structured data, have become the choice of many algorithms due to the high quality and wide scale of the data, and therefore many recommendation algorithms combined with knowledge graphs have emerged as a popular new direction in recommendation systems. These algorithms are able to preserve the rich connections between different entities. Moreover, when constructing the features of an entity, the entities that are far away from the central entity can also be utilized. Entities are no longer only directly connected to each other. To address the shortcomings of existing recommendation algorithms, this paper designs the recommendation algorithm GPRE using graph neural networks. GPRE focuses on expressing the user's features. The graph neural network provides GPRE with a strong generalization capability for modeling, which can provide long-range semantics between users and entities, as well as selective entity selection in the auxiliary graph neural network. Explicit semantic links are established between remote and central nodes to reduce the introduction of noise. In this paper, experiments are conducted on real-world datasets and the results are compared with baselines. The experimental results show that GPRE performs well on the experimental dataset.

Preprint TECHNICAL NOTE | doi:10.20944/preprints202211.0220.v2

SG-ColBase: A Relational Column Database Kernel

Tong Zhou, Tingzhen Liu

Subject: Computer Science And Mathematics, Information Systems Keywords: Relational Database; Columnar Storage; Bloom Filter; Skip List; Field Level Lock; Read Write Concurrency; OLTP; OLAP; LSM-Tree; Token Bucket Algorithm

Online: 14 November 2022 (03:02:09 CET)

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Preprint TECHNICAL NOTE | doi:10.20944/preprints202211.0220.v1

SG-ColBase: A Relational Column Database Kernel

Tong Zhou, Tingzhen Liu

Subject: Computer Science And Mathematics, Information Systems Keywords: Relational Database; Columnar Storage; Bloom Filter; Skip List; Field Level Lock; Read Write Concurrency; OLTP; OLAP; LSM-Tree

Online: 11 November 2022 (07:35:23 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0190.v1

IoT based Smart Waste Management Using Deep Reinforcement Learning

Kazim Raza Rajani, Anuroop Gaddam, Jyotheesh Gaddam

Subject: Computer Science And Mathematics, Information Systems Keywords: sustainability; smart cities; Internet of Things (IoT); multi-agent deep reinforcement learning; smart waste management; smart sensors

Online: 10 November 2022 (04:49:09 CET)

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Ever-increasing need for improving the livability of a city and improve outcomes for its residents, over the last decade, the adoption of technology to develop urbanised societies around the world has given rise to the need for developing smart cities. The speed at which the world population is growing, the use of Internet of Things in smart cities have really advanced the quality of life. One significant area of concern within the smart city framework is waste management. If the waste within a city is not adequately managed, then it leads to issues in the health of the citizens. Additionally, the waste management has such a high impact on the environmental footprint, hence the need to have a smart way of managing waste is of critical importance. Through our research, we analyse the challenges of waste management within a city to understand the impact of the problem on to the citizens and overall city operations. We then investigate ways in which we can solve these problems using the emerging technologies, such as the Internet of Things, to collect valuable data of large volumes arriving at an astronomical rate, then apply multi-agent deep reinforcement learning algorithms to harness the power of big data to extract meaningful information and actionable insights. We ingest data generated by our Internet of Things into our algorithm for three main purposes including providing the notifications to an external system, for example, a map navigation engine out of the scope for this project but a future extension for route optimisation and waste vehicle tracking; extracting and reporting the actionable insights from the underlying data; and consuming the extracted data for predictive forecasting to draw out the unknown patterns of waste fill levels within various geographical locations and again send out triggers and notification to external systems for example a waste collection authority who can efficiently schedule the waste collection vehicles and optimise the route. To achieve the above mentioned outcomes, we propose a framework that is agnostic of the hardware that it connects to and can effectively interface with a wide variety of hardware keeping a level of abstraction in the architecture.

Preprint REVIEW | doi:10.20944/preprints202211.0161.v1

Data Locality in High Performance Computing, Big Data, and Converged Systems: An Analysis of the Cutting Edge and A Future System Architecture

Sardar Usman, Rashid Mehmood, Iyad Katib, Aiiad Albeshri

Subject: Computer Science And Mathematics, Information Systems Keywords: High Performance Computing (HPC); big data; High Performance Data Analytics (HPDS); con-vergence; data locality; spark; Hadoop; design patterns; process mapping; in-situ data analysis

Online: 9 November 2022 (01:38:34 CET)

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

Preprint ARTICLE | doi:10.20944/preprints202209.0202.v2

Dynamically Updated Alive Publication Date

Mikhail Gorbunov-Posadov

Subject: Computer Science And Mathematics, Information Systems Keywords: alive publication; dynamic component of bibliographic reference; latest revision date; Crossref; arXiv.org

Online: 7 November 2022 (11:00:38 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0111.v1

A Graph Neural Network-based Video Recommendation Model Combining Users’ Long-term and Short-term Preference

mildred brook, David Rohlf, Andrea Bishop, Matt Jones

Subject: Computer Science And Mathematics, Information Systems Keywords: Recommendation, GNN, Preference

Online: 7 November 2022 (08:38:06 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0093.v1

Automatic XML Extraction from Word and Formatting of E-book Formats: Insight into the Open Source Academic Publishing Suite (OS-APS)

Carsten Borchert, Roberto Cozatl, Frederik Eichler, Astrid Hoffmann, Markus Putnings

Subject: Computer Science And Mathematics, Information Systems Keywords: automatic typesetting; media-neutral publishing; open access; open source; scholarly publishing; XML/HTML conversion

Online: 4 November 2022 (13:17:34 CET)

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Preprint ARTICLE | doi:10.20944/preprints202211.0034.v1

SAUSA: Securing Access, Usage, and Storage of 3D Point Clouds Data by a Blockchain-based Authentication Network

Ronghua Xu, Yu Chen, Genshe Chen, Erik Blasch

Subject: Computer Science And Mathematics, Information Systems Keywords: Blockchain; Smart Contract; Point Cloud; Security; Privacy Preservation; Software-Defined Network (SND); Big Data; Assurance; Resilience.

Online: 2 November 2022 (02:18:50 CET)

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The rapid development of three-dimensional (3D) acquisition technology based on 3D sensors provides a large volume of data, which is often represented in the form of point clouds. Point cloud representation can preserve the original geometric information along with associated attributes in a 3D space. Therefore, it has been widely adopted in many scene-understanding-related applications such as virtual reality (VR) and autonomous driving. However, the massive amount of point cloud data aggregated from distributed 3D sensors also poses challenges for secure data collection, management, storage, and sharing. Thanks to the characteristics of decentralization and security nature, Blockchain has a great potential to improve point cloud services and enhance security and privacy preservation. Inspired by the rationales behind Software Defined Network (SDN) technology, this paper envisions SAUSA, a blockchain-based authentication network that is capable of recording, tracking, and auditing the access, usage, and storage of 3D point cloud data sets in their life-cycle in a decentralized manner. SAUSA adopts an SDN-enabled point cloud service architecture which allows for efficient data processing and delivery to satisfy diverse Quality-of-Service (QoS) requirements. A blockchain-based authentication framework is proposed to ensure security and privacy preservation in point cloud data acquisition, storage, and analytics. Leveraging smart contracts for digitizing access control policies and point cloud data on the blockchain, data owners have full control of their 3D sensors and point clouds. In addition, anyone can verify the authenticity and integrity of point clouds in use without relying on a third party. Moreover, SAUSA integrates a decentralized storage platform to store encrypted point clouds while recording references of raw data on the distributed ledger. Such a hybrid on-chain and off-chain storage strategy not only improves robustness and availability but also ensures privacy preservation for sensitive information in point cloud applications. A proof-of-concept prototype is implemented and tested on a physical network. The experimental evaluation validates the feasibility and effectiveness of the proposed SAUSA solution.

Preprint BRIEF REPORT | doi:10.20944/preprints202210.0208.v1

Investigation of Early-Stage Breast Cancer Detection Using Quantum Neural Network

Musaddiq Al Ali, Amjad Sahib, Muazez Al Ali

Subject: Computer Science And Mathematics, Information Systems Keywords: Quantum neural network; Breast cancer; Classical neural network; Machine learning; Mammography

Online: 14 October 2022 (10:11:54 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202210.0129.v1

The Scientific Productivity of Collective Subjects Based on the Time-Weighted Pagerank Method with Citation Intensity

Alexander Kuchansky, Andrii Biloshchytskyi, Yurii Andrashko, Svitlana Biloshchytska, Adil Faizullin

Subject: Computer Science And Mathematics, Information Systems Keywords: PageRank; Time-Weighted PageRank; collective subjects; citation intensity; scientific research; research productivity; scientometrics

Online: 10 October 2022 (14:04:52 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202210.0064.v1

Secure Approximate String Matching using Homomorphic Encryption for Privacy-preserving Record Linkage

Shahidul Islam Khan, Rakib Hosen, Iqbal Hossain

Subject: Computer Science And Mathematics, Information Systems Keywords: Homomorphic Encryption; Privacy-preserving Record Linkage; approximate string matching

Online: 6 October 2022 (10:31:25 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202210.0043.v1

An Approach to Forecasting Commodity Futures Prices Using Machine Learning

Gabriel Schneider de Jesus, Aurélio Faustino Hoppe, Andreza Sartori, Valderi Reis Quietinho Leithardt

Subject: Computer Science And Mathematics, Information Systems Keywords: Commodities; Long Short-Term Memory; Machine Learning; Neural Networks; Prediction; Technical analysis

Online: 5 October 2022 (13:39:16 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0413.v1

An Approach for Data Privacy Management for Banking Using Consortium Blockchain

Shady Nabih, hanan fahmy, sayed abdelgaber

Subject: Computer Science And Mathematics, Information Systems Keywords: Consortium Blockchain; Ring signature; Blockchain privacy; Blockchain security; Access Control; Blockchain big data

Online: 27 September 2022 (07:35:53 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0358.v1

Iterative Qubits Management for Quantum Search

Wenrui Mu

Subject: Computer Science And Mathematics, Information Systems Keywords: Quantum Search; Qubit Management; Iterative Search

Online: 23 September 2022 (05:25:28 CEST)

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Preprint REVIEW | doi:10.20944/preprints202209.0338.v1

Identifying Influencing Factors of Road Accidents in Emerging Road Accident Black spots

Mehdi Gheisari

Subject: Computer Science And Mathematics, Information Systems Keywords: Road; Accidents; Black spots; spatial analysis; Factor analysis

Online: 22 September 2022 (09:41:07 CEST)

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Preprint DATA DESCRIPTOR | doi:10.20944/preprints202209.0323.v1

CoviRx: A User-Friendly Interface for Systematic Down-Selection of Repurposed Drug Candidates for COVID-19

Hardik Ashish Jain, Vinti Agarwal, Chaarvi Bansal, Anupama Kumar, Faheem Faheem, Muzaffar-Ur-Rehman Mohammed, Sankaranarayanan Murugesan, Moana M Simpson, Avinash V Karpe, Rohitash Chandra, Christopher A MacRaild, Ian K Styles, Amanda L Peterson, Matthew A Cooper, Carl M.J. Kirkpatrick, Rohan M Shah, Enzo A Palombo, Natalie L Trevaskis, Darren J Creek, Seshadri S Vasan

Subject: Computer Science And Mathematics, Information Systems Keywords: COVID-19; Open-source dataset; Drug Repurposing; Database system; Web application devel-opment; software development; Drug fingerprints; Bulk upload

Online: 21 September 2022 (10:14:11 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0306.v1

Artificial Intelligence of Things Applied to Assistive Technology: A Systematic Literature Review

Maurício Pasetto de Freitas, Vinícius Aquino Piai, Ricardo Heffel Farias, Anita M. R. Fernandes, Anubis Graciela de Moraes Rossetto, Valderi Reis Quietinho Leithardt

Subject: Computer Science And Mathematics, Information Systems Keywords: AIoT; Artificial Intelligence; Assistive Technology; Deep Learning; Machine Learning

Online: 20 September 2022 (10:45:15 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0295.v1

XOR Chain and Perfect Encryption at the Dawn of the Quantum Era

Luis Adrián Lizama-Perez

Subject: Computer Science And Mathematics, Information Systems Keywords: Authentication; Encryption; Blockchain

Online: 20 September 2022 (05:51:42 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0286.v1

Horus: An Effective and Reliable Framework for Code-Reuse Exploits Detection in Data Stream

Gang Yang, Xingtong Liu, Chaojing Tang

Subject: Computer Science And Mathematics, Information Systems Keywords: intrusion detection; vulnerability exploit; machine learning; code-reuse attack; malware detection

Online: 20 September 2022 (03:10:37 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0202.v1

Dynamically Updated Alive Publication Date

Mikhail Gorbunov-Posadov

Subject: Computer Science And Mathematics, Information Systems Keywords: alive publication; dynamic component of bibliographic reference; latest revision date; Crossref; arXiv.org

Online: 14 September 2022 (09:13:08 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0199.v1

Unblocking Recognition: A Token System for Acknowledging Academic Contribution

James Lee, Mario Moroso, Timothy Mackey

Subject: Computer Science And Mathematics, Information Systems Keywords: Blockchain; Token; Recognition; Reward; Peer Review; Research Management; NIHR

Online: 14 September 2022 (08:41:28 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0283.v2

Impact of Information Technology in the Hiring Process of People

Muhammad Azam, Muhammad Nouman, Ahsan Rehman Gill

Subject: Computer Science And Mathematics, Information Systems Keywords: information technology; impact; society; future

Online: 13 September 2022 (08:27:14 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0109.v1

Moving Object Detection Based on a Combination of Kalman Filter and Median Filtering

Diana Kalita, Pavel Lyakhov

Subject: Computer Science And Mathematics, Information Systems Keywords: Kalman filter; median filter; impulse noise; estimate prediction; object distance determination; lidar; value calibration; point cloud.

Online: 7 September 2022 (10:20:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0094.v1

Architecture using blockchain data privacy for healthcare data management

Anubis Rossetto, Christofer Sega, Valderi Reis Quietinho Leithardt

Subject: Computer Science And Mathematics, Information Systems Keywords: Blockchain; Cryptography; DApp; Health Data; Privacy.

Online: 7 September 2022 (03:06:09 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202209.0009.v1

A Lightweight Authentication Framework for Privacy-Preserving Virtual Health Services

Qian Qu, Ronghua Xu, Han Sun, Yu Chen

Subject: Computer Science And Mathematics, Information Systems Keywords: Digital Twin; Internet-of-Medical-Things (IoMT); Security; Privacy; Blockchain; Non-fungible Token (NFT); Virtual Healthcare Services; Access Control; Data Sharing

Online: 1 September 2022 (07:21:25 CEST)

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Seniors safety is a compelling need, which necessitates 24/7 real-time monitoring and timely dangerous action recognition. Being able to mirror characteristics of physical objects (PO) to corresponding logical objects (LO) and seamlessly monitor their footprints thus detect anomaly parameters, Digital Twins (DT) has been considered a practical way to provide virtual health services for seniors safety. Meanwhile, widely adopted Internet of Medical Things (IoMT) consisting of wearable sensors and non-contact optical cameras for self and remote health data monitoring also raises concerns on information security and privacy violation. Therefore, security of POs, LOs and reliable data sharing among healthcare professionals are challenging as constructing trust and privacy-preserving virtual health services. Thanks to characteristics of decentralization, traceability and unalterability, Blockchain is promising to enhance security and privacy properties in many areas like data analysis, finance and healthcare. This paper envisions a lightweight authentication framework (LAF) to enable secure and privacy-preserving virtual healthcare services. Leveraging Non-Fungible Token (NFT) technology to tokenize LOs and data streams on blockchain, anyone can certify the authenticity of a digital LO along with its synchronized data between PO without relying on a third-party agency. In addition, the NFT-based tokenization not only allows owners fully control their IoMT devices and data, but it also enables verifiable ownership and traceable transferability during data sharing process. Moreover, NFT only contains references to encrypted raw data that are saved on off-chain storage like local files or distributed databases, such a hybrid storage strategy ensures privacy-preservation for sensitive information. A proof-of-concept prototype is implemented and tests are conducted on a case study of seniors safety. The experimental evaluation shows the feasibility and effectiveness of the proposed LAF solution.

Preprint ARTICLE | doi:10.20944/preprints202208.0427.v1

Cloud-native Observability: The Many-faceted Benefits of Structured and Unified Logging - A Case Study

Nane Kratzke

Subject: Computer Science And Mathematics, Information Systems Keywords: cloud-native; observability; cloud computing; logging; structured logging; logs; metrics; traces; distributed tracing; log aggregation; log forwarding; log consolidation

Online: 25 August 2022 (07:32:18 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0382.v1

New Traceback Approach Deployment in Smart Mesh Microgrids against DoS Attacks

Mouna Gassara, Bilel Gassara

Subject: Computer Science And Mathematics, Information Systems Keywords: IP traceback; smart mesh Microgrid; NS-3; real secure testbed

Online: 22 August 2022 (11:16:04 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0353.v1

Multimodal Fusion Context-aware Semi-supervised Recommendation Method

Soren Pham, Arne Kamen, Jeffrey P Alarcón, Yehuda Hachulla

Subject: Computer Science And Mathematics, Information Systems Keywords: recommender; multimodal; context-aware

Online: 19 August 2022 (03:07:04 CEST)

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The advent of the era of big data will bring more convenience to people and greater development to society. But at the same time, it will also bring people the problem of 'information overload', i.e., when people are faced with huge information data, there are many redundant and worthless data. The redundant and worthless data information seriously interferes with the accurate selection of information data. Even though people can use Internet search engines to access information data, they cannot meet the individual needs of specific users in specific contexts. The personalized needs of a particular user in a particular context. Therefore, how to find useful and valuable information quickly has become one of the key issues in the development of big data. With the advent of the era of big data, recommendation systems, as an important technology to alleviate information overload, have been widely used in the field of e-commerce. Recommender systems suffer from a key problem: data sparsity. The sparsity of user history rating data causes insufficient training of collaborative filtering recommendation models, which leads to a significant decrease in the accuracy of recommendations. In fact, traditional recommendation systems tend to focus on scoring information and ignore the contextual context in which users interact. There are various contextual modal information in people's real life, which also plays an important role in the recommendation process. In this paper we achieve data degradation and feature extraction, solving the problem of sparse data in the recommendation process. An interaction context-aware sub-model is constructed based on a tensor decomposition model with interaction context information to model the specific influence of interaction context in the recommendation process. Then an attribute context-aware sub-model is constructed based on the matrix decomposition model and using attribute context information to model the influence of user attribute contexts and item attribute contexts on recommendations. In the process of building the model, the method not only utilizes the explicit feedback rating information of users in the original dataset, but also utilizes the interaction context and attribute context information of the implicit feedback and the unlabeled rating data. We evaluate our model by extensive experiments. The results illustrate the effectiveness of our recommender model.

Preprint ARTICLE | doi:10.20944/preprints202208.0331.v1

Modeling DTA by Combining Multiple-Instance Learning with a Private-Public Mechanism

Chunyu Wang, Yuanlong Chen, Lingling Zhao, Junjie Wang, Naifeng Wen

Subject: Computer Science And Mathematics, Information Systems Keywords: Drug-Target Binding Affinity; Multi-Instance Learning; Transformer

Online: 18 August 2022 (03:58:34 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202208.0023.v1

Enabling Blockchain Services for IoE with Zk-Rollups

Thomas Lavaur, Jérôme Lacan, Caroline Ponzoni Carvalho Chanel

Subject: Computer Science And Mathematics, Information Systems Keywords: IoT; IoE; Blockchain; Rollup; Zero-Knowledge; Zk-Rollup; Scalability

Online: 1 August 2022 (11:46:06 CEST)

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Internet of Things includes all connected objects from small embedded systems with low computational power and storage capacities to efficient ones, as well as moving objects like drones and autonomous vehicles. The concept of Internet of Everything expands upon this idea by adding people, data and processing. The adoption of such systems is exploding and becoming ever more significant, bringing with it questions related to the security and the privacy of these objects. A natural solution to data integrity, confidentiality and single point of failure vulnerability is the use of blockchains. Blockchains can be used as an immutable data layer for storing information, avoiding single point of failure vulnerability via decentralization and providing strong security and cryptographic tools for IoE. However, the adoption of blockchain technology in such heterogeneous systems, containing light devices, presents several challenges and practical issues that need to be overcome. Indeed, most of the solutions proposed to adapt blockchains to devices with low resources confront difficulty in maintaining decentralization or security. The most interesting are probably the Layer 2 solutions which build offchain systems strongly connected to the blockchain. Among these, zk-rollup is a promising new generation of Layer 2/off-chain schemes which can remove the last obstacles to blockchain adoption in IoT, or more generally, in IoE. Despite their promises illustrated by recent systems proposed by startups and private companies, very few scientific publications explaining or applying this barely-known technology have been published, especially for non-financial systems. In this context, the objective of our paper is to fill this gap for IoE systems in two steps. We first propose a synthetic review of recent proposals to improve scalability including onchain (consensus, blockchain organization, ...) and offchain (sidechain, rollups) solutions and we demonstrate that zk-rollups are the most promising ones. In a second step, we focus on IoE by describing several interesting features (scalability, dynamicity, data management, ...) that are illustrated with various general IoE use cases.

Preprint ARTICLE | doi:10.20944/preprints202207.0461.v1

A Dependable Digital System Model for Malaria Monitoring

Carine Pierrette Mukamakuza, Emmanuel Tuyishimire, Aimable Mbituyumuremyi, Timothy X. Brown, Didier Iradukunda, Ofentse Phuti, Rose Mary Happiness

Subject: Computer Science And Mathematics, Information Systems Keywords: Malaria; digital; epidemic; mixed infections; reinforcement

Online: 29 July 2022 (11:25:46 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202206.0172.v3

MonkeyPox2022Tweets: The First Public Twitter Dataset on the 2022 MonkeyPox Outbreak

Nirmalya Thakur

Subject: Computer Science And Mathematics, Information Systems Keywords: Monkeypox; monkey pox; Twitter; Dataset; Tweets; Social Media; Big Data; Data Mining; Data Science

Online: 25 July 2022 (09:41:19 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202204.0299.v3

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof of Concept

Nirmalya Thakur, Chia Y. Han

Subject: Computer Science And Mathematics, Information Systems Keywords: elderly; aging population; ambient intelligence; fall detection; indoor localization; real-world implementation; sensors; activities of daily living; assisted living

Online: 21 July 2022 (10:46:08 CEST)

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Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct Activities of Daily Living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with ADLs to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADL.s while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as – the lack of functionalities to detect both falls and indoor locations, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an Ambient Assisted Living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real world. Proof of concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparison studies with prior works in this field are also presented to uphold the novelty of this work. The first comparison study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparison study shows that the cost for the development of this system is the least as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

Preprint DATA DESCRIPTOR | doi:10.20944/preprints202206.0146.v2

A Large-Scale Dataset of Twitter Chatter about Online Learning during the Current COVID-19 Omicron Wave

Nirmalya Thakur

Subject: Computer Science And Mathematics, Information Systems Keywords: COVID-19; COVID; Omicron; online learning; remote learning; online education; Twitter; dataset; Tweets; social media; Big Data

Online: 21 July 2022 (08:05:19 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202206.0383.v2

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Nirmalya Thakur

Subject: Computer Science And Mathematics, Information Systems Keywords: Exoskeleton; Twitter; Tweets; Big Data; social media; Data Mining; dataset; Data Science; Natural Language Processing; Information Retrieval

Online: 21 July 2022 (04:06:53 CEST)

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Preprint REVIEW | doi:10.20944/preprints202207.0259.v1

A Systematic Review of Blockchain Technology in Current Epoch: Applications, Adoption Challenges, and Opportunities

Muhammad Nouman, Muhammad Azam, Ahsan Rehman Gill

Subject: Computer Science And Mathematics, Information Systems Keywords: Blockchain; Blockchain Technology; Cryptocurrency; Applications; Challenges; Opportunities

Online: 18 July 2022 (10:10:46 CEST)

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Preprint REVIEW | doi:10.20944/preprints202207.0190.v1

Representative Service Providers and Their Selection in Cloud Computing Domain; A Comprehensive Overview

Pranish Dahal, Ritika Prasai

Subject: Computer Science And Mathematics, Information Systems Keywords: cloud computing; data storage; users; service provider; software; hardware

Online: 13 July 2022 (04:52:59 CEST)

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Preprint REVIEW | doi:10.20944/preprints202007.0642.v2

Internet of Things and Blockchain-Based Framework for Coronavirus (COVID-19) Disease

Tanweer Alam, Mohamed Benaida

Subject: Computer Science And Mathematics, Information Systems Keywords: COVID-19; IoT; Blockchain; Contact Tracing; Mobile Applications

Online: 8 July 2022 (08:04:39 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202205.0238.v2

An Exploratory Study of Tweets about the SARS-CoV-2 Omicron Variant: Insights from Sentiment Analysis, Language Interpretation, Source Tracking, Type Classification, and Embedded URL Detection

Nirmalya Thakur, Chia Y. Han

Subject: Computer Science And Mathematics, Information Systems Keywords: COVID-19; SARS-CoV-2; Omicron; Twitter; tweets; sentiment analysis; big data; Natural Language Processing; Data Science; Data Analysis

Online: 7 July 2022 (08:36:40 CEST)

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This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, knowledge, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 12028 tweets about the Omicron variant were studied, and the specific characteristics of tweets that were analyzed include - sentiment, language, source, type, and embedded URLs. The findings of this study are manifold. First, from sentiment analysis, it was observed that 50.5% of tweets had the ‘neutral’ emotion. The other emotions - ‘bad’, ‘good’, ‘terrible’, and ‘great’ were found in 15.6%, 14.0%, 12.5%, and 7.5% of the tweets, respectively. Second, the findings of language interpretation showed that 65.9% of the tweets were posted in English. It was followed by Spanish or Castillian, French, Italian, Japanese, and other languages, which were found in 10.5%, 5.1%, 3.3%, 2.5%, and <2% of the tweets, respectively. Third, the findings from source tracking showed that “Twitter for Android” was associated with 35.2% of tweets. It was followed by “Twitter Web App”, “Twitter for iPhone”, “Twitter for iPad”, “TweetDeck”, and all other sources that accounted for 29.2%, 25.8%, 3.8%, 1.6%, and <1% of the tweets, respectively. Fourth, studying the type of tweets revealed that retweets accounted for 60.8% of the tweets, it was followed by original tweets and replies that accounted for 19.8% and 19.4% of the tweets, respectively. Fifth, in terms of embedded URL analysis, the most common domains embedded in the tweets were found to be twitter.com, which was followed by biorxiv.org, nature.com, wapo.st, nzherald.co.nz, recvprofits.com, science.org, and other URLs. Finally, to support similar research and development in this field centered around the analysis of tweets, we have developed an open-access Twitter dataset that comprises tweets about the SARS-CoV-2 omicron variant since the first detected case of this variant on November 24, 2021.

Preprint ARTICLE | doi:10.20944/preprints202207.0056.v1

Intelligent 3D Network Protocol for Multimedia Data Classification using Deep Learning

Arslan Syed, Eman A Aldhahri, Muhammad Munwar Iqbal, Abid Ali, Ammar Muthanna, Harun Jamil, Faisal Jamil

Subject: Computer Science And Mathematics, Information Systems Keywords: deep learning; convolutional neural networks; classification; machine learning; IoT

Online: 5 July 2022 (04:22:49 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202207.0035.v1

Quality of Experience Experimentation Prediction Framework Through Programmable Network Management

Ahmed Osama Basil Al-Mashhadani, Mu Mu, Ali Al-Sharbaz

Subject: Computer Science And Mathematics, Information Systems Keywords: QoE; Fairness; SDN; Classification Prediction; DASH; Multimedia

Online: 4 July 2022 (06:08:03 CEST)

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Preprint ARTICLE | doi:10.20944/preprints202206.0390.v1

Multi-Scale Object Detection with Pixel Attention Mechanism in Complex Background

Jinsheng Xiao, Haowen Guo, Yuntao Yao, Shuhao Zhang, Jian Zhou, Zhijun Jiang

Subject: Computer Science And Mathematics, Information Systems Keywords: Object detection; Feature fusion network; Multiple feature selection; Angle prediction; Pixel Attention Mechanism

Online: 29 June 2022 (03:09:52 CEST)

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Preprint COMMUNICATION | doi:10.20944/preprints202206.0383.v1

Twitter Big Data as A Resource for Exoskeleton Research: A Large-Scale Dataset of about 140,000 Tweets and 100 Research Questions

Nirmalya Thakur

Subject: Computer Science And Mathematics, Information Systems Keywords: exoskeleton; Twitter; Tweets; Big Data; social media; Data Mining; dataset; Data Science; Natural Language Processing; Information Retrieval

Online: 28 June 2022 (10:00:38 CEST)

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Computer Science and Mathematics