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SSRN 2023年

作者： Xu, Jiawei Zadorozhny, Vladimir Grant, John Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh PA United States Department of Computer Science UMIACS University of Maryland MD United States

FIP: Fake News Detection System Using Incompatibility ProbabilityJiawei Xu, Vladimir Zadorozhny, John Grant,We propose FIP, a new method for fake news detection. In this framework a topic is a statement about an event, such as a headline. News articles may refer to or elaborate on the supposed event. Our technique relies on calculating the incompatibility probability for news articles with respect to a topic based on their stances determined by a reviewer. In the relevant cases where a news article is related to a topic, the stances are agree, disagree, and discuss, where the last option reflects uncertainty. As we show experimentally, the news articles with the highest incompatibility probability values are the best candidates for being fake news. © 2023, The Authors. All rights reserved.

关键词： Fake detection

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The Continuous Use of a Highway Traffic Management System (HTMS) Services on Mobile Application

The Continuous Use of a Highway Traffic Management System (H...

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HCI and UX Conference in Indonesia (CHIuXiD), International

作者： Norizan Anwar Masni Aida Majid Norzuraiza Rina Ahmad Yohannes Kurniawan College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Malaysia Testing & Commissioning Department CabNet System (M) Sdn. Bhd. Selangor Malaysia Information Systems Department School of Information Systems Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798350380439

ISBN: (纸本)9798350380446

Mobile applications are used across various fields and for different purposes. M-commerce, social media, education, and navigation are just a few examples of applications available on mobile platforms. These applications enable users to access them anytime and anywhere. However, there is a limited number of mobile applications that specifically provide information about traffic, emergency services (SOS), toll fares, amenities, and more. As one of the largest highway concessionaires in Malaysia, XYZ has also developed a mobile application to disseminate information to highway users. Thus, the purpose of this study is to examine the factors that influence the continued use of the XYZ mobile application. The results show that user experience factors—such as Effort Use (EU), Social Influence (SI), Accuracy (ACC), Completeness (COM), Pragmatic Quality (PQ), Stimulation Quality (SQ), Identity Quality (IQ), and Attractiveness (ATT)—positively affect the continued use of the XYZ mobile application.

关键词： Road transportation Accuracy Social networking (online) Navigation Education Emergency services User experience Mobile applications Pragmatics Information integrity

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On the Effectiveness of Dimensionality Reduction Techniques on High Dimensionality Datasets 1

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12th International Conference on Information systems and Advanced Technologies, ICISAT 2022

作者： Henouda, Salah Eddine Laallam, Fatima Zohra Kazar, Okba Harous, Saad Houfani, Djihane Department of Computer Science LINATI Laboratory Kasdi Merbah University Ouargla Algeria Department of Computer Science LINFI Laboratory Mohamed Khider University Biskra Algeria Department of Information Systems and Security College of Information Technology United Arab Emirate University Abu Dhabi United Arab Emirates Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (数字)9783031253447

ISBN: (纸本)9783031253430

The present work strives to investigate the effect of using dimensionality reduction techniques (DRTs) on breast cancer (BC) classification problem. Primarily, we focused on the following five (DRTs): Auto-Encoders (AE), T-Distributed Stochastic Neighbor Embedding (T- SNE), Recursive Feature Elimination (RFE), Isometric Feature Mapping (Isomap), and Principle Component analysis (PCA). These methods are combined with two famous classifiers that are Support Vector Machine (SVM) and Multilayer perceptron (MLP). They are used for BC classification. Breast Cancer Wisconsin Diagnostic (WDBC) data set was used to validate the experiments of this work. The former was provided by the University of California, Irvine (UCI) machine learning repository. The results demonstrated that combining MLP with the chosen (DRTs) methods increased the classification accuracy for almost all built models by at least 0.7%. In addition, they revealed a decrease in the classification accuracy using SVM as a classifier for almost all built models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Breast cancer prediction Deep learning Dimensionality reduction Machine learning Medical dataset Neural networks

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Secure and Reliable 6G Communications with BCH-Based GLDPC and Kyber

Secure and Reliable 6G Communications with BCH-Based GLDPC a...

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2025 IEEE International Conference on Consumer Electronics, ICCE 2025

作者： Nguyen, Linh Min, Cheol-Hong Lee, Hanho Nguyen, Tuy Tan School of Informatics Computing and Cyber Systems Northern Arizona University United States Department of Electrical and Computer Engineering University of St. Thomas United States Department of Electrical and Computer Engineering Inha University Korea Republic of

ISBN: (纸本)9798331521165

Channel coding in sixth-generation (6G) networks must attain exceptionally low bit error rate (BER), typically in the range of 10-6 to 10-9, to ensure the requisite level of reliability. While fifth-generation (5G) New Radio (NR) coding techniques have made substantial progress, the inherent limitations of the low error floor impede the attainment of such stringent BER targets. Compared to the low-density parity check (LDPC) codes employed in 5G communications, generalized low-density parity check (GLDPC) codes offer a more advantageous trade-off between computational complexity and error correction performance in 6G networks. GLDPC codes enhance traditional LDPC structures by incorporating more complex local decoding units within their check nodes. When coupled with cryptographic techniques, GLDPC codes can establish a robust information security framework. In this paper, we propose a new security-based GLDPC that uses a number of BCH matrices for the GLDPC decoding process combined with the Kyber algorithm (BCH-based KyGLDPC) before transmitting data to increase reliability and solve the security problem against quantum computer attacks. The system's performance is assessed using both image data and data encrypted prior to transmission in a white additive Gaussian noise environment. The results indicate that the system provides strong error correction and data security. © 2025 IEEE.

关键词： Qubits

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Improved Data Encoding for Emerging computing Paradigms: From Stochastic to Hyperdimensional computing

arXiv

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arXiv 2025年

作者： Moghadam, Mehran Shoushtari Aygun, Sercan Najafi, M. Hassan Electrical Computer and Systems Engineering Department Case Western Reserve University ClevelandOH44106 United States School of Computing and Informatics University of Louisiana at Lafayette LafayetteLA70503 United States

Data encoding is a fundamental step in emerging computing paradigms, particularly in stochastic computing (SC) and hyperdimensional computing (HDC), where it plays a crucial role in determining the overall system performance and hardware cost efficiency. This study presents an advanced encoding strategy that leverages a hardware-friendly class of low-discrepancy (LD) sequences, specifically powers-of-2 bases of Van der Corput (VDC) sequences (VDC-2n), as sources for random number generation. Our approach significantly enhances the accuracy and efficiency of SC and HDC systems by addressing challenges associated with randomness. By employing LD sequences, we improve correlation properties and reduce hardware complexity. Experimental results demonstrate significant improvements in accuracy and energy savings for SC and HDC systems. Our solution provides a robust framework for integrating SC and HDC in resource-constrained environments, paving the way for efficient and scalable AI implementations. © 2025, CC BY.

关键词： Encoding (symbols)

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CKKS-Based Homomorphic Encryption Architecture using Parallel NTT Multiplier

CKKS-Based Homomorphic Encryption Architecture using Paralle...

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： Tuy Tan Nguyen Jisu Kim Hanho Lee School of Informatics Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA Department of Information and Communication Engineering Inha University Incheon Korea

This paper presents a high-throughput CKKS-based encryption architecture for homomorphic encryption. By de-ploying a parallel number theoretic transform (NTT) multiplier architecture, the polynomial multiplication is significantly accel-erated. Additionally, the modular multiplier is also improved by efficiently implementing using digital signal processing resources. The proposed NTT multiplier and homomorphic encryption architecture are evaluated using Xilinx Vivado and Xilinx XCU250 FPGA board. The evaluation results demonstrate that the proposed NTT multiplier helps improve the throughput of polynomial multiplication by at least 1.5 x compared to the most recent works. The efficiency of the proposal NTT multiplier, calculated by throughput per L UT or Slice, is much better than that of existing studies. The proposed homomorphic encryption architecture using the proposed NTT multiplier offers a high throughput of 32.7 Gbps.

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Secure- and Privacy-Preserving Policies for Distributed Cooperative Control of Multiple Vehicle systems

Secure- and Privacy-Preserving Policies for Distributed Coop...

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Autonomous systems: Sensors, Processing, and Security for Ground, Air, Sea, and Space Vehicles and Infrastructure 2023

作者： Nguyen, Binh T. Nghiem, Truong X. Nguyen, Linh Nguyen, Tuy Tan Nguyen, Thang The Department of Engineering Texas A&M University-Corpus Christi Corpus ChristiTX78412 United States School of Informatics Computing and Cyber Systems Northern Arizona University FlagstaffAZ86011 United States Institute of Innovation Science and Sustainability Federation University Australia ChurchillVIC3842 Australia

ISBN: (纸本)9781510661967

Conventional distributed formation control of multiple vehicle systems (MVSs) has two drawbacks: inflexible formation changes and explicit-value exchanges of vehicles' information such as position and velocity among all vehicles. Since formation changes are needed, each vehicle is required to update its relative position which is quite difficult in large spatial applications. Firstly, the explicit-value exchanges possibly result in two critical issues. When each vehicle policy needs to keep its information confidential from another unexpected listener, the explicit-value exchanges are invalid for the privacy policies. Additionally, the explicit-value storing or exchanging signals or parameters are much more vulnerable and dangerous to security threats. This work proposes an approach to overcome the above challenges by taking advantage of model predictive control-consensus algorithms to achieve desired formations. We will also allow the computation to be effectively distributed among the vehicle agents according to their computational capabilities. Secondly, we use the highly secure encryption scheme that empowers all computations carried out in encrypted forms, including system parameters and signals. Our results are verified by the formation control of multiple vehicles working in large-scale environments where a ground station does not touch all vehicles due to limited communication ranges and security problems. Compared to cutting-edge studies, the formation of vehicles is still able to be changed securely by the ground station without updating new formations to all vehicles. Besides, the data privacy of each vehicle is preserved by encrypting all physical signals. © 2023 SPIE. All rights reserved.

关键词： Vehicles

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Securing the Future after Pager Bombs:Lifecycle Protection of Smart Devices via Blockchain Intelligence

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IEEE/CAA Journal of Automatica Sinica 2024年第12期11卷 2355-2358页

作者： Fei Lin Qinghua Ni Jing Yang Juanjuan Li Nan Zheng Levente Kovács Radu Prodan Mariagrazia Dotoli Qing-Long Han Fei-Yue Wang the Department of Engineering Science Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the School of Artificial Intelligence University of Chinese Academy of SciencesBei-jing 100049China IEEE Obuda University John von Neumann Faculty of InformaticsBecsi Str.96/bH-1034 BudapestHungary the Institute of Information Technology University of Klagenfurt9020 Klagenfurt am WortherseeAustria the Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190

THE Lebanese wireless device explosion incident has drawn widespread attention,involving devices such as pagers,walkie-talkies,and other common devices[1].This event has revealed and highlighted the security vulnerabilities in global supply chains from raw material manufacturing and distribution to the usage of devices and equipment,signaling the onset of a new wave of"supply chain warfare"[2].

关键词： Smart cycle chains

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AR Intelligent Real-time Method for Cultural Heritage Object Recognition 5

AR Intelligent Real-time Method for Cultural Heritage Object...

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5th IEEE International Conference on Advanced Information and Communication Technologies, AICT 2023

作者： Pisnyi, Oleh Kit, Ivan Lipianina-Honcharenko, Khrystyna Sieck, Jurgen Sachenko, Anatoliy Dobrowolski, MacIej Sapozhnyk, Grygoriy Master's Degree in Computer Science West Ukrainian National University Ternopil Ukraine Systems and Control West Ukrainian National University Department for Information-Computing Ternopil Ukraine Hochschule für Technik und Wirtschaft Berlin Berlin Germany Research Institute for Intelligent Computer Systems West Ukrainian National University Ternopil Ukraine Kazimierz Pulaski University of Technology and Humanities in Radom Department of Informatics and Teleinformatics Radom Poland

ISBN: (纸本)9798350372571

In a world of rapid technological changes and growing interest in cultural heritage, the development of innovative tools for travelers and history enthusiasts becomes incredibly important. Even in the digital age, interactive ways of exploring historical landmarks remain relevant and attractive. In this research, an AR Intelligent Real-time Method for Cultural Heritage Object Recognition has been developed. The method is implemented through a chatbot, which, when using a smartphone's camera, recognizes objects of cultural heritage and provides descriptions of these objects. The chatbot opens up new possibilities for travelers, allowing them to enjoy history and cultural heritage using intelligent tools and augmented reality. © 2023 IEEE.

关键词： Augmented reality

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A Hybrid Ensemble Deep Learning Approach for Emotion Classification

A Hybrid Ensemble Deep Learning Approach for Emotion Classif...

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IEEE International Conference on Big Data

作者： Christos Karras Aristeidis Karras Dimitrios Tsolis Markos Avlonitis Spyros Sioutas Computer Engineering and Informatics Department Decentralized Systems Computing (DSC) Group University of Patras Patras Greece Department of Informatics Ionian University Corfu Greece

ISBN: (纸本)9781665480468

Speech processing, the field of analysing input speech signals and methods of processing them has emerged in the recent days. Additionally, the development of a speech processing system involves several components in the design phase with probabilistic approximations for enhanced audio sampling and de-noising. In this work, we focus into use of Gaussian random variables while modelling and filtering noise that gets added after being passed through an additive noise channel in a communication system, and the applications of Hidden Markov models. Moreover, we apply deep learning methods for emotion classification via a robust and accurate ensemble learning scheme that is applied to a joint deep network which incorporates audiovisual inputs and generates the emotion prediction effectively reaching satisfactory accuracy.

关键词： Deep learning Additive noise Filtering Hidden Markov models Speech recognition Markov processes Big Data

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