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IEEE International Conference on Consumer Electronics (ICCE)

作者： Jiadao Zou Qingxue Zhang Luddy School of Informatics Computing and Engineering Indiana University Department of Computer Science and Department of Biomedical Engineering Indiana University School of Informatics Computing and Engineering Indiana University Luddy Indianapolis

ISBN: (数字)9798331521165

ISBN: (纸本)9798331521172

Brain dynamics big data is of increasing promise for many applications like epilepsy detection and cognitive understanding, with the advancements of consumer technology. However, the deep-source brain measurement is difficult. In this study, we target the brain electroencephalogram (EEG) application, and investigate the deep-source EEG generation from surface EEG towards convenient big data. The deep learning algorithm has been developed to mine different configurations of the surface EEG streams, including the single-channel and multi-channel cases, for deep-source EEG generation. Promising experiments on the epilepsy application have been conducted, demonstrating the great promise of deep-learning-empowered deep-source EEG generation. This study will greatly advance brain dynamics mining towards smart consumer technology.

关键词： Deep learning Heuristic algorithms Smart healthcare Epilepsy Big Data Electroencephalography Streams Consumer electronics

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New Cloud Computing Authentication Based on Secure Hash Algorithm (SHA-3) and Lightweight Sosemanuk Algorithm 2nd

New Cloud Computing Authentication Based on Secure Hash Algo...

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2nd International Conference on Cyber Intelligence and Information Retrieval, CIIR 2023

作者： Niaf, Jolan Rokan Kadhim, Aqeel Kamil Mohammed, Qutaiba Humadi Hoomod, Haider K. Salman, Muna Muhsen Iraqi Commission for Computers & Informatics Informatics Institute for Postgraduate Studies Baghdad Iraq Computer Techniques Engineering Department Imam Al-Kadhim College Baghdad Iraq College of Nursing University of Baghdad Baghdad Iraq College of Education Al-Mustansiriyah University Baghdad Iraq Ministry of Science and Technology Department of Information Technology Baghdad Iraq

ISBN: (纸本)9789819776023

The term "cloud computing" refers to a vast network of linked, virtually hosted, and easily updatable computer resources. That are capable of consisting of a number of different apps and offering a number of different services to customers, and that have the potential to do so. Customers are required to pay for the cost of using the Services in accordance with the company's "pay-for-use only" policy. However, there are certain firms, such as Google Inc, that provide customers with particular free storage space even though the services provided by the company are limited, specifically with regard to the public cloud. A third-party service provider frequently owns and maintains the infrastructure required to run cloud computing services. But because more and more data and files pertaining to individuals and businesses are being stored in the cloud, worries have started to grow about how safe it actually is. Among these are the protection of software for virtualization and distributed computing, the safety of application software, the administration of identities, and the authentication and restriction of access. Nevertheless, efficient user authentication is the most essential need of cloud computing, as it limits unauthorized usage of a cloud server. Cloud computing has become increasingly popular in recent years. In this research, we present a new authentication method between users and online computing systems that is based on the suggested secure hash algorithm (SHA-3) and makes use of the lightweight Sosemanuk method. We created this technique. That system is going to be known as the SoSha3 system. The design that has been proposed for the system deals with data in three different sizes: 128, 256, and 512 bits. In order to make these authentication systems suitable for cloud computing at high rates, some modifications were made to them. The three-dimensional chaotic system, also known as the Lorenz system, will be combined with the authentication architecture tha

关键词： Cloud security

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Generative AI, IoT, and blockchain in healthcare: application, issues, and solutions

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Discover Internet of Things 2025年第1期5卷 1-23页

作者： Mazhar, Tehseen khan, Sunawar Shahzad, Tariq khan, Muhammad Amir Saeed, Mamoon M. Awotunde, Joseph Bamidele Hamam, Habib School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Department of Computer Science International Islamic University Islamabad44000 Pakistan Department of Computer Science COMSATS University Islamabad Sahiwal Campus Sahiwal57000 Pakistan School of Computing Sciences College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Sana’a Yemen Department of Computer Science Faculty of Communication and Information Sciences University of Ilorin Ilorin Nigeria Faculty of Engineering Uni de Moncton MonctonNBE1A3E9 Canada Department of Computer Science and Information Technology School Education Department Government of Punjab 31200 Layyah Pakistan School of Electrical Engineering University of Johannesburg Johannesburg2006 South Africa Av. Grandes Ecoles LibrevilleBP 1989 Gabon Bridges for Academic Excellence Spectrum Tunis Tunisia

This article discusses Blockchain and Generative AI in healthcare, including their uses, difficulties, and solutions. Blockchain technology improves EHR security, privacy, and interoperability, while smart contracts streamline supply chain management and administrative procedures. Blockchain verifies and secures IoT data, improving medical care and treatment, according to case studies. Generative AI systems like ChatGPT have transformed healthcare by personalizing therapy, diagnostics, and predictive analytics. AI systems can examine massive databases to diagnose diseases early, anticipate dangers, and personalize therapies. By providing timely information, boosting treatment adherence, and giving continuous support, AI-powered virtual health assistants have enhanced patient involvement. Generative AI has additionally enhanced medical research and drug development, cutting the time and expense of introducing new medicines. Generative AI and Blockchain provide safe patient data storage, high-quality AI training datasets, and efficient healthcare operations. Scalability, energy usage, and interoperability issues remain. Scalable Blockchain designs and standardized data integration and exchange protocols are suggested by this study. These technologies could improve medical research and therapy by making them safer, more effective, and more individualized. © The Author(s) 2025.

关键词： Electronic health record

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MatrikalinDiabetes: User-Centered Design of a mHealth App for Gestational Diabetes Mellitus Management and Education Among Bangladeshi Women 26th

MatrikalinDiabetes: User-Centered Design of a mHealth App ...

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26th International Conference on Human-computer Interaction, HCII 2024

作者： Ratul, Mohammad Arshad Hossain Yanoor Bristy, Tunisha Sayeed, Noorjahan Islam, Ashraful Chaudhry, Beenish Moalla Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Dhaka Bangladesh School of Computing and Informatics University of Louisiana at Lafayette Louisiana United States

ISBN: (纸本)9783031768088

This study employs the User-Centered Design (UCD) methodology to develop a mobile health (mHealth) application (app) specifically tailored for Bangladeshi women with Gestational Diabetes Mellitus (GDM). GDM affects approximately 10% of pregnant women globally and around 35% within Bangladesh, as reported by the World Health Organization (WHO). Despite its high prevalence, there is a significant lack of awareness and education about GDM in Bangladesh, compounded by language barriers that make existing digital solutions less accessible. The MatrikalinDiabetes mHealth app aims to overcome these barriers by providing comprehensive management and educational resources for GDM in the Bangla language, addressing both pre-pregnancy and post-pregnancy needs. To ensure the app meets the actual needs of potential users, personas were created based on a literature review, and a survey was conducted. This process informed the development of both a low-fidelity, paper-based prototype and a high-fidelity digital prototype. Key features of the MatrikalinDiabetes app include tracking of food and water intake, physical activity monitoring, reminders, GDM education, chat forums for community support, blood glucose level (BGL) monitoring, an SOS button for emergency contacts, and quizzes for user engagement. Preliminary feedback indicated that 53.8% of participants favored the integrated features and expressed willingness to use and recommend the app. The survey of the high-fidelity prototype yielded positive responses, with 57.9% of participants strongly agreeing and 42.1% agreeing that the features were beneficial. The user-friendly design of the high-fidelity prototype ensures intuitive interaction, aiding in health management, enhancing GDM understanding, and promoting a healthier lifestyle among pregnant women in Bangladesh. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Obstetrics

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Questionable Fairness in Federated Learning 8

Questionable Fairness in Federated Learning

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8th International Conference on Data Science and Machine Learning Applications, CDMA 2025

作者： Alharbi, Raed Nguyen, Truc Yousefzadeh, Nooshin Aljohani, Ahmed National Renewable Energy Laboratory Computatioual Science Center GoldenCO United States University of Florida Computer and Information Science and Engineering Department GainesvilleFL United States College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia University of North Texas Computer Science and Engineering Denton United States

ISBN: (纸本)9798331539696

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. This paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. The experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning. © 2025 IEEE.

关键词： Federated learning

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Secure Lattice-Based Signature Scheme for Internet of Things Applications

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IEEE Access 2025年 13卷 75985-75999页

作者： Prajapat, Sunil Gautam, Deepika Kumar, Pankaj Jangirala, Srinivas Das, Ashok Kumar Sikdar, Biplab Srinivasa Ramanujan Department of Mathematics Central University of Himachal Pradesh Dharamshala 176206 India Jindal Global Business School O. P. Jindal Global University Haryana 131001 India Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad 500 032 India Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea Department of Electrical and Computer Engineering National University of Singapore 117583 Singapore

The enormous benefits of Internet of Things (IoT) technology have driven its deployment in various applications. Additionally, the development of quantum computers has directed attention towards lattice-based cryptography. Consequently, the computational capabilities of quantum computers pose a threat to the security of the existing IoT signature mechanisms. Quantum computers are proficient at unraveling the complexity bound of computationally hard problems like the integer factorization problem (IFP) and the discrete logarithm problem (DLP). As a result, security is an essential requirement for the IoT communication network against quantum attacks. The amalgamation of certificateless public key cryptosystems (CL-PKC) and lattice-based cryptography (LBC) is one of the solution for alleviating these security menaces. Lucidly, CL-PKC prevents key escrow issues and key management problems;LBC prevents quantum attacks. The Shortest Integer Solution (SIS) problem, which the NTRU lattices offer, serves as the basis for this paper’s introduction of a certificateless signature mechanism for IoT environments. By adopting the Random Oracle Model, we demonstrated the security of the suggested mechanism against Type 1 and Type 2 attackers. Furthermore, security analysis and performance evaluation demonstrate robust communication, as evidenced by metrics such as the computational cost of CL-Sign and CL-Verify phases at 536µs, 376.81µs and communication cost of KGC at 418 bits, CL-Sign at 532 bits and CL-Verify at 446 bits. Also, we calculate the cost of single-message signature generation and verification on an IoT device. These results show that the suggested mechanism’s security and computational efficiency are more reliable, and efficient than other relevant competing frameworks. © 2013 IEEE.

关键词： Certificateless public key cryptosystem Internet of Things (IoT) lattice-based cryptography Security Signature

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Bibliometric and Content Analysis of Large Language Models Research in Software engineering: The Potential and Limitation in Software engineering

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International Journal of Advanced computer Science and Applications 2025年第4期16卷 344-356页

作者： Damayanti, Annisa Dwi Gani, Hamdan Zhipeng, Feng Gani, Helmy Zuhriyah, Sitti Nurani Djabir, St. Nurhayati Wardani, Nur Ilmiyanti Department of Environmental Engineering Faculty of Engineering Hasanuddin University Makassar Indonesia Department of Machinery Automation System ATI Makassar Polytechnic Makassar Indonesia School of Culture Creativity and Media Hangzhou Normal University Zhejiang Hangzhou China Department of Industrial Hygiene Faculty of Public Health Occupational Health and Safety Makassar College of Health Sciences Indonesia Department of Computer System Universitas Handayani Makassar Makassar Indonesia Department of Information Systems and Technology Institut Teknologi dan Bisnis Nobel Indonesia Jl. Sultan Alauddin No.212 Makassar90221 Indonesia Department of Informatics Engineering Universitas Handayani Makassar Makassar Indonesia

Large Language Models (LLM) is a type of artificial neural network that excels at language-related tasks. The advantages and disadvantages of using LLM in software engineering are still being debated, but it is a tool that can be utilized in software engineering. This study aimed to analyze LLM studies in software engineering using bibliometric and content analysis. The study data were retrieved from Web of Science and Scopus. The data were analyzed using two popular bibliometric approaches: bibliometric and content analysis. VOS Viewer and Bibliometrix software were used to conduct the bibliometric analysis. The bibliometric analysis was performed using science mapping and performance analysis approaches. Various bibliometric data, including the most frequently referenced publications, journals, and nations, were evaluated and presented. Then, the synthetic knowledge method was utilized for content analysis. This study examined 235 papers, with 836 authors contributing. The publications were published in 123 different journals. The average number of citations per publication is 1.44. Most publications were published in Proceedings International Conference on Software engineering and ACM International Conference Proceeding Series, with China and the United States emerging as the leading countries. It was discovered that international collaboration on the issue was inadequate. The most often used keywords in the publications were "software design," "code (symbols)," and "code generation." Following the content analysis, three themes emerged: 1) Integration of LLM into software engineering education, 2) application of LLM in software engineering, and 3) potential and limitation of LLM in software engineering. The results of this study are expected to provide researchers and academics with insights into the current state of LLM in software engineering research, allowing them to develop future conclusions. © (2025), (Science and Information Organization). All Rights Res

关键词： Software design

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Addressing Domain Mismatch in Unsupervised Neural Machine Translation

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 472-482页

作者： Youyuan Lin Rui Wang Chenhui Chu Graduate School of Informatics Kyoto University Kyoto Japan Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

Pretrained models have taken full advantage of monolingual corpora and achieved impressive results in training Unsupervised Neural Machine Translation (UNMT) models. However, when adapting UNMT models with in-domain monolingual corpora for domain-specific translation tasks, one of the languages may lack in-domain corpora, resulting in the unequal amount and proportion of in-domain monolingual corpora in each language. This problem situation is known as Domain Mismatch (DM). This study investigates the impact of DM in UNMT. We find that DM causes a translation quality disparity. That is, while in-domain monolingual corpora of a language can enhance the in-domain translation quality into that particular language, this enhancement cannot be generalized to the other language, and the translation quality into the other language remains deficient. To address this problem, we propose Domain-Aware Adaptation (DAA), which can be embedded in the vanilla UNMT model training process. By passing sentence-level domain information to the model during training and inference, DAA gives higher weight to in-domain data from open-domain corpora related to specific domains to alleviate domain mismatch. The experimental results on German-English and Romanian-English translation tasks specified in the IT, Koran, medical, and TED2020 domains demonstrate that DAA can efficiently exploit open-domain corpora to mitigate the quality disparity of translation caused by DM.

关键词： Training Adaptation models Translation Neural machine translation Data models Speech processing

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HELP4DNS: Leveraging the programmable data plane for effective and robust defense against DDoS attacks on DNS

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Journal of Network and computer Applications 2025年 240卷

作者： Şahin, Mehmet Emin Demirci, Mehmet Department of Information Systems Graduate School of Informatics Gazi University Ankara Turkey Ankara Turkey Department of Computer Engineering Faculty of Engineering Gazi University Ankara Turkey

DNS is a critical component of the Internet infrastructure, and securing it has been an active research domain, with a particular emphasis on countering DDoS attacks. With the rise of programmable data planes, novel defensive strategies taking advantage of their flexibility and line-rate packet processing capabilities have been developed to counter a range of DDoS attacks. This study proposes two novel methodologies against DNS flood and DNS amplification attacks within programmable data planes using P4. The first approach involves constraining the concurrent active queries per client to mitigate DNS query flood attacks, thereby ensuring that clients generating a high volume of requests adhere to predetermined limits. The proposed method uses concurrent query limits per client by employing a modified token bucket algorithm within an updatable Bloom filter data structure to track and limit DNS queries. This approach effectively rate limits malicious client requests, preventing server overload and safeguarding benign users from any resulting disruptions. The second method is a DNS firewall implemented on the P4 switch situated on the victim's side to prevent DNS amplification attacks. The proposed firewall utilizes an updatable Bloom filter on a P4 switch, enabling stateful processing of DNS queries at the application layer. Additionally, it supports stateful tracking of fragmented DNS responses resulting from the Extension Mechanisms for DNS. While IP fragmentation occurs at the IP layer, the proposed approach achieves stateful tracking of fragmented DNS responses at the application layer. In this manner, only the responses corresponding to legitimate requests are forwarded among the received DNS responses by the victim, while responses stemming from DNS amplification attacks are blocked. Evaluation results have demonstrated that the proposed approach effectively blocks high-volume DNS amplification attack packets with minimal memory space requirements. © 2025 Elsevi

关键词： computer system firewalls

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Stock Price Forecasting in Finance with Artificial Neural Networks 5

Stock Price Forecasting in Finance with Artificial Neural Ne...

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5th Asia Conference on Information engineering, ACIE 2025

作者： Daloglu, Turgut Osi, Garibe Aslan, Murat Beskirli, Mehmet Cizre District Directorate of National Education Şirnak University Şirnak Turkey Şirnak University Department of Computer Engineering Şirnak Turkey Karamanoǧlu Mehmetbey University Faculty of Medicine Departmen of Medical Education and Informatics Karaman Turkey

ISBN: (纸本)9798331528409

This paper examines the applications of Artificial Neural Networks (ANNs) in the field of finance, particularly in stock price forecasting. Artificial neural networks are mathematical models that mimic biological nervous systems and are used to solve complex problems such as financial data analysis. Stock price forecasting is an important topic for investors and finance professionals. Artificial neural networks have been widely used in this field due to their ability to process large amounts of financial data, learn complex relationships and predict future price movements. This study deals with stock price forecasting with artificial neural networks using historical stock prices, economic indicators and other financial data. Neural networks attempt to predict future price movements by learning the complex relationships between these data. Stock price forecasting with artificial neural networks can offer a more flexible and adaptive approach than traditional statistical methods. However, the success of the model depends on factors such as the quality of the data set used, network architecture and training parameters. This study was conducted to understand the potential of artificial neural networks in finance and to evaluate their effectiveness in stock price forecasting. The purpose of this study is to explore how artificial neural networks can be used in financial data analysis, and in particular their potential in stock price forecasting. The study is designed to understand the advantages of applying ANN in finance, to evaluate the stock price forecasting performance of this technology and to provide a new perspective that can influence future investment strategies. © 2025 IEEE.

关键词： Neural network models

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