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Privacy Enhanced Mobile User Authentication Method Using Motion Sensors

computer Modeling in Engineering & sciences 2024年第3期138卷 3013-3032页

作者： Chunlin Xiong Zhengqiu Weng Jia Liu Liang Gu Fayez Alqahtani Amr Gafar Pradip Kumar Sharma Shenzhen Institutes of Advanced Technology Chinese Academy of SciencesShenzhen518052China Sangfor Technologies Inc. Shenzhen518055China School of Data Science and Artificial Intelligence Wenzhou University of TechnologyWenzhou325035China College of Computer Science&Technology Zhejiang University of TechnologyHangzhou310023China Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Math&Computer Science Department Faculty of ScienceMenofia UniversityShebin El-KomEgypt Computing Science Department University of AberdeenAberdeenUK

With the development of hardware devices and the upgrading of smartphones,a large number of users save privacy-related information in mobile devices,mainly smartphones,which puts forward higher demands on the protection of mobile users’privacy *** present,mobile user authenticationmethods based on humancomputer interaction have been extensively studied due to their advantages of high precision and non-perception,but there are still shortcomings such as low data collection efficiency,untrustworthy participating nodes,and lack of *** this end,this paper proposes a privacy-enhanced mobile user authentication method with motion sensors,which mainly includes:(1)Construct a smart contract-based private chain and federated learning to improve the data collection efficiency of mobile user authentication,reduce the probability of the model being bypassed by attackers,and reduce the overhead of data centralized processing and the risk of privacy leakage;(2)Use certificateless encryption to realize the authentication of the device to ensure the credibility of the client nodes participating in the calculation;(3)Combine Variational Mode Decomposition(VMD)and Long Short-TermMemory(LSTM)to analyze and model the motion sensor data of mobile devices to improve the accuracy of model *** experimental results on the real environment dataset of 1513 people show that themethod proposed in this paper can effectively resist poisoning attacks while ensuring the accuracy and efficiency of mobile user authentication.

关键词： Mobile authentication blockchain federated learning smart contract certificateless encryption VMD LSTM

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Optimizing Coverage in Wireless Sensor Networks Using the Tumbleweed Algorithm

Journal of Network Intelligence

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Journal of Network Intelligence 2025年第1期10卷 250-261页

作者： Zhang, Mengfei Chu, Shu-Chuan Liu, Nengxian Huang, Kuan-Chun Pan, Jeng-Shyang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China College of Computer and Data Science Fuzhou University Fuzhou350108 China Department of Industrial and Systems Engineering Chung Yuan Christian University Taoyuan320314 Taiwan School of Artificial Intelligence Nanjing University of Information Science and Technology Nanjing210044 China Department of Information Management Chaoyang University of Technology Taichung41349 Taiwan

Wireless sensor networks (WSNs) have found extensive applications across various fields, significantly enhancing the convenience in our daily lives. Hence, an in-creasing number of researchers are directing their attention toward the performance of sensor nodes, with a particular emphasis on addressing the coverage challenges in WSNs. Node coverage plays a crucial role in determining the detection performance of WSNs, making it imperative to attain maximum coverage with a given number of sensor nodes. This paper applies the Tumbleweed Algorithm (TA) to the coverage problem in WSNs and validates its effectiveness through simulation experiments. The results demonstrate its unique advantages in global search and avoiding local optima, providing a new solution for the WSN coverage problem and offering valuable references for future research and practical applications. The TA algorithm simulates the lifecycle of tumbleweed from seedling to mature plant and includes the concept of growth cycles. This paper compares the TA algorithm with PSO, GWO, DE, BOA, SCA, and AOA, setting different numbers of wireless sensor nodes and varying sensing radii for the experiments. The experimental results show that the TA algorithm significantly improves coverage rates under various configurations and consistently outperforms other commonly used algorithms. © 2025, Taiwan Ubiquitous Information CO LTD. All rights reserved.

关键词： Sensor nodes

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DFU_XAI: A Deep Learning-Based Approach to Diabetic Foot Ulcer Detection Using Feature Explainability

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Biomedical Materials and Devices 2024年第2期2卷 1225-1245页

作者： Biswas, Shuvo Mostafiz, Rafid Paul, Bikash Kumar Uddin, Khandaker Mohammad Mohi Hadi, Md. Abdul Khanom, Fahmida Department of Information and Communication Technology Mawlana Bhashani Science and Technology University Institute of Information Technology Noakhali Science and Technology University Department of Software Engineering Daffodil International University Department of Computer Science and Engineering Dhaka International University Department of Computer Science: Information Technology University of Nebraska Omaha Department of Mathematics American International University – Bangladesh

Diabetic foot ulcer (DFU) is a potentially fatal complication of diabetes. Traditional techniques of DFU analysis and therapy are more time-consuming and costly. Artificial intelligence (AI), particularly deep neural networks, has demonstrated remarkable effectiveness in medical applications. Despite this, the lack of explainability of deep learning models is currently viewed as a key hurdle to using these approaches in actual clinical settings. In this research, we present the DFU_XAI framework for assessing the interpretability of explainable-driven deep learning (DL) models. DFU_XAI evaluates five DL models (Xception, DenseNet121, ResNet50, InceptionV3, and MobileNetV2) to establish a transparent DL framework using three state-of-the-art explanation methods: Shapley additive explanation (SHAP), local interpretable model-agnostic explanations (LIME), and gradient-weighted class activation mapping (Grad-CAM). ResNet50 outperformed the other four models with remarkable results: 98.75% accuracy, 99.2% precision, 97.6% recall, 98.4% F1-score, and 98.5% AUC. For the most part, it can locate diabetic foot ulcers precisely on a diabetic foot and discriminate between diabetic foot ulcers and healthy feet in the DFU dataset. A heat map will indicate the precise location of the ulcer that needs care. © The Author(s), under exclusive licence to Springer science+Business Media, LLC 2024.

关键词： Convolutional neural networks Deep learning Diabetic foot ulcers Explainable AI

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A Rearrangement and Restore-Based Mixer Model for Target-Oriented Multimodal Sentiment Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 3109-3119页

作者： Jia, Li Ma, Tinghuai Rong, Huan Sheng, Victor S. Huang, Xuejian Xie, Xintong Nanjing University of Information Science and Technology School of Computer Science Nanjing Jiangsu210044 China Nanjing University of Information Science and Technology School of Computer and Software Nanjing Jiangsu210044 China Jiangsu Ocean University School of Computer Engineering Lianyungang Jiangsu222005 China Nanjing University of Information Science and Technology School of Artificial Intelligence Nanjing Jiangsu210044 China Texas Tech University Department of Computer Science LubbockTX79409 United States

With the development of fine-grained multimodal sentiment analysis tasks, target-oriented multimodal sentiment (TMSC) analysis has received more attention, which aims to classify the sentiment of target with the help of textual and associated image features. Existing methods focus on exploring fine-grained image features and incorporate transformer-based complex fusion strategies, while ignoring the heavy computational burden. Recently, some lightweight multilayer perceptrons (MLP)-based methods have been successfully applied to multimodal sentiment classification tasks. In this article, we propose an effective rearrangement and restore mixer model (RR-Mixer) for TMSC, which dedicates the interaction of image, text, and targets along the modal-axis, sequential-axis, and feature channel-axis through rearrangement and restore operations. Specifically, we take vision transformer (ViT) and robustly optimized BERT (RoBERTa) pretrained models to extract image and textual features, respectively. Further, we adopt cosine similarity to select the most semantically relevant image features. Then, an RR-Mixer module is designed for mixed multimodal features, with the core technology consisting of rolling, grouping rearrangement and restore operations. Moreover, we introduce MLP Unit to learn the information of different modalities for intermodal interaction. The results show that our model achieves superior performance on two benchmark multimodal datasets, TWITTER-15 and TWITTER-17, with a significant improvement of 4.66% and 1.26% in terms of macro-F1. © 2020 IEEE.

关键词： Feature extraction

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NeuroProbe: A Performance Profiling Feed Forward Neural Network Simulator for Hardware Interaction Analysis

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SN computer science 2025年第5期6卷 1-22页

作者： Ayyappan, Binu Kumar, G. Santhosh Department of Computer Science Cochin University of Science and Technology Kochi India Department of Artificial Intelligence and Data Science Rajagiri School of Engineering & Technology Kochi India

NeuroProbe is a simple neural network simulator designed by authors specifically for educational purposes focusing on simulating inference phase on a computationally capable embedded hardware, aiming to provide a deeper understanding of hardware interactions and performance improvement. This simulator incorporates performance profiling capabilities, allowing students and educators to analyze and optimize the performance of a simulated trained neural network. By simulating the intricate hardware interactions, NeuroProbe enables users to gain insights into how various components impact the overall performance of neural networks. Using this simulator, learners can experiment with different configurations and architectures, identify potential bottlenecks, and explore optimization strategies to enhance neural network performance before deploying the model on an Edge Computing environment. It also enriches computer science education by enabling learners to gain deeper insights into hardware interactions and performance improvement. By identifying performance bottlenecks, learners can refine their understanding of neural networks and develop skills in optimizing their models for enhanced performance. In conclusion, NeuroProbe empowers users to bridge the gap between theoretical knowledge and practical implementation of neural network on an embedded device, fostering a more comprehensive understanding of neural networks and their hardware requirements. Currently, it is focused solely on single-threaded CPU-based computation enabling SSE, AVX. At present, NeuroProbe does not support SIMD extensions such as AVX-FMA, AVX-512, multi-threaded computation, and accelerators such as NPU, TPU, and GPU. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.

关键词： Hardware–software interactions Neural network simulator NeuroProbe Performance bottlenecks Performance profiling Simulations

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A Comparison Study of Prominent International university Ranking Systems: Exploring the Potential of Yemeni Universities for Rankings 1

A Comparison Study of Prominent International University Ran...

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1st International Conference on Emerging Technologies for Dependable Internet of Things, ICETI 2024

作者： Al-Hagree, Salah Al-Shalabi, Ahmed Abdullah Al-Sanabani, Maher Al-Majma, Nashwan Ahmed Abdulrazzak, Fuaad Hasan Al-Gaphari, Ghaleb Al-Dilami, Redwan Alawdi, Ala'a Computer Science Department Faculty of Computer and Information Technology Sana’a University Yemen Computer Science Department Faculty of Sciences Ibb University Yemen Aljazeera University Yemen Computer Science Department Faculty of Computer and Information Technology Sana’a University Sana’a Yemen Computer Science Department Faculty of Computer and Information Technology Thamar University Yemen Faculty of Computing and IT University of Science and Technology Sana’a Yemen

ISBN: (纸本)9798331533557

Specifically targeting Yemeni universities, the focus lies on their potential to enter these rankings and enhance their positions such as Quacquarelli Symonds (QS) World university Ranking (QS), Academic Ranking of World Universities (ARWU), Webometrics Ranking, Times Higher Education (THE) World university Ranking, and SCImago Institutions (SIR) Rankings. Its objective is to encourage Yemeni universities to prioritize the criteria outlined in these rankings and view them as a means to access other international rankings. The research tackles the challenge of comprehending the criteria and application process required for Yemeni universities to participate in these rankings, as well as the rankings they have achieved. The study underscores the importance of this research by providing Yemeni university administrators with valuable insights into the criteria used for international rankings and the rankings obtained by Yemeni universities. This understanding can facilitate efforts to enhance the quality of higher education within these institutions. The findings of the study reveal that there are two Yemeni universities listed in the QS. The university of science and technology, ranks 151-170, while Thamar university, ranks 171-200 for 2023. While in 2024, the university of science and technology ranked first locally and the university of Aden ranked second. This paper investigates the participation of Yemeni universities in global ranking systems such as ARWU, THE, QS, and SIR, with the aim of enhancing their international standing. It highlights the challenges and opportunities for Yemeni universities to meet ranking criteria, offering insights that can help improve the quality of higher education. © 2024 IEEE.

关键词： Quacquarelli Symonds Ranking Yemen research Yemeni Universities

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PFHaD: Page Fault Handler Assisted Defense Against Data-Oriented Attacks 1

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3rd International Conference on Advances in Computing Research, ACR 2025

作者： Zhang, Tianning Huang, Hao School of Computer Jiangsu University of Science and Technology Zhenjiang China Department of Computer Science and Technology Nanjing University Nanjing China

ISBN: (数字)9783031876479

ISBN: (纸本)9783031876462

As memory corruption vulnerabilities evolve, attackers have shifted focus from traditional control-flow attacks to non-control data attacks, which manipulate data influencing a program′s behavior without altering its control flow. This shift bypasses conventional defenses like Control Flow Integrity (CFI), posing new security risks. In response, we propose a novel defense mechanism called Page Fault Handler-assisted Defense (PFHaD). PFHaD leverages hardware page faults to protect critical data in memory, providing robust defense against non-control data attacks with minimal performance overhead. Unlike traditional defenses, PFHaD also prevents information leakage by making critical data pages inaccessible. The solution does not require modifications to hardware, compilers, or operating systems, making it ideal for commercial-off-the-shelf (COTS) software. Experimental results demonstrate that PFHaD effectively mitigates a wide range of non-control data attacks while maintaining low performance impact, offering a scalable and efficient solution to modern memory corruption threats. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Program compilers

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Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems

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Digital Government: Research and Practice 2024年第4期5卷 1-21页

作者： Billiris, Grace Gill, Asif Oppermann, Ian Niazi, Mahmood Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Computer Science University of Technology SydneyNSW Australia University of Technology SydneyNSW Australia King Fahd University of Petroleum and Minerals Department of Information and Computer Science Dhahran Saudi Arabia

Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents - the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this article aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of 10 legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study. © 2024 Copyright held by the owner/author(s).

关键词： Generative Artificial Intelligence Copyright Concern GAI Governance Copyright Regulations Independent Intellectual Effort Authorship

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Investigation of Casual Factors on Traffic Accidents Using Mutual Information 21

Investigation of Casual Factors on Traffic Accidents Using M...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Badounmar Min, Nandar Win Faculty of Computer Science Myanmar Department of Computer Science University of Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

In contemporary times, the prevalence of traffic accidents worldwide has escalated, prompting a pressing need to understand the causal factors contributing to these incidents. The concept of causal effect in the context of traffic accidents pertains to the influence or impact that specific variables or factors exert on the occurrence, severity, or outcome of accidents. Thus, it becomes paramount for researchers to delve into the root causes of these accidents. The primary challenge lies in identifying the causal relationships between various risk factors - such as weather conditions, human factors, and road conditions, among others - and the incidence of traffic accidents. Addressing this challenge forms the crux of this study. In this research endeavor, we propose a novel system that leverages mutual information-based quantification to elucidate the causal factors underlying traffic accidents. Furthermore, we employ machine learning techniques, such as support vector machines (SVM) and logistic regression, to predict the fatality of accidents. According to the evaluation results, the mutual information-based quantification of causal effects yields an impressive 94% accuracy with logistic regression in predicting accident fatalities. © 2024 IEEE.

关键词： Support vector machines

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A cloud-based IoT smart water distribution framework utilising BIP component: Jordan as a model

A cloud-based IoT smart water distribution framework utilisi...

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作者： Alshattnawi, Sawsan AlSobeh, Anas M.R. Computer Science Department Faculty of Computer Science and Technology Yarmouk University Irbid Jordan

Jordan is one of the poorest countries in terms of water resources, estimated to be below the poverty line. Due to high population growth and development, water supply and demand need a novel distribution water regime in Jordan. This paper presents a design-based smart water distribution model (SWDM), which integrates various technology solutions, such as behaviour-interaction-priority (BIP) components, cloud computing, and internet of things (IoT). This paper proposes a BIP-IoT model to introduce the SWDM, which provides a dynamic smart-design scalable model that is implemented over cloud components to cope with the increasing challenges of the water distribution regime in Jordan. The paper analyses the viability of this model and investigates an advantage in the reusable automation dynamic of SWDM’s architecture. A composition component is integrated into the architecture that employs intelligence domain-independent planning to control execution. It also presents a high-level prototype cloud-based implementation of our proposed architectural model using AI data analysis algorithm. © 2024 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Cloud computing

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