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Advancing Smartphone Sensor-Based Keystroke Dynamics for Implicit and Active Authentication: Addressing Challenges and Enhancing Usability Control 11th

Advancing Smartphone Sensor-Based Keystroke Dynamics for Imp...

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11th International Symposium on Applied Computing for Software and Smart Systems, ACSS 2024

作者： Roy, Soumen Roy, Utpal Sinha, Devadatta Pal, Rajat Kumar Department of Computer Science Bagnan College Khalore Bagnan Howrah711303 India Department of Computer and System Sciences VisvaBharati Santiniketan731235 India Department of Computer Science and Engineering University of Calcutta Acharya Prafulla Chandra Roy Siksha Prangan JD-2 Sector-III Kolkata Saltlake City700106 India

ISBN: (纸本)9789819797615

Smartphone sensors play a pivotal role in delineating user behaviour, particularly in the context of keystroke dynamics-based implicit and active/continuous authentication, as well as liveness detection. The acquisition of multifaceted sensory data during typing serves as a fundamental source of behavioural attributes, yet the field faces several intricate challenges requiring careful consideration to recalibrate the delicate balance between security and usability. Notable challenges encompass the capricious nature of anomaly detectors when confronted with unavoidable influencing factors, the substantial battery drainage associated with prolonged sensor operation, the computational inefficiency intrinsic to smartphones, and the imperative need for usability control in frequently switching applications and balancing security in both device and application levels, especially in scenarios with varying security requirements. This study endeavours to mitigate these challenges through the implementation of cutting-edge anomaly detection techniques, incorporating four distinct schemes—namely, column medians template formation, synthetic samples, soft biometrics and score-level fusion. The study further leverages advanced sensory patterns, including gyroscope, accelerometer and rotational information, captured during abbreviated typing intervals at a low sampling rate to mitigate battery consumption. In the pursuit of enhanced usability control, a Watchman score is assigned to each running application, dictating security adjustments based on genuineness evaluations of micro-behaviours within predefined temporal windows. A novel rule-based mechanism is introduced to govern device and application security within a continuous authentication mode. This empirical investigation, supported by robust statistical evidence, aspires to yield more precise, energy-efficient, and user-friendly implicit and active user identity verification mechanisms. The findings present an avenue for f

关键词： Smartphones

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Novel hybrid optimization based adaptive deep convolution neural network approach for human activity recognition system

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Multimedia Tools and Applications 2025年第9期84卷 6519-6543页

作者： Ashwin, M. Jagadeesan, D. Raman Kumar, M. Murugavalli, S. Chaitanya Krishna, A. Ammisetty, Veeraswamy Department of Computer Science and Engineering Presidency University Karnataka Bangalore India Andhra Pradesh Madanapalle517325 India Nalla Malla Reddy Engineering College Telangana Hyderabad500088 India Department of Artificial Intelligence K. Ramakrishnan College of Technology Trichy -2 Kariyamanickam Rd Tamil Nadu Tiruchirappalli621112 India Department of ECE St. Martin’s Engineering College Telangana Secunderabad India Department of Computer Science & Engineering Koneru Lakshmaiah Education Foundation Guntur Andhra Pradesh Vaddeswaram India

Human Activity Recognition (HAR) has emerged as a crucial area of research in computer vision, signal processing, and machine learning, enabling to automatically identify human activities from sensor data. Although various models are developed for automatic HAR, they face challenges like limited accuracy, large training time, etc. To resolve these issues, we developed an innovative hybrid strategy using the combination of hybrid meta-heuristic optimization algorithms and deep learning. The developed work commences with the collection of HAR database and the collected database was pre-processed to enhance image quality. Further, segmentation was performed using optimized Otsu’s methodology to extract the most significant features. Then, an innovative classification model was developed using the combination of Improved Spotted Hyena algorithm with Seagull Optimization Algorithm (ISHO-SOA) and Adaptive Deep Convolutional Neural Networks (ADCNN) for identifying and classifying human activities. The novelty of the algorithm lies in the seamless integration of different meta-heuristic optimization algorithms and deep learning for HAR. In the proposed algorithm, the ADCNN was trained using the database to classify human activities, while the ISHO-SOA for refining the hyperparameters of ADCNN to optimize its training and improve classification performances. The proposed algorithm was modeled in MATLAB software, and validated using HAR dataset from the Kaggle database. The experimental results depict that the developed algorithm achieved higher accuracy of 99.84%, and greater precision of 99.90%. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Classification (of information)

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Channel-Feedback-Free Transmission for Downlink FD-RAN:A Radio Map Based Complex-Valued Precoding Network Approach

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China Communications 2024年第4期21卷 10-22页

作者： Zhao Jiwei Chen Jiacheng Sun Zeyu Shi Yuhang Zhou Haibo Xuemin(Sherman)Shen School of Electronic Science and Engineering Nanjing UniversityNanjing 210023China College of Information Science and Electronic Engineering Zhejiang UniversityHangzhou 310058China Department of Mathematics and Theories Peng Cheng LaboratoryShenzhen 518000China Department of Electrical and Computer Engineering University of WaterlooWaterlooOntarioN2L 3G1Canada

As the demand for high-quality services proliferates,an innovative network architecture,the fully-decoupled RAN(FD-RAN),has emerged for more flexible spectrum resource utilization and lower network ***,with the decoupling of uplink base stations and downlink base stations in FDRAN,the traditional transmission mechanism,which relies on real-time channel feedback,is not suitable as the receiver is not able to feedback accurate and timely channel state information to the *** paper proposes a novel transmission scheme without relying on physical layer channel ***,we design a radio map based complex-valued precoding network(RMCPNet)model,which outputs the base station precoding based on user *** comprises multiple subnets,with each subnet responsible for extracting unique modal features from diverse input ***,the multimodal embeddings derived from these distinct subnets are integrated within the information fusion layer,culminating in a unified *** also develop a specific RMCPNet training algorithm that employs the negative spectral efficiency as the loss *** evaluate the performance of the proposed scheme on the public DeepMIMO dataset and show that RMCPNet can achieve 16%and 76%performance improvements over the conventional real-valued neural network and statistical codebook approach,respectively.

关键词： beamforming complex neural networks deep learning FD-RAN

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E-waste Classification Using Pre-trained Deep Learning CNN Model 2nd

E-waste Classification Using Pre-trained Deep Learning CNN M...

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2nd International Conference on Innovations in Data Analytics, ICIDA 2023

作者： Al Hossain, Mirsad Akash, Saiful Islam Fahim, Sajid Faysal Arifin Zaman, Md. Motaharul Islam, Md. Department of Computer Science and Engineering East West University Aftabnagar A/2 Jahurul Islam Avenue Jahurul Islam City Dhaka1212 Bangladesh Department of Computer Science and Engineering United International University Dhaka-Madani Avenue United City1212 Bangladesh

ISBN: (纸本)9789819749270

Electronic waste is one of the fastest-growing waste streams in the world, posing a threat to human health and the environment. E-waste classification is an essential step for adequately managing and recycling E-waste. In this research, we have proposed a method for E-waste classification using YOLOv7 as an E-waste detection model. After that, a pre-trained model is used as a classification model. First, we have used YOLOv7 to detect E-waste objects in images and crop them into separate regions. Then, we have used a pre-trained model to classify each part into one of the different categories of E-waste composition. Finally, we have got 93.5% mAP for the E-waste detection model and 99.7% accuracy for the classification of E-waste. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Deep learning

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The Amalgamation of Federated Learning and Explainable Artificial Intelligence for the Internet of Medical Things: A Review

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Recent Advances in computer science and Communications 2024年第4期17卷 1-19页

作者： Govardanan, Chemmalar Selvi Murugan, Ramalingam Yenduri, Gokul Gurrammagari, Deepti Raj Bhulakshmi, Dasari Kandati, Dasaradharami Reddy Supriya, Yarradoddi Gadekallu, Thippa Reddy Rathore, Rajkumar Singh Jhaveri, Rutvij School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India Department of Computer Science Cardiff School of Technologies Cardiff Metropolitan University Llandaff Campus Western Avenue CardiffCF5 2YB United Kingdom Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University Gujarat Gandhinagar382007 India Lovely Professional University Phagwara India College of Information Science and Engineering Jiaxing University 314001 China Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Zhongda Group Zhejiang Province HaiyanCounty Jiaxing City314312 China

The Internet of Medical Things (IoMT) has emerged as a paradigm shift in healthcare, integrating the Internet of Things (IoT) with medical devices, sensors, and healthcare systems. From peripheral devices that monitor vital signs to remote patient monitoring systems and smart hospitals, IoMT provides a vast array of applications that empower healthcare professionals. However, the integration of IoMT presents numerous obstacles, such as data security, privacy concerns, interoperability, scalability, and ethical considerations. For the successful integration and deployment of IoMT, addressing these obstacles is essential. Federated Learning (FL) permits collaborative model training while maintaining data privacy in distributed environments like IoMT. By incorporating Explainable Artificial Intelligence (XAI) techniques, the resulting models become more interpretable and transparent, enabling healthcare professionals to comprehend the underlying decision-making processes. This integration not only improves the credibility of Artificial Intelligence models but also facilitates the detection of biases, errors, and peculiar patterns in the data. The combination of FL and XAI contributes to the development of more privacy-preserving, trustworthy, and explainable AI systems, which are essential for the development of dependable and ethically sound IoMT applications. Hence, the aim of this paper is to conduct a literature review on the amalgamation of FL and XAI for IoMT. © 2024 Bentham science Publishers.

关键词： Decision making

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Guest Editorial: Special Issue on Human-Machine Fusion Decision-Making for Emergency Handling

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IEEE Transactions on Automation science and engineering 2025年 22卷 4427-4433页

作者： Wu, Edmond Q. Li, Jianqiang Chen, Guimin Yuce, Mehmet R. Islam, Shafiqul Ser, Javier Del Yu, Hui Liu, Peter Xiaoping Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China College of Computer and Software Engineering Shenzhen University Shenzhen518060 China State Key Laboratory for Manufacturing Xi'an Jiaotong University Xi'an710049 China Department of Electrical and Computer Systems Engineering Monash University MelbourneVIC3800 Australia Xavier University of Louisiana New OrleansLA70125 United States Tecnalia Institute Bizkaia48160 Spain School of Creative Technologies University of Portsmouth PortsmouthPO1 2UP United Kingdom Department of Systems and Computer Engineering Carleton University OttawaONKIS 5B6 Canada

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Reinforcement Learning for Online Dispatching Policy in Real-Time Train Timetable Rescheduling

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IEEE Transactions on Intelligent Transportation Systems 2024年第1期25卷 478-490页

作者： Yue, Peng Jin, Yaochu Dai, Xuewu Feng, Zhenhua Cui, Dongliang Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China University of Bielefeld Nature Inspired Computing and Engineering Group Bielefeld33619 Germany University of Surrey Department of Computer Science GuildfordGU2 7XH United Kingdom University of Surrey School of Computer Science and Electronic Engineering GuildfordGU2 7XH United Kingdom

Train Timetable Rescheduling (TTR) is a crucial task in the daily operation of high-speed railways to maintain punctuality and efficiency in the presence of unexpected disturbances. However, it is challenging to promptly create a rescheduled timetable in real time. In this study, we propose a reinforcement-learning-based method for real-time rescheduling of high-speed trains. The key innovation of the proposed method is to learn a well-generalized dispatching policy from a large amount of samples, which can be applied to the TTR task directly. At first, the problem is transformed into a multi-stage decision process, and the decision agent is designed to predict dispatching rules. To enhance the training efficiency, we generate a small yet good-quality action set to reduce invalid explorations. Besides, we propose an action sampling strategy for action selection, which implements forward planning with consideration of evaluation uncertainty, thus improving search efficiency. Extensive experimental results demonstrate the effectiveness and competitiveness of the proposed method. It has been proven that the local policies trained by the proposed method can be applied to numerous problem instances directly, rendering it unnecessary to use human-designed rules. © 2000-2011 IEEE.

关键词： Reinforcement learning

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Generative artificial intelligence: a systematic review and applications

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Multimedia Tools and Applications 2024年 1-40页

作者： Sengar, Sandeep Singh Hasan, Affan Bin Kumar, Sanjay Carroll, Fiona Cardiff School of Technologies Cardiff Metropolitan University CardiffCF5 2YB United Kingdom Department of Computer Science and Engineering Delhi Technological University New Delhi110042 India

In recent years, the study of artificial intelligence (AI) has undergone a paradigm shift. This has been propelled by the groundbreaking capabilities of generative models both in supervised and unsupervised learning scenarios. Generative AI has shown state-of-the-art performance in solving perplexing real-world conundrums in fields such as image translation, medical diagnostics, textual imagery fusion, natural language processing, and beyond. This paper documents the systematic review and analysis of recent advancements and techniques in Generative AI with a detailed discussion of their applications including application-specific models. Indeed, the major impact that generative AI has made to date, has been in language generation with the development of large language models, in the field of image translation and several other interdisciplinary applications of generative AI. Moreover, the primary contribution of this paper lies in its coherent synthesis of the latest advancements in these areas, seamlessly weaving together contemporary breakthroughs in the field. Particularly, how it shares an exploration of the future trajectory for generative AI. In conclusion, the paper ends with a discussion of Responsible AI principles, and the necessary ethical considerations for the sustainability and growth of these generative models. © Crown 2024.

关键词： Generative adversarial networks

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Leveraging Machine Learning for Crime Intent Detection in Social Media Posts 1st

Leveraging Machine Learning for Crime Intent Detection in ...

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1st International Conference on AI-generated Content, AIGC 2023

作者： Bokolo, Biodoumoye George Onyehanere, Praise Ogegbene-Ise, Ebikela Olufemi, Itunu Tettey, Josiah Nii Armah Department of Computer Science Sam Houston State University HuntsvilleTX77340 United States Department of Architecture Technology and Engineering University of Brighton BrightonBN2 4AT United Kingdom Department of Computer and Software Engineering Kennesaw State University KennesawGA United States Department of Computer Science and Quantitative Methods Austin Peay State University ClarksvilleTN37044 United States Department of Computer Science and Engineering Wright State University DaytonOH45435 United States

ISBN: (纸本)9789819975860

Detecting crime intent from user-generated content on social media platforms has become increasingly important for law enforcement and crime prevention. This paper presents a comprehensive approach for crime intent detection from user tweets using machine learning techniques. The study utilizes a dataset of about 400,000 tweets and applies data preprocessing, feature selection, and model training with logistic regression, ridge regression classifier, Stochastic Gradient Descent (SGD) classifier, Random Forests, and support vector machine models. Evaluation metrics such as accuracy, precision, recall, and F1 score are employed to assess the models’ performance. The results reveal that the logistic regression model achieves the highest accuracy ratio of 0.981 in detecting crime intent from tweets. This research showcases the effectiveness of machine learning and advanced transformer-based models in leveraging social media data for crime analysis. The findings provide valuable insights into the potential for early detection and monitoring of crime intent using online platforms, contributing to the field of crime prevention and law enforcement. The utilization of machine learning techniques offers new avenues for understanding and analyzing crime-related sentiments expressed by social media users. By accurately detecting crime intent from user-generated content, law enforcement agencies can enhance their proactive measures, monitor public sentiment towards crime, and shape policies and interventions to address public concerns effectively. The research highlights the significance of leveraging social media data for crime detection and emphasizes the potential impact of advanced machine learning models in improving public safety and crime prevention efforts. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Sentiment analysis

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Multiplication of Sparse Matrices and their Transpose Using Compressed Sparse Diagonals

Multiplication of Sparse Matrices and their Transpose Using ...

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2024 IEEE High Performance Extreme Computing Conference, HPEC 2024

作者： Haque, Sardar Anisul Parvez, Mohammad Tanvir Hossain, Shahadat Doha Qatar College of Computer Qassim University Department of Computer Engineering Buraydah52571 Saudi Arabia University of Northern British Columbia Department of Computer Science Prince GeorgeBCV2N 4Z9 Canada

ISBN: (纸本)9798350387131

Matrix-matrix multiplication is one of the most important kernel in linear algebra operations with a multitude of applications in scientific and engineering computing. Sparse matrix computation on modern High-performance Computing (HPC) architecture presents challenges such as load balancing, data locality optimization, and computational scalability. Data structures to store sparse matrices are designed to minimize overhead information as well as to optimize the operations count and memory access. In this study, we present a new data structure, 'compressed sparse diagonal' (CSD), to efficiently store and compute with general sparse matrices. The CSD builds upon the previously developed diagonal storage - an orientation-independent uniform scheme to compute with 'structured' matrices [1]. Compared with the widely used compressed sparse row/column (CSR/CSC), the CSD scheme avoids explicit transposition operation when multiplying a matrix with its transpose. The results from preliminary numerical experiments with the aforementioned types of matrices demonstrate the CSD scheme's effectiveness in matrix-transposed matrix multiplication. © 2024 IEEE.

关键词： Matrix algebra

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