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26th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2024

作者： Costi, Flavia Onchis, Darian Istin, Codruta Florea, Adina Magda Hogea, Eduard West University of Timisoara Department of Computer Science Romania Politehnica University of Timisoara Department of Computer and Information Technology Romania National University of Science and Technology Politehnica Bucharest Department of Computer Science Romania

ISBN: (纸本)9798331532833

This paper aims to provide a comprehensive analysis of the benefits of employing GraphLIME (Local Interpretable Model Explanations for Graph Neural Networks) for reliable diabetes mellitus prediction. Our focus is on highlighting the advantages of integrating GraphLIME with a features attention-mechanism, compared to the standard pairing of deep learning neural networks with the original LIME explainability method. This system enabled us to develop an effective approach for identifying the most relevant features and applying the attention mechanism solely to those features. We conducted a detailed comparison of the performance metrics between the two approaches. By incorporating the attention mechanism, the model reached an accuracy of 92.6% in addressing the problem. The model's performance is thoroughly illustrated, with results further assessed using the Receiver Operating Characteristic (ROC) curve. Applying this technique to a dataset of 768 patients with or without diabetes mellitus, we enhanced the model's performance by over 18%. © 2024 IEEE.

关键词： Self-supervised learning

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Fusion of speech and handwritten signatures biometrics for person identification

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International Journal of Speech technology 2023年第4期26卷 833-850页

作者： Abushariah, Ahmad A. M. Abushariah, Mohammad A. M. Gunawan, Teddy Surya Chebil, J. Alqudah, Assal A. M. Ting, Hua-Nong Mustafa, Mumtaz Begum Peer Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Computer Information Systems King Abdullah II School of Information Technology The University of Jordan Amman Jordan ECE Department Faculty of Engineering International Islamic University Malaysia Kuala Lumpur53100 Malaysia Department of Computer Science Faculty of Science and Information Technology Al-Zaytoonah University of Jordan Amman Jordan Department of Biomedical Engineering Faculty of Engineering University of Malaya Kuala Lumpur50603 Malaysia Department of Software Engineering Faculty of Computer Science and Information Technology University of Malaya Kuala Lumpur50603 Malaysia

Automatic person identification (API) using human biometrics is essential and highly demanded compared to traditional API methods, where a person is automatically identified using his/her distinct characteristics including speech, fingerprint, iris, handwritten signatures, and others. The fusion of more than one human biometric produces bimodal and multimodal API systems that normally outperform single modality systems. This paper presents our work towards fusing speech and handwritten signatures for developing a bimodal API system, where fusion was conducted at the decision level due to the differences in the type and format of the features extracted. A data set is created that contains recordings of usernames and handwritten signatures of 100 persons (50 males and 50 females), where each person recorded his/her username 30 times and provided his/her handwritten signature 30 times. Consequently, a total of 3000 utterances and 3000 handwritten signatures were collected. The speech API used Mel-Frequency Cepstral Coefficients (MFCC) technique for features extraction and Vector Quantization (VQ) for features training and classification. On the other hand, the handwritten signatures API used global features for reflecting the structure of the hand signature image such as image area, pure height, pure width and signature height and the Multi-Layer Perceptron (MLP) architecture of Artificial Neural Network for features training and classification. Once the best matches for both the speech and the handwritten signatures API are produced, the fusion process takes place at decision level. It computes the difference between the two best matches for each modality and selects the modality of the maximum difference. Based on our experimental results, the bimodal API obtained an average recognition rate of 96.40%, whereas the speech API and the handwritten signatures API obtained average recognition rates of 92.60% and 75.20%, respectively. Therefore, the bimodal API system is a

关键词： Biometrics

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Evaluation of Reliability in Request-segregated Clouds through Remiadiations Predicting Hardware Failure Scheme

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Recent Patents on Engineering 2025年第1期19卷 112-127页

作者： Sharma, Rohit Yadav, Vibhash Singh, Raghuraj Department of Computer Science and Engineering Dr. Ambedkar Institute of Technology for Handicapped U.P. Kanpur208024 India Department of Information Technology Rajkiya Engineering College Banda210201 India Department of Computer Science and Engineering Harcourt Butler Technical University Kanpur208002 India

Background: Cloud services have become a popular approach for offering efficient services for a wide range of activities. Predicting hardware failures in a cloud data center can minimize downtime and make the system more reliable and fault-tolerant. Objective: This research aims to analyze a predictive hardware failure model based on machine learning that anticipates the required remediations for undiagnosed failures in a cloud computing system serving multiclass requests. Methods: The model is tested on a carefully designed cloud data center that categorizes incoming requests as web, compute, storage, and dedicated server requests. To demonstrate improved reliability, a carefully designed test case is run on ReliaCloud-NS, which is a simulator for creating a CCS and computing its reliability. Results: The work found that using this model considerably enhanced the reliability of cloud computing systems when compared to not using the model. Conclusion: Although various estimation methods are patented to evaluate the system reliability of a cloud computing network, the emphasis of this study was mostly on improving the reliability of request-segregated clouds upon failing hardware resources like CPU, memory, bandwidth, and hard disc. Moreover, the prediction model might potentially be expanded to other system resources such as GPUs, software, and database packages. © 2025 Bentham science Publishers.

关键词： Cloud platforms

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Network Intrusion Detection Based on Feature Selection and Hybrid Metaheuristic Optimization

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computers, Materials & Continua 2023年第2期74卷 2677-2693页

作者： Reem Alkanhel El-Sayed M.El-kenawy Abdelaziz A.Abdelhamid Abdelhameed Ibrahim Manal Abdullah Alohali Mostafa Abotaleb Doaa Sami Khafaga Department of Information Technology College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Information Systems Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of System Programming South Ural State UniversityChelyabinsk454080Russia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Applications of internet-of-things(IoT)are increasingly being used in many facets of our daily life,which results in an enormous volume of *** computing and fog computing,two of the most common technologies used in IoT applications,have led to major security *** are on the rise as a result of the usage of these technologies since present security measures are *** artificial intelligence(AI)based security solutions,such as intrusion detection systems(IDS),have been proposed in recent *** technologies that require data preprocessing and machine learning algorithm-performance augmentation require the use of feature selection(FS)techniques to increase classification accuracy by minimizing the number of features *** the other hand,metaheuristic optimization algorithms have been widely used in feature selection in recent *** this paper,we proposed a hybrid optimization algorithm for feature selection in *** proposed algorithm is based on grey wolf(GW),and dipper throated optimization(DTO)algorithms and is referred to as *** proposed algorithm has a better balance between the exploration and exploitation steps of the optimization process and thus could achieve better *** the employed IoT-IDS dataset,the performance of the proposed GWDTO algorithm was assessed using a set of evaluation metrics and compared to other optimization approaches in 2678 CMC,2023,vol.74,no.2 the literature to validate its *** addition,a statistical analysis is performed to assess the stability and effectiveness of the proposed *** results confirmed the superiority of the proposed approach in boosting the classification accuracy of the intrusion in IoT-based networks.

关键词： Feature selection grey wolf optimization dipper throated optimization intrusion detection internet-of-things(IoT)

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Efficient Saliency Map Detection for Low-Light Images Based on Image Gradient

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IEEE Transactions on Circuits and systems for Video technology 2024年第2期34卷 852-865页

作者： Lin, Chun-Yi Haq, Muhamad Amirul Chen, Jiun-Han Ruan, Shanq-Jang Naroska, Edwin National Taiwan University of Science and Technology Department of Electronic and Computer Engineering Taipei10607 Taiwan Hochschule Niederrhein University of Applied Science Faculty of Electrical Engineering and Computer Science Krefeld47805 Germany

Recently, deep learning has been widely employed across various domains. The Convolution Neural Network (CNN), a popular deep learning algorithm, has been successfully utilized in object recognition tasks, such as face recognition, vehicle recognition, and license plate recognition. However, conventional methods for object recognition may not be appropriate for low-light image recognition due to information loss in the dark regions and unexpected noise that can impair object quality. Therefore, the development of specialized techniques for low-light image enhancement has become a major research focus for object detection. This paper proposed a gradient-based saliency map detection method with an improved ResNet architecture that outperforms previous works in detecting multiple or large objects. Additionally, the proposed method enhances images with the object as the center and emphasizes foreground-background differences. Compared with previous works, this paper achieves1.28× improvements in the parameters and 1.32× faster inference speed than the original ResNet architecture. © 1991-2012 IEEE.

关键词： Neural networks

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m2Vib: 2D Vibration Measurement Using a Single mmWave Radar 30

m2Vib: 2D Vibration Measurement Using a Single mmWave Radar

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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Wang, Haolin Xu, Wei Li, Zezhao Yang, Yanni Huang, Zhijian Shandong University School of Computer Science and Technology Qingdao China National Key Laboratory of Science and Technology on Information System Security China

ISBN: (纸本)9798331515966

Two-dimensional vibration measurement is essential for monitoring structure and machine health, especially providing more information than those 1D solutions. However, most existing 2D vibration monitoring methods require multi-dimension sensors, which can be costly for large-scale deployment. In this paper, we propose m^2 Vib, the first work that can realize accurate 2D vibration measurement using a single mmWave radar. Our system is achieved by creating a virtual radar with non-line-of-sight (NLoS) signals from a nearby reflector (e.g., wall) to offer the 2nd-dimension vibration observations. We further establish a geometric model to unveil the relationship between 2D vibration displacement and mmWave signal. Such an idea still encounters challenges from significantly attenuated NLoS signals and low-resolution geometric parameters from the commercial mmWave radar. Thus, we design a new retroreflector to enhance NLoS signals and advanced signal processing methods to obtain fine-grained geometric parameters. Extensive experimental results show that we can achieve sub-mm level 2D vibration measurement with an average vibration displacement error of 62.2 um and frequency error of 0.01 Hz in the X-axis and Y-axis. © 2024 IEEE.

关键词： Vibration measurement

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Multiple-Person Tracking with Criss-Cross Attention and Deep Matching 8

Multiple-Person Tracking with Criss-Cross Attention and Deep...

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8th International Conference on Signal and Image Processing, ICSIP 2023

作者： Liu, Zhuoyang Zhang, Liming University of Macau Faculty of Science and Technology Department of Computer and Information Science Macau China

ISBN: (纸本)9798350397932

Multi-pedestrian tracking is one of the important application fields of computer vision, among which the real-time tracking of pedestrians and the ability to solve occlusion problems are two big challenges to improve the recognition accuracy. In this paper, we propose a new standardized tracking-by-detection framework that can effectively alleviate the above two problems. We first introduce the Criss-Cross Attention (CCA) mechanism in the detector. The CCA module can reduce the computational complexity of the detector and improve the detection accuracy. Then a pedestrian deep feature extractor fused with cosine metric learning for two-layer convolution is added to further improve the tracking accuracy and real-time performance. Finally, Kalman Filter prediction combined with pedestrian deep features effectively reduces identity switching under the occlusion. We conduct experiments on the MOT16 datasets and evaluate on HOTA. Experimental results demonstrate the competitiveness of our method against some selected state-of-the-art methods. © 2023 IEEE.

关键词： computer vision

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Taxonomy of Multi-Agent systems Attacks and their Defense Tactics in Certifying Security of Cyber Physical systems

Taxonomy of Multi-Agent Systems Attacks and their Defense Ta...

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2023 International Conference on Advances in Computation, Communication and information technology, ICAICCIT 2023

作者： Jain, Garima Solomon Jebaraj, N.R. Noida Institute of Engineering and Technology Department of Computer Science & Business System Uttar Pradesh Greater Noida India Department of Computer Science and Information Technology Bangalore India

ISBN: (纸本)9798350344387

The utilization of multi-agent systems has been increasingly prevalent across various sectors, owing to their notable efficacy in execution. However, a multitude of hazards exist that possess the capability to undermine the integrity of the agent and pose a threat to the overall security of the system. Therefore, it is crucial to carefully evaluate and address security vulnerabilities during the process of creating Multi-Agent systems (MAS). This survey assesses different models that aim to guarantee the security of Multi-Agent systems (MAS). These models were developed based on principles related to the agents' functionality and communication. This study offers a comprehensive examination and categorization of the prevailing threats targeting Multi-Agent systems (MASs). Following this, we conduct a comprehensive analysis and assessment of diverse security strategies described in scholarly sources, classifying them into prevention, detection, and resiliency approaches based on their established credibility and reliability. In conclusion, we offer a suggestion for the optimal security approach to address particular types of threats, considering the most recent breakthroughs in the field of study. © 2023 IEEE.

关键词： Attacks Cyber Physical systems Multi Agent systems Resiliency Security

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Remote Inter-beat Interval Estimation: A Signal Reconstruction Approach with Multi-channel Input and Channel-Wise Attention Mechanism

Remote Inter-beat Interval Estimation: A Signal Reconstructi...

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2024 IEEE International Conference on E-Health Networking, Application and Services, HealthCom 2024

作者： Wang, Shengze Bouazizi, Mondher Ohtsuki, Tomoaki Graduate School of Science and Technology Keio University Yokohama Japan Department of Information and Computer Science Faculty of Science and Technology Keio University Yokohama Japan

ISBN: (纸本)9798350350548

This paper presents a radar-based heart rate monitoring and Inter-beat interval (IBI) estimation. Traditional IBI estimation relies on peak detection combined with signal processing techniques. Our approach uses signal reconstruction with a neural network, which significantly improves the accuracy and robustness of IBI estimation compared to conventional methods. By utilizing multi-channel input data, our approach effectively mitigates various sources of noise and interference, resulting in highly accurate and reliable IBI predictions. Instead of relying solely on peak detection, we use the U-net architecture and cross-channel attention mechanism to reconstruct the triangular waveform generated from the ground truth ECG signals. The incorporation of multi-channel input and channel-wise attention mechanism improves our model’s ability to discriminate and emphasize critical features from different input channels, further improving IBI estimation accuracy. To evaluate the accuracy and robustness of our proposed method, we trained our model on the open-source dataset [1], and then performed leave-one-subject-out validation, which ensures our approach to be evaluated only on unseen independent subjects. We have achieved a Root Mean Square Error (RMSE) of 26.7 ms for the IBI of each heart-beat. Our results demonstrate the transformative potential of adopting signal reconstruction methods supported by state-of-the-art deep learning techniques. This shift in perspective promises more accurate and robust heart rate and IBI estimation, opening new avenues for improving the accuracy and reliability of cardiac monitoring systems. © 2024 IEEE.

关键词： Heart

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Propagation-aware Q-coverage and Q-connectivity network design in relay-aided IoT sensor systems using heuristic and genetic algorithms

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Neural Computing and Applications 2025年 1-28页

作者： Thang, Nguyen Xuan Hanh, Nguyen Thi Son, Nguyen Van Tan, Nguyen Phuc Hung, To Quang Chien, Trinh Van Binh, Huynh Thi Thanh Faculty of Computer Science Hanoi University Nguyen Trai Hanoi Viet Nam Faculty of Interdisciplinary Digital Technology Phenikaa University Yen Nghia Ha Dong Hanoi12116 Viet Nam Faculty of Computer Science Phenikaa University Yen Nghia Ha Dong Hanoi12116 Viet Nam School of Information and Communication Technology Hanoi University of Science and Technology Dai Co Viet Hanoi Viet Nam

Wireless sensor networks play a crucial role in Internet of Things systems, whose development and prevalence have given rise to numerous challenges. One of the most notable problems regarding wireless sensor networks is the sensor deployment problem, where the objective is to minimize the number of nodes used and to satisfy the constraints of coverage and connectivity. In reality, sensors may be prone to errors and failures;therefore, simple coverage and simple connectivity cannot guarantee fault tolerance for a system. This leads to the concepts of Q-coverage and Q-connectivity, which indicates that each target needs q covering sensors and q node-disjoint paths to the base station. To minimize the number of nodes while still ensuring Q-coverage and Q-connectivity, we propose a heuristic algorithm and an algorithm based on the genetic algorithm. Besides, we construct various experiment scenarios and a realistic dataset to evaluate the efficiency of our proposed methods. The experiment results show that our proposed algorithms significantly outperform existing methods in both solution quality and running time. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Connectivity Heuristic algorithm IoT networks Target coverage Wireless sensor network

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