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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Joseph, Geethu Moothedath, Shana Lin, Jiabin Delft University of Technology Faculty of Electrical Engineering Mathematics and Computer Science CD Delft2628 Netherlands Iowa State University Department of Electrical and Computer Engineering IA50011 United States

ISBN: (纸本)9798350316339

This paper studies the problem of modifying the input matrix of a structured system to make the system strongly structurally controllable. We focus on the generalized structured systems that rely on zero/nonzero/arbitrary structure, i.e., some entries of system matrices are zeros, some are nonzero, and the remaining entries can be zero or nonzero (arbitrary). We derive the feasibility conditions of the problem, and if it is feasible, we reformulate it into another equivalent problem. This new formulation leads to a greedy heuristic algorithm. However, we also show that the greedy algorithm can give arbitrarily poor solutions for some special systems. Our alternative approach is a randomized Markov chain Monte Carlo-based algorithm. Unlike the greedy algorithm, this algorithm is guaranteed to converge to an optimal solution with high probability. Finally, we numerically evaluate the algorithms on random graphs to show that the algorithms perform well. © 2024 IEEE.

关键词： Markov chains

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Augmenting Internet of Medical Things Security:Deep Ensemble Integration and Methodological Fusion

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computer Modeling in engineering & sciences 2024年第12期141卷 2185-2223页

作者： Hamad Naeem Amjad Alsirhani Faeiz MAlserhani Farhan Ullah Ondrej Krejcar Faculty of Informatics and Management Center for Basic and Applied ResearchUniversity of Hradec KraloveHradec Kralove50003Czech Republic Department of Computer Science College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Department of Computer Engineering&Networks College of Computer and Information SciencesJouf UniversityAl Jouf72388Saudi Arabia Cybersecurity Center Prince Mohammad Bin Fahd UniversityAl JawharahKhobarDhahran34754Saudi Arabia

When it comes to smart healthcare business systems,network-based intrusion detection systems are crucial for protecting the system and its networks from malicious network *** protect IoMT devices and networks in healthcare and medical settings,our proposed model serves as a powerful tool for monitoring IoMT *** study presents a robust methodology for intrusion detection in Internet of Medical Things(IoMT)environments,integrating data augmentation,feature selection,and ensemble learning to effectively handle IoMT data *** rigorous preprocessing,including feature extraction,correlation removal,and Recursive Feature Elimi-nation(RFE),selected features are standardized and reshaped for deep learning *** using the BAT algorithm enhances dataset *** deep learning models,Transformer-based neural networks,self-attention Deep Convolutional Neural Networks(DCNNs),and Long Short-Term Memory(LSTM)networks,are trained to capture diverse data *** predictions form a meta-feature set for a subsequent meta-learner,which combines model *** classifiers validate meta-learner features for broad algorithm *** comprehensive method demonstrates high accuracy and robustness in IoMT intrusion *** were conducted using two datasets:the publicly available WUSTL-EHMS-2020 dataset,which contains two distinct categories,and the CICIoMT2024 dataset,encompassing sixteen *** results showcase the method’s exceptional performance,achieving optimal scores of 100%on the WUSTL-EHMS-2020 dataset and 99%on the CICIoMT2024.

关键词： Cyberattack ensemble learning feature selection intrusion detection smart cities machine learning BAT augmentation

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A Parallel Hybrid Testing Technique for Tri-Programming Model-Based Software Systems

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computers, Materials & Continua 2023年第2期74卷 4501-4530页

作者： Huda Basloom Mohamed Dahab Abdullah Saad AL-Ghamdi Fathy Eassa Ahmed Mohammed Alghamdi Seif Haridi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddah21493Saudi Arabia KTH Royal Institute of Technology StockholmSweden

Recently,researchers have shown increasing interest in combining more than one programming model into systems running on high performance computing systems(HPCs)to achieve exascale by applying parallelism at multiple *** different programming paradigms,such as Message Passing Interface(MPI),Open Multiple Processing(OpenMP),and Open Accelerators(OpenACC),can increase computation speed and improve *** the integration of multiple models,the probability of runtime errors increases,making their detection difficult,especially in the absence of testing techniques that can detect these *** studies have been conducted to identify these errors,but no technique exists for detecting errors in three-level programming *** the increasing research that integrates the three programming models,MPI,OpenMP,and OpenACC,a testing technology to detect runtime errors,such as deadlocks and race conditions,which can arise from this integration has not been ***,this paper begins with a definition and explanation of runtime errors that result fromintegrating the three programming models that compilers cannot *** the first time,this paper presents a classification of operational errors that can result from the integration of the three *** paper also proposes a parallel hybrid testing technique for detecting runtime errors in systems built in the C++programming language that uses the triple programming models MPI,OpenMP,and *** hybrid technology combines static technology and dynamic technology,given that some errors can be detected using static techniques,whereas others can be detected using dynamic *** hybrid technique can detect more errors because it combines two distinct *** proposed static technology detects a wide range of error types in less time,whereas a portion of the potential errors that may or may not occur depending on the 4502 CMC,2023,vol.74,no.2 operating environme

关键词： Software testing hybrid testing technique OpenACC OpenMP MPI tri-programming model exascale computing

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Optimizing Service Placement in Edge-to-Cloud AR/VR Systems Using a Multi-Objective Genetic Algorithm 14

Optimizing Service Placement in Edge-to-Cloud AR/VR Systems ...

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14th International Conference on Cloud Computing and Services science, CLOSER 2024

作者： Herabad, Mohammadsadeq G. Taheri, Javid Ahmed, Bestoun S. Curescu, Calin Department of Mathematics and Computer Science Karlstad University Karlstad Sweden School of Electronics Electrical Engineering and Computer Science Queen’s University Belfast Belfast United Kingdom Department of Computer Science Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Ericsson AB Stockholm Sweden

ISBN: (纸本)9789897587016

Augmented Reality (AR) and Virtual Reality (VR) systems involve computationally intensive image processing algorithms that can burden end-devices with limited resources, leading to poor performance in providing low latency services. Edge-to-cloud computing overcomes the limitations of end-devices by offloading their computations to nearby edge devices or remote cloud servers. Although this proves to be sufficient for many applications, optimal placement of latency sensitive AR/VR services in edge-to-cloud infrastructures (to provide desirable service response times and reliability) remain a formidable challenging. To address this challenge, this paper develops a Multi-Objective Genetic Algorithm (MOGA) to optimize the placement of AR/VR-based services in multi-tier edge-to-cloud environments. The primary objective of the proposed MOGA is to minimize the response time of all running services, while maximizing the reliability of the underlying system from both software and hardware perspectives. To evaluate its performance, we mathematically modeled all components and developed a tailor-made simulator to assess its effectiveness on various scales. MOGA was compared with several heuristics to prove that intuitive solutions, which are usually assumed sufficient, are not efficient enough for the stated problem. The experimental results indicated that MOGA can significantly reduce the response time of deployed services by an average of 67% on different scales, compared to other heuristic methods. MOGA also ensures reliability of the 97% infrastructure (hardware) and 95% services (software). © 2024 by SCITEPRESS – science and Technology Publications, Lda.

关键词： Augmented reality

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Impact of Skin Lesion Segmentation Labeling Errors on Convolutional Neural Networks 66

Impact of Skin Lesion Segmentation Labeling Errors on Convol...

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66th International Symposium ELMAR, ELMAR 2024

作者： Sojo, Robert Bencevic, Marin Habijan, Marija Galic, Irena Josip Juraj Strossmayer University of Osijek Faculty of Electrical Engineering Computer Science and Information Technology Osijek Croatia

ISBN: (纸本)9798350375411

Accurate segmentation of skin lesions is critical for dermatological diagnosis and treatment. This study investigates the impact of label errors on the performance of convolutional neural networks in skin lesion segmentation. We conducted experiments using ground truth and simulated erroneous labels to train models and analyzed the effects of the errors using regression analysis. To simulate errors, we assume that errors are more likely to occur in more complex border regions. In addition, we add parameters to bias the errors towards either false positives or false negative regions. The overall effect of the false positive and false negative errors on the Dice coefficient is deceptively low. However, when we observe the false negative or false positive rate of the predictions, we see that they are significantly influenced by the bias of the label errors. In addition, label false positives have a more severe impact on prediction results than label false negatives. We recommend focusing on minimizing label false positives and suggest moving beyond mean Dice Score Coefficients for model evaluation. © 2024 IEEE.

关键词： Convolutional neural networks

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Application of Machine Learning in Mental Diseases Diagnosis 25

Application of Machine Learning in Mental Diseases Diagnosis

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Alsawi, Rahaf Alazaidah, Raed Samara, Ghassan Alzoui, Haneen Alidmat, Omar Elsoud, Esraa Abu Talat Khouj, Mohammed Katrawi, Anwar Alluwaici, Mo'Ath Zarqa University Faculty of Information Technology Department of Data Science and Ai Zarqa Jordan Applied Science Private University Faculty of Information Technology Department of Computer Science Amman Jordan Zarqa University Faculty of Information Technology Department of Computer Science Zarqa Jordan University of Business and Technology Department of Electrical Engineering Jeddah21432 Saudi Arabia Zarqa University Faculty of It Department of Computer Science Zarqa Jordan

ISBN: (纸本)9798331540012

Mental diseases, mental disorders, or psychological disorders is a large group of symptoms which causes changes to the state of mood and reflect this on the behavior of the person with the mental disorder. It affects the patient's way of thinking, which limits his ability to perform his simple daily needs, and thus, becomes a burden on the individual, and on the surrounding society as well. There are common types of mental disorders such as depression, anxiety disorders, schizophrenia, and bipolar disorder. In this research, two main objectives are considered. The first objective is determining which is the best classifier for predicting a person's mental disorder among six well-known classifiers. The second objective is determining what is the best feature selection technique to use with mental disorders datasets considering six popular techniques, and with respect to several evaluation metrics. The results revealed that Logistic Regression and Naïve Bayes are the two best classification models, while Info Gain Ratio is the best feature selection method. © 2024 IEEE.

关键词： Adversarial machine learning

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Secure speaker identification in open and closed environments modeled with symmetric comb filters

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Multimedia Tools and Applications 2025年第18期84卷 19147-19189页

作者： Shafik, Amira Monir, Mohamad El-Shafai, Walid Khalaf, Ashraf A. M. Nassar, M.M. El-Fishawy, Adel S. El-Din, M. A. Zein Dessouky, Moawad I. El-Rabaie, El-Sayed M. Abd El-Samie, Fathi E. Department Electronics and Electrical Communications Engineering Faculty of Electronic Engineering Menoufia University Menouf32952 Egypt Security Engineering Laboratory Department of Computer Science Prince Sultan University Riyadh11586 Saudi Arabia Electrical Engineering Department Faculty of Engineering Minia University Minia61519 Egypt Department of Information Technology College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia

Speech is a fundamental means of human interaction. Speaker Identification (SI) plays a crucial role in various applications, such as authentication systems, forensic investigation, and personal voice assistance. However, achieving robust and secure SI in both open and closed environments remains challenging. To address this issue, researchers have explored new techniques that enable computers to better understand and interact with humans. Smart systems leverage Artificial Neural Networks (ANNs) to mimic the human brain in identifying speakers. However, speech signals often suffer from interference, leading to signal degradation. The performance of a Speaker Identification System (SIS) is influenced by various environmental factors, such as noise and reverberation in open and closed environments, respectively. This research paper is concerned with the investigation of SI using Mel-Frequency Cepstral Coefficients (MFCCs) and polynomial coefficients, with an ANN serving as the classifier. To tackle the challenges posed by environmental interference, we propose a novel approach that depends on symmetric comb filters for modeling. In closed environments, we study the effect of reverberation on speech signals, as it occurs due to multiple reflections. To address this issue, we model the reverberation effect with comb filters. We explore different domains, including time, Discrete Wavelet Transform (DWT), Discrete Cosine Transform (DCT), and Discrete Sine Transform (DST) domains for feature extraction to determine the best combination for SI in case of reverberation environments. Simulation results reveal that DWT outperforms other transforms, leading to a recognition rate of 93.75% at a Signal-to-Noise Ratio (SNR) of 15 dB. Additionally, we investigate the concept of cancelable SI to ensure user privacy, while maintaining high recognition rates. Our simulation results show a recognition rate of 97.5% at 0 dB using features extracted from speech signals and their DCTs. Fo

关键词： Speech enhancement

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A Comparative Analysis of the Junction Formation Mechanisms of the Boron on Silicon Junction 33

A Comparative Analysis of the Junction Formation Mechanisms ...

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33rd International Scientific Conference Electronics, ET 2024

作者： Fang, Piet Nihtianov, Stoyan TU Delft Faculty of Electrical Engineering Mathematics and Computer Science Department of Microelectronics Mekelweg 4 Delft Netherlands

ISBN: (纸本)9798350376449

Photodiodes based on the Boron on Silicon junction (B-Si) show excellent responsivity to DUV and VUV photons, radiation hardness, and impressive electrical characteristics. However, the proposed models describing the junction formation mechanism do not sufficiently predict the junction's properties. We analyze two previously proposed models: the ultra-shallow p-n junction model and the charge transfer heterojunction model. We additionally apply the Schottky-Mott theory, a semiconductor-metal heterojunction model. Both the commonalities and incompatibilities between these models are discussed. © 2024 IEEE.

关键词： Heterojunctions

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Odometry-Less Indoor Dynamic Object Detection and Localization with Spatial Anticipation

Odometry-Less Indoor Dynamic Object Detection and Localizati...

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2024 IEEE/SICE International Symposium on System Integration, SII 2024

作者： Shen, Zhengcheng Gao, Yi Kästner, Linh Lambrecht, Jens Berlin Institute of Technology Industry Grade Networks and Clouds Faculty of Electrical Engineering and Computer Science Berlin Germany

ISBN: (纸本)9798350312072

The continually evolving image segmentation methods in computer vision can further broaden the cognitive abilities of the robot. As humans, we won't judge if the object is moving by accuracy speed estimation but through semantic information based on visual input. With the existing video segmentation method, the robot can pay more attention to the foreground objects, which are more important for path planning and navigation in most cases. The proposed methods try to deploy the first-person view segmentation method with an RGBD sensor and get the location information and locally egocentric map. The error of the segmentation methods will cause unexpected objects in the egocentric map, and the self-occlusion will influence the location and distance estimation. To further improve the methods, a spatial anticipate unit is embedded into the framework. We improve 5.6% the localization accuracy(RMSE), 12.9% the distance estimation(MAE) with the trained object, and 28.9% the recall for the trained object. The methods also show the margin on the untrained object dataset for similar objects. © 2024 IEEE.

关键词： Semantics

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Governance Framework for Academic Verifiable Credentials Using KERI and ACDC 9th

Governance Framework for Academic Verifiable Credentials Us...

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9th Future Technologies Conference, FTC 2024

作者： Taneska, Marija Trpcheska, Hristina Mihajloska Faculty of Computer Science and Engineering Skopje Macedonia Ss. Cyril and Methodius University in Skopje Skopje Macedonia

ISBN: (纸本)9783031731273

The management of academic credentials is becoming increasingly complex in today’s dynamic environment. This paper proposes a novel governance framework for academic verifiable credentials (aVCs) leveraging Key Event Receipt Infrastructure (KERI) and Authentic Chained Data Containers (ACDCs). This framework aims to establish a secure, transparent, and efficient ecosystem for issuing and verifying academic credentials. Traditional physical credentials like diplomas are susceptible to loss, damage, and forgery. Verifiable credentials offer a digital alternative with enhanced security and functionality. They can be easily revoked, contain rich data structures, and reveal only necessary information to verifiers. Our framework incorporates cryptographic roots of trust, comprehensive auditability, and a focus on quantum-resistant design to ensure long-term security. The framework is guided by principles that promote trustworthiness, universal access, open standards, and data privacy. By utilizing KERI for secure key management and ACDCs for tamper-proof record keeping, the framework addresses existing limitations. It also offers scalability and interoperability across different educational institutions and platforms, ensuring broad adoption. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Records management

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