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IEEE International Conference on Communications (ICC)

作者： Yingying Pei Mushu Li Huaqing Wu Qiang Ye Conghao Zhou Shisheng Hu Xuemin Shen Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Canada Department of Electrical and Software Engineering University of Calgary Calgary AB Canada Department of Computer Science Memorial University of Newfoundland St. John's NL Canada

In this paper, we investigate joint caching and computing resource reservation for supporting location-aware augmented reality (AR) applications in an edge-assisted two-tier radio access network. We aim at minimizing the caching and computing resource consumption while satisfying the AR service delay requirement. Specifically, to capture the spatio-temporal AR service dynamics, the resource consumption minimization problem is formulated as a long-term stochastic optimization problem. Due to the time-varying service demands and tightly coupled multi-resource reservation decisions, we propose a novel resource reservation algorithm based on the Lyapunov optimization technique to solve the problem. We first transform the original long-term problem into multiple one-shot optimization problems, each of which is then solved by our designed iterative algorithm in an online manner. Simulation results demonstrate that the proposed algorithm can significantly reduce the overall resource consumption compared to benchmark algorithms.

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IoT-enabled model for Digital Twin of Mental Stress (DTMS)

IoT-enabled model for Digital Twin of Mental Stress (DTMS)

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2021 IEEE Globecom Workshops, GC Wkshps 2021

作者： Ferdousi, Rahatara Hossain, M. Anwar El Saddik, Abdulmotaleb Canada King Saud University College of Computer Information Sciences Department of Software Engineering Saudi Arabia

ISBN: (纸本)9781665423908

Stress has become one of the mental health adversaries of the COVID-19 pandemic. Several stressors like fear of infection, lockdown, and social distancing are commonly accountable for the stress. The existing stress prediction systems are less compatible to handle diversly changing stressors during COVID-19. The traditional approaches often use incomplete features from limited sources (e.g., only wearable sensor or user device) and static prediction techniques. The Edge Artificial Intelligence (Edge AI) employs machine learning to make data from these sources usable for decision making. Therefore, In this study, we propose a Digital Twin of Mental Stress (DTMS) model that employs IoT-based multimodal sensing and machine learning for mental stress prediction. We obtained 98% accuracy for four widely used Machine Learning(ML) algorithms Naïve Bayes(NB), Random Forest(RF), Multilayer Perceptron(MLP), and Decision Tree (DT). The optimal Digital Twin Features (DTF) could reduce the classification time. © 2021 IEEE.

关键词： COVID-19

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EEG Spectral Analysis for Inattention Detection in Academic Domain

EEG Spectral Analysis for Inattention Detection in Academic ...

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Frontiers in Education (FIE) Conference

作者： Sreekanth Gopi Nasrin Dehbozorgi Department of Computer Science Kennesaw State University Kennesaw GA Department of Software Engineering Kennesaw State University Kennesaw GA

The increasing rate of attention deficit among students, attributed to social media, has far-reaching consequences on their academic performance. Inattention or lack of attention is a state of absent-mindedness or not paying enough attention to the details. A rich body of literature suggests that it is highly associated with underachievement in the academic context. In particular, students who have been clinically diagnosed with Attention Deficit Hyperactivity Disorder(ADHD), a neuro-developmental disorder characterized by inattention, hyperactivity, and impulsivity symptoms are more at risk of under-performance and retention, with some leaving school without a terminal degree. In addition to the academic domain, inattention generally impacts the quality of life and future occupations. Research suggests that ADHD is also attributed to societal and unemployment excess costs as well as productivity loss and healthcare expenses which are estimated to be over $14K per adult in the United States (US). Although more than eight million adults were identified with ADHD in the US by 2018, not all inattention cases are associated with ADHD. Inattention could be caused by several other factors such as stress or anxiety and its early detection and timely intervention is critical, especially in the academic domain. There are existing studies that analyze brain signals by Electroencephalogram (EEG) scans to identify individuals who have ADHD. In this study, we developed a Machine learning (ML) pipeline model that is trained on both ADHD and inattention data to determine if a person is having an attention problem. In the first phase of developing the model, we trained a few different classifiers on a 19-channel public EEG dataset of 60 ADHD and 61 non-ADHD participants. Data analysis showed K-Nearest Neighbor (KNN) classifier outperformed other classifiers with an accuracy of 89%. While many of the existing papers focus on ADHD data, in this work we expand our model to analyze the

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Federated Non-Intrusive Load Monitoring for Smart Homes Utilizing Attention-Based Aggregation

Federated Non-Intrusive Load Monitoring for Smart Homes Util...

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IEEE International Conference on Industry 4.0, Artificial Intelligence, and Communications Technology (IAICT)

作者： Shamisa Kaspour Abdulsalam Yassine Department of Computer Science Lakehead University Thunder Bay Canada Department of Software Engineering Lakehead University Thunder Bay Canada

Nowadays, Non-Intrusive Load Monitoring (NILM) with Federated Learning (FL) framework has become a growing study towards providing a secure energy disaggregation system in smart homes. This study aims at deploying an attention-based aggregation (FedAtt) approach in FL to emphasize agents’ behavioral differences when consuming energy from various appliances. The goal of the proposed technique is to minimize the weighted distance between the parameters of the local model and the global model to better represent each local model’s characteristics. In this paper, we examine two different models for NILM: Short Sequence-to-Point (SS2P) and Variational Auto-Encoder (VAE). Our goal is to evaluate the effectiveness of FedAtt. The evaluation of the framework was carried out using the UK-DALE and REFIT datasets. The obtained results were then compared against centralized approaches of the models as well as FedAvg. Our findings show that FedAtt generates comparable results to the centralized model and FedAvg while improving the stability of FL at different values of added noise to local parameters.

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Explainable Artificial Intelligence Approaches: Challenges and Perspectives

Explainable Artificial Intelligence Approaches: Challenges a...

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2021 IEEE International Conference "Quality Management, Transport and Information Security, Information Technologies", T and QM and IS 2021

作者： Shevskaya, Natalya V. Saint Petersburg Electrotechnical University 'LETI' Software Engineering and Computer Applications Department Saint Petersburg Russia

ISBN: (纸本)9781665433020

Artificial intelligence systems are used in many areas, for example, in finance and medicine. Every year, intelligent systems get for processing more and more data and make more and more decisions. All these decisions are exerting more and more influence on human destinies. Mistrust of fully non-human, autonomous artificial intelligence systems become the cornerstone. The key of mistrust is hidden in the lack of understanding as to why intelligent systems make this or that decision. To solve this problem, methods of explainable artificial intelligence are used. This article provides an overview of such methods and discusses the main trends in their development, as well as the implementation difficulties that one has to face during development and use. © 2021 IEEE.

关键词： Intelligent systems

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Enhanced Security Protocol for VLSI Systems: Modified AES Algorithm for Robust Data Transmission

Enhanced Security Protocol for VLSI Systems: Modified AES Al...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： Jinsha Lawrence Dinesh Kumar Budagam P. Mukilan Kavitha Veerappan E. Chandrasekhar K. Barathi Department of Computer Science and Engineering Faculty of Engineering Karpagam Academy of Higher Education (Deemed to be University) Coimbatore India Department of MS in Software Engineering Security Architect at VISA California United States Department of Electronics and Communication Engineering Dhanalakshmi Srinivasan College of Engineering Coimbatore India Department of Electronics and Communication Engineering Kongu Engineering College Erode India Department of Electronics and Communication Engineering Chaitanya Bharathi Institute of Technology (A) Hyderabad India Department of Electrical and Electronics Engineering Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

The development of compact and efficient devices has been made possible by the growth of Very Large Scale Integration (VLSI) technologies, which has transformed modern electronics. However, there are cautions regarding the security of data, especially when it comes to transmission due to the extensive usage of technology. To address these, this study proposes a modified Advanced Encryption Standard (AES) algorithm as a solution for improved security protocol in VLSI systems. The proposed algorithm are intended to support the encryption procedure, increasing stronger production against cryptographic attacks while preserving the system's effectiveness and speed factors that are critical aspects for VLSI applications. Through extensive simulations and testing, the modified AES algorithm demonstrated significant security enhancements the operational efficiency of the VLSI system. The attained outcomes of the proposed work shows that the Modified AES strategy provides a workable technique to protect data in VLSI-based devices while maintaining data communication integrity and confidentiality. The proposed modified AES algorithm demonstrates a significant improvement in performance with a propagation delay of 7 ns, power consumption of 29 mW, and a computational overhead of 62 bits, leading to enhanced efficiency in cryptographic operations. This study highlights that the modified AES algorithm improve security in modern electronic systems and maintain performance.

关键词： Protocols Power demand System performance Very large scale integration Throughput Hardware Encryption Security Data communication Standards

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Quantized and Interpretable Learning Scheme for Deep Neural Networks in Classification Task

arXiv

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arXiv 2024年

作者： Maleki, Alireza Lavaei, Mahsa Bagheritabar, Mohsen Beigzad, Salar Abadi, Zahra Department of Technology Management University of Tehran Tehran Iran Electrical and Computer Engineering University of Tehran Tehran Iran Electrical Engineering Department University of Cincinnati Ohio United States Software and Engineering University of St. Thomas Minnesota MN United States Electrical Engineering Department Tehran University Tehran Iran

Deep learning techniques have proven highly effective in image classification, but their deployment in resource-constrained environments remains challenging due to high computational demands. Furthermore, their interpretability is of high importance which demands even more available resources. In this work, we introduce an approach that combines saliency-guided training with quantization techniques to create an interpretable and resource-efficient model without compromising accuracy. We utilize Parameterized Clipping Activation (PACT) to perform quantization-aware training, specifically targeting activations and weights to optimize precision while minimizing resource usage. Concurrently, saliency-guided training is employed to enhance interpretability by iteratively masking features with low gradient values, leading to more focused and meaningful saliency maps. This training procedure helps in mitigating noisy gradients and yields models that provide clearer, more interpretable insights into their decision-making processes. To evaluate the impact of our approach, we conduct experiments using famous Convolutional Neural Networks (CNN) architecture on the MNIST and CIFAR-10 benchmark datasets as two popular datasets. We compare the saliency maps generated by standard and quantized models to assess the influence of quantization on both interpretability and classification accuracy. Our results demonstrate that the combined use of saliency-guided training and PACT-based quantization not only maintains classification performance but also produces models that are significantly more efficient and interpretable, making them suitable for deployment in resource-limited settings. © 2024, CC BY-NC-SA.

关键词： Convolutional neural networks

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Semantic, Technical and Legal Interoperability of European Company Open Data in Practice: The STIRData Approach

Semantic, Technical and Legal Interoperability of European C...

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12th International Conference on Data Science, Technology and Applications, DATA 2023

作者： Klımek, Jakub Chortaras, Alexandros Mısek, Jakub Yang, Jim J. Skagemo, Steinar Tzouvaras, Vassilis Charles University Faculty of Mathematics and Physics Department of Software Engineering Czech Republic School of Electrical and Computer Engineering National Technical University of Athens Greece Institute of Law and Technology Masaryk University Czech Republic Norwegian Digitalisation Agency Norway Brønnøysund Register Centre Norway

ISBN: (纸本)9789897586644

As part of the Open Data Directive, the European Commission has published a list of high-value datasets (HVDs) that public sector bodies must make available as open data. The list also contains specific data items that must be included in these datasets. However, it does not prescribe any technical means of how the data should be published, severely hindering the interoperability of the datasets once they are published. One of the HVD topics is company data. In this practice report paper, we present results of STIRData, a project co-financed by the Connecting Europe Facility programme of the European Union, focusing on technical, semantic, and legal interoperability of open data from business registries, covering the company data HVDs topic. The results include a data architecture and a data specification to make the published data technically and semantically interoperable, and legal interoperability guidelines to ensure legal interoperability of the published data. Moreover, proof-of-concept transformations of data from selected European business registries are shown using open source tools and according to the specification. Finally, a user-orientated platform for browsing and analysing the data is presented as an example of the possibilities of using the data published in an interoperable way. © 2023 by SCITEPRESS–Science and Technology Publications, Lda.

关键词： Data consistency

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Automated Area Based Explosive Trace Detection System

Automated Area Based Explosive Trace Detection System

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Science, engineering and Business for Sustainable Development Goals (SEB-SDG), International Conference on

作者： Monday F. Ohemu Ambrose A. Azeta Ibrahim A. Adeyanju Nuradeen Maidoki Department of Artiftial Intelligent ACETEL-National Open University of Nigria Nigeria Department of Software Engineering Namibia University of Science and Technology Namibia Department of Computer Engineering Federal University Oye-Ekiti Nigeria Natview Technology Abuja Nigeria

ISBN: (数字)9798350358155

ISBN: (纸本)9798350358162

Explosive based attacks on people and sensitive places in the form of terrorism has become a global challenge that is making organizations such as airports, train station, security agencies and the government to do anything possible to secure the lives of people and some essential infrastructures. Several approaches have been adopted to prevent its occurrence that ranges from the use of animals, analytical means and sensor based approach to manual screaming by human personnel. With the development in the field of Artificial Intelligent, efforts are being made to deploy Sensor base system that leverage of machine learning technology to make the system highly accurate and sensitive in detection of explosive trace within an environment. This study presents the architecture of area base explosive trace detection utilizing deep learning model. The experiment shows the concept of area based explosive trace detection from trace properties of Carbon, Nitrogen, Oxygen and Hydrogen (CNOH). The deep leaning model recorded an accuracy of 98% on the explosive trace data. The system uses this information to detect the presence of explosion within an area.

关键词： Wireless sensor networks Accuracy Oxygen Terrorism Explosives Data models Real-time systems

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Mobile Based Solution to Weight Loss Planning for Children (with Obesity) in Sri Lanka 3

Mobile Based Solution to Weight Loss Planning for Children (...

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3rd International Conference on Advancements in Computing, ICAC 2021

作者： Rajapakse, R.M.M.P.K. Mudalige, J.M.A.I. Perera, L.A.D.Y.S. Warakagoda, R.N.A.M.S.C.B. Siriwardana, Samanthi Sri Lanka Institute of Information Technology Department of Computer Science and Software Engineering Malabe Sri Lanka

ISBN: (纸本)9781665408622

Obesity is a condition where there is excess fat in the body, and it is one of the world's most extreme and dangerous dietary diseases. Genetic factors, lack of physical activity, unhealthy eating patterns, or a combination of these factors are the most common causes of obesity. This is important because it influences every part of a child's life. More, in particular, this disorder leads to poor health and negative social standing with perceptions. Nowadays, children are paying keen interest in technology and related devices. Therefore, in this research, we are planning to give a mobile-based solution with a smart band that is used to monitor the child. In this solution, we are mainly focusing on Sri Lankan children with obesity who are aged between 5-10. In our solution, there are four main sections which are, monitoring child activities, recognizing the activities, and getting relevant data, then based on those data and previous activity completion levels, this solution will suggest activities for losing weight, provide specific diet plans for each child considering the health conditions and predict the probability of having main obesity-related diseases by using previously gathered data. © 2021 IEEE.

关键词： Deep learning

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