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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Fadhil, Saif Aamer Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Integrated Work Package KBR SouthbankVIC Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

Depending on a device's encryption mechanism, a wide variety of tangible details could be exposed. These leaks are used in side-channel analysis, which is used to get keys. Due to deep learning's sensitivity to the characteristics of the data being processed, using such algorithms can significantly improve the accuracy and efficiency of side channel analysis. However, classic neural networks are now used for the vast majority of the work that is being done. When the number of nodes in a network grows, so does the efficiency with which key recovery can function. However, the method's computing complexity grows in direct proportion. Overfitting, inadequate capacity for feature extraction, and inefficient training are all potential issues. In this study, we develop a compact convolutional neural network by enhancing a previously existing combination of neural networks. Novel neural network along with previous neural network both have their own implementations of the side-channel analysis used in comparative trials. Statistically, the new network has better accuracy, quicker convergence, and more robustness. As part of the research, heatmaps were provided as a means of data visualisation. The critical interval concentration is higher and the heat value is higher in the new network. Conventional neural networks, which serve as the foundation for various kinds of neural networks, perform much worse than side channel studies based on feature fusion networks. © 2023 IEEE.

关键词： Efficiency

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Detection of Crucial Power Side Channel Data Leakage in Neural Networks 33

Detection of Crucial Power Side Channel Data Leakage in Neur...

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33rd International Telecommunication Networks and Applications Conference, ITNAC 2023

作者： Ahmed, Amjed Abbas Hasan, Mohammad Kamrul Satar, Nurhizam Safie Mohd Nafi, Nazmus Shaker Aman, Azana Hafizah Islam, Shayla Fadhil, Saif Aamer Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia Department of Computer Techniques Engineering Baghdad10011 Iraq Integrated Work Package KBR SouthbankVIC Australia Institute of Computer Science and Digital Innovation UCSI University Kuala Lumpur56000 Malaysia

ISBN: (纸本)9798350317138

Neural network (NN) accelerators are now extensively utilized in a range of applications that need a high degree of security, such as driverless cars, NLP, and image recognition. Due to privacy issues and the high cost, hardware implementations contained within NN Propagators were often not accessible for general populace. Additionally with power and time data, accelerators also disclose critical data by electro-magnetic (EM) sided channels. Within this study, we demonstrate a side-channel information-based attack that can successfully steal models from large-scale NN accelerators deployed on real-world hardware. The use of these accelerators is widespread. The proposed method of attack consists of two distinct phases: 1) Using EM side-channel data to estimate networking's underlying architecture;2) Using margin-dependent, attackers learning actively in estimating parameters, notably weights. Deducing the underlying network structure from EM sidechannel data. Inferring the underlying network structure from EM sidechannel data. Experimental findings demonstrate that the disclosed attack technique can be used to precisely retrieve the large-scale NN via the use of EM side-channel information leaking. Overall, our attack shows how critical it is to conceal electromagnetic (EM) traces for massive NN accelerators in practical settings. © 2023 IEEE.

关键词： Side channel attack

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PUF-Based Privacy-Preserving Simultaneous Authentication Among Multiple Vehicles in VANET

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IEEE Transactions on Vehicular technology 2024年第5期73卷 6727-6739页

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Akram, Muhammad Wahid Hussain, Shafiq Das, Ashok Kumar Park, Youngho Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China National Yunlin University of Science and Technology Graduate School of Intelligent Data Science Yunlin64002 Taiwan University of Lahore Department of Software Engineering Punjab Lahore54590 Pakistan University of Sahiwal Department of Computer Science Punjab Sahiwal57000 Pakistan International Institute of Information Technology Center for Security Theory and Algorithmic Research Telangana Hyderabad500032 India Kyungpook National University School of Electronics Engineering Daegu41566 Korea Republic of

The intellectual and immense attainment of a smart city and auto-mobile vehicles are gradually growing. This growth drives Vehicular Ad-Hoc Network (VANET), a broadly operated complex network for inter-vehicular transmission to acquire information relevant to the vehicle location, road condition, traffic congestion, and speed. Generally, this information is procured through an open-source communication, which is susceptible to various security threats. In broad terms, the privacy of data in VANET is considered an imperative task. In this regard, many authentication protocols are formerly initiated for VANET. Most of these protocols are precarious owing to eavesdropping, replay, and impersonation attacks, etc. Furthermore, most existing protocols operates on enormous communication and computation rate, which turns them inadequate for VANET. This paper exhibits a lightweight privacy-preserving authentication protocol for n number of vehicles in VANET using the physical unclonable function (PUF) technique, and offers efficient and secure communication via public channels. Moreover, we evaluate our protocol formally and informally, showing that it is scalable, lightweight, and safe compared to formerly protocols. The rate of computation and communication is less than most related protocols. We also simulate the proposed scheme using the NS-3 simulator to measure its various network performance parameters. As a result, it is apparent through the beheld statistics that the presented protocol is beyond practical, efficient, and reliable for the secure dissemination of information in VANET. © 1967-2012 IEEE.

关键词： Vehicular ad hoc networks

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Prediction of Adsorption and Desorption Isotherms for Atmospheric Water Harvesting 21

Prediction of Adsorption and Desorption Isotherms for Atmosp...

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21st International Learning and technology Conference, L and T 2024

作者： Kabbaj, Narjisse El-Amin, Mohamed F. Effat University Effat College of Engineering Energy and Technology Research Center Electrical and Computer Engineering Department Jeddah Saudi Arabia Effat University Nat. Science Math and Tech Unit Effat College of Engineering Energy and Technology Research Center Jeddah Saudi Arabia

ISBN: (纸本)9798350393569

This study improves the mathematical modeling for the potential of atmospheric water harvesting (AWH) using desiccant materials. This research is crucial in highlighting the sustainable water management strategies of AWH systems in converting atmospheric moisture into a vital water source. The primary focus of our research methodology was the analytical derivation of sorption isotherms, essential for the hygrothermal simulation of desiccant materials. This was accomplished through the application of two established models, namely, the Guggenheim-Anderson-de Boer (GAB) and Van Genuchten (VG). Experimental data on various anhydrous salts from existing literature have been used. An in-depth comparative analysis of these models reveals that the VG model aligns more closely with the experimental data, thus asserting its superiority in enhancing the selection and efficiency of desiccant materials in AWH systems. By confirming the VG model's superiority in accurately modeling sorption isotherms, our research not only improves the model of AWH systems but also, importantly, contributes to the development of advanced water harvesting technologies. © 2024 IEEE.

关键词： Water conservation

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Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking 38

Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Fo...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Chen, Xi Qin, Chuan Fang, Chuyu Wang, Chao Zhu, Chen Zhuang, Fuzhen Zhu, Hengshu Xiong, Hui University of Science and Technology of China China Computer Network Information Center Chinese Academy of Sciences China Baidu Inc. China Institute of Artificial Intelligence Beihang University China SKLSDE School of Computer Science Beihang University China AI Thrust The Hong Kong University of Science and Technology Guangzhou China Department of Computer Science and Engineering The HongKong University of Science and Technology Hong Kong

In a rapidly evolving job market, skill demand forecasting is crucial as it enables policymakers and businesses to anticipate and adapt to changes, ensuring that workforce skills align with market needs, thereby enhancing productivity and competitiveness. Additionally, by identifying emerging skill requirements, it directs individuals towards relevant training and education opportunities, promoting continuous self-learning and development. However, the absence of comprehensive datasets presents a significant challenge, impeding research and the advancement of this field. To bridge this gap, we present Job-SDF, a dataset designed to train and benchmark job-skill demand forecasting models. Based on millions of public job advertisements collected from online recruitment platforms, this dataset encompasses monthly recruitment demand. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels. We benchmark a range of models on this dataset, evaluating their performance in standard scenarios, in predictions focused on lower value ranges, and in the presence of structural breaks, providing new insights for further research. Our code and dataset are publicly accessible via the https://***/Job-SDF/benchmark. © 2024 Neural information processing systems foundation. All rights reserved.

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LSTM Based Neural Network Model for Anomaly Event Detection in Care-Independent Smart Homes

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computer Modeling in engineering & sciences 2024年第9期140卷 2689-2706页

作者： Brij B.Gupta Akshat Gaurav Razaz Waheeb Attar Varsha Arya Ahmed Alhomoud Kwok Tai Chui Department of Computer Science and Information Engineering Asia UniversityTaichung413TaiwanChina Symbiosis Centre for Information Technology(SCIT) Symbiosis International UniversityPune411057India Center for Interdisciplinary Research University of Petroleum and Energy Studies(UPES)Dehradun248007India Computer Engineering Ronin InstituteMontclairNJ 07043USA Management Department College of Business AdministrationPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Business Administration Asia UniversityTaichung413TaiwanChina Department of Electrical and Computer Engineering Lebanese American UniversityBeirut1102Lebanon Department of Computer Sciences Faculty of Computing and Information TechnologyNorthern Border UniversityRafha91911Saudi Arabia School of Science and Technology Hong Kong Metropolitan University(HKMU)Hong KongChina

This study introduces a long-short-term memory(LSTM)-based neural network model developed for detecting anomaly events in care-independent smart homes,focusing on the critical application of elderly fall *** balances the dataset using the Synthetic Minority Over-sampling Technique(SMOTE),effectively neutralizing bias to address the challenge of unbalanced datasets prevalent in time-series classification *** proposed LSTM model is trained on the enriched dataset,capturing the temporal dependencies essential for anomaly *** model demonstrated a significant improvement in anomaly detection,with an accuracy of 84%.The results,detailed in the comprehensive classification and confusion matrices,showed the model’s proficiency in distinguishing between normal activities and *** study contributes to the advancement of smart home safety,presenting a robust framework for real-time anomaly monitoring.

关键词： LSTM neural networks anomaly detection smart home health-care elderly fall prevention

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Single-pixel Compressive Imaging with Single Photon Counting

Single-pixel Compressive Imaging with Single Photon Counting

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CLEO: Applications and technology in CLEO 2024, CLEO: A and T 2024 - Part of Conference on Lasers and Electro-Optics

作者： Li, Lili Thomas, Matthew Kumar, Santosh Huang, Yu-Ping Department of Physics Stevens Institute of Technology HobokenNJ07030 United States Center for Quantum Science and Engineering Stevens Institute of Technology HobokenNJ07030 United States Department of Computer Science Stevens Institute of Technology HobokenNJ07030 United States

We experimentally demonstrate a compressive imaging system by utilizing single-pixel detection at a single-photon level and a deep neural network. As a benchmark test, MNIST handwritten digits could be reconstructed a... 详细信息

关键词： Deep neural networks

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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2023 IEEE World AI IoT Congress, AIIoT 2023

作者： Sakib, Sheikh Rahmatulla Sara, Kamarun Nahar Hossain Rasel, Md. Tahmid Asif, Md. Masudul Islam Nahid, Md. Aynul Hasan Rahman, Md. Saifur Mridha, M.F. Islam, Ashraful Bangladesh University of Business and Technology Department of Computer Science and Engineering Dhaka Bangladesh Daffodil International University Department of Computer Science and Engineering Dhaka Bangladesh American International University-Bangladesh Department of Computer Science Dhaka Bangladesh Independent University Bangladesh Center for Computational and Data Sciences Dhaka Bangladesh

ISBN: (纸本)9798350337617

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM2.5, PM10, SO2, CO, NO2, and O3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city's air quality. The model's prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures. © 2023 IEEE.

关键词： Air quality

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Fog enabling technologies in healthcare: A review

Fog enabling technologies in healthcare: A review

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International Conference on Artificial Intelligence, Blockchain, Computing and Security, ICABCS 2023

作者： Yadav, Aditya Pradhan, Onesimus Chandra Khanam, Ruqaiya Amrita Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India Department of EECE Center for AI in Medicine Imaging and Forensic Sharda University U.P Greater Noida India Center for Cyber Security and Cryptology Computer Science and Engineering Sharda School of Engineering and Technology Sharda University U.P Greater Noida India

ISBN: (纸本)9781032669663

This paper provides an overview of fog computing healthcare technologies that are playing a huge role in the current healthcare Industry. In this paper, we suggest the Data mining is the most effective option for disease prediction. The suggested framework intends to support health care based on disease symptoms, and health-related data. Gathering and analyzing activity-specific data that may be utilized to gauge the health benefits of exercise and its intensity as well as provide suggestions. Health Informatics makes extensive use of wireless sensor networks (WSNs). Patients at risk for chronic illnesses are increasingly being monitored via wireless and wearable sensors. This ensures that patients follow their treatment schedules and protects them from unexpected assaults. Along with its abilities, the fog computing framework also brings over from the cloud computing platform a number of security flaws that must be solved for the benefit of the user. Fog computing is crucial because it aids in the system’s elimination of latency problems, which are crucial in the healthcare sector. © 2024 The Author(s).

关键词： Fog computing

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Clustering Single-Cell RNA-seq Data: Impact of Data Binarization on Algorithmic Performance

Clustering Single-Cell RNA-seq Data: Impact of Data Binariza...

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18th International Joint Conference on Biomedical engineering Systems and Technologies, BIOSTEC 2025

作者： Widzisz, Karolina Kania, Mateusz Zyla, Joanna Polański, Andrzej Department of Computer Graphics Vision and Digital Systems Silesian University of Technology Akademicka 16 Gliwice Poland Department of Applied Informatics Silesian University of Technology Akademicka 16 Gliwice Poland Department of Data Science and Engineering Silesian University of Technology Akademicka 16 Gliwice Poland

The primary objective of this study was to test the hypothesis that the binary information on the presence or absence of gene expression can sufficiently capture the inherent heterogeneity within single-cell RNA se quencing (scRNA-seq) data. This hypothesis posits that even without detailed expression levels, valuable insights about cellular diversity can be obtained. Utilizing this method can be particularly advantageous when analyzing large datasets, a common scenario in the field of scRNA-seq. In this paper, we evaluate clustering performance and cluster separability of a variety of model-based algorithms and distance-based methods to an alyze both expression level data and threshold-encoded binarized data. We examined the performance of the Bernoulli-mixture model and Gaussian-mixture model. These were compared against traditional clustering techniques such as hierarchical clustering, K-means, and the Louvain algorithm on a range of scRNA-seq datasets. Our findings reveal that mixture models exhibit a lower dependence on the specific dataset compared to distance-based methods. Mixture models, particularly, demonstrate greater efficacy in accurately estimat ing the number of clusters present within the data. Among analyzed algorithms, the Bernoulli-mixture model stands out, outperforming distance-based approaches in several key aspects. Binary data, presence/absence of gene expression, seem to be indeed adequate to capture the heterogeneity of scRNA-seq data when clus tering with methods specifically designed for binary datasets. The implications of this finding are significant, as it opens up new possibilities for simplifying data analysis in scRNA-seq studies without compromising the accuracy of the results. © 2025 by SCITEPRESS– science and technology Publications, Lda.

关键词： Data accuracy

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