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CNN-based Human Activity Recognition on Edge Computing Devices

CNN-based Human Activity Recognition on Edge Computing Devic...

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Omni-layer Intelligent Systems (COINS), International Conference on

作者： Amandeep Singh Tiziana Margaria Florenc Demrozi Dep. of Computer Science and Information Systems University of Limerick Ireland Dep. of Electrical Engineering and Computer Science University of Stavanger Norway

Human Activity Recognition (HAR) is a research area that involves wearable devices integrating inertial and/or physiological sensors to classify human actions and status across various application domains, such as healthcare, sports, industry, and entertainment. However, executing HAR algorithms on remote devices or the cloud can lead to issues such as latency, bandwidth requirements, and energy consumption. Transitioning towards Edge HAR can be a more effective and versatile solution, overcoming the challenges of traditional HAR techniques. We present a novel HAR model for computation on edge devices: we design a Convolutional Neural Network (CNN) Deep Learning approach and compare its performance with cloud-computing HAR models. The paper is accompanied by a self-collected dataset. The experiments on this dataset demonstrate that the proposed edge computing model achieves promising results ( $\geq$ 92 %) in terms of Precision, Recall, and Fl-score. Furthermore, the model exhibits significantly reduced latency, with only 117 ms, and utilizes minimal memory, with a peak of 18.8 Kb RAM and 956 Kb Flash memory.

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Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbors, and Support Vector Machines

Detecting 5G Narrowband Jammers with CNN, k-nearest Neighbor...

引用

IEEE International Workshop on Information Forensics and Security (WIFS)

作者： Matteo Varotto Florian Heinrichs Timo Schürg Stefano Tomasin Stefan Valentin Dep. of Computer Science Darmstadt University of Applied Sciences Germany Dep. of Medical Engineering and Technomathematics FH Aachen University of Applied Sciences Germany Dep. of Information Engineering Dep. of Mathematics University of Padova Italy Sciences Germany

ISBN: (数字)9798350364422

ISBN: (纸本)9798350364439

5G cellular networks are particularly vulnerable against narrowband jammers that target specific control sub-channels in the radio signal. One mitigation approach is to detect such jamming attacks with an online observation system, based on machine learning (ML). We propose to detect jamming at the physical layer with an ML model that performs binary classification. Based on data from an experimental 5G network, we study the performance of different classification models. A convolutional neural network will be compared to support vector machines and k-nearest neighbors, where the last two methods are combined with principal component analysis. The obtained results show substantial differences in terms of classification accuracy and computation time.

关键词： Support vector machines Accuracy 5G mobile communication Computational modeling Wireless networks Nearest neighbor methods Convolutional neural networks Jamming Principal component analysis Narrowband

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Review of Cloud AI for Real-Time Fraud Detection 10

Review of Cloud AI for Real-Time Fraud Detection

引用

10th International Conference on Automation, Robotics, and Applications, ICARA 2024

作者： Aabitova, Gulnara Abalkanov, Miras Abitova, Gulnara A. Shuteyeva, Gulnur Aitmukhanbetova, Elvira Kulniyazova, Korlan Astana IT University Dep. Computer Engineeering Astana Kazakhstan State Univerity of New York at Binghamton Dept. Electical Engineering and Computer Science Binghamton United States Eurasian National University Dept. System Anslysis and Managemnt Astana Kazakhstan

ISBN: (纸本)9798350394245

The paper presents a comprehensive analysis of the integration of Cloud AI in real-time fraud detection, highlighting significant advancements and practical applications. It is identifying a key challenge such as data privacy and the need for scalable solutions, while also exploring the potential of innovative AI algorithms and strategic partnerships to enhance fraud detection capabilities. Through detailed case studies, the work is demonstrated the effective application of Cloud AI across various sectors. The study concludes that Cloud AI, despite not being a complete solution, plays a crucial role in strengthening fraud detection systems. Our findings suggest that strategic implementation of Cloud AI can lead to more adaptive and efficient systems, thereby significantly contributing to the economic sector's resilience against fraudulent activities. This paper underscores the transformative impact of Cloud AI in fraud detection, offering insights into its future potential and laying the groundwork for further advancements in this field. © 2024 IEEE.

关键词： Crime

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Usage of Blockchain Technology for Improving computer Security 25

Usage of Blockchain Technology for Improving Computer Securi...

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Almomani, Adai Al Refai Mohammed, N. Aburomman, Ahmad Alidmat, Omar Khair Alla Saber, Qotadeh Alshariedeh, Fuad Khouj, Mohammed Zarqa University Department of Computer Science Zarqa Jordan Zarqa University Software Engineering Dep. Zarqa Jordan Zarqa University Department of Data Science and A Zarqa Jordan Univeristy of Business and Technology Department of Electronic Enginnering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

Cyber threats have grown increasingly sophisticated, surpassing the capabilities of traditional security measures. Blockchain technology, with its decentralized and immutable structure, offers a potential solution to these challenges. This study examines the role of blockchain tools such as smart contracts, decentralized identity management, and secure data sharing in enhancing cybersecurity. A review of key literature and analysis of survey data from cybersecurity professionals demonstrate blockchain's effectiveness in improving data integrity, access control, and secure communications. The results show significant correlations between the adoption of blockchain technologies and the mitigation of threats like data breaches and unauthorized access. While blockchain presents promising advancements, scalability, regulatory barriers, and energy consumption remain challenges. The study provides valuable insights for researchers, practitioners, and policymakers on how blockchain can be integrated into modern cybersecurity strategies to create more secure digital environments. © 2024 IEEE.

关键词： Cybersecurity

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Standardization Review on Non-Terrestrial Networks 39

Standardization Review on Non-Terrestrial Networks

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39th International Conference on Information Networking, ICOIN 2025

作者： Kyung, Yeunwoong Lee, Jaewook Ko, Haneul Oh, Seungmin Lee, Jaehwan Kim, Youngjun Kongju National University Div. of Info. & Comm. Eng Cheonan South Korea Pukyong National University Dep. of Info. and Comm. Engineering Busan South Korea Kyung Hee University Dep. of Electronic Eng Yongin-si South Korea Kongju National University Dep. of Computer Science and Eng Cheonan South Korea Kyungnam University School of Computer Science and Eng Changwon South Korea

ISBN: (纸本)9798331506940

As the demand for global connectivity increases, non-terrestrial networks (NTNs), particularly satellite-based systems, have gained significant attention in communication network standardization. The 3rd Generation Partnership Project (3GPP) has been at the forefront of developing standards that incorporate satellite communications into the broader terrestrial network framework. This paper reviews the progression of NTN standardization, focusing on three key stages: (1) the use of satellites as relays or the next generation NodeBs (gNBs), (2) the integration of user plane functions (UPF) within satellites, and (3) the inclusion of control plane network functions (NFs) within satellites. These developments in standardizations reflect a shift from basic relay functions to fully integrated satellite networks. © 2025 IEEE.

关键词： 3GPP Standards Control Plane Network Functions Non-Terrestrial Networks

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Blockchain-based Device Identity Management with Consensus Authentication for IoT Devices 19

Blockchain-based Device Identity Management with Consensus A...

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19th IEEE Annual Consumer Communications and Networking Conference, CCNC 2022

作者： Mukhandi, Munkenyi Damiao, Francisco Granjal, Jorge Vilela, Joao P. University of Coimbra Cisuc Dep. of Informatics Engineering Coimbra Portugal Pdmfc Lisbon Portugal University of Porto Faculty of Sciences Cracs Inesctec Cisuc Dep. of Computer Science Porto Portugal

ISBN: (纸本)9781665431620

To decrease the IoT attack surface and provide protection against security threats such as introduction of fake IoT nodes and identity theft, IoT requires scalable device identity and authentication management. This work proposes a blockchain-based identity management approach with consensus authentication as a scalable solution for IoT device authentication management. The proposed approach relies on having a blockchain secure tamper proof ledger and a novel lightweight consensus-based identity authentication. The results show that the proposed decentralised authentication system is scalable as we increase number of nodes. © 2022 IEEE.

关键词： Authentication

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Design of Technology for Secure File Storage Based on Hybrid Cryptography Methods: Short Overview

Design of Technology for Secure File Storage Based on Hybrid...

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Smart Information Systems and Technologies (SIST), IEEE International Conference on

作者： Gulnara A. Abitova Abu Said Manap Korlan Kulniyaziva Vladimir Nikulin Dep. of Electrical Engineering and Computer Science State University of New York Binghamton NY USA Dep. of Computer Engineering Astana IT University Astana Kazakhstan Master’s Degree Astana IT University Astana Kazakhstan Dep. of System Analysis and Management L.N. Gumilyov Eurasian National University Astana Kazakhstan Dep. of Electrical Engineering and Computer Science Binghamton University Binghamton NY USA

ISBN: (数字)9798350374865

ISBN: (纸本)9798350374872

In the era of pervasive digital data, ensuring secure file storage has become a paramount concern. This paper explores the significance of hybrid cryptography for design of an advanced tools and techniques for secure file storage. Hybrid cryptography, combining symmetric and asymmetric encryption, offers robust protection against unauthorized access, tampering, and data loss. The article reviews recent cryptography literature, highlighting the importance of secure file storage in today's interconnected world and examining the benefits of hybrid cryptography. The analysis of articles on cryptography reveals emerging trends and challenges. Post-quantum cryptography addresses concerns about quantum threats, while blockchain-based cryptography enhances security in IoT data sharing. Homomorphic encryption enables computations on encrypted data, and cryptographic protocols that protect privacy enable safe multi-party computation. Machine learning's intersection with cryptanalysis introduces efficiency but raises ethical considerations. The paper further discusses advancements and trends in cryptography techniques, including post-quantum cryptography, homomorphic encryption, zero-knowledge proofs, post-quantum key exchange, secure multi-party computation, and post-quantum signature schemes. These developments aim to ensure long-term security against quantum attacks, enable privacy-preserving computations, and enhance the confidentiality, integrity, and authentication of digital communication and data storage. In conclusion, the paper advocates for the adoption of hybrid cryptography in secure file storage systems. Its combination of symmetric and asymmetric encryption, along with its adaptability to evolving security landscapes, positions hybrid cryptography as a formidable approach to data protection. By embracing hybrid cryptography and staying informed about the latest advancements, organizations can navigate the digital age with confidence, ensuring the confidentiality

关键词： Navigation Reviews Organizations Machine learning Market research Multi-party computation Complexity theory

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Performance assessment of low-cost sensors under representative indoor air conditions 17

Performance assessment of low-cost sensors under representat...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin NTNU Dep. Energy and Process Engineering Trondheim Norway NTNU Dep. Industrial Economics and Technology Management Trondheim Norway DTU Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Recently many indoor air quality (IAQ) low-cost sensors (LCS) have arrived in the market. Being cost-effective encourages sensor installation in many different situations for IAQ-monitoring and sometimes for control of HVAC systems. However, very often these sensors are delivered with incomplete information and there are many doubts about their performance, reliability, and response to aging or drift. In this article, an evaluation of the performance of eight equal in-house mounted IAQ stations is presented. Pre-calibrated LCS measuring CO2, temperature, RH, and particulate matter were connected to a RaspberryPi. Some tests were repeated with 6 months of difference and some with one year. In general, all stations responded similarly to the events to which they were exposed, the inter-sensor correlations values were higher than 0.95. LCS reacted similarly to the sources (trends) but with quantitative disparities. Sensor calibration is needed but when included, the tested LCS seem fit for IAQ measurements. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Carbon dioxide

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Pollutant correlation analysis in measurements at four classrooms in four Norwegian schools 17

Pollutant correlation analysis in measurements at four class...

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17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022

作者： Alonso, Maria Justo Jørgensen, Rikke Bramming Madsen, Henrik Mathisen, Hans Martin Dep. Energy and Process Engineering Trondheim Norway Dep. Industrial Economics and Technology Management Trondheim Norway Technical University of Denmark Applied Mathematics and Computer Science Kgs. Lyngby Denmark

Demand-controlled ventilation (DCV) is often used in school buildings to save energy by modulating the airflow rates based on the demand. However, other pollutants and indoor air parameters may affect health and are not necessarily correlated to CO2. In this study investigation of correlations between PM2.5, formaldehyde, absolute humidity, CO2 and temperature are performed. Measurements of these parameters were performed between two months and one year, positioned in the supply air in four CO2-based DCV classrooms at four different schools. The time series of measurements were de-trended to avoid overestimating cross-correlation functions. From the measurements we have concluded the need to measure: 1) pollutants related to human activities (CO2) 2) pollutants that infiltrate from processes like combustion or traffic outdoors (PM2.5), and 3) parameters related to other "non-combustion-related activities" indoors, heat sources and moisture loads (temperature). Formaldehyde from building materials seems to be mostly degassed in these older than one-year buildings, but this study should be repeated on newer buildings. © 2022 17th International Conference on Indoor Air Quality and Climate, INDOOR AIR 2022. All rights reserved.

关键词： Formaldehyde

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Intelligent Recognition of Immune Cells: Analysis and Research 19

Intelligent Recognition of Immune Cells: Analysis and Resear...

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19th IEEE International Conference on e-Business engineering, ICEBE 2023

作者： Li, Chih-En Liu, Wei-Lun Lai, Kun-Yi Chiang, Yu-Hsiang Yan, Xiaowei Chen, Hao-Xiang Li, Hsu-Ching Cheng, Wan-Shu National Kaohsiung University of Science and Technology Department of Electrical Engineering Kaohsiung Taiwan Institute for Systems Biology Seattle Washington United States Providence University Dep. of Computer Science and Information Management Taichung Taiwan

ISBN: (纸本)9798350325553

Immunotherapy utilizes the potential of the patient's immune system to fight disease. T-lymphocytes have become the central focus of the immune system in the fight against cancer. One of the fundamental studies in immunotherapy is how to accurately identify various types of immune cells. Therefore, the study of immune-related cells will be an important reference for the treatment of COVID19 or other related diseases. In this paper, a series of experiments were conducted with COVID-19 patients as the starting point to design a strategy to identify the important subtypes of CD$4^{+}$T cells. In this paper, an intelligent machine learning algorithm is proposed according to the results of different experiments of linear and nonlinear dimension reduction algorithms, integrating the important characteristics of these two algorithms and improving the performance. Our experimental results show that the algorithm designed based on the front-end experiments can effectively distinguish the important lymphocytes. The proposed algorithm not only improves the performance, but also the dimension reduction procedure keeps the important features of both global and regional data. The experimental results of this paper will be beneficial for the subsequent analysis of the clustering in identifying important subtypes of CD$4^{+}$T cells. Most importantly, this wm significantly improve the procedure of manually integrating and labeling the recorded data after several tedious and monotonous analyses with external software. © 2023 IEEE.

关键词： COVID-19

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