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24th International Conference on Human-computer Interaction, HCII 2022

作者： Kokkinos, Panagiotis Margaris, Dionisis Spiliotopoulos, Dimitris Institute of Communication and Computer Systems NTUA Athens Greece Department of Digital Systems University of the Peloponnese Sparta Greece Department of Management Science and Technology University of the Peloponnese Tripoli Greece

ISBN: (纸本)9783031064166

Cloud infrastructures handle processing and storage options for a multitude of applications and services. Expert users are tasked to verify assigned resources and select optimal combinations to accommodate the infrastructure operations. For the technical users (engineers) in this specialised environment, user intent is not modelled in the traditional HCI application sense, but rather by intentionally combining the functional and non-functional requirements of the infrastructure through provider recommendations that are used as features. This work reports on the design, development and evaluation of a user interface that enable intent transfer from the specialised technical level of the expert user to the provider recommendation evaluation by the same users. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2022.

关键词： User interfaces

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Heterogeneous Federated Learning systems for Time-Series Power Consumption Prediction with Multi-Head Embedding Mechanism

arXiv

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arXiv 2025年

作者： Syu, Jia-Hao Lin, Jerry Chun-Wei Srivastava, Gautam Yun, Unil Department of Computer Science and Information Engineering National Taiwan University Taiwan Department of Distributed Systems and Informatic Devices Silesian University of Technology Poland Department of Math and Computer Science Brandon University Brandon Canada Department of Computer Science and Engineering Sejong University Korea Republic of

Time-series prediction is increasingly popular in a variety of applications, such as smart factories and smart transportation. Researchers have used various techniques to predict power consumption, but existing models lack discussion of collaborative learning and privacy issues among multiple clients. To address these issues, we propose Multi-Head Heterogeneous Federated Learning (MHHFL) systems that consist of multiple head networks, which independently act as carriers for federated learning. In the federated period, each head network is embedded into 2-dimensional vectors and shared with the centralized source pool. MHHFL then selects appropriate source networks and blends the head networks as knowledge transfer in federated learning. The experimental results show that the proposed MHHFL systems significantly outperform the benchmark and state-of-the-art systems and reduce the prediction error by 24.9% to 94.1%. The ablation studies demonstrate the effectiveness of the proposed mechanisms in the MHHFL (head network embedding and selection mechanisms), which significantly outperforms traditional federated average and random transfer. © 2025, CC BY-NC-SA.

关键词： Federated learning

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Decentralized Security in IoT: Lightweight Blockchain with Optimized CA-LSTM for Improved Performance and Privacy 5

Decentralized Security in IoT: Lightweight Blockchain with O...

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5th International Conference on Data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Selvakumarasamy, K. Kumar, T. Rajesh Meenambika, A. Balamanigandan, R. Mahaveerakannan, R. Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Electronics and Communication Engineering Chennai602105 India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Department of Computer Science and Engineering Tamil Nadu Chennai602105 India Psna College of Engineering and Technology Computer Science and Business Systems India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350389609

An existing infrastructure can be linked to billions of devices, or "things,"through the Internet of Things (IoT). This allows for machine-to-machine communication as well as human-to-human communication. Massive volumes of data are being generated due to the proliferation of devices globally, which permeates every facet of everyday life. Thus, new problems are emerging as a result of the development and use of existing technologies, and these problems pertain to new applications, regulations, cloud computing, security, and privacy. With its decentralized nature, the blockchain has the potential to protect users' and data's privacy. This study introduces a novel method that makes use of lightweight blockchain technology to significantly lessen the computing load usually seen in traditional blockchain systems. Implementation time and computational complexity can be significantly reduced by connecting this lightweight blockchain with IoT devices. Improved safety is achieved by the use of CA-LSTM (channel attention long short-term memory) technology for attack detection. In order to improve the CA-LSTM algorithm's solution, the suggested system uses the RMSSO algorithm as a hyperparameter tuning technique. Similarly, to found very little power consumption and physical memory utilization;for example, Raspberry Pi devices used 0.2 GB of memory and NVIDIA Jetson devices used 0.42 GB. With the deployed model, power consumption increased by an average of 15% per device. This technology makes it easier to build certain types of business models and gives decentralized apps the ability to securely compute on encrypted data without compromising user privacy. The findings confirm that the proposed system is very secure, on par with conventional blockchain implementations. © 2024 IEEE.

关键词： Differential privacy

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The Role of Soft Skills in Engineering Education 53

The Role of Soft Skills in Engineering Education

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53rd IEEE ASEE Frontiers in Education International Conference, FIE 2023

作者： Fojcik, Marcin Fojcik, Martyna Katarzyna Ziebinski, Adam Pollen, Bjarte Kampen, Anne-Lena Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Førde Norway Volda University Collage Department of Humanities and Education Volda Norway Silesian University of Technology Department of Distributed Systems and Informatics Devices Gliwice Poland Western Norway University of Applied Sciences Department of Computer Science Electrical Engineering and Mathematical Sciences Bergen Norway

ISBN: (纸本)9798350336429

The rapid but uneven education development worldwide is creating many challenges. Changing conditions and rules in engineering fields such as programming, automation, and electronics are not always followed by changes in education. Employers expect potential candidates to be current with the latest domain competencies and soft skills. The idea is to hire candidates with both knowledge (theoretical and practical) and experience in reflection and collaboration skills. Universities can not always keep up with the development of technology and the adaptation of curricula because change happens so quickly. Therefore, upgrades are needed in engineering education. Focusing education solely on measurable knowledge and results is a simple but often insufficient approach to presenting the entire curriculum within the time frame of a course. Our study of about 150 engineering students in Poland and Norway included the role of students' experiences in hands-on activities, especially labs. The research aimed to determine what students expect, what influences their results, and what complications may arise. The study results show that students often struggle with time management, prioritizing, and understanding practical tasks. In addition, there is a difference between students with and without practical work experience. Our research showed that both groups of students did not achieve all their intended educational goals for various reasons. Therefore, adapting the current teaching system to the student's needs and requirements is important. © 2023 IEEE.

关键词： Bolognia process culture model engineering education laboratory teaching soft skills

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Vulnerability Testing on IoT Networks Using Machine Learning Algorithm

Vulnerability Testing on IoT Networks Using Machine Learning...

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2024 International Conference on Advances in Data Engineering and Intelligent Computing systems, ADICS 2024

作者： Mohandas, R. Prasanna, D.S.John Deva Baiju, B.V. Nathiya, S. Rajkumar, T. Dhiliphan Sivashankar, C. Srm Institiute Of Science And Technology Department Of Computational Intelligence Chennai Kattankulathur India Srm Institiute Of Science And Technology Department Of Datascience And Business Systems Chennai Kattankulathur India Vit University TamilNadu Vellore India Prince Shri Venkateshwara Padmavathi Engineering College Department Of Electronics And Communication Engineering Ponmar Chennai India Kalasalingam Academy Of Research And Education Dept. Of Computer Science And Engineering Tamilnadu Krishnankovil India Department Of Computer Science And Engineering Chennai India

ISBN: (纸本)9798350364828

The increasing growth of Internet of Things (IoT) devices has created a wide attack surface for cyber criminals to carry out more destructive cyber attacks;therefore, the number of cyber attacks in the information security industry is increasing rapidly. As attackers use new and innovative methods to launch cyber-oriented attacks, many of these strikes have succeeded in their malicious goals. Anomaly-based intrusion detection systems (IDS) use machine learning techniques to detect and classify attacks on IoT networks. Faced with unpredictable network technologies and various infiltration methods, traditional machine learning technologies are powerless. In abounding areas of research, kernel learning methods prove that they accurately determine pathological abilities. © 2024 IEEE.

关键词： Internet of things

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Quantum Computing's Double-Edged Sword Unravelling the Vulnerabilities in Quantum Key Distribution for Enhanced Network Security

Quantum Computing's Double-Edged Sword Unravelling the Vulne...

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2023 IEEE International Conference on Next Generation Electronics, NEleX 2023

作者： Anantraj, I. Umarani, B. Karpagavalli, C. Usharani, C. Lakshmi, S. Jothi Sri Krishna College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Kongunadu College of Engineering and Technology Department of Electronics and Communication Engineering Namakkal India Ramco Institute of Technology Department of Artificial Intelligence and Data Science Rajapalayam India

ISBN: (纸本)9798350319088

Quantum computing has emerged as both a boon and a bane in the realm of network security. This paper delves into the intriguing interplay between quantum computing and the vulnerabilities of Quantum Key Distribution (QKD) protocols, a cornerstone of secure quantum communication. While QKD offers unmatched security through the principles of quantum mechanics, it is not impervious to attacks in the age of rapidly advancing quantum computing. We examine these vulnerabilities, highlighting the challenges of ensuring the long-term security of quantum networks in a post-quantum world. Through a comprehensive exploration of threat models and the practicality of QKD, we uncover potential weaknesses in quantum network security. In particular, we investigate QKD's susceptibility to evolving quantum algorithms, hardware vulnerabilities, and post-processing security concerns. This work aims to provide a roadmap for understanding and addressing these vulnerabilities, ensuring the continued robustness of quantum communication networks, and fostering the development of post-quantum cryptographic solutions. As quantum computing advances, its implications for quantum network security are far-reaching. This study underlines the critical need for research and innovation in securing quantum communication. By unravelling the vulnerabilities within Quantum Key Distribution and exploring post-quantum cryptographic alternatives, this research contributes to the development of more resilient and secure quantum networks, safeguarding sensitive information and communications in an increasingly quantum-enabled world. © 2023 IEEE.

关键词： Quantum cryptography

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Artificial Butterfly Optimization based Cluster Head Selection with Energy Efficient Data Aggregation model for Heterogeneous WSN Environment 7

Artificial Butterfly Optimization based Cluster Head Selecti...

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7th International Conference on Computing Methodologies and communication, ICCMC 2023

作者： Venkatasubramanian, S. Vijay, R. HariPrasath, S. Department of Computer Science and Business Systems Saranathan College of Engineering Trichy India Department of Electrical and Electronics Engineering Saranathan College of Engineering Trichy India Department of Electronics and Communication Engineering Saranathan College of Engineering Trichy India

ISBN: (纸本)9781665464086

The WSN is a new and urgent technology with many potential uses, including but not limited to health security, environmental monitoring, etc. Due to lower battery capacity, WSN has a restricted-energy resource. In order to solve the issue of unequal energy consumption among nodes, it is necessary to choose a sensor node from a cluster with more than enough power to make up for the weaker nodes. This paper develops the idea of heterogeneous WSN (H-WSN), which provides supplementary energy to the heterogeneity network. One method that has shown promise in overcoming this difficulty is the clustering technique, which optimizes energy consumption and extends the useful life of a sensor network. Even if the existing approaches function well, the computational complexity may rise due to the usage of a single mobile sink in their studies. As an alternative to communication among each CH and sink through a separate hop, the network uses Multiple Mobile Sinks (MMSs). The combination of the data collection and aggregation mechanism (DCA) and artificial butterfly optimization (ABO) based on CH selection allows for energy-efficient data transfer using MMSs in H-WSN. The CH assortment uses the distance parameter, remaining energy, and regular energy for the suggested energy efficiency model. The NS2 platform hosts the final product of the projected H-WSN. The suggested ABO-CH-DCA approach is superior to the baseline protocols in simulations on various measures, including throughput, network lifespan, remaining energy, dead nodes, and live nodes. © 2023 IEEE.

关键词： Energy utilization

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PpNNT: Multiparty Privacy-Preserving Neural Network Training System

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 370-383页

作者： Feng, Qi He, Debiao Shen, Jian Luo, Min Choo, Kim-Kwang Raymond Wuhan University School of Cyber Science and Engineering Wuhan430072 China Shandong Provincial Key Laboratory of Computer Networks Jinan250014 China School of Information Science and Technology Zhejiang Sci-Tech University Hangzhou310018 China Shanghai Technology Innovation Centre of Distributed Privacy-Preserving Artificial Intelligence Matrix Elements Technologies Shanghai201204 China University of Texas at San Antonio Department of Information Systems and Cyber Security San AntonioTX78249 United States

By leveraging smart devices [e.g., industrial Internet of Things (IIoT)] and real-time data analytics, organizations, such as production plants can benefit from increased productivity, reduced costs, enhanced self-monitoring, and autonomous decision-making. In such a setting, machine learning plays an important role in data analytics, but the use of conventional centralized machine learning solutions may raise uncomfortable concerns about data privacy. Hence, one can explore the use of federated learning. In this article, we propose privacy-preserving deep neural network training (PpNNT), which is designed to support federated learning in the multiparty setting. To minimize the overall costs, we further design a hybrid architecture to fully maximize resource utilization. Our proposed design allows the PpNNT system to provide high security, efficiency, and scalability for IIoT data analytics, as evidenced by our theoretical security proof and experimental results on the CIFAR10 dataset. © 2020 IEEE.

关键词： Decision making

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Advancements in Multi-Cloud Applications for Enhanced E-Healthcare Services 1

Advancements in Multi-Cloud Applications for Enhanced E-Heal...

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1st International Conference on Artificial Intelligence for Innovations in Healthcare Industries, ICAIIHI 2023

作者： Naidu, P Ramesh Gowda, Dankan Sarma, Parismita Arora, Dinesh Suneetha, Sampathirao Patil, Sandip Ramesh Nitte Meenakshi Institute of Technology Department of Computer Science and Engineering Karnataka Bangalore India Bms Institute of Technology and Management Department of Electronics & Communication Engineering Karnataka Bangalore India Gauhati University Assam Department of Information Technology Assam Guwahati India Chandigarh Engineering College Department of Electronics and Communication Punjab Mohali India Andhra University Department of Computer Science & Systems Engineering Andhra Pradesh Visakhapatnam India Bharati Vidyapeeth College of Engineering Department of Communication Skill Navi Mumbai India

ISBN: (纸本)9798350330915

E-Health Record Security Research on a Cloud-Based Multi-Layer Framework reaches its climax in a string of noteworthy discoveries, demonstrating how the framework may transform cloud health data security. The framework's multi-tiered design proved to be an effective barrier against a wide range of cyber threats, protecting the privacy and security of patients' medical records. An important factor in the varied healthcare industry is the fact that it can be easily scaled and adjusted to meet the needs of healthcare providers of varied sizes and capabilities. Nevertheless, there are obstacles that need to be addressed, according to the report. These include the difficulty of implementation and the ongoing need for upgrades and modifications to address changing cyber threats and technical advances. Healthcare firms must continuously implement educational programs due to the reliance on user compliance and training. With an eye toward the future, this structure lays the groundwork for more sophisticated studies in the subject. Potential research directions include improving cross-platform compatibility, optimizing resource utilization to reduce performance implications, and integrating AI and ML for automated threat response and predictive analytics. © 2023 IEEE.

关键词： Cloud Computing Data Security Healthcare Optimization Multi-Cloud Applications Patient Data Privacy Scalability

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Optimal Machine Learning Enabled Performance Monitoring for Learning Management systems

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computer systems science & Engineering 2023年第3期44卷 2277-2292页

作者： Ashit Kumar Dutta Mazen Mushabab Alqahtani Yasser Albagory Abdul Rahaman Wahab Sait Majed Alsanea Department of Computer Science and Information Systems College of Applied SciencesAlMaarefa UniversityAd Diriyah13713RiyadhKingdom of Saudi Arabia Physical Therapy Department Majmaah UniversityMajmaah11952Kingdom of Saudi Arabia Department of Computer Engineering College of Computers and Information TechnologyTaif UniversityTaif21944Kingdom of Saudi Arabia Department of Archives and Communication King Faisal UniversityAl Ahsa31982HofufKingdom of Saudi Arabia Department of Computing Arabeast CollegesRiyadh11583Kingdom of Saudi Arabia

Learning Management System(LMS)is an application software that is used in automation,delivery,administration,tracking,and reporting of courses and programs in educational *** LMS which exploits machine learning(ML)has the ability of accessing user data and exploit it for improving the learning *** recently developed artificial intelligence(AI)and ML models helps to accomplish effective performance monitoring for *** the different processes involved in ML based LMS,feature selection and classification processesfind *** this motivation,this study introduces Glowworm-based Feature Selection with Machine Learning Enabled Performance Monitoring(GSO-MFWELM)technique for *** key objective of the proposed GSO-MFWELM technique is to effectually monitor the performance in *** pro-posed GSO-MFWELM technique involves GSO-based feature selection techni-que to select the optimal ***,Weighted Extreme Learning Machine(WELM)model is applied for classification process whereas the parameters involved in WELM model are optimallyfine-tuned with the help of May-fly Optimization(MFO)*** design of GSO and MFO techniques result in reduced computation complexity and improved classification *** presented GSO-MFWELM technique was validated for its performance against benchmark dataset and the results were inspected under several *** simulation results established the supremacy of GSO-MFWELM technique over recent approaches with the maximum classification accuracy of 0.9589.

关键词： Learning management system data mining performance monitoring machine learning feature selection

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