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Automatics and Informatics (ICAI), International Conference

作者： Liuchang Zhang Haoping Wang Yang Tian Xincheng Zhuang Ahmad Taher Azar Saim Ahmed Dept. School of Automation Nanjing University of Science and Technology Nanjing China Dept. Automated Systems & Soft Computing Lab Prince Sultan University Riyadh Saudi Arabia Dept. College of Computer and Information Sciences Prince Sultan University Riyadh Saudi Arabia

ISBN: (数字)9798350353907

ISBN: (纸本)9798350353914

This paper puts forward a fractional order sliding-mode-assisted disturbance observer-based model-free control (FO-SAMDOMFC) methodology for fractional order system with disturbance. First, the system dynamic model is rewritten as a fractional order ultra-local model (ULM), and a SMADO is designed to estimate the lumped disturbance term. Then, A closed-loop control law is proposed on the basis of the ULM with the objective of guaranteeing the convergence of the tracking error. Next, a proof of the stability of the closed-loop system is given based on the fractional extension of Lyapunov’s direct method. Finally, the simulation compared with other controllers based on MATLAB/Simulink is realized, and the simulation results validate the proposed method.

关键词： Accuracy System dynamics Simulation Noise Mathematical models Disturbance observers Stability analysis Noise measurement MATLAB Informatics

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A Conceptual Framework for AI Governance in Public Administration – A Smart Governance Perspective

A Conceptual Framework for AI Governance in Public Administr...

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International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC)

作者： Manikandan Rajagopal Ramkumar Sivasakthivel Gobinath Ramar Mansurali A Sathesh Kumar Karuppasamy Lean Operations and Systems School of Business and Management CHRIST (Deemed to be University) Bangalore India Department of Computer Science School of Sciences CHRIST (Deemed to be University) Bangalore India Department of Commerce and Management Central University of Tamilnadu India Department of Computer Science & Information Technology School of Computing Kalasalingam Academy of Research and Education Krishnankoil India

With the public governance lagging behind the fast evolving of AI in their attempts to yield sufficient governance, corresponding principles are necessary to be in par with this dynamic advancement. As AI becomes more pervasive and integrated into various domains, there is a growing need for AI governance models that can ensure that the development and deployment of AI systems align with ethical, legal, and social standards. There are some answers that literature puts forward to the question onthe way the government and public administration has to react to the huge concerns related to AI and usage of policies to avoid the emerging challenges. In this survey, AI problems and the prior AI regulation techniques are analyzed. In this research study, a governance model for AI is proposed by combining all the facets and also implements a new procedure for governing AI. This study will help the decision makers to make smart government a reality by using AI governance framework.

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Rethinking Few-shot Class-incremental Learning: Learning from Yourself

arXiv

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arXiv 2024年

作者： Tang, Yu-Ming Peng, Yi-Xing Meng, Jingke Zheng, Wei-Shi School of Computer Science and Engineering Sun Yat-sen University China Peng Cheng Laboratory Shenzhen China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Guangdong Province Key Laboratory of Information Security Technology Sun Yat-sen University China

Few-shot class-incremental learning (FSCIL) aims to learn sequential classes with limited samples in a few-shot fashion. Inherited from the classical class-incremental learning setting, the popular benchmark of FSCIL uses averaged accuracy (aAcc) and last-task averaged accuracy (lAcc) as the evaluation metrics. However, we reveal that such evaluation metrics may not provide adequate emphasis on the novel class performance, and the continual learning ability of FSCIL methods could be ignored under this benchmark. In this work, as a complement to existing metrics, we offer a new metric called generalized average accuracy (gAcc) which is designed to provide an extra equitable evaluation by incorporating different perspectives of the performance under the guidance of a parameter α. We also present an overall metric in the form of the area under the curve (AUC) along the α. Under the guidance of gAcc, we release the potential of intermediate features of the vision transformers to boost the novel-class performance. Taking information from intermediate layers which are less class-specific and more generalizable, we manage to rectify the final features, leading to a more generalizable transformer-based FSCIL framework. Without complex network designs or cumbersome training procedures, our method outperforms existing FSCIL methods at aAcc and gAcc on three datasets. See codes at 1 Copyright © 2024, The Authors. All rights reserved.

关键词： Complex networks

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Meta-Analysis and Systematic Review for Anomaly Network Intrusion Detection Systems: Detection Methods, Dataset, Validation Methodology, and Challenges

arXiv

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arXiv 2023年

作者： Maseer, Ziadoon K. Yusof, Robiah Al-Bander, Baidaa Saif, Abdu Kadhim, Qusay Kanaan Faculty of Computer Technology Engineering Bilad Al Rafidain University College Iraq Faculty of Information and Communication Technology Universiti Teknikal Malaysia Melaka Malaysia School of Computing Keele University United Kingdom Faculty of Engineering Taiz University Taiz Yemen Department of Computer Science College of Science University of Diyala Diyala Baqubah32001 Iraq

Intrusion detection systems (IDSs) built on artificial intelligence (AI) are presented as latent mechanisms for actively detecting fresh attacks over a complex network. Although review papers are used systematic review or simple methods to analyse and criticize the anomaly NIDS works, the current review uses a traditional way as a quantitative description to find current gaps by synthesizing and summarizing the data comparison without considering algorithms performance. This paper presents a systematic and meta-analysis study of AI for network intrusion detection systems (NIDS) focusing on deep learning (DL), and machine learning (ML) approaches in network security. Deep learning algorithms are explicated their structure and data intrusion network is justified based on an infrastructure of networks and attack types. By conducting a meta-analysis and debating the validation of the DL and ML approach by effectiveness, used dataset, detected attacks, classification task, and time complexity, we offer a thorough benchmarking assessment of the current NIDS-based publications-based systematic approach. The proposed method is considered reviewing works for the anomaly-based network intrusion detection system (anomaly-NIDS) models. Furthermore, the effectiveness of proposed algorithms and selected datasets are discussed for the recent direction and improvements of ML and DL to the NIDS. The future trends for improving an anomaly-IDS for continuing detection in the evolution of cyberattacks are highlighted in several research studies. © 2023, CC BY-SA.

关键词： Network security

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Blockchain Solution For SMEs Cybersecurity Threats In E-Commerce

Blockchain Solution For SMEs Cybersecurity Threats In E-Comm...

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Computer science and Emerging Technologies (CSET), International Conference on

作者： Firas H. Zawaideh Waheeb Abu-Ulbeh Salameh A. Mjlae Yousef A. Baker El-Ebiary Yazeed Al Moaiad Sunanda Das Department of Cyber Security Faculty of Science and Informaiton Technology Irbid National University Irbid Jordan Cybersecurity Department Faculty of Administrative sciences and Informatics Al-Istiqlal University Palestine CS Department Prince Abdullah bin Ghazi Faculty of Information Technology Al-Balqa Applied University (BAU) Jordan Faculty of Informatics and Computing UniSZA Malaysia Faculty of Computer and Information Technology MEDIU Malaysia School of Computer Science and Engineering Jain University Bangalore India

The rapid growth of electronic commerce (e-commerce) has revolutionized the way Small and Medium-sized Enterprises (SMEs) conduct business, enabling them to reach global markets and unlock new growth opportunities. However, this digital transformation has also exposed SMEs to an escalating array of cybersecurity threats that could compromise their data, financial integrity, and reputation. Blockchain technology, known for its decentralized and immutable nature, has emerged as a potential solution to enhance the security and resilience of e-commerce operations for SMEs. This research paper aims to investigate the current state of cybersecurity preparedness among SMEs engaged in e-commerce and identify the prevalent threats they face in the digital landscape. To achieve this goal, the study incorporates systematic literature review (SLR) method to examine the best technological solution to address the cybersecurity issues faced by e-commerce organizations in this paper of SMEs operating in e-commerce to gather data on their security practices, incident history, and perceptions of cybersecurity risks. In the context of this research, blockchain technology will be explored as a potential mechanism to enhance the security of e-commerce operations for SMEs. The inherent features of blockchain, such as decentralization, transparency, and immutability, could help protect sensitive data and thwart cyberattacks. By exploring the integration of blockchain into e-commerce systems, the research aims to shed light on the potential benefits and challenges associated with its adoption by SMEs. The research findings will not only shed light on the prevailing cybersecurity practices within SMEs and highlight their vulnerabilities but will also explore how blockchain technology can be leveraged to address these challenges. By understanding these patterns and exploring the feasibility of blockchain integration, the research aims to provide comprehensive recommendations for enhancing cy

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Federated Graph Learning via Constructing and Sharing Feature Spaces for Cross-Domain IoT

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IEEE Internet of Things Journal 2025年

作者： Chen, Jiale Zhuo, Shengda He, Jinchun Qiu, Wangjie Zhang, Qinnan Xiong, Zehui Zheng, Zhiming Tang, Yin Chen, Min Wang, Changdong Huang, Shuqiang Jinan University College of Cyber Security Guangzhou China Beihang University Institute of Artificial Intelligence Beijing Advanced Innovation Center for Future Blockchain and Privacy Computing Beijing100191 China Zhongguancun Laboratory Beijing China Singapore University of Technology and Design Information Systems Technology and Design 487372 Singapore Jinan University School of Management Guangzhou China South China University of Technology School of Computer Science and Engineering Guangzhou China Sun Yat-sen University School of Computer Guangzhou China Jinan University College of Cyber Security of Jinan University China Guangdong Key Laboratory of Data Security and Privacy Preserving Guangzhou China

The Internet of Things (IoT) collects large volumes of diverse data, with graph data as a critical component, and extensively utilizes Federated Graph Learning (FGL) to process this data while preserving data security. However, the graph data collected by different IoT institutions are relatively independent due to various factors (e.g., data collection methods, geographical locations), and data access is restricted to local environments due to privacy constraints, IoT institutions typically possess heterogeneous feature spaces. Aggregating under these conditions could potentially contaminate local graph representations. Unfortunately, most existing FGL methods tend to overlook this issue. To address this challenge, we propose Federated Graph Learning via Constructing and Sharing Features (FedCSF), a novel FGL framework designed to build a globally consistent feature space and share it among IoT institutions. To construct and extract the feature space, we employ an uniform feature initialization across IoT institutions and design an encoder to extract both global and local feature relationships, thereby facilitating effective collaboration across data from different IoT institutions. Furthermore, we introduce an independent adaptive aggregation strategy to eliminate the integration of harmful knowledge, ensuring that the contributions from each IoT institution are effectively integrated into the global model. We theoretically analyze the convergence of FedCSF. To validate the effectiveness of FedCSF, we conducted extensive experiments under various settings (i.e., cross-datasets, and cross-domains), demonstrating its significant advantages of FedCSF in terms of performance, convergence speed, and practical adaptability © 2014 IEEE.

关键词： Federated learning

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Performance Evaluation of Data Transfer API for Rank Level Approximate computing on HPC Systems

Performance Evaluation of Data Transfer API for Rank Level A...

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IEEE International Symposium on Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Yoshiyuki Morie Yasutaka Wada Ryohei Kobayashi Ryuichi Sakamoto Faculty of Fukuoka Medical Technology Teikyo University Fukuoka JAPAN School of Information Science Meisei University Tokyo JAPAN Center for Computational Sciences University of Tsukuba Ibaraki JAPAN Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo JAPAN

ISBN: (数字)9781665497473

ISBN: (纸本)9781665497480

Approximate computing (AC) has attracted much attention to optimize tradeoffs among performance, power con-sumption, and computation results accuracy by adjusting data precision in applications. Even on HPC systems, AC is demanded to maximize performance under the limited power budget and hardware resources. To apply AC for HPC applications, we need to consider the character of each MPI rank in an application and optimize it with its appropriate data precision. However, we also need to perform data transfer while converting the precision of the target data. This paper proposes data pack/unpack APIs, which are applicable for standard MPI programs for HPC systems, for converting the data precision of the target data, and shows its performance evaluation. We can express data transfer among ranks with different data precision with the proposed APIs. The performance evaluation reveals the break-even point to apply AC for HPC applications from the perspective of data transfer volume.

关键词： Performance evaluation Distributed processing Conferences Approximate computing Data transfer Hardware Standards

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CHECKWHY: Causal Fact Verification via Argument Structure

arXiv

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arXiv 2024年

作者： Si, Jiasheng Zhao, Yibo Zhu, Yingjie Zhu, Haiyang Lu, Wenpeng Zhou, Deyu Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology Shandong Academy of Sciences China School of Computer Science and Engineering Southeast University China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications Southeast University Ministry of Education China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science China

With the growing complexity of fact verification tasks, the concern with "thoughtful" reasoning capabilities is increasing. However, recent fact verification benchmarks mainly focus on checking a narrow scope of semantic factoids within claims and lack an explicit logical reasoning process. In this paper, we introduce CHECKWHY, a challenging dataset tailored to a novel causal fact verification task: checking the truthfulness of the causal relation within claims through rigorous reasoning steps. CHECKWHY consists of over 19K "why" claim-evidence-argument structure triplets with supports, refutes, and not enough info labels. Each argument structure is composed of connected evidence, representing the reasoning process that begins with foundational evidence and progresses toward claim establishment. Through extensive experiments on state-of-the-art models, we validate the importance of incorporating the argument structure for causal fact verification. Moreover, the automated and human evaluation of argument structure generation reveals the difficulty in producing satisfying argument structure by fine-tuned models or Chain-of-Thought prompted LLMs, leaving considerable room for future improvements1 Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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A Visualization Method for Data of InSAR Geological Subsidence Risk 2

A Visualization Method for Data of InSAR Geological Subsiden...

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2021 2nd International Conference on Machine Learning and Computer Application, ICMLCA 2021

作者： Liu, Chenxu Lu, Ming Fan, Yue Shao, Yongheng Dong, Zengshou Shenyang Institute of Computing Technology Chinese Academy of Sciences Liaoning Shenyang China University of Chinese Academy of Sciences Beijing China Infrastructure & Cloud Service SSG Lenovo Beijing China School of Electronic Information Engineering Taiyuan University of Science and Technology Shanxi Taiyuan030024 China

ISBN: (纸本)9783800757398

The ground deformation problem caused by excessive exploitation of underground resources is very prominent in our country. Nowadays, InSAR technology has become an important tool for monitoring ground deformation. In this paper, we process regular hexagonal resampling for InSAR ground subsidence data, use the cumulative amount of risk measurement points as the clustering index, and use the DBSCAN algorithm to realize the risk area *** clustering based on regular hexagonal resampling is not restricted, and the parameters of the DBSCAN algorithm are difficult to choose, both with high flexibility. This experiment compares the speed of presentation with the effect of partitioning, and the results show that our method of region division is more intuitive. The results can be displayed within millisecond response, and the presentation speed is 1297.5 times faster than direct on-page plotting. Our method has a high practical application value for InSAR geological subsidence data. © VDE VERLAG GMBH · Berlin · Offenbach.

关键词： Subsidence

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Development of Healthcare Monitoring System with Pollution Control in Industrial Sectors Using the Internet of Things

Development of Healthcare Monitoring System with Pollution C...

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International Conference on Inventive Research in computing Applications (ICIRCA)

作者： N Raja Rajeswari G Arunkumar M Senthil Vadivu Manish Gupta R Sethuraman M Rajkumar Department of Mechanical Engineering Saveetha Engineering College Chennai Tamil Nadu India Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Madanapalle Andhra Pradesh India Department of Electronics and Communication Engineering Sona College of Technology Salem Tamil Nadu India Division of Research and Development Lovely Professional University Phagwara Punjab India Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai Tamil Nadu India Department of Smart Computing School of Information Technology & Engineering Vellore Institute of Technology VIT University Vellore Tamil Nadu India

Rapid improvement in industrialization and urbanization leads to various impacts on environmental conditions. This leads to a rise in pollution that causes various constraints on human health. Earlier prediction and maintenance of pollution is the solution to obtain a greener ecosystem. This is done through machine learning techniques adopting the Internet of Things. The Internet of Things helps to obtain a smarter environment through real-time monitoring with the control system. Thus machine learning helps to analyze the amount of toxic substances that are emitted from the industries and recorded periodically. They are represented as online controlling and monitoring systems. The sensors and actuators with control devices are used to record the external environmental conditions. The prior intimation of the healthcare is monitored appropriately and intimated to solve earlier. The classification and detection of infections and diseases are done through image processing techniques. Thus the proposed system helps to obtain automatic detection of healthcare and pollution detection systems through machine learning with computational systems.

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