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IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia)

作者： Vincent Lew Kok Seng Au Thien Wan S. H. Shah Newaz Tatsuhiro Tsuchiya School of Computing and Informatics Universiti Teknologi Brunei Gadong Brunei Darussalam Graduate School of Information Science and Technology Osaka University Osaka Japan

ISBN: (数字)9798331530839

ISBN: (纸本)9798331530846

Blockchain has been paving the way for new business opportunities as we have witnessed over the last few years. It will continue to proliferate further and be adopted in increasingly diverse sectors. One area of research in Blockchain is how it can be used in Internet of Things (IoT) based applications like Unmanned Aerial Vehicles (UAV), Smart cities and Healthcare. However, with the advent of quantum technology, some widely used security algorithms in blockchain are vulnerable (i.e., quantum algorithms can exponentially solve cryptographic algorithms used to digitally sign or encrypt data), thereby diminishing communication security. Thus, post-quantum algorithms are at the forefront of research to secure data before quantum technologies can fully mature. Early standardized post-quantum algorithms such as hash-based one-time signatures, relies on the proven one-way-ness of hashing algorithm. Hash-based one-time signatures require robust secret key management systems to prevent security degradation. To safeguard IoT applications from quantum threats, this paper proposes a solution which utilizes both hash-based one-time signatures and blockchain to cover each technology's weaknesses.

关键词： Degradation Energy consumption Quantum algorithm Smart cities Medical services Bandwidth Autonomous aerial vehicles Blockchains Internet of Things Business

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Integration Communication and Detection Scheme Based on Superimposed Waveform

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IEEE Transactions on Communications 2025年

作者： Wang, Yifei Wang, Junfeng Zhou, Mingzhang Li, Dingzhao Jiang, Yu Li, Ning Fuyao University of Science and Technology School of Computing and Artificial Intelligence Fuzhou350109 China Tianjin University of Technology School of Integrated Circuit Science and Engineering Tianjin300384 China Xiamen University School of Informatics Fujian Xiamen361005 China

Integrated detection and communication (IDC) is vital for development of underwater integration technology. Existing approaches heavily rely on either communication or detection signals, requiring complex integration structures and demodulation techniques. In IDC systems, the superimposed waveforms offer enhanced flexibility, simplified waveform design and improved spectral efficiency. However, a significant challenge of superimposed waveforms is the interference between the communication and detection signal. To address this problem, a novel superimposed waveform adopting detection interference cancellation scheme based on compressed sensing is investigated in this paper. This scheme effectively eliminates detection signal and obtains residual communication signal. By combining the sparse estimation of channel impulse response with prior knowledge of detection signals, it reconstructs the detection interference at the receiver. Furthermore, we demonstrate the superiority of proposed scheme based on compressing sensing by comparing it with correlation matching estimation. Considering the influence of multi-path effects and ambient noise on residual communication signal, this paper introduces a virtual time reversal mirror (VTRM) for channel equalization and Gaussian kernel soft decision algorithm. The multi-path effects and ambient noise are modeled by the Bellhop and Middleton Class A model, respectively. Simulation results and sea trial show that the effectiveness of the proposed scheme. © 1972-2012 IEEE.

关键词： Compressed sensing

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Vision-Based PM2.5 Concentration Estimation with Natural Scene Statistical Analysis

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第6期5卷 2805-2815页

作者： Wang, Guangcheng Shi, Quan Wang, Han Gu, Ke Wei, Mengting Wong, Lai Kuan Wang, Mingxing Nantong University School of Transportation and Civil Engineering Nantong226019 China Beijing University of Technology Faculty of Information Technology Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing Laboratory of Smart Environmental Protection Beijing Artificial Intelligence Institute Beijing100124 China Chinese Academy of Sciences Institute of Software Beijing100045 China Multimedia University Faculty of Computing and Informatics Cyberjaya63100 Malaysia Environmental Science and Technology Company Ltd. Beijing102100 China

As the primary pollutant in China's urban atmosphere, particulate matter (PM)2.5 poses a great threat to the health of residents and ecological stability. An efficient and effective PM2.5 concentration monitoring is essential. Nonetheless, the popular devices for PM2.5 monitoring are developed based on two standards: the microoscillation balance method and the β-ray method, which have high purchase and maintenance costs and slow calculation rates. To this end, we put forward a real-time and reliable vision-based estimation algorithm of PM2.5 concentration. To be specific, the proposed method first develops two natural scene statistical analysis-based visual priors to measure saturation and structural information losses caused by the 'haze' formed by PM2.5. Moreover, we develop a lightweight deep belief network-deep neural network-based PM2.5 concentration estimation model, which learns the mapping from the designed visual priors to PM2.5 concentrations. Experiments confirm the superiority of our vision-based PM2.5 concentration estimation method by comparison with state-of-the-art photo-based PM2.5 monitoring methods. © 2020 IEEE.

关键词： Entropy

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SYN Flood Attack Detection and Prevention Technique (SFaDMT) in Distributed Environment 4

SYN Flood Attack Detection and Prevention Technique (SFaDMT)...

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4th International Conference on Emerging Trends in Networks and Computer Communications, ETNCC 2024

作者： Jalil Fufan, Halawati Abd Riskhan, Basheer Hussain, Khalid Jazri, Husin Bin Alajmani, Samah H. Sharma, Komal School of Computing and Informatics Albukhary International University Alor Setar Malaysia School of Computer Science Taylor's University Subang Jaya Malaysia Taif University Department of Information Technology Taif Saudi Arabia Lovely Professional University Division of Research and Development Phagwara144411 India KKR and KSR institute of technology and sciences Andhra Pradesh Guntur India

ISBN: (纸本)9798350353266

In the modern digital landscape, cyber-attacks have become highly advanced and difficult to detect, especially in distributed systems. These Denial of Service (DoS) or Distributed Denial of Service (DDoS) attacks lead to resource unavailability for legitimate users, causing financial losses for organizations. To counter these threats, researchers have proposed efficient mitigation techniques. According to Kaspersky's Q3 report, SYN Flood is the most prevalent type of D DoS attack, increasing from 76% to 81% compared to the Q2 report. DDoS attacks can be executed directly or indirectly. In a direct attack, control traffic is sent straight from the attackers to the zombies to target the victim host. Conversely, in an indirect DDoS attack, control traffic is sent indirectly to reflectors by zombies to compromise the target host. Reflectors are uncompromised systems that only respond to requests. To mitigate such attacks, traffic shaping and Pushback techniques are employed. Our research introduces an adaptive heuristic-based method called SYN Flood Attack Detection and Mitigation Technique (SFaDMT) to detect DDoS SYN flood attacks. This study proposes an efficient technique for detecting and blocking SYN attacks. The SFaDMT technique is based on a decision support mechanism. The proposed model is simulated and analyzed in the OMNET simulator, and a comparison is made with the latest technique. The results demonstrate an improvement in detection with the proposed solution. © 2024 IEEE.

关键词： Heuristic methods

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Entropic Model Predictive Optimal Transport for Underactuated Linear Systems

arXiv

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arXiv 2023年

作者： Ito, Kaito Kashima, Kenji The School of Computing Tokyo Institute of Technology Yokohama Japan The Graduate School of Informatics Kyoto University Kyoto Japan

This letter investigates dynamical optimal transport of underactuated linear systems over an infinite time horizon. In our previous work, we proposed to integrate model predictive control and the celebrated Sinkhorn algorithm to perform efficient dynamical transport of agents. However, the proposed method requires the invertibility of input matrices, which severely limits its applicability. To resolve this issue, we extend the method to (possibly underactuated) controllable linear systems. In addition, we ensure the convergence properties of the method for general controllable linear systems. The effectiveness of the proposed method is demonstrated by a numerical example. Copyright © 2023, The Authors. All rights reserved.

关键词： Linear systems

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Adaptive iterated local search algorithm for dynamic patient admission scheduling problems

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Soft computing 2025年 1-11页

作者： Turky, Ayad Sabar, Nasser R. Song, Andy Hussain, Abir Liatsis, Panos Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Science and Information Technology La Trobe University MelbourneVIC3086 Australia School of Computing Technologies RMIT University Melbourne Australia Department of Electrical Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi United Arab Emirates

Healthcare resource management is essential for ensuring the quality of patient care. However, it can be a complex and costly task. This work addresses the patient admission scheduling (PAS) problem, a complex aspect of healthcare resource management. PAS aims to allocate patients to hospital beds within a planning horizon, subject to a variety of healthcare constraints. The goal is to maximize management efficiency and patient comfort to improve medical treatment. In this work, we consider a practical variant of PAS known as dynamic PAS (DPAS). DPAS considers several factors and constraints, such as the daily registration of new patients, urgent patients, uncertainties in stay lengths, operating theatre resources, and the surgery scheduling process. An effective and efficient adaptive iterated local search (AILS) algorithm is proposed to solve DPAS. To enable the search to explore the search space efficiently, the proposed AILS adaptively integrates a number of components. Two adaptive perturbation strategies are devised to locate unexplored areas in the search space. To exploit the newly discovered areas effectively, we propose an adaptive local search mechanism as an intensification strategy to find a high-quality solution. The proposed AILS algorithm is compared to benchmark problems used by existing algorithms. The experimental results demonstrate the effectiveness and efficiency of the proposed approach. Specifically, out of 30 tested instances, AILS obtains 17 of the best-known results using less computational time. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Resource allocation

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A Knowledge Base of Argumentation Schemes for Multi-Agent Systems 26

A Knowledge Base of Argumentation Schemes for Multi-Agent Sy...

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26th International Conference on Enterprise Information Systems, ICEIS 2024

作者： Ferreira, Carlos Eduardo A. Engelmann, Débora C. Bordini, Rafael H. Carbonera, Joel Luis Panisson, Alison R. Informatics Institute Federal University of Rio Grande do Sul Porto Alegre Brazil Taquara Brazil School of Technology Pontifical Catholic University of Rio Grande do Sul Porto Alegre Brazil Department of Computing Federal University of Santa Catarina Araranguá Brazil

ISBN: (纸本)9789897586927

Argumentation constitutes one of the most significant components of human intelligence. Consequently, argumentation has played a significant role in the community of Artificial Intelligence, in which many researchers study ways to replicate this intelligent behaviour in intelligent agents. In this paper, we describe a knowledge base of argumentation schemes modelled to enable intelligent agents’ general (and domain-specific) argumentative capability. To that purpose, we developed a knowledge base that not only enables agents to reason and communicate with other software agents using a computation model of arguments, but also with humans, using a natural language representation of arguments which results from natural language templates modeled alongside their respective argumentation scheme. To illustrate our approach, we present a scenario in the legal domain where an agent employs argumentation schemes to reason about a crime, deciding whether the defendant intentionally committed the crime or not, a decision that could significantly impact the severity of the sentence handed down by a legal authority. Once a conclusion is reached, the agent provides a natural language explanation of its reasoning. Copyright © 2024 by SCITEPRESS – Science and technology Publications, Lda.

关键词： Multi agent systems

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Homogeneous Graph Extraction: An Approach to Learning Heterogeneous Graph Embedding

Homogeneous Graph Extraction: An Approach to Learning Hetero...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Shihao Gao Xiaoyan Yu Yu Cai Xulong Zhang Jianzong Wang Taisong Jin Key Laboratory of Multimedia Trusted Perception and Efficient Computing Ministry of Education of China School of Informatics Xiamen University China Ping An Technology Co. Ltd. Shenzhen China School of Computer Science and Technology Beijing Institute of Technology China School of Informatics Xiamen University China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Heterogeneous Graph Neural Networks (HGNNs) aim to embed rich structural and semantic information of heterogeneous graphs into low-dimensional node representations. While HGNNs extend the foundational work of homogeneous Graph Neural Networks, the methodology for effectively transforming heterogeneous graphs into homogeneous graphs and then learning node representations remains under-explored. In this paper, we propose a novel heterogeneous graph embedding method via the Homogeneous Graph Extraction strategy, termed HGE. Specifically, the proposed method ingeniously harnesses information clusters and metapaths to extract tailored homogeneous graphs from the complex heterogeneous graph. Subsequently, these distilled homogeneous graphs are fed into a weight-shared homogeneous graph encoder to obtain embeddings with diverse semantic information. Finally, we employ an attention mechanism, which adeptly fuses embeddings derived from distinct homogeneous graphs, resulting in the more expressive capability of the nodes. The effectiveness of the proposed architecture was demonstrated through experiments on three real heterogeneous graph datasets.

关键词： Representation learning Attention mechanisms Fuses Semantics Signal processing Graph neural networks Acoustics Data mining Speech processing

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A Review on Solar Panel Adoption in Residential Malaysia: Trends, Benefits and Technologies 2nd

A Review on Solar Panel Adoption in Residential Malaysia: Tr...

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2nd International Conference on Intelligent Manufacturing and Robotics, ICIMR 2024

作者： Quan, Teoh Chee Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Majeed, Anwar P. P. Abdul Luo, Yang Department of Smart Computing and Cyber Resilience School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Petaling Jaya Selangor Sunway City47500 Malaysia Department of Data Science and Artificial Intelligence School of Computing and Artificial Intelligence Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Engineering and Technology Sunway University No. 5 Jalan Universiti Bandar Sunway Selangor Petaling Jaya47500 Malaysia Faculty of Informatics Engineering University of Aleppo Aleppo Syria School of Intelligent Manufacturing Ecosystem Xi’an Jiaotong-Liverpool University Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

This survey provides detailed perspectives on Malaysian solar energy system adoption in relation to the economic outcome and environmental and policy context. This work has been done by using comprehensive keyword lists and inclusion criteria to confirm that the findings of this survey capture as many relevant publications as possible from the research period from 2010 to 2024. Unlike numerous papers that investigate such systems with mere commercial or industrial perspective, this survey aims at assessing residential installation, and providing accurate cost benefit analysis while showing the business belief of such schemes. It also examines various opinions, especially the Malaysian government policies and strategic incentives and staking its success on public awareness and acceptance. We equally allocate a considerable portion of this survey to discussing the environmental effects and the life cycle analyses comparing solar energy with other energy types. Notably, the survey is also under sustainable development goal 7 with the goal of achieving universal access to affordable reliable, sustainable and modern energy. Such a focus also makes the identified topics relevant for Malaysia, which is useful for references in subsequent projects and activities. These would include use of web-survey, the coverage on the residential sector, the ability to incorporate policy analysis and emphasis on sustainability to the study on solar energy usage in residential Malaysia. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Clean energy

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Explainable Hybrid Model for Presaging Tumor Stage Classification with Survival Survey

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SN Computer Science 2025年第4期6卷 1-24页

作者： Manna, Sweta Mistry, Sujoy Zhao, Zhongming Dahal, Keshav Department of Computer Science and Engineering Maulana Abul Kalam Azad University of Technology Nadia W.B Kolkata 741249 India Center for Precision Health School of Biomedical Informatics The University of Texas Health Science Center at Houston Houston TX United States School of Engineering and Computing University of the West of Scotland Paisley PA1 2BE United Kingdom

Tumor stage classification with a survival survey during detection time is the most crucial part of cancer treatment. The survivability period is directly associated with the early detection of the stage. This study developed a hybrid model to accurately classify the tumor (T) stages with the survivability analysis. The study aims to provide an explainable Artificial Intelligence framework (such as SHAP, and SHAPASH), through which the trustability and interpretability of the proposed model can increase. Along with the XAI, this hybrid approach uses two statistical models, ANOVA and LASSO with the standard TNM (tumor, node, metastasis) method for the T-stage classification. Afterward, the survivability analysis for each T stage is shown by the Kaplan–Meier (KM) method, proportion hazard ratio (Cox). Where KM is used to represent the survivability of individual tumor stages in months and the Cox regression shows the risk factors of the events. Initially, the proposed model was examined on a full dataset that contained malignant and benign data for evaluating the performance of the model, therefore stage classification for T1, T2, and T3 performs for malignant data. The K-fold and repeated-stratified K-fold cross-validation are used to measure the accuracy of the whole dataset for lung and breast cancer. For lung cancer, maximum accuracy was attained by MLP–97.64 in K-fold cross-validation. In breast cancer maximum accuracy achieved by GPC-99.57 in repeated-stratified K-fold cross validation. The results of both datasets are compared with the state-of-the-art models. The study contributed by representing the significance of individual features for tumor staging using the advanced approach of XAI, the SHAPASH interface. The accuracy of the T1, T2, and T3 stages based on malignant data was demonstrated with various classifier models with cross-validation. Finally, it is expected that executing that approach will improve patient outcomes. By using, advanced artificial in

关键词： ANOVA Breast cancer Deep learning Explainable artificial intelligence F-score Gaussian process classification LASSO Lung cancer Machine learning Random-Forest Shapely additive explanation XGBoost

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