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5th International Conference on Advancements in Computing, ICAC 2023

作者： Mudunkotuwa, Kavindi Senadeera, Chamath Gunawardana, Beuni Pathiraja, Onara Wijendra, Dinuka Fernando, Harinda Sri Lanka Institute of Information Technology Department of Information Technology Malabe Sri Lanka Sri Lanka Institute of Information Technology Department of Computer Systems and Network Engineering Malabe Sri Lanka

ISBN: (纸本)9798350358131

This study presents a comprehensive assessment of water quality in Sri Lankan reservoirs using available data sources. The study focuses on the identification of microorganisms through image analysis, watercolor analysis in satellite-based images detection of water quality, and the inference of water quality index using relevant physical parameters. By analyzing images, the study quantifies microorganisms, providing insights into microbial diversity and potential risks. Image processing techniques enable the identification of contaminants based on watercolor analysis. Utilizing satellite data, it examines variations in water quality parameters, providing a comprehensive assessment of overall water quality. The derived water quality index, using relevant physical parameters, quantitatively assesses and compares water quality across different reservoirs. The findings contribute to understanding water quality in Sri Lankan reservoirs, supporting informed decision-making and sustainable water resource management. © 2023 IEEE.

关键词： Water quality

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DTGCN: a method combining dependency tree and graph convolutional networks for Chinese long-interval named entity relationship extraction

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Journal of Ambient Intelligence and Humanized Computing 2023年第10期14卷 13361-13373页

作者： Li, Taoyuan Ma, Liangli Qin, Jiwei Ren, Wei College of Electronic Engineering Naval University of Engineering Wuhan China School of Computer Science China University of Geosciences Wuhan China Key Laboratory of Network Assessment Technology CAS Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China Henan Key Laboratory of Network Cryptography Technology Zhengzhou China

The extraction of relationships between military entities and descriptive entities is a key task for automated construction of military knowledge graphs. This category of text unstructured imposed problems such as unfixed entity length, possibly overlong intervals between entities, and excessive pronouns. Many studies rely on traditional pattern matching and statistical learning methods. Thanks to the expansion of data volume, deep learning methods are generally used to address similar problems. However, the traditional single neural network model lacks the ability to extract the relationship information of long-interval entities and cannot tackle the above problems well. To solve these problems, a new named entity relationship extraction model called DTGCN is proposed combining dependency tree and GCN (Graph Convolutional networks). By comparing with traditional models, the experimental results demonstrate that the method proposed in this paper can enhance F1-value by 2.28–11.11% and can provide support for the extraction of Chinese long-interval relationships. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Knowledge graph

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FedIERF: Federated Incremental Extremely Random Forest for Wearable Health Monitoring

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Journal of computer Science & technology 2023年第5期38卷 970-984页

作者：胡春雨忽丽莎袁林陆佃杰吕蕾陈益强 Key Laboratory of Computing Power Network and Information Security Ministry of EducationShandong Computer Science Center(National Supercomputer Center in Jinan)Qilu University of Technology(Shandong Academy of Sciences)Jinan 250353China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer ScienceJinan 250000China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan 25000China Institute of Information Technology Hebei University of Economics and BusinessShijiazhuang 050061China School of Information Science and Engineering Shandong Normal UniversityJinan 250358China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China

Wearable health monitoring is a crucial technical tool that offers early warning for chronic diseases due to its superior portability and low power ***,most wearable health data is distributed across dfferent organizations,such as hospitals,research institutes,and companies,and can only be accessed by the owners of the data in compliance with data privacy *** first challenge addressed in this paper is communicating in a privacy-preserving manner among different *** second technical challenge is handling the dynamic expansion of the federation without model *** address the first challenge,we propose a horizontal federated learning method called Federated Extremely Random Forest(FedERF).Its contribution-based splitting score computing mechanism significantly mitigates the impact of privacy protection constraints on model *** on FedERF,we present a federated incremental learning method called Federated Incremental Extremely Random Forest(FedIERF)to address the second technical *** introduces a hardness-driven weighting mechanism and an importance-based updating scheme to update the existing federated model *** experiments show that FedERF achieves comparable performance with non-federated methods,and FedIERF effectively addresses the dynamic expansion of the *** opens up opportunities for cooperation between different organizations in wearable health monitoring.

关键词： federated learning incremental learning random forest wearable health monitoring

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Triple:The Interpretable Deep Learning Anomaly Detection Framework based on Trace-Metric-Log of Microservice 31

Triple:The Interpretable Deep Learning Anomaly Detection Fra...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Ren, Rui Wang, Yang Liu, Fengrui Li, Zhenyu Xie, Gaogang Institute of Computing Technology Cas China University of Chinese Academy of Sciences China Cas Computer Network Information Center China

ISBN: (纸本)9798350399738

Existing anomaly detection approaches based on deep-learning just could simultaneously dig out key information from two dimensions in the traces, metrics or logs. Besides, they just output simple binary result, which ignores the key artificial statement information in the log. In this paper, we propose Triple, an interpretable anomaly detection approach based on deep learning for microservice system. More importantly, Triple aims to help engineers to establish trust in the system decision from key metrics and the artificial statements in logs. Triple leverages graph representation to describe the complicated dependency relationship in the traces with the logs and metrics embedded into the node features. Based on the graph representation, Triple trains a Spatial-Temporal Graph Convolutional network(STGCN) to capture the key information and generate decision boundary by deep SVDD, which detects the system's anomaly. In addition, we design an interpreter to transfer the simple binary result into a humanly understandable result, including log, metrics and trace, to facilitate engineers' understanding and handling of the incoming incident. Our work has four aims. First, to the best of our knowledge, we are the first to simultaneously apply three data sources to finish anomaly detection in the domain. Second, we design a new anomaly detection method that is an STGCN based on SVDD. Third, we design an interpreter that makes the decision not only a simple binary result. The interpretable result could capture the key artificial statement information in the log and assist engineers in incident troubleshooting. Finally, we design a series experiments to validate our method's effectiveness in the real-world system's dataset. Our results show that Triple consistently achieves improvements over other state-of-the-art models by 11%-65%. © 2023 IEEE.

关键词： Anomaly detection

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ML-based Intrusion Detection as a Service: Traffic Split Offloading and Cost Allocation in a Multi-tier Architecture

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IEEE Transactions on Services Computing 2025年第3期18卷 1557-1572页

作者： Sudyana, Didik Lai, Yuan-Cheng Lin, Ying-Dar Cholda, Piotr National Cheng Kung University Computer and Network Center Tainan701 Taiwan National Taiwan University of Science and Technology Department of Information Management Taipei106 Taiwan National Yang Ming Chiao Tung University Department of Computer Science Hsinchu300 Taiwan AGH University of Krakow Institute of Telecommunications Kraków Poland

An Intrusion Detection System (IDS) employing machine learning (ML) solutions is crucial for identifying network intrusions. To minimize operational expenses and enhance performance, enterprises have begun outsourcing IDS management to service providers, giving rise to the concept of Intrusion Detection as a Service (IDaS). Earlier research primarily aimed at enhancing the accuracy of ML-based IDS models or expediting their computational process. However, from the service provider's perspective, an optimal architecture ensuring minimal computation cost and processing delay is crucial to increasing revenue. This study evaluates the performance of IDaS in a multi-tier architecture, utilizing traffic split offloading to enhance performance by mapping three in-sequence ML-based IDS tasks (pre-processing, binary detection, multi-class classification) to the architectures as the offloading destinations. We employ a simulated annealing-based traffic offloading and cost allocation (SA-TOCA) algorithm to determine the offloading ratio for each traffic path and the cost requirements for each tier. The results indicate that the edge-cloud architecture is 15% and four times more cost-effective compared to the fog-edge and fog-cloud architectures, respectively, and it demonstrates superior performance in minimizing processing delays. Offloading the majority of traffic to the edge and the remainder to the cloud proves to be an efficient strategy, reducing both computation costs and average delays. © 2008-2012 IEEE.

关键词： Computation offloading

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Cognition:Accurate and Consistent Linear Log Parsing Using Template Correction

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Journal of computer Science & technology 2023年第5期38卷 1036-1050页

作者：田冉刁祖龙姜海洋谢高岗 University of Chinese Academy of Sciences Beijing 100049China Institute of Computing Technology Chinese Academy of SciencesBeijing 100190China Purple Mountain Laboratories Nanjing 211111China Computer Network Information Center Chinese Academy of SciencesBeijing 100083China

Logs contain runtime information for both systems and *** many of them use natural language,a typical log-based analysis needs to parse logs into the structured format *** parsing approaches often take two *** first step is to find similar words(tokens)or ***,parsers extract log templates by replacing different tokens with variable ***,we observe that most parsers concentrate on precisely grouping similar tokens or *** they do not have a well-designed template extraction process,which leads to inconsistent accuracy on particular *** root cause is the ambiguous definition of variable placeholders and similar *** consequences include abuse of variable placeholders,incorrectly divided templates,and an excessive number of templates over *** this paper,we propose our online log parsing approach *** redefines variable placeholders via a strict lower bound to avoid ambiguity ***,it applies our template correction technique to merge and absorb similar *** eliminates the interference of commonly used parameters and thus isolates template *** through 16 public datasets shows that Cognition has better accuracy and consistency than the state-of-the-art *** also saves up to 52.1%of time cost on average than the others.

关键词： log analysis log parsing template correction

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Enhancing students' learning achievements, self-efficacy, and motivation using mobile augmented reality

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Journal of computer Assisted Learning 2024年第4期40卷 1823-1837页

作者： Aldeeb, Fatimah H. Sallabi, Omar M. Elaish, Monther M. Hwang, Gwo-Jen Computer Network College of Computer Technology Benghazi Libya Computer Science The Libyan Academy Benghazi Libya Software Engineering Information Technology Faculty University of Benghazi Benghazi Libya Computer Science Information Technology Faculty University of Benghazi Benghazi Libya Graduate Institute of Educational Information and Measurement National Taichung University of Education Taichung City Taiwan Graduate Institute of Digital Learning and Education National Taiwan University of Science and Technology Taipei City Taiwan Yuan Ze University Taoyuan City Taiwan

Background: This paper examines the use of augmented reality (AR) as a concept-association tool in schools, with the aim of enhancing primary school students' learning outcomes and engagement. Conflicting findings exist in previous studies regarding the cognitive load of AR-enriched learning, with some reporting reduced load and others indicating increased demand and poorer performance. Understanding these implications is essential for effectively leveraging AR in education. The study offers a fresh perspective on the potential of AR technology in improving educational experiences. Objectives: The primary goal of this research was to evaluate the effectiveness of an AR-assisted concept-association strategy for improving essential knowledge acquisition and skills outcomes, increasing cognitive load, and increasing self-efficacy and learning motivation among primary school students. Methods: A quasi-experimental design with a control group was employed to investigate the effectiveness of the intervention. Results and Conclusions: The results demonstrate that the implementation of the AR-assisted concept-association strategy effectively enhanced essential knowledge acquisition and skills outcomes, increased cognitive load, and increased self-efficacy and learning motivation among primary school students. These findings highlight the potential of AR technology to improve the learning experience and engagement of primary school students. The study contributes to the existing literature on the effectiveness of AR technology in education, suggesting future research directions. Ultimately, it offers a practical solution for improving the learning experience by presenting a new approach for teaching using AR technology. © 2024 John Wiley & Sons Ltd.

关键词： augmented reality biology education cognitive load learning motivation mobile learning self-efficacy

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Dysarthric speech recognition: an investigation on using depthwise separable convolutions and residual connections

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Neural Computing and Applications 2025年第12期37卷 7991-8005页

作者： Shahamiri, Seyed Reza Mandal, Krishnendu Sarkar, Sudeshna DeepNet Discovery Network Faculty of Engineering and Design University of Auckland Private Bag 92019 Building 405 Level 6 Room 669 3 Garfton Road Auckland1142 New Zealand Computer Science and Engineering Department Indian Institute of Technology Kharagpur India

Dysarthria is a neurological condition resulting from impairments affecting muscle control involved in speech articulation, leading to reduced intelligibility or unintelligible speech, which affects communication abilities. Although Automatic Speech Recognition (ASR) technologies hold the potential to improve the lives of people with dysarthria significantly, ASR systems designed for normal speech have shown limited effectiveness when presented with impaired speech. Consequently, researchers have focused on developing ASR systems specifically tailored for dysarthria. However, progress in this area has been gradual due to the scarcity of dysarthric speech for training and the increased variability of speech among dysarthric individuals, necessitating a larger dataset of dysarthric utterances. One potential solution to enhance the robustness of dysarthric ASR is to deepen the architecture of the acoustic model, which maps the speech signal to words or phonetic units. However, deeper architectures require more training data and pose challenges in dealing with issues such as the vanishing gradient problem and representational bottlenecks in deep learning models. In this study, we expanded on our previous findings and investigated the applications of Depthwise Separable Convolution neurons and the inclusion of Residual Connections to propose a deep dysarthric acoustic model, tackling both vanishing gradients and representational bottleneck issues in dysarthric ASR. Multiple speaker-adaptive dysarthric ASRs were trained and evaluated for 15 UA-Speech dysarthric subjects, then benchmarked against the state-of-the-art and our previous dysarthric ASRs. Our proposed architectures have delivered up to 22.58% word recognition rate (WRR) improvements over the reference models. We observed an average of 10.81% better WRRs over the base traditional dysarthric ASR for all speakers. Likewise, the proposed acoustic model outperformed the state-of-the-art Transformer-based dysarthric

关键词： Convolution

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Scale-Based Slicer Placement in Elastic Optical networks

Scale-Based Slicer Placement in Elastic Optical Networks

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2023 Opto-Electronics and Communications Conference, OECC 2023

作者： Kitsuwan, Nattapong Pavarangkoon, Praphan The University of Electro-Communications Department of Computer and Network Engineering Tokyo Japan King Mongkut's Institute of Technology Ladkrabang School of Information Technology Bangkok Thailand

ISBN: (纸本)9781665462136

This paper proposes a scale-based slicer placement scheme to reduce bandwidth blocking rate (BBR) in elastic optical networks (EONs). In EONs, a slicing-stitching technology is adopted to overcome a spectrum slot allocation rule that the spectrum slots of the same request must be consecutive on the same link. This technology is done by slicers to split a spectrum band into multiple sub-spectrum components. In EONs with limited slicers, determining equally the number of slicers on every node may not result in a low BBR since the traffic volume on each node is not the same. We investigate and determine the number of slicers needed for each node as a scale-based policy. The simulation results show that our scheme achieves a BBR reduction of 91%, compared to the conventional scheme. © 2023 IEEE.

关键词： Fiber optic networks

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Achieving Fuzzy Matching Data Sharing for Secure Cloud-Edge Communication

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China Communications 2022年第7期19卷 257-276页

作者： Chuan Zhang Mingyang Zhao Yuhua Xu Tong Wu Yanwei Li Liehuang Zhu Haotian Wang School of Cyberspace Science and Technology Beijing Institute of TechnologyBeijing 100081China School of Computer Science and Technology Beijing Institute of TechnologyBeijing 100081China Yangtze Delta Region Academy of Beijing Institute of Technology Jiaxing 314019China National Computer Network Emergency Response Technical Team/Coordination Center of China Beijing 100029China College of Arts and Science University of PennsylvaniaPhiladelphia 19104USA

In this paper,we propose a novel fuzzy matching data sharing scheme named FADS for cloudedge *** allows users to specify their access policies,and enables receivers to obtain the data transmitted by the senders if and only if the two sides meet their defined certain policies ***,we first formalize the definition and security models of fuzzy matching data sharing in cloud-edge ***,we construct a concrete instantiation by pairing-based cryptosystem and the privacy-preserving set intersection on attribute sets from both sides to construct a concurrent matching over the *** the matching succeeds,the data can be ***,nothing will be *** addition,FADS allows users to dynamically specify the policy for each time,which is an urgent demand in practice.A thorough security analysis demonstrates that FADS is of provable security under indistinguishable chosen ciphertext attack(IND-CCA)in random oracle model against probabilistic polynomial-time(PPT)adversary,and the desirable security properties of privacy and authenticity are *** experiments provide evidence that FADS is with acceptable efficiency.

关键词： fuzzy-matching privacy-preserving set intersection cloud-edge communication data sharing

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