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12th IEEE Conference on systems, Process and Control, ICSPC 2024

作者： Li, Brandon Kah Wei Jasser, Muhammed Basheer Issa, Bayan Ajibade, Samuel-Soma M. Chua, Hui Na Wong, Richard T.K. Hamzah, Muzaffar Sunway University No. 5 Jalan Universiti School of Engineering and Technology Department of Computing and Information Systems Selangor Darul Ehsan Bandar Sunway47500 Malaysia School of Engineering and Technology Selangor Darul Ehsan Bandar Sunway47500 Malaysia University of Aleppo Faculty of Informatics Engineering Syria Istanbul Ticaret University Department of Computer Engineering Istanbul Turkey Universiti Malaysia Sabah Faculty of Computing and Informatics Sabah Kota Kinabalu88450 Malaysia

ISBN: (纸本)9798350391398

Satellite images that are publicly available on the internet such as Google Maps are mostly low-quality and blurry which is not very useful for research or decision-making due to the lack of details. Recently image upscaling has been increasing in popularity to address several real-world problems where one approach is using Convolutional Neural Networks (CNNs). However, CNNs have difficulty in restoring fine details and this has led to the use of Generative adversarial networks (GANs) for image scaling. In this paper, three GAN models are trained which are Enhanced Super-Resolution Generative Adversarial Networks (Real-ESRGAN), Image Restoration Using Swin Transformer (SwinIR-Medium), and Dual Aggregation Transformer (DAT-2). The models are evaluated with well-known image metrics to determine each model's effectiveness in upscaling low-resolution satellite images. The results show that all models can reconstruct the image to some degree where the best out of the three is DAT-2. Further fine-tuning of the best model by changing the loss functions has shown improvements in structural integrity for the upscaled image. © 2024 IEEE.

关键词： Generative adversarial networks

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Energy Theft Detection in Smart Grids:Taxonomy,Comparative Analysis,Challenges,and Future Research Directions

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IEEE/CAA Journal of Automatica Sinica 2022年第4期9卷 578-600页

作者： Mohsin Ahmed Abid Khan Mansoor Ahmed Mouzna Tahir Gwanggil Jeon Giancarlo Fortino Francesco Piccialli the Department of Computer Science COMSATS University Islamabad the Department of Computer Science School of ComputingEngineering and Digital TechnologiesTeesside University the Innovative Value Institute Maynooth University the Department of Computer Science Bahria University the School of Electronic Engineering Xidian University the Department of Embedded Systems Engineering Incheon National University IEEE the Department of Informatics ModelingElectronics and SystemsUniversity of Calabria the Department of Mathematics and Applications“R.Caccioppoli” University of Naples FedericoⅡ

Electricity theft is one of the major issues in developing countries which is affecting their economy *** with the introduction of emerging technologies,this issue became more *** many new energy theft detection(ETD)techniques have been proposed by utilising different data mining(DM)techniques,state&network(S&N)based techniques,and game theory(GT)***,a detailed survey is presented where many state-of-the-art ETD techniques are studied and analysed for their strengths and *** levels of taxonomy are presented to classify state-of-the-art ETD *** types and ways of energy theft and their consequences are studied and summarised and different parameters to benchmark the performance of proposed techniques are extracted from *** challenges of different ETD techniques and their mitigation are suggested for future *** is observed that the literature on ETD lacks knowledge management techniques that can be more effective,not only for ETD but also for theft *** can help in the prevention of energy theft,in the future,as well as for ETD.

关键词： Challenges comparative analysis energy theft detection future research directions smart grid taxonomy

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Bayesian estimation-based sentiment word embedding model for sentiment analysis

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CAAI Transactions on Intelligence Technology 2022年第2期7卷 144-155页

作者： Jingyao Tang Yun Xue Ziwen Wang Shaoyang Hu Tao Gong Yinong Chen Haoliang Zhao Luwei Xiao Guangdong Provincial Key Laboratory of Quantum Engineering and Quantum Materials School of Physics and Telecommunication EngineeringSouth China Normal UniversityGuangzhouChina College of Mathematics and Informatics&College of Software Engineering South China Agricultural UniversityGuangzhouChina School of Foreign Languages Zhejiang University of Finance&EconomicsHangzhouZhejiangChina Educational Testing Service PrincetonNew JerseyUSA School of Computing Informatics and Decision Systems EngineeringArizona State UniversityTempeUSA

Sentiment word embedding has been extensively studied and used in sentiment analysis ***,most existing models have failed to differentiate high-frequency and low-frequency ***,the sentiment information of low-frequency words is insufficiently captured,thus resulting in inaccurate sentiment word embedding and degradation of overall performance of sentiment analysis.A Bayesian estimation-based sentiment word embedding(BESWE)model,which aims to precisely extract the sentiment information of low-frequency words,has been *** the model,a Bayesian estimator is constructed based on the co-occurrence probabilities and sentiment proba-bilities of words,and a novel loss function is defined for sentiment word embedding *** experimental results based on the sentiment lexicons and Movie Review dataset show that BESWE outperforms many state-of-the-art methods,for example,C&W,CBOW,GloVe,SE-HyRank and DLJT1,in sentiment analysis tasks,which demonstrate that Bayesian estimation can effectively capture the sentiment information of low-frequency words and integrate the sentiment information into the word embedding through the loss *** addition,replacing the embedding of low-frequency words in the state-of-the-art methods with BESWE can significantly improve the performance of those methods in sentiment analysis tasks.

关键词： function embedding estimation

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Evaluating the Effectiveness and Efficiency of A Website Using Cognitive Walkthrough Method 11

Evaluating the Effectiveness and Efficiency of A Website Usi...

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11th International Conference on Cyber and IT Service Management, CITSM 2023

作者： Haryanto, Tri Sholihah, Mar'Atus Yuniarto, Dwi Sopandi, Ajang Kaffah, Faiz Muqorrir Subiyakto, Aang Asia e University School of Science Technology Kuala Lumpur Malaysia Uin Syarif Hidayatullah Jakarta Department of Information Systems South Tangerang Indonesia Sebelas April University Department of Informatics Engineering Sumedang Indonesia University of Muhammadiyah Tangerang Department of Informatics Engineering Tangerang Indonesia Iain Syekh Nurjati Cirebon Department of Informatics Engineering Cirebon Indonesia

ISBN: (纸本)9798350305968

The quality of a website is said to be good if the display has a combination of elements consisting of system design, content on the system, navigation, and functionality on the system so that the website layout is aligned and balanced. Therefore, an assessment is needed from the user side who wants to see if the website is good or still some bugs that can be fixed later. The method used in this research is a cognitive walkthrough, which involves users in assessing the usability of a website. This research aimed to identify and analyze problems that users face accessing the sampled website and also provide recommendations and suggestions for improving the user interface. The results show that the proportion the effectiveness and efficiency obtained by 64% and 66.31%. It is below the standard set by Jeff Sauro of 78%. Besides that, the majority of participants failed in the fourth task scenario (ST4: finding address and contact us information) and found errors in the second task scenario (ST2: searching for company profile information) and the third scenario (ST3: finding product information). It was found that the proportion of error rates in the ST2 and ST4 are 40% and 100%. Moreover, the ST1 and ST3 task scenarios have error rates below average, by 0% and 13.33% respectively. © 2023 IEEE.

关键词： Efficiency

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Iterative Design of a Decision Support System for Fall Risk Detection in residential care facilities

Iterative Design of a Decision Support System for Fall Risk ...

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作者： Tschorn, Niklas Keuchel, Maren Müller, Inga Milkov, Sarah Potthoff, Christian Windrath, Dioselina Weber, Yvonne Meister, Sven Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Dortmund / Health Care Informatics Faculty of Health School of Medicine Witten/Herdecke University Speicherstraße 6 Witten Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany HS Gesundheit Bochum Bochum Germany Diakonie Michaelshoven Cologne Germany Connext Communication GmbH Paderborn Germany Health Care Informatics Faculty of Health School of Medicine Witten/Herdecke University Witten Germany

Nursing care jobs are predominantly described as burdensome. In particular, the workload and the time available to deal with it are in a state of imbalance. Nurses are very often under time pressure and have to make decisions under psychological, emotional and physical stresses. Machine learning methods and in particular, Decision Support systems (DSS) can be used to support nurses with improved information (e.g. visualization of individual health risks) for decisions that are not easy to make. As falls are one of the most common health problems in care facilities, we will present a concept of a DSS for the prevention of falls that was developed in the PFLIP research project. We explain our user-centered design process based on ISO 9241-210-2019. User feedback is obtained at each phase so that nurses can identify problems at an early stage. The result is a conceptual design, visualized as a click dummy, to identify individual fall risks and predict preventive measures to reduce the risk of falling. © 2024 The Author(s), published by De Gruyter.

关键词： Electronic health record

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Generating robotic elliptical excisions with human-like tool-tissue interactions

Generating robotic elliptical excisions with human-like tool...

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IEEE International Conference on Robotics and Automation (ICRA)

作者： Artūras Straižys Michael Burke Subramanian Ramamoorthy School of Informatics University of Edinburgh Department of Electrical and Computer Systems Engineering Monash University

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

In surgery, the application of appropriate force levels is critical for the success and safety of a given procedure. While many studies are focused on measuring in situ forces, little attention has been devoted to relating these observed forces to surgical techniques. Answering questions like "Can certain changes to a surgical technique result in lower forces and increased safety margins?" could lead to improved surgical practice, and importantly, patient outcomes. However, such studies would require a large number of trials and professional surgeons, which is generally impractical to arrange. Instead, we show how robots can learn several variations of a surgical technique from a smaller number of surgical demonstrations and interpolate learnt behaviour via a parameterised skill model. This enables a large number of trials to be performed by a robotic system and the analysis of surgical techniques and their downstream effects on tissue. Here, we introduce a parameterised model of the elliptical excision skill and apply a Bayesian optimisation scheme to optimise the excision behaviour with respect to expert ratings, as well as individual characteristics of excision forces. Results show that the proposed framework can successfully align the generated robot behaviour with subjects across varying levels of proficiency in terms of excision forces.

关键词： Force measurement Force Surgery Safety Bayes methods Robots Optimization

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A Comprehensive Review of Redirected Walking Techniques:Taxonomy,Methods,and Future Directions

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Journal of Computer Science & Technology 2022年第3期37卷 561-583页

作者： Yi-Jun Li Frank Steinicke Miao Wang State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Department of Informatics Universität HamburgHamburg 20146Germany Peng Cheng Laboratory Shenzhen 518055China

Virtual reality(VR)allows users to explore and experience a computer-simulated virtual environment so that VR users can be immersed in a totally artificial virtual world and interact with arbitrary virtual ***,the limited physical tracking space usually restricts the exploration of large virtual spaces,and VR users have to use special locomotion techniques to move from one location to *** these techniques,redirected walking(RDW)is one of the most natural locomotion techniques to solve the problem based on near-natural walking *** core idea of the RDW technique is to imperceptibly guide users on virtual paths,which might vary from the paths they physically walk in the real *** a similar way,some RDW algorithms imperceptibly change the structure and layout of the virtual environment such that the virtual environment fits into the tracking *** this survey,we first present a taxonomy of existing RDW *** on this taxonomy,we compare and analyze both contributions and shortcomings of the existing methods in detail,and find view manipulation methods offer satisfactory visual effect but the experience can be interrupted when users reach the physical boundaries,while virtual environment manipulation methods can provide users with consistent movement but have limited application ***,we discuss possible future research directions,indicating combining artificial intelligence with this area will be effective and intriguing.

关键词： virtual reality locomotion redirected walking

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SGNR: A Social Graph Neural Network Based Interactive Recommendation Scheme for E-Commerce

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Tsinghua Science and Technology 2023年第4期28卷 786-798页

作者： Dehua Ma Yufeng Wang Jianhua Ma Qun Jin School of Communications and lnformation Engineering Nanjing University of Posts and TelecommunicationsNanjing 210003China Department the Faculty of Computer and Information SciencesHosei UniversityTokyo 194-0298Japan Networked Information Systems Laboratory Department of Human Informatics and Cognitive SciencesFaculty of Human SciencesWaseda UniversityTokyo 169-8050Japan

Interactive Recommendation(IR)formulates the recommendation as a multi-step decision-making process which can actively utilize the individuals’feedback in multiple steps and optimize the long-term user benefit of *** Reinforcement Learning(DRL)has witnessed great application in IR for ***,user cold-start problem impairs the learning process of the DRL-based recommendation ***,most existing DRL-based recommendations ignore user relationships or only consider the single-hop social relationships,which cannot fully utilize the social *** fact that those schemes can not capture the multiple-hop social relationships among users in IR will result in a sub-optimal *** address the above issues,this paper proposes a Social Graph Neural network-based interactive Recommendation scheme(SGNR),which is a multiple-hop social relationships enhanced DRL *** this framework,the multiple-hop social relationships among users are extracted from the social network via the graph neural network which can sufficiently take advantage of the social network to provide more personalized recommendations and effectively alleviate the user cold-start *** experimental results on two real-world datasets demonstrate that the proposed SGNR outperforms other state-of-the-art DRL-based methods that fail to consider social relationships or only consider single-hop social relationships.

关键词： Interactive Recommendation(IR) Deep Reinforcement Learning(DRL) Graph Neural Network(GNN)

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Distributed intelligence for IoT-based smart cities: a survey

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Neural Computing and Applications 2024年第27期36卷 16621-16656页

作者： Hashem, Ibrahim Abaker Siddiqa, Aisha Alaba, Fadele Ayotunde Bilal, Muhammad Alhashmi, Saadat Mehmood Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates School of Computing College of Arts and Sciences Universiti Utara Malaysia Kedah Sintok06010 Malaysia Department of Computer Science Federal College of Education Zaria Kaduna Tudun Wada810282 Nigeria Department of Pharmaceutical Outcomes and Policy University of Florida GainesvilleFL32610 United States Department of Computing and Information Systems School of Engineering and Technology Sunway University Selangor Petaling Jaya47500 Malaysia Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah27272 United Arab Emirates

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Smart city

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Can we trust the evaluation on ChatGPT? 3

Can we trust the evaluation on ChatGPT?

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3rd Workshop on Trustworthy Natural Language Processing, TrustNLP 2023, co-located with ACL 2023

作者： Aiyappa, Rachith An, Jisun Kwak, Haewoon Ahn, Yong-Yeol Center for Complex Networks & Systems Luddy School of Informatics Computing & Engineering United States Indiana University Network Science Institute Indiana University BloomingtonIN47408 United States

ISBN: (纸本)9781959429869

ChatGPT, the first large language model with mass adoption, has demonstrated remarkable performance in numerous natural language tasks. Despite its evident usefulness, evaluating ChatGPT's performance in diverse problem domains remains challenging due to the closed nature of the model and its continuous updates via Reinforcement Learning from Human Feedback (RLHF). We highlight the issue of data contamination in ChatGPT evaluations, with a case study in stance detection. We discuss the challenge of preventing data contamination and ensuring fair model evaluation in the age of closed and continuously trained models. © 2023 Proceedings of the Annual Meeting of the Association for Computational Linguistics. All rights reserved.

关键词： Reinforcement learning

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