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

作者： Yarham, Sara Behjati, Mehran Alobaidy, Haider A. H. Majeed, Anwar P. P. Abdul Zheng, Yufan Department of Computing and Information Systems School of Engineering and Technology Sunway University Bandar Sunway47500 Malaysia Department of Information and Communications Engineering College of Information Engineering Al-Nahrain University Baghdad64046 Iraq Suzhou Taicang215400 China

ISBN: (纸本)9789819639489

Monitoring air quality and environmental conditions is crucial for public health and effective urban planning. Current environmental monitoring approaches often rely on centralized data collection and processing, which pose significant privacy, security, and scalability challenges. Federated Learning (FL) offers a promising solution to these limitations by enabling collaborative model training across multiple devices without sharing raw data. This decentralized approach addresses privacy concerns while still leveraging distributed data sources. This paper provides a comprehensive review of FL applications in air quality and environmental monitoring, emphasizing its effectiveness in predicting pollutants and managing environmental data. However, the paper also identifies key limitations of FL when applied in this domain, including challenges such as communication overhead, infrastructure demands, generalizability issues, computational complexity, and security vulnerabilities. For instance, communication overhead, caused by the frequent exchange of model updates between local devices and central servers, is a notable challenge. To address this, future research should focus on optimizing communication protocols and reducing the frequency of updates to lessen the burden on network resources. Additionally, the paper suggests further research directions to refine FL frameworks and enhance their applicability in real-world environmental monitoring scenarios. By synthesizing findings from existing studies, this paper highlights the potential of FL to improve air quality management while maintaining data privacy and security, and it provides valuable insights for future developments in the field. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Environmental monitoring

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A survey for light field super-resolution

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High-Confidence computing 2024年第1期4卷 118-129页

作者： Mingyuan Zhao Hao Sheng Da Yang Sizhe Wang Ruixuan Cong Zhenglong Cui Rongshan Chen Tun Wang Shuai Wang Yang Huang Jiahao Shen State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Key Laboratory of Data Science and Intelligent Computing International Innovation InstituteBeihang UniversityHangzhou 311115China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Compared to 2D imaging data,the 4D light field(LF)data retains richer scene’s structure information,which can significantly improve the computer’s perception capability,including depth estimation,semantic segmentation,and LF ***,there is a contradiction between spatial and angular resolution during the LF image acquisition *** overcome the above problem,researchers have gradually focused on the light field super-resolution(LFSR).In the traditional solutions,researchers achieved the LFSR based on various optimization frameworks,such as Bayesian and Gaussian *** learning-based methods are more popular than conventional methods because they have better performance and more robust generalization *** this paper,the present approach can mainly divided into conventional methods and deep learning-based *** discuss these two branches in light field spatial super-resolution(LFSSR),light field angular super-resolution(LFASR),and light field spatial and angular super-resolution(LFSASR),***,this paper also introduces the primary public datasets and analyzes the performance of the prevalent approaches on these ***,we discuss the potential innovations of the LFSR to propose the progress of our research field.

关键词： Light field super-resolution Convolutional neural network Transformer Sub-aperture image Epipolar-plane image

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Emotion Recognition systems Must Embrace Ambiguity 12

Emotion Recognition Systems Must Embrace Ambiguity

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12th International Conference on Affective computing and Intelligent Interaction Workshops and Demos, ACIIW 2024

作者： Wu, Jingyao Dang, Ting Sethu, Vidhyasaharan Ambikairajah, Eliathamby School of Electrical Engineering and Telecommunications University of New South Wales Sydney Australia School of Computing and Information Systems University of Melbourne Melbourne Australia

ISBN: (纸本)9798331516451

Automatic Emotion Recognition (AER) systems aim to emulate human-like perception of emotions. However, existing techniques often neglect the inherent ambiguity in emotion perception. This paper argues that this oversight fundamentally inhibits the ability of such systems to mimic human-like abilities and advocates for a more nuanced approach to emotion modeling-one that embraces ambiguity. We provide a thorough exploration of positions related to ambiguity-aware emotions, delving into both supportive and opposing viewpoints. Our argument emphasises that ambiguity should be represented through probability distributions, and continuous modelling of emotions over time is crucial to capturing human-like perceptions. By integrating these elements, AER systems can achieve more natural and effective interactions with users, better reflecting the complexities of human emotional perception. We conclude by offering guidelines for advancing ambiguity-aware emotion modeling in future research. In summary, embracing ambiguity in AER systems leads to more robust and contextually sensitive emotion recognition, enhancing their practical utility and bridging the gap between human and machine understanding. © 2024 IEEE.

关键词： emotion ambiguity emotion recognition rater variability

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INNSE: Invertible neural network-based DNA image storage with self-correction encoding

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Computational and Structural Biotechnology Journal 2025年 27卷 2492-2502页

作者： Zheng, Yanfen Zhang, Xiaokang Sun, Lijun Cao, Ben Wang, Bin Zhang, Qiang Zheng, Pan School of Computer Science and Technology Dalian University of Technology Liaoning Dalian116024 China The Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian University Liaoning Dalian116622 China Department of Accounting and Information Systems University of Canterbury Upper Riccarton Christchurch8140 New Zealand

DNA storage has become an alternative to silicon-based storage of media. However, when used to store multimodal data such as images, it fails to take full advantage of the characteristics of high correlation and variability present in such data, and currently remains inferior in terms of encoding density and time complexity. It also has the shortcoming of weak performance in terms of error correction methods to address insertion and deletion errors caused by error crosstalk. Therefore, we proposed an invertible neural network-based DNA image storage self-correction encoding method (INNSE) that uses the invertibility of network to perform up-down sampling processing on image and video data, thereby greatly reducing the number of DNA sequences required for data storage. A codebook generator was then designed to encode low-dimensional vectors into DNA sequences that meet various constraints. Finally, a self-correction method was constructed to achieve DNA sequence error correction at the base level without adding any redundant information. Our experimental results showed that, compared to the Yin-Yang algorithm, the encoding density of INNSE was increased by 3×, encoding time complexity was reduced by 95.26%. Under a simulated error rate of 2%, PSNR and SSIM were raised by 80.88% and 63.82%, respectively, proving that INNSE has significant advantages in encoding performance and error correction ability. © 2025

关键词： Network coding

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Smart Watchdog Mechanism for Fault Detection in RISC-V

Smart Watchdog Mechanism for Fault Detection in RISC-V

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IEEE International Symposium on Circuits and systems (ISCAS)

作者： David Simpson Jim Harkin Malachy McElholm Liam McDaid School of Computing Engineering and Intelligent Systems Ulster University Derry UK

ISBN: (数字)9798350356830

ISBN: (纸本)9798350356847

Modern micro-processors face reliability challenges due to manufacturing defects, aging or when operating in harsh environments like deep-sea or space. Watchdog mechanisms are essential for detecting both permanent and transient faults, but they must exhibit minimal overheads in terms of power and area consumption. Nonetheless, additional research is required to ensure the dependability of the watchdog circuit as if compromised, could pose a significant threat to the system. This paper proposes a smart watchdog paradigm based on spiking neural networks (SNNs) that could realise a reliable, low power and area efficient solution. The smart watchdog presented in this paper was trained to monitor control flow at the execute stage of a RISC-V processor with results showing high fault coverage of 98%, independent of the software application executed. The smart watchdog was able to detect faults not recognised by the trap handler of the RISC-V core. An FPGA implementation of the smart watchdog validates the in-circuit fault detection capability when deployed with a RISC-V processor.

关键词： Fault detection Process control Spiking neural networks Transforms Electrical fault detection Hardware Robustness Software reliability Transient analysis Monitoring

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Development of Fast Prediction System of User Action for a Fully Autonomous Smart Home System 4

Development of Fast Prediction System of User Action for a F...

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4th International Conference on Sentiment Analysis and Deep Learning, ICSADL 2025

作者： Rajalakshmi, N.R. Sharmila, L. Prabu, S. Veeramakali, T. Arulkumaran, G. Chourasia, Pranshu Vel Tech Rangarajan Dr.Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Tamil Nadu India School of Computing Srm Institute of Science and Technology Department of Computing Technologies Kattankulathur Chennai India School of Computing Srm Institute of Science and Technology Department of Data Science and Business Systems Kattankulathur Chennai India School of Computing and Information Technology Reva University Karnataka Bangalore560064 India

ISBN: (纸本)9798331523923

The HVAC system, energy storage building, distributed power supply, and other equipment are integrated into the scheduling algorithm, which is aimed at reducing household electricity consumption. It is also assumed that users can provide energy to the grid according to their own conditions. Taking electricity cost and comfort level as optimization targets, a home energy optimization control model for the coordinated management of hybrid energy sources is built. A smart scheduling mechanism based on the improved adaptive particle swarm optimization approach is proposed in order to derive the best time intervals for electric appliances, necessary power for the control of the room temperature for every time frame, and power for charging and discharging of the storage battery at various moments. Simulation results show that through the incorporation of distributed photovoltaic power generation, backup storage by battery, and home energy optimization control, the system efficiently balances between user comfort and electricity consumption. This offers great technical support to the development of home energy management systems. By using time-of-use electricity price for energy acquisition and supply, the optimization control goal is minimizing both power use and cost as well as preserving comfort levels. The hybrid energy management's proposed home energy optimization control model uses an adaptive particle swarm optimization algorithm to find the optimal operation schedules of the electrical appliances, the required power for temperature control in a room, and the charge/discharge power level of the storage battery at each time interval. As per the optimization principle, the proposed dynamic programming algorithm converts the multi-stage problem into a sequence of single-stage problems and solves them separately. This method successfully resolves intricate problems that cannot be addressed through greedy algorithms or divide-and-conquer. In this research, management ac

关键词： Decision making

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Perception Gaps Between Hashtag Stakeholders on X (Twitter) - Application on the Australian National Disability Insurance Scheme (#NDIS) 20

Perception Gaps Between Hashtag Stakeholders on X (Twitter) ...

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20th IEEE International Conference on e-Business engineering, ICEBE 2024

作者： Zhang, Yu Bui, Hang Thanh Zipperle, Michael Buick, Fiona Chand, Satish Hussain, Omar Wang, Min Mo, Huadong Zhang, Wenjie School of Business Unsw Canberra Canberra Australia University of Canberra Faculty of Science and Technology Canberra Australia School of Systems & Computing Unsw Canberra Canberra Australia School of Computer Science and Engineering Unsw Sydney Sydney Australia

ISBN: (纸本)9798350365856

A hashtag on X (Twitter) can attract a range of stakeholders with diverse perceptions on the hashtag. Under-standing and considering the differences between these stake-holders' perceptions on specific hashtags can be highly valuable for policy making in both public and private sectors. This paper proposes a framework to analyse the depth and width of the perception gap between stakeholders of a hashtag using sentiment analysis and zero-shot learning on large scale Twitter data. The framework was applied to the hashtag '#NDIS', the National Disability Insurance Scheme, Australia's pilot personalised disability service which provides funding to support people with disability. Over 155,000 tweets were collected and analysed to identify and assess the perception gap between the two most significant stakeholders (NDIS participants and service providers) on six main topics during the implementation of the NDIS. The study verified the effectiveness of the proposed framework, revealing a widespread perception gap between the two NDIS stakeholders, as well as differences in their online behaviours using Twitter. The results suggest that the NDIS policymakers and service providers should pay attention to the utilisation of social media, including hearing voice from the broader public and creating channels for transparent engagement with other NDIS stakeholders. © 2024 IEEE.

关键词： Tweets

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Trustworthy DNN partition for blockchain-enabled digital twin in wireless IIoT networks

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Science China(Information Sciences) 2024年第11期67卷 373-374页

作者： Xiumei DENG Jun LI Long SHI Kang WEI Ming DING Yumeng SHAO Wen CHEN Shi JIN Pillar of Information Systems Technology and Design Singapore University of Technology and Design National Mobile Communications Research Laboratory Southeast University School of Electronic and Optical Engineering Nanjing University of Science and Technology Department of Computing Hong Kong Polytechnic University Data61 CSIRO Department of Electronics Engineering Shanghai Jiao Tong University

The integration of artificial intelligence(AI) and digital twin(DT) technology has revolutionized the industrial Internet of Things(IIoT), enabling advanced automation and intelligent manufacturing [1]. Through sophisticated interactions between physical entities and their virtual counterparts,AI-driven DTs facilitate performance monitoring, analysis,simulation, and optimization of physical assets, enabling predictive maintenance and informed decision-making [2].

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A Swarm Optimization Method for Gas Plume Boundary Detection and Monitoring 4

A Swarm Optimization Method for Gas Plume Boundary Detection...

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4th International Conference on Robotics, Automation, and Artificial Intelligence, RAAI 2024

作者： Meselhi, Mohamed Garratt, Matt Anavatti, Sreenatha Kasmarik, Kathryn Tran, Phi Islam, Rafiqul School of Engineering & Technology University of New South Wales Canberra Australia School of Systems & Computing University of New South Wales Canberra Australia

ISBN: (纸本)9798331520038

Detecting the boundary of gas plumes is critical for environmental monitoring, disaster response, and industrial safety. Traditional fixed-sensor networks struggle to cover large areas efficiently, while autonomous drone swarms using Particle Swarm Optimization (PSO) show a scalable and flexible solution. Inspired by natural creatures, such as bees, that detect environmental boundaries using collective intelligence, we explore a multi-drone technique inspired by PSO to track and detect the dynamic boundaries of gas plumes in complex environments. We explored how plume detection is influenced by several factors: wind direction, the number of particles, and wind speed. The results indicate the effectiveness of the proposed algorithm in accurately detecting the plume boundary. © 2024 IEEE.

关键词： environmental monitoring gas source localization Particle Swarm Optimization plume boundary detection robotics

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Optimal Tracking Performance of Networked Control systems Under DoS and Its Applications

Optimal Tracking Performance of Networked Control Systems Un...

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2024 China Automation Congress, CAC 2024

作者： Ren, Xinyu Li, Bo Zhao, Feng Chen, Xiangyong Zhang, Xianhe Jiang, Xiaowei School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Finance Anhui University of Finance and Economics Bengbu China Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Automation and Electrical Engineering Key Laboratory of Complex systems and Intelligent Computing in Universities of Shandong Linyi University Linyi China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

ISBN: (纸本)9798350368604

This paper investigates the optimal tracking performance (OTP) of multiple-input multiple-output (MIMO) discrete networked control systems (NCSs) under the influence of Denial of Service (DoS) attack, channel noise, and bandwidth constraints. The channel noise is assumed to be additive Gaussian white noise (AWGN). Due to the randomness of DoS attack, a Bernoulli distribution is used for DoS modeling. Utilizing frequency domain analysis methods, coprime factorization, all-pass decomposition, and Youla parameterization, an explicit expression for the optimal tracking performance (OTP) of the system under the two-degree-of-freedom (TDOF) controller is derived. The analysis indicates that non-minimum phase (NMP) zeros, unstable poles (UPs), DoS attack, and network constraints affect the OTP of NCSs. Finally, the theoretical results are validated through simulations of MIMO NCS and vehicle control system. © 2024 IEEE.

关键词： White noise

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