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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Boonprakong, Nattapat Tag, Benjamin Goncalves, Jorge Dingler, Tilman School of Computing and Information Systems University of Melbourne ParkvilleVIC Australia School of Computer Science and Engineering University of New South Wales SydneyNSW Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

ISBN: (纸本)9798400713941

computing systems are increasingly designed to adapt to users' cognitive states and mental models. Yet, cognitive biases affect how humans form such models and, therefore, they can impact their interactions with computers. To better understand this interplay, we conducted a scoping review to chart how Human-Computer Interaction (HCI) researchers study cognitive biases. Our findings show that computing systems not only have the potential to induce and amplify cognitive biases but also can be designed to steer users' behaviour and decision-making by capitalising on biases. We describe how HCI researchers develop algorithms and sensing methods to detect and quantify the effects of cognitive biases and discuss how we can use their understanding to inform system design. In this paper, we outline a research agenda for more theory-grounded research and highlight ethical issues when researching and designing computing systems with cognitive biases in mind as they affect real-world behaviour. © 2025 Copyright held by the owner/author(s).

关键词： Real time systems

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Guardians of Truth: Exploring Techniques for Veracity Identification in social media during an Infodemic 3

Guardians of Truth: Exploring Techniques for Veracity Identi...

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3rd International Conference on Artificial Intelligence For Internet of Things, AIIoT 2024

作者： Suthanthiradevi, P. Revathy, G. Sathish Kumar, P. Karthikeyan, B. Muthu, Lakshmi.V SRM institute of Science and Technology Data Science and Business Systems School of Computing India Vels Institute of science Technology and Advanced studies Department of Computer science and engineering India Rajalakshmi institute of technology Computer and Communication Engineering India SASTRA Deemed University School of Computing India St. Joseph's Institute of Technology Department of Computer Science and Engineering India

ISBN: (纸本)9798350372120

With the recent advent of technology, social networks are accessible 24/7 using mobile devices. During covid-19 pandemic the propagation of misinformation are mostly related to the disease, its cures and prevention. With Twitter as the medium this information was spreaded to millions of people who lack domain specific knowledge. This can lead to bloster fear and direct damage to the people. This research focuses on identifying the veracity of the Twitter posts behind pandemic situation. The authors have proposed a Transformer Model using Bat Algorithm for identifying the fake news. This model is trained and evaluated using infodemic metadata. Our experimental results shows by which proposed model accurately detected the fake news and achieved 96.5% of f1-score. © 2024 IEEE.

关键词： Social networking (online)

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A Novel Recommendation Algorithm Integrates Resource Allocation and Resource Transfer in Weighted Bipartite Network

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Big Data Mining and Analytics 2024年第2期7卷 357-370页

作者： Qiang Sun Leilei Shi Lu Liu Zixuan Han Liang Jiang Yan Wu Yeling Zhao School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang 212013China School of Computing and Mathematical Sciences University of LeicesterLeicesterLE17RHUK School of Ocean College Jiangsu University of Science and TechnologyZhenjiang 212003China School of Finance and Economics Jiangsu UniversityZhenjiang 212013China

Grid-based recommendation algorithms view users and items as abstract nodes,and the information utilised by the algorithm is hidden in the selection relationships between users and *** these relationships can be easily handled,much useful information is overlooked,resulting in a less accurate recommendation *** aim of this paper is to propose improvements on the standard substance diffusion algorithm,taking into account the influence of the user’s rating on the recommended item,adding a moderating factor,and optimising the initial resource allocation vector and resource transfer matrix in the recommendation *** average ranking score evaluation index is introduced to quantify user satisfaction with the recommendation *** are conducted on the MovieLens training dataset,and the experimental results show that the proposed algorithm outperforms classical collaborative filtering systems and network structure based recommendation systems in terms of recommendation accuracy and hit rate.

关键词： cloud computing link prediction bipartite graph network recommendation algorithm cold start problem

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Accumulated-state-error-based event-triggered sampling scheme and its application to H∞ control of sampled-data systems

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Science China(Information Sciences) 2024年第6期67卷 328-341页

作者： Xian-Ming ZHANG Qing-Long HAN Bao-Lin ZHANG Xiaohua GE Dawei ZHANG School of Science Computingand Engineering TechnologiesSwinburne University of Technology College of Automation and Electronic Engineering Qingdao University of Science & Technology School of Mathematics Shandong University

This paper is concerned with event-triggered H∞control of sampled-data systems. Its novelties lie in three aspects:（i） A novel accumulated-state-error-based event-triggered scheme is introduced by comparing the integral of the state error from tk to t with the system state sampled at tk. This condition works well due to the fact that the so-called Zeno behaviour does not occur.（ii） A novel Lyapunov functional is constructed to establish a criterion to ensure some certain H∞performance of the closed-loop system. This Lyapunov functional is dependent on the integral of the state error involved in the event-triggered scheme.（iii） Under the event-triggered sampling scheme, suitable state-feedback controllers can be designed rather than be given a priori. Moreover, a self-triggered implementation of the proposed event-triggered sampling scheme is presented as well. Finally, a batch reactor model and an inverted pendulum system are given to demonstrate the effectiveness of the proposed method.

关键词： event-triggered sampling self-triggered implementation H ∞ control looped-functional

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ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED CRIMINAL DETECTION USING A DEEP LEARNING APPROACH

ASSESSING THE ACCURACY AND ETHICAL IMPLICATIONS OF AUTOMATED...

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2023 IEEE International Conference on Research Methodologies in Knowledge Management, Artificial Intelligence and Telecommunication engineering, RMKMATE 2023

作者： Jagadeesan, S. Sujigarasharma, K. Manjula, R. Vetriselvi, T. School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamil Nadu Vellore India Department of Computing Technologies School of Computing Faculty of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Chennai India Department of IOT School of Computer Science and Engineering Vellore Institute of Technology Tamil Nadu Vellore India

ISBN: (纸本)9798350305708

This study examines the accuracy and ethical implications of using convolutional neural networks (CNN) for automated crime detection. A CNN model was trained on a dataset of criminal mugshots to identify potential criminal behaviour based on facial features. This study analyzed the performance of the model and achieved a high accuracy rate in identifying criminals. However, the ethical implications of automated criminal detection are also explored, including bias, privacy and human rights violations. The findings of this study highlight the need for caution and ethical considerations when implementing automated crime detection technologies. It is important to ensure that such technologies are not used to violate the rights of individuals or perpetuate societal biases. © 2023 IEEE.

关键词： CNN Criminal detection Face detection Machine learning

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Inductive Relation Prediction by Disentangled Subgraph Structure

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Tsinghua Science and technology 2024年第5期29卷 1566-1579页

作者： Guiduo Duan Rui Guo Wenlong Luo Guangchun Luo Tianxi Huang Laboratory of Intelligent Collaborative Computing University of Electronic Science and Technology of ChinaChengdu 611731Chinaand also with Trusted Cloud Computing and Big Data Key Laboratory of Sichuan ProvinceChengdu 611731China School of Computer Science and Engineering University of Electronic Science and Technology of ChinaChengdu 611731China Hangzhou NetEase Cloud Music Technology Co. Ltd.Hangzhou 310052China Department of Fundamental Courses Chengdu Textile CollegeChengdu 611700China

Currently,most existing inductive relation prediction approaches are based on subgraph structures,with subgraph features extracted using graph neural networks to predict ***,subgraphs may contain disconnected regions,which usually represent different semantic *** not all semantic information about the regions is helpful in relation prediction,we propose a relation prediction model based on a disentangled subgraph structure and implement a feature updating approach based on relevant semantic *** indirectly achieve the disentangled subgraph structure from a semantic perspective,the mapping of entity features into different semantic spaces and the aggregation of related semantics on each semantic space are *** disentangled model can focus on features having higher semantic relevance in the prediction,thus addressing a problem with existing approaches,which ignore the semantic differences in different subgraph ***,using a gated recurrent neural network,this model enhances the features of entities by sorting them by distance and extracting the path information in the ***,it is shown that when there are numerous disconnected regions in the subgraph,our model outperforms existing mainstream models in terms of both Area Under the Curve-Precision-Recall(AUC-PR)and Hits@*** prove that semantic differences in the knowledge graph can be effectively distinguished and verify the effectiveness of this method.

关键词： disentangled subgraph structure knowledge graph completion Gated Recurrent Unit(GRU) feature updating

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ONNXPruner: ONNX-Based General Model Pruning Adapter

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 5806-5817页

作者： Ren, Dongdong Li, Wenbin Ding, Tianyu Wang, Lei Fan, Qi Huo, Jing Pan, Hongbing Gao, Yang Nanjing University School of Electronic Science and Engineering Nanjing210093 China Nanjing University Suzhou Campus State Key Laboratory for Novel Software Technology School of Intelligence Science and Technology Suzhou215163 China Microsoft Corporation Applied Sciences Group RedmondWA98052 United States University of Wollongong School of Computing and Information Technology WollongongNSW2522 Australia

Recent advancements in model pruning have focused on developing new algorithms and improving upon benchmarks. However, the practical application of these algorithms across various models and platforms remains a significant challenge. To address this challenge, we propose ONNXPruner, a versatile pruning adapter designed for the ONNX format models. ONNXPruner streamlines the adaptation process across diverse deep learning frameworks and hardware platforms. A novel aspect of ONNXPruner is its use of node association trees, which automatically adapt to various model architectures. These trees clarify the structural relationships between nodes, guiding the pruning process, particularly highlighting the impact on interconnected nodes. Furthermore, we introduce a tree-level evaluation method. By leveraging node association trees, this method allows for a comprehensive analysis beyond traditional single-node evaluations, enhancing pruning performance without the need for extra operations. Experiments across multiple models and datasets confirm ONNXPruner’s strong adaptability and increased efficacy. Our work aims to advance the practical application of model pruning. © 1979-2012 IEEE.

关键词： Adaptation Models Training Filters Deep Learning Computational Modeling Software Interoperability Load Modeling Software Algorithms Electronic Mail General Model Pruning ONNX Tree Level Evaluation Deep Neural Network Model Pruning Deep Learning Model Architecture Deep Learning Framework Hardware Platform Superior Performance Deep Neural Network Feature Maps Py Torch Image Net Training Images Sparse Matrix Root Node Type Of Operation Single Node Deep Neural Network Model Validation Images Extra Components Importance Of Filtering Pruning Method Squeeze Net

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An Interpretation of the Challenges and Solutions for Agriculture-based Supply Chain Management using Blockchain and IoT 7

An Interpretation of the Challenges and Solutions for Agricu...

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7th International Conference on computing Methodologies and Communication, ICCMC 2023

作者： Surya, M. Manohar, S. Department of Computer Science and Engineering School of Computing SRM Institute of Science and Technology Tamilnadu India

ISBN: (纸本)9781665464086

Healthcare, the smart grid, and supply chain management are some of the industries where IoT(Internet of Things) technology is improving. Also, it improves the way humans communicate with each other, the atmosphere, and the people around them. Internet of things achieves this by using wearable technology, embedded sensors, and automated systems. The number, types, and sizes of IoT devices are increasing. There are issues with security, authenticity, reliability, and scalability. Because of the expected growth in IoT, IoT systems must be reliable and maintainable. Blockchain has helped solve IoT network problems. Blockchain improves trust, immutability, transparency, and release of agricultural data collection, processing, and sharing. Blockchain and IOT will streamline precision farming and transform normal farms into the Internet of Smart Farms. This study describes the IoT systems have issues, and blockchain-based solutions are suggested. Additionally, it discusses current research on the degree to which blockchain technology and Internet of Things networks. IoT integration inside the blockchain issues are also covered. This article proposes to double connect IoT and blockchain using recent computing techniques. This research work intends to make data processing and storage decentralized, secure and anonymous, and seamless and fast using blockchain technology and services. © 2023 IEEE.

关键词： Cloud computing architecture

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DRL-Based Time-Varying Workload Scheduling With Priority and Resource Awareness

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2838-2852页

作者： Liu, Qifeng Fan, Qilin Zhang, Xu Li, Xiuhua Wang, Kai Xiong, Qingyu Chongqing University School of Big Data and Software Engineering Key Laboratory of Dependable Service Computing in Cyber Physical Society of Ministry of Education Chongqing400044 China Nanjing University School of Electronic Science and Engineering Nanjing210023 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China Haihe Laboratory of Information Technology Application Innovation Tianjin300072 China Harbin Institute of Technology School of Computer Science and Technology Weihai264209 China Shandong Key Laboratory of Industrial Network Security Weihai264209 China

With the proliferation of cloud services and the continuous growth in enterprises’ demand for dynamic multidimensional resources, the implementation of effective strategy for time-varying workload scheduling has become increasingly significant. In this paper, we propose a deep reinforcement learning (DRL)-based method for time-varying workload scheduling, aiming to allocate resources efficiently across servers in the cluster. Specifically, we integrate a classifier and queue scorer to construct a priority queue that exploits temporal resource utilization patterns across different workload classes. Then, we design parallel graph attention layers to capture the dimensional features and temporal dynamics of cloud server cluster. Moreover, we propose a DRL algorithm to generate scheduling strategies that can adapt to dynamic environments. Validation on real-world traces from Google cluster demonstrates that our method outperforms existing approaches in key metrics of cloud server cluster management. © 2004-2012 IEEE.

关键词： Cloud platforms

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Remaining Useful Life Prediction of Degraded Systems Based on Multiple Performance Indicators Constructed by Multiple Features

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IEEE Sensors Journal 2025年第11期25卷 19613-19631页

作者： Wu, Bin Shi, Hui Zeng, Jianchao Zhang, Xiaohong Wang, Zhijian Wang, Zuolu Taiyuan Institute of Technology Department of Computer Engineering Taiyuan030008 China Taiyuan University of Science and Technology School of Electronic Information Engineering Taiyuan030024 China Institute for Big Data and Visual Computing North University of China Taiyuan030051 China Taiyuan University of Science and Technology School of Economics and Management Taiyuan030024 China North University of China School of Mechanical Engineering Taiyuan030051 China University of Huddersfield Centre for Efficiency and Performance Engineering HuddersfieldHD1 3DH United Kingdom

The health status assessment of industrial equipment is critical to fault prediction and health management technologies. To fully reflect system degradation and accurately predict the remaining useful life (RUL) of a system, the construction of multiple performance indicators is important. Thus, a new state-space model and RUL prediction method are proposed herein based on multiple indicators, considering the three correlation relationships among features and degradation indicators. First, according to the extracted and selected feature signals, various relationships such as the correlations among multiple features, multiple features and indicators, and multiple indicators are analyzed. Then an intuitive multi-indicator state-space model is established considering the three correlations and the identified degradation patterns. On this basis, three failure modes of the system are defined according to the multi-indicator complex correlation conditions, and the corresponding probability density function (pdf) of the system RUL is calculated. Finally, the PHM2012 Challenge and XJTU-SY datasets are used as examples for experimental verification. Compared with other prediction methods, the proposed method can intuitively reflect the correlation between features and indicators, to solve the problem caused by the hidden random correlation. © 2001-2012 IEEE.

关键词： Prediction models

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