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Proceedings of the 2025 CHI Conference on Human Factors in computing Systems

作者： Nattapat Boonprakong Saumya Pareek Benjamin Tag Jorge Goncalves Tilman Dingler School of Computing and Information Systems University of Melbourne Parkville Victoria Australia School of Computing and Information Systems University of Melbourne Melbourne Victoria Australia School of Computer Science and Engineering University of New South Wales Sydney New South Wales Australia School of Computing and Information Systems University of Melbourne Melbourne Australia Industrial Design Engineering Delft University of Technology Delft Netherlands

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Digital Video Forensics: Review, Challenges, Parameters and Future Direction

Digital Video Forensics: Review, Challenges, Parameters and ...

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Technological Advancements in Computational sciences (ICTACS), International Conference on

作者： Gurvinder Singh Raj Gaurang Tiwari Ambuj Kumar Agarwal Himani Maheshwari Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Department of Computer Science Engineering Sharda School of Engineering and Technology Sharda University Greater Noida School of Computing Graphic Era Hill University Dehradun Uttarakhand

ISBN: (数字)9798350387490

ISBN: (纸本)9798350387506

Digital videos are recorded in everyday circumstances for a variety of reasons, including protection, preserving life-related experiences, and providing accurate proof. The growth of multimedia technology has led to share the digital videos on social media platforms. A variety of editing tools is used to alter the information presented in the digital videos with software. Consequently, it is more significant to secure the information in the digital videos. This work provides a review of digital video forensics. Firstly, digital video forensics has been introduced with its types firstly and after that, various kinds of digital video forgery like intra-frame forgery and inter-frame forgery are discussed. These forgery kinds have been further divided into different categories. The types of existing passive techniques have also been described for the detection of digital video forgeries in detail. Moreover, this paper delivers the new and innovative challenges within the digital video forensics. Besides, the evaluation parameters which are used in the existing techniques are discussed in detail. At last, future directions in the realm of digital video forensics are detailed in this paper.

关键词： Visualization Reviews Social networking (online) Forensics Streaming media Feature extraction Forgery Software Smart phones Videos

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AdapTraj: A Multi-Source Domain Generalization Framework for Multi-Agent Trajectory Prediction

AdapTraj: A Multi-Source Domain Generalization Framework for...

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International Conference on Data Engineering

作者： Tangwen Qian Yile Chen Gao Cong Yongjun Xu Fei Wang Chinese Academy of Sciences Institute of Computing Technology Beijing China University of Chinese Academy of Sciences Beijing China School of Computer Science and Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Multi-agent trajectory prediction, as a critical task in modeling complex interactions of objects in dynamic systems, has attracted significant research attention in recent years. Despite the promising advances, existing studies all follow the assumption that data distribution observed during model learning matches that encountered in real-world deployments. However, this assumption often does not hold in practice, as inherent distribution shifts might exist in the mobility patterns for deploy-ment environments, thus leading to poor domain generalization and performance degradation. Consequently, it is appealing to leverage trajectories from multiple source domains to mitigate such discrepancies for multi-agent trajectory prediction task. However, the development of multi-source domain generalization in this task presents two notable issues: (1) negative transfer; (2) inadequate modeling for external factors. To address these issues, we propose a new causal formulation to explicitly model four types of features: domain-invariant and domain-specific features for both the focal agent and neighboring agents. Building upon the new formulation, we propose AdapTraj, a multi-source domain generalization framework specifically tailored for multi-agent trajectory prediction. AdapTraj serves as a plug-and-play module that is adaptable to a variety of models. Extensive experiments on four datasets with different domains demonstrate that AdapTraj consistently outperforms other baselines by a substantial margin.

关键词： Degradation Adaptation models Buildings Predictive models Data engineering Data models Trajectory

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Optimization of Memory March X Test Algorithm Based on Nuclear Safety Level DCS System Platform 1

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9th International Conference on Energy Engineering and Environmental Engineering, ICEEEE 2022

作者： Bai, Yongfei Wu, Zhiqiang Liu, Jie Jiang, Wei Zhong, Ke Han, Wenxing Chen, Zhi School of Computer Science University of South China Hengyang421200 China Nuclear Power Institute of China Chengdu610000 China Intelligent Equipment Software Evaluation Engineering Technology Research Center of Hunan Hengyang China CNNC Key Laboratory on High Trusted Computing Hunan Hengyang China

ISBN: (数字)9783031302336

ISBN: (纸本)9783031302329

At present, the testing algorithm of nuclear power safety level DCS system for memory is GALPAT algorithm. This algorithm has a high time complexity and low fault coverage for memory. During the testing process, additional storage space is required, and the security of additional memory cannot be guaranteed. Based on the memory fault testing theory, this paper analyzes and studies the March X algorithm. It is found that the algorithm can not meet the coverage requirements for memory faults when applied to the nuclear power safety level DCS system. A March X+ algorithm is proposed. The algorithm can meet the index requirements of the nuclear power safety level DCS system for memory testing algorithms, and the fault detection rate reaches more than 95%, And it innovatively proposes to save the data to the register during the detection process, which does not require additional memory and improves the security. To verify the feasibility of the algorithm designed in this paper, the algorithm test time and functionality were verified in the main control PBIST module under a nuclear power safety level DCS system platform. The results show that the proposed test algorithm has certain advantages in test time and memory fault coverage. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Nuclear energy

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Efficient UAV Path Planning and Energy Optimization with Cooperative System Model for Vehicular Adhoc Networks

Efficient UAV Path Planning and Energy Optimization with Coo...

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2023 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2023

作者： Veeramuthu, A. Mahalingam, Shalini Ilakkia, T. Sampathkumar, R. Subha, T.D. Sathyabama Institute of Science and Technology School of Computing Department of Information Technology Chennai India St.Joseph's College of Engineering College Department of Computer Science and Engineering Chennai India Sona College of Technology Department of Electrical and Electronics Engineering Tamilnadu Salem India KPR Institute of Engineering and Technology Department of Mechatronics Engineering Coimbatore India R.M.K. Engineering College Department of Electronics and Communication Engineering Tamilnadu Chennai India

ISBN: (纸本)9798350319200

Vehicular Ad Hoc Networks (VANETs) is a highly trending technology which occupies lots of applications of intelligent communications in day to day life. Typically, vehicles in a vehicular network move at rapid speeds with unpredictable mobility, leading to frequent changes in the network's structure. Consequently, this might cause some problems while transmitting data between vehicles. In addition, external activities might lead to data loss. In order to overcome certain drawbacks in recent times ground level to air level data connectivity is concentrated which includes the concept of Unmanned Aerial Networks (UAV). The primary obstacles in the construction of UAV is that its network formation and path finding process. For the purpose of overcoming these kinds of drawbacks in this article effective UAV path planning and the energy optimization (EUPEO) is concentrated in the vehicular communication. Through this process the communication is carried out in an effective manner with intelligent path planning of UAV and as well it reduces the delay and energy consumption. The idea is implemented in ns3 and from the final outcome it is obviously proven that the EUPEO method achieved superior results than previous methods concerned with efficiency and as well the delivery rate of the information transmitted. © 2023 IEEE.

关键词： Effective UAV Path Planning and Energy Optimization Unmanned Aerial Networks (UAV) Vehicular Ad Hoc Networks (VANETs)

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Multi-Modal Data Analysis Based Game Player Experience Modeling Using LSTM-DNN

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computers, Materials & Continua 2021年第9期68卷 4087-4108页

作者： Sehar Shahzad Farooq Mustansar Fiaz Irfan Mehmood Ali Kashif Bashir Raheel Nawaz KyungJoong Kim Soon Ki Jung School of Computer Science and Engineering Kyungpook National UniversityDaeguKorea Faculty of Engineering&Informatics School of MediaDesign and TechnologyUniversity of BradfordBradfordBD71DPUK Department of Computing and Mathematics Manchester Metropolitan UniversityManchesterUK Department of Operations TechnologyEvents and Hospitality ManagementManchester Metropolitan UniversityUK Institute of Integrated Technology Gwangju Institute of Science and TechnologyKorea

Game player modeling is a paradigm of computational models to exploit players’behavior and experience using game and player *** modeling refers to descriptions of players based on frameworks of data derived from the interaction of a player’s behavior within the game as well as the player’s experience with the *** behavior focuses on dynamic and static information gathered at the time of *** experience concerns the association of the human player during gameplay,which is based on cognitive and affective physiological measurements collected from sensors mounted on the player’s body or in the player’s *** this paper,player experience modeling is studied based on the board puzzle game“Candy Crush Saga”using cognitive data of players accessed by physiological and peripheral *** Short-Term Memory-based Deep Neural Network(LSTM-DNN)is used to predict players’effective states in terms of valence,arousal,dominance,and liking by employing the concept of transfer *** learning focuses on gaining knowledge while solving one problem and using the same knowledge to solve different but related *** homogeneous transfer learning approach has not been implemented in the game domain before,and this novel study opens a new research area for the game industry where the main challenge is predicting the significance of innovative games for entertainment and players’*** not only from a player’s point of view,it is also a benchmark study for game developers who have been facing problems of“cold start”for innovative games that strengthen the game industrial economy.

关键词： Game player modeling experience modeling player analytics deep learning LSTM game play data Candy Crush Saga

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FDSC-YOLOv8:Advancements in Automated Crack Identification for Enhanced Safety in Underground Engineering

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computer Modeling in Engineering & sciences 2024年第9期140卷 3035-3049页

作者： Rui Wang Zhihui Liu Hongdi Liu Baozhong Su Chuanyi Ma 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)Jinan250353China School of Qilu Transportation Shandong UniversityJinan250100China Geotechnical and Structural Engineering Center Shandong UniversityJinan250061China Shandong Lairong High-Speed Railway Co. Ltd.Weihai264200China Shandong High-Speed Group Co. Ltd.Jinan250014China

In underground engineering,the detection of structural cracks on tunnel surfaces stands as a pivotal task in ensuring the health and reliability of tunnel ***,the dim and dusty environment inherent to under-ground engineering poses considerable challenges to crack *** paper proposes a crack segmentation algorithm termed as Focused Detection for Subsurface Cracks YOLOv8(FDSC-YOLOv8)specifically designed for underground engineering structural ***,to improve the extraction of multi-layer convolutional features,the fixed convolutional module is replaced with a deformable convolutional ***,the model’s receptive field is enhanced by introducing a multi-branch convolutional module,improving the extraction of shallow features for small ***,the Dynamic Snake Convolution module is incorporated to enhance the extraction capability for slender and weak ***,the Convolutional Block Attention Module(CBAM)module is employed to achieve better target *** FDSC-YOLOv8s algorithm’s mAP50 and mAP50-95 reach 96.5%and 66.4%,according to the testing data.

关键词： Crack segmentation improved YOLOv8 engineering applications feature extraction

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Multi-Grained Feature Pruning for Video-Based Human Pose Estimation

Multi-Grained Feature Pruning for Video-Based Human Pose Est...

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

作者： Zhigang Wang Shaojing Fan Zhenguang Liu Zheqi Wu Sifan Wu Yingying Jiao College of Computer Science and Technology Zhejiang Gongshang University Hangzhou China School of Computing National University of Singapore Singapore The State Key Laboratory of Blockchain and Data Security Zhejiang University Hangzhou China Hangzhou High-Tech Zone (Binjiang) Institute of Blockchain and Data Security Hangzhou China College of Computer Science and Technology Jilin University Changchun China College of Computer Science and Technology Zhejiang University of Technology Hangzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Human pose estimation, with its broad applications in action recognition and motion capture, has experienced significant advancements. However, current Transformer-based methods for video pose estimation often face challenges in managing redundant temporal information and achieving fine-grained perception because they only focus on processing low-resolution features. To address these challenges, we propose a novel multi-scale resolution framework that encodes spatiotemporal representations at varying granularities and executes fine-grained perception compensation. Furthermore, we employ a density peaks clustering method to dynamically identify and prioritize tokens that offer important semantic information. This strategy effectively prunes redundant feature tokens, especially those arising from multi-frame features, thereby optimizing computational efficiency without sacrificing semantic richness. Empirically, it sets new benchmarks for both performance and efficiency on three large-scale datasets. Our method achieves a 93.8% improvement in inference speed compared to the baseline, while also enhancing pose estimation accuracy, reaching 87.4 mAP on the PoseTrack2017 dataset.

关键词： Accuracy Pose estimation Semantics Signal processing algorithms Benchmark testing Signal processing Motion capture Computational efficiency Spatiotemporal phenomena Speech processing

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Cross-Device Control and Interaction for Workstations using Mobile Devices

Cross-Device Control and Interaction for Workstations using ...

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Electronic Systems and Intelligent computing (ICESIC), International Conference on

作者： Nivethitha V G S Rakshika Suthir Sriram Thangavel M Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Chennai India College of Information and Technology United Arab Emirates University Al Ain Dubai

ISBN: (数字)9798331522988

ISBN: (纸本)9798331522995

The proliferating use of mobile devices has paved the way for new possibilities in enhancing user interactions with workstations. This research paper investigates and demonstrates the practicality of utilizing mobile devices as controllers for workstations. We investigate a wide range of technologies, design considerations, and factors related to user experience. While analyzing the case studies and conducting experiments, we put forth both the positive and adverse effects observed with this novel approach. It is proved by the present study results that the application of mobile devices as controllers of workstations can bring substantial raise in productivity and ease to the end-user. Based on existing studies, this device-free approach addresses the problem of traditional workstation complementing peripherals with mobile technology at the present. This change in how humans interact with computers provokes a variety of efficiency advantages but at the same time provides new challenges such as security and interdevice integration. Focusing on all these complexities will allow the possibility of the full potential of the mobile devices in the capacity of the controller for the workstations.

关键词： Productivity computers Focusing Switches Control systems Data transfer Mobile handsets User experience Workstations Security

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Reinforcement Learning with Token-level Feedback for Controllable Text Generation

arXiv

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arXiv 2024年

作者： Li, Wendi Wei, Wei Xu, Kaihe Xie, Wenfeng Chen, Dangyang Cheng, Yu Cognitive Computing and Intelligent Information Processing Laboratory School of Computer Science and Technology Huazhong University of Science and Technology China China Ping An Property & Casualty Insurance company of China China The Chinese University of Hong Kong Hong Kong

To meet the requirements of real-world applications, it is essential to control generations of large language models (LLMs). Prior research has tried to introduce reinforcement learning (RL) into controllable text generation while most existing methods suffer from overfitting issues (finetuning-based methods) or semantic collapse (post-processing methods). However, current RL methods are generally guided by coarse-grained (sentence/paragraph-level) feedback, which may lead to suboptimal performance owing to semantic twists or progressions within sentences. To tackle that, we propose a novel reinforcement learning algorithm named TOLE which formulates TOken-LEvel rewards for controllable text generation, and employs a "first-quantize-then-noise" paradigm to enhance the robustness of the RL algorithm. Furthermore, TOLE can be flexibly extended to multiple constraints with little computational expense. Experimental results show that our algorithm can achieve superior performance on both single-attribute and multi-attribute control tasks. We have released our codes at https://***/WindyLee0822/CTG. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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