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

作者： Alexander G Minton Howe Yuan Zhu Hsiang-Ting Chen Yu-Kai Wang Zhuoli Zhuang Gina Notaro Raquel Galvan James Allen Matthias D Ziegler Chin-Teng Lin School of Computer Science FEIT University of Technology Sydney Sydney New South Wales Australia Discipline of Design University of Sydney Camperdown New South Wales Australia School of Computer and Mathematical Sciences University of Adelaide Adelaide South Australia Australia University of Technology Sydney Sydney NSW Australia School of Computer Science FEIT University of Technology Sydney Sydney NSW Australia Advanced Technology Laboratories Lockheed Martin Cherry Hill New Jersey USA Lockheed Martin Arlington Virginia USA Advance Technology Laboratories Lockheed Martin Cherry Hill New Jersey USA Australian AI Institute School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Ultimo Australia

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MVD-HuGaS: Human Gaussians from a Single Image via 3D Human Multi-view Diffusion Prior

arXiv

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arXiv 2025年

作者： Xiong, Kaiqiang Feng, Ying Zhang, Qi Jiao, Jianbo Zhao, Yang Liang, Zhihao Gao, Huachen Wang, Ronggang School of Electronic and Computer Engineering Peking University China Peng Cheng Laboratory China Migu Culture Technology Co. Ltd China Co. Ltd. China vivo Mobile Communication Co. Ltd China School of Computer Science University of Birmingham United Kingdom School of Computer and Information Hefei University of Technology China South China University of Technology China

3D human reconstruction from a single image is a challenging problem and has been exclusively studied in the literature. Recently, some methods have resorted to diffusion models for guidance, optimizing a 3D representation via Score Distillation Sampling(SDS) or generating one back-view image for facilitating reconstruction. However, these methods tend to produce unsatisfactory artifacts (e.g. flattened human structure or over-smoothing results caused by inconsistent priors from multiple views) and struggle with real-world generalization in the wild. In this work, we present MVD-HuGaS, enabling free-view 3D human rendering from a single image via a multi-view human diffusion model. We first generate multi-view images from the single reference image with an enhanced multi-view diffusion model, which is well fine-tuned on high-quality 3D human datasets to incorporate 3D geometry priors and human structure priors. To infer accurate camera poses from the sparse generated multi-view images for reconstruction, an alignment module is introduced to facilitate joint optimization of 3D Gaussians and camera poses. Furthermore, we propose a depth-based Facial Distortion Mitigation module to refine the generated facial regions, thereby improving the overall fidelity of the reconstruction. Finally, leveraging the refined multi-view images, along with their accurate camera poses, MVD-HuGaS optimizes the 3D Gaussians of the target human for high-fidelity free-view renderings. Extensive experiments on Thuman2.0 and 2K2K datasets show that the proposed MVD-HuGaS achieves state-of-the-art performance on single-view 3D human rendering. © 2025, CC BY.

关键词： 3D reconstruction

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Riding Over Two-Way Carrier: A Dual-Sided RIS-Enabled Symbiotic Backscatter System

Riding Over Two-Way Carrier: A Dual-Sided RIS-Enabled Symbio...

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IEEE Conference on Wireless Communications and Networking

作者： Xiaoyi Huang Haiyang Ding Gang Yang Maged Elkashlan Jules M. Moualeu Chau Yuen School of Information and Communications National University of Defense Technology Wuhan China National Key Laboratory of Wireless Communications University of Electronic Science and Technology of China China School of Electronic Engineering and Computer Science Queen Mary University of London London U.K. School of Electrical and Information Engineering University of the Witwatersrand Johannesburg South Africa School of Electrical and Electronic Engineering Nanyang Technological University Singapore

ISBN: (数字)9798350368369

ISBN: (纸本)9798350368376

In this paper, we propose a two-way backscatter communication system assisted by an active and passive dualsided reconfigurable intelligent surface (RIS) where the active RIS element contains an amplifier while the passive one does not. By altering the switch status within each RIS element, different transmission and reflection coefficients can be achieved, enabling a binary backscatter modulation. Moreover, a maximal-ratio-combining (MRC)-based detector is proposed to decode the backscatter signal and the end-user's signal, and the corresponding symbol error rate (SER) and throughput are subsequently analyzed. Numerical results show that by avoiding the additive thermal noise within each RIS element, passive dual-sided RISenabled communications outperform the active dual-sided RISenabled communications in terms of SER, and it is also revealed that the throughput can be significantly improved through backscatter modulation.

关键词： Symbiosis Error analysis Communication systems Modulation Detectors Switches Reconfigurable intelligent surfaces Throughput Thermal noise Backscatter

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Spilled Deep Capsule Neural Network with Skill Optimization Algorithm for Breast Cancer Recognition in Mammograms

Spilled Deep Capsule Neural Network with Skill Optimization ...

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International Conference on Inventive Computation Technologies (ICICT)

作者： Jayasimha S R Rajendra V. Patil Yashashwini S Prabu M Srisathirapathy S R Maranan Deptarment of MCA JSS Academy of Technical Education Affiliated to Visvesvaraya Technological University Bengaluru Karnataka India Department of Computer Science and Engineering SSVPS Bapusaheb Shivajirao Deore College of Engineering Mumbai Maharashtra India Department of Computer Science and Engineering Cambridge Institute of Technology Bengaluru Karnataka India Department of Computer Science and Engineering J.N.N Institute of Engineering Chennai Tamil Nadu India Department of Computer Science and Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Tamil Nadu India Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu India

ISBN: (数字)9798331512248

ISBN: (纸本)9798331512255

Breast cancer stands as the main reason for cancer deaths among women worldwide so early detection plays an essential role in raising both survival statistics and treatment effectiveness. The common breast cancer screening method known as mammography leads radiologists to struggle when evaluating mammograms because they often make incorrect diagnoses that result in delayed medical procedures. Standard assessment techniques experience difficulties in identifying faint abnormalities which causes both incorrect positive and negative results. The proposed research implements a new methodology that utilizes MIAS dataset to detect breast cancer. The Adaptive Morphological Wavelet Perona-Malik Filter Algorithm operates on mammogram images for preprocessing to optimize quality and save vital image characteristics. A Spilled Deep Capsule Neural Network function (SDCN) utilizes the network for effective mammographic image detection and feature extraction. The Skill Optimization Algorithm (SOA) serves as a parameter optimization tool which boosts both accuracy and efficiency of the model. The developed method succeeded in reaching 99.9% accuracy while surpassing previous standard examination methods. The research demonstrates how advanced deep learning and optimization algorithms support radiologists by detecting breast cancer properly and expeditiously which creates better patient healthcare outcomes.

关键词： Accuracy Feature extraction Breast cancer Mammography Optimization Medical diagnostic imaging Biological neural networks Standards Testing Residual neural networks

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Low Complexity Wireless Interference Identification in Antagonistic Environments Based on Multi-Task Learning

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IEEE Transactions on Vehicular technology 2025年

作者： Zhao, Ruosi Jiang, Weiheng Li, Donggen Niyato, Dusit Xiong, Zehui Fu, Shu Lu, Jie Chongqing University School of Microelectronics and Communication Engineering Chongqing400044 China Nanyang Technological University School of Computer Science and Engineering Singapore Singapore University of Technology and Design Pillar of Information Systems Technology and Design Singapore Singapore

As the fundamental premise of anti-interference communication, wireless interference identification (WII) has garnered extensive research and yielded substantial results, especially for deep learning (DL)-enabled WII. However, existing studies are conducted typically under the closed-set assumption, whereby the type of interference is known. This aspect poses a challenge in identifying the unknown interference signals under the d-set assumption. To tackle this issue, this paper proposes a multi-task learning-enabled WII (MTL-WII) algorithm in antagonistic environments. Firstly, we generate the semantic feature space of known classes, forming the mapping relationship from the original signal data to the semantic feature space while calculating the semantic center vectors of each known class through multi-task learning. Secondly, we obtain the semantic feature space of the test set incorporating interference signals of known and unknown classes through the established mapping relationships. Subsequently, the similarity between the semantic feature vectors of the interference signals under test and the semantic center vectors of the interference signals within each class gets calculated using the clustering method. Finally, the attributed class is determined by comparing the similarity against a threshold. Nevertheless, the computational complexity of the proposed method limits its applicability across various scenarios. To this end, this paper further proposes a binarized MTL-WII (BMTL-WII) algorithm. By binarizing the semantic spatial generative network in MTL-WII, both the weights and activation of the semantic spatial generative network replace the 32-bit floating-point numbers with 1-bit fixed-point numbers, which can save memory and improve the speed of the operations. Experimental results show that the MTL-WII model achieves an average identification accuracy of 95.4%, with an 89.3% accuracy for unknown interference signals. Compared to the MTL-WII algorithm, th

关键词： Multi-task learning

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ANNS: An Intelligent Advanced Non-Convex Non-Smooth Scheme for IRS-Aided Next Generation Mobile Communication Networks

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IEEE Transactions on Mobile Computing 2025年

作者： Chen, Miaojiang Liu, Anfeng Xiong, Neal N. Ren, Yingying Song, Houbing Herbert Guangxi University School of Computer Electronics and Information China Guangxi Key Laboratory of Multimedia Communications and Network Technology Nanning530004 China Central South University School of Computer Science and Engineering Changsha410083 China Sul Ross State University Department of Computer Science and Mathematics AlpineTX79830 United States Department of Information Systems BaltimoreMD21250 United States

Enhancing the communication rate and quality has become the primary goal for the development of next-generation mobile communication networks, and traditional techniques such as MIMO and increasing the transmit power of the base station (BS) have not achieved a leapfrog effect. The emergence of Intelligent Reflective Surfaces (IRS) provides more reliable technical support for providing high-energy and high-rate communications. However, IRS-aided joint optimization with communication rate and quality of service constraints is a non-convex non-smooth optimization problem, and the optimal global solution cannot be obtained due to its computational complexity. In this paper, we propose an intelligent Advanced Non-convex Non-smooth Scheme (ANNS) in IRS-aided Next Generation Mobile Communication Networks for making the transmission rate of mission communication and communication quality effective. To ensure that the inequality constraints in the joint optimization problem are not violated and the equation constraints are satisfied, a hybrid deep reinforcement learning and data-experience-driven constraint security layer network is proposed, which maps the constraint violations into the safe feasible domain by mapping the constraint variables into the constraint variables mapping method, and the convergence of the algorithm is theoretically demonstrated. Experimental results show that the proposed ANNS performs superior optimization compared to SAC, DDPG, and A2C for solving non-convex non-smooth problems. The proposed ANNS has the potential to be generalized to other mobile computing applications with non-convex non-smooth characteristics. © 2002-2012 IEEE.

关键词： Deep reinforcement learning

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NVSRLO: A FeFET-Based Non-Volatile and SEU-Recoverable Latch Design with Optimized Overhead

NVSRLO: A FeFET-Based Non-Volatile and SEU-Recoverable Latch...

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Design, Automation and Test in Europe Conference and Exhibition

作者： Aibin Yan Wangjin Jiang Han Bao Zhengfeng Huang Tianming Ni Xiaoqing Wen Patrick Girard School of Microelectronics Hefei University of Technology Hefei China School of Integrated Circuits Anhui Polytechnic University Wuhu China Graduate School of Computer Science and Systems Engineering Kyushu Institute of Technology Fukuoka Japan LIRMM University of Montpellier / CNRS Montpellier France

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

This paper presents a FeFET-based non-volatile and single-event upset (SEU) recoverable latch, namely NVSRLO, which does not require any extra control signals. Simulation results show that the proposed latch provides non-volatility and SEU-recovery with optimized overhead. Compared with existing non-volatile latches, NVSRLO significantly reduces delay, power, and delay-power-area product at the cost of area.

关键词： Latches Costs Radiation hardening (electronics) Simulation Single event upsets Europe Delays Energy harvesting FeFETs

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IoT-Integrated Convolutional Neural Network for Accurate Vehicle Acoustic Signal Analysis

IoT-Integrated Convolutional Neural Network for Accurate Veh...

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Integrated Circuits and Communication Systems (ICICACS), IEEE International Conference on

作者： P. Sasikala K Subha Sharmini Venkatesan Sorakka Ponnappan K. R. Kavitha K. Gunasekaran C. Srinivasan Department of Mathematics Vinayaka Mission's Kirupananda Variyar Engineering College Vinayaka Mission's Research Foundation (Deemed to be University) Salem India Department of Electrical and Electronics Engineering SRM Institute of Science and Technology Kattankulathur Chennai India Department of Mechanical Engineering Sathyabama Institute of Science and Technology Chennai India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Computer Science and Engineering Panimalar Engineering College Chennai India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India

ISBN: (数字)9798331508456

ISBN: (纸本)9798331508463

This paper examines the use of IoT-integrated Convolutional Neural Networks (CNNs) for precise analysis of vehicle audio signals. The context highlights the growing intricacy of vehicle systems and the need for advanced monitoring methods. Acoustic signals generated by different vehicle components may provide significant insights about vehicle health; nevertheless, traditional analysis techniques are often ineffective. The Internet of Things (IoT) technology allows real-time data collection from sensors installed on vehicles, which is then analyzed using CNNs to identify certain patterns and abnormalities in auditory signals. The approaches used include using IoT sensors to record various vehicle noises and utilizing CNNs for feature extraction and classification, providing great defect detection precision. The results indicate the system's capacity to detect engine faults, braking deficiencies, and other mechanical concerns early, enabling quick treatments. It presents a novel methodology for vehicle monitoring, enhancing predictive maintenance, safety, and performance, with prospects for future developments in autonomous diagnostics. The proposed system improved vehicle acoustic signal processing using IoT and CNN models, achieving high accuracy of 99% in anomaly detection, preventive maintenance, and real-time traffic monitoring.

关键词： Training Accuracy Noise Real-time systems Acoustics Sensor systems Sensors Convolutional neural networks Internet of Things Intelligent sensors

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A Trust-Based Personalized Differential Privacy Guarantees for Online Social Networks

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IEEE Transactions on Network and Service Management 2025年

作者： Chen, Jiajun Hu, Chunqiang Sheng, Weihong Li, Ruinian Zhao, Ruifeng Yu, Jiguo Chongqing University College of Computer Science Chongqing401331 China Chongqing University School of Big Data and Software Engineering Chongqing401331 China Bowling Green State University Computer Science Department United States Electric Power Dispatching and Control Center of Guangdong Power Grid Co. Ltd Guangzhou China University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu611731 China Qilu University of Technology Big Data Institute Jinan250353 China

Online social networks have emerged as a significant data source, but the extensive collection and utilization of personal information have given rise to profound concerns regarding privacy. From a legislative and policy perspective, and in alignment with the concept of privacy as control, users have the right to control their personal privacy information. However, users often encounter challenges in terms of understanding and effectively managing their privacy settings to align with their specific privacy requirements. To address this issue, in this paper, we incorporate the concept of trust and propose a trust-based personalized differential privacy model for online social networks, denoted as TPDP, which relies on a trusted central server to facilitate its operation. Specifically, when a user requests access to another user's personal information, the TPDP mechanism provides a privacy response, where the privacy level is determined based on the direct and indirect trust values among users, calculated automatically by the trusted central server. Furthermore, the proposed TPDP model offers user-to-user personalized differential privacy protection from the perspectives of network structures, trust-related factors, and trust propagation patterns. Finally, we validate the model's feasibility and assess the privacy-utility trade-off, as well as its robustness against attacks, through theoretical analysis and performance evaluation. © 2004-2012 IEEE.

关键词： Differential privacy

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Smart Plant Irrigation System with Arduino Uno and Soil Moisture Sensor

Smart Plant Irrigation System with Arduino Uno and Soil Mois...

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Computational Intelligence, Communication technology and Networking (CICTN), International Conference on

作者： Jagdish Chandra Patni Vuppu Surya Prakash Gajjala Sri Vardhan Reddy Anjana Snehith Santhosh Yammanuru Chaitanya Reddy Nilesh Bhaskarrao Bahadure Akhilesh Kumar Srivastava Dept. of CSE Alliance School of Advanced Computing Alliance University Bengaluru India Department of Computer Science and Engineering School of Technology GSFC University Vadodara Gujarat India ABES Engineering College Ghaziabad Uttar Pradesh India

ISBN: (数字)9798331530389

ISBN: (纸本)9798331530396

Automatic Smart Plant Watering System is a smarter solution that works on Arduino Uno, which can automatically operate for plant watering at optimal moisture conditions of soil. It checks the moisture with the help of a soil moisture sensor and, once the level of moisture goes down below a threshold, initiates a water pump. This user-friendly system is also energy efficient, making it suitable for busy or travel users, further contributing to sustainability plant care, thereby making it ideal for residential and nursery settings while emphasizing the promising use of smart agriculture and IoT technologies. This paper focuses on developing a smart water framework designed to efficiently manage water distribution based on real-time soil and environmental conditions.

关键词： Irrigation Wireless sensor networks Soil moisture Moisture Systems architecture Water pumps Time factors Sustainable development Water resources Farming

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