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Companion Proceedings of the ACM on Web Conference 2025

作者： Xu Zhang Peng Wang Chen Wang Zhe Xu Xiaohua Nie Wei Wang Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shang Hai China Shanghai Key Laboratory of Data Science School of Computer Science and Technology Fudan University Shanghai China National Engineering Research Center for Big Data Software Tsinghua University Beijing China School of Software Tsinghua University Beijing China National Key Laboratory of Strength and Structural Integrity Aircraft Strength Research Institute of China Xi'an Shaanxi China

ISBN: (纸本)9798400713316

Strain Gauge Status (SGS) recognition is crucial in the field of intelligent manufacturing based on the Internet of Things, as accurate identification helps timely detection of failed mechanical components, avoiding accidents. The loading and unloading sequences generated by strain gauges can be identified through time series classification (TSC) algorithms. Recently, deep learning models, e.g., convolutional neural networks (CNNs) have shown remarkable success in the TSC task, as they can extract discriminative local features from the subsequences to identify the time series. However, we observe only the local features may not be sufficient for expressing the time series, especially when the local sub-sequences between different time series are very similar, e.g., SGS data of aircraft wings in static strength experiments. Nevertheless, CNNs suffer from the limitation in extracting global features due to the nature of convolution operations. For extracting global features to more comprehensively represent the SGS time series, we propose two insights: (i) Constructing global features through feature engineering. (ii) Learning high-order relationships between local features to capture global features. To realize and utilize them, we propose a hypergraph-based global feature learning and fusion framework, which learns and fuses global features for semantic consistency to enhance the representation of SGS time series, thereby improving recognition accuracy. Our method designs are validated on industrial SGS and public UCR datasets, showing better generalization for unseen data in SGS recognition.

关键词： deep learning models

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Intelligent Network Selection Algorithm for Multiservice Users in 5G Heterogeneous Network System: Nash Q-Learning Method

Intelligent Network Selection Algorithm for Multiservice Use...

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作者： Ma, Mingfang Zhu, Anqi Guo, Songtao Yang, Yuanyuan The College of Computer Science Chongqing University Chongqing China Robotics Research Center College of Intelligence Science and Technology National University of Defense Technology Changsha China Department of Electrical and Computer Engineering Stony Brook University Stony BrookNY United States

The 5G heterogeneous network architecture integrates different radio access technologies (RATs), which will support the large-scale communication connection of massive Internet-of-Things (IoT) devices. However, as the rapid growth of IoT connections, personalized requirements of services requested and heterogeneity deepening of the network system, how to design an intelligent network selection scheme for user devices (UDs) is becoming a crucial challenge in the 5G heterogeneous network system. Most of the existing network selection methods only optimize the selection strategies from the user side or network side, which results in heavy network congestion, poor user experience, and system performance degradation. Accordingly, we propose a multiagent $Q$ -learning network selection (MAQNS) algorithm based on Nash $Q$ -learning, which can learn a joint optimal selection strategy to improve system throughput and reduce user blocking on the premise of ensuring the requirements of IoT services. In particular, we apply the discrete-time Markov chains to model the network selection, and the analytic hierarchy process (AHP) and gray relation analysis (GRA) are jointly utilized to obtain user preferences for each network. Finally, performance evaluation demonstrates that comparing to the existing schemes, MAQNS proposed cannot only improve system throughput and reduce user blocking but also promote user experience on average energy efficiency and delay. © 2014 IEEE.

关键词： Internet of things

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Dual Driven Approaches for General Purposed Brain Inspired Computing

Dual Driven Approaches for General Purposed Brain Inspired C...

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IEEE Electron Devices technology and Manufacturing Conference (EDTM)

作者： Luping Shi Yuqing Cong Wei Zhang Center for Brain-Inspired Computing Research (CBICIR) China Department of Precision Instrument Tsinghua University Optical Memory National Engineering Research Center Beijing China IDG/McGovern Institute for Brain Research Tsinghua University Beijing China Department of Computer Science and Technology Tsinghua University China China Nanhu Academy of Electronics and Information Technology Jiaxing Zhejiang China China Electronics Technology HIK Group Co. Ltd Hangzhou Zhejiang China

ISBN: (数字)9798331504168

ISBN: (纸本)9798331504175

Modern Von Neumann-based computers are increasingly inadequate to meet current computational demands. Braininspired computing (BIC) offers a promising alternative, aiming to emulate the efficiency, adaptability, and learning capabilities of the human brain. Despite significant progress, a unified technological solution for BIC has yet to emerge. This talk reviews recent BIC advancements, covering theories, chips, software, and systems. Key challenges and potential solutions for robust BIC systems and the development of general purposed BIC are also examined.

关键词： computers computer science Neuroscience Reviews Neuromorphic engineering Software Manufacturing

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On Mobile Covert Communication in Wireless Networks with a Full-Duplex Receiver

On Mobile Covert Communication in Wireless Networks with a F...

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International Conference on Networking and Network Applications (NaNA)

作者： Qifeng Miao Yizhi Cao Ranran Sun Ji He Xiaochen Li Huihui Wu Hangzhou Institute of Technology Xidian University Hangzhou China School of Computer Science and Technology Xidian University Xi’an China PLA Strategic Support Force Information Engineering University Zhengzhou China Department of Automation Beijing National Research Center for Information Science and Technology (BNRist) Tsinghua University Beijing China

ISBN: (数字)9798350376777

ISBN: (纸本)9798350376784

This paper investigates the covert communication in a mobile wireless system where a mobile transmitter wants to send covert messages to a full-duplex receiver without being detected by a warden. To assist the covert communication, the receiver emits artificial noise to confuse the warden. We first provide the theoretical modeling for the probabilities of false alarm and missed detection, and the detection error probability. Then, we analyze the optimal threshold and minimum detection error probability of the warden. Finally, extensive numerical results are presented to verify the correctness of our analysis and explore the impact of mobility on warden’s detection performance. We can have a deep insight that the mobile transmitter can significantly decrease the detection performance of warden and contribute to the covert communication.

关键词： Transmitters Error probability Wireless networks Noise Receivers Full-duplex system Trajectory

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NTIRE 2023 Challenge on Night Photography Rendering

NTIRE 2023 Challenge on Night Photography Rendering

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2023 IEEE/CVF Conference on computer Vision and Pattern Recognition Workshops, CVPRW 2023

作者： Shutova, Alina Ershov, Egor Perevozchikov, Georgy Ermakov, Ivan Banić, Nikola Timofte, Radu Collins, Richard Efimova, Maria Terekhin, Arseniy Zini, Simone Rota, Claudio Buzzelli, Marco Bianco, Simone Schettini, Raimondo Lei, Chunxia Wang, Tingniao Wang, Song Liu, Shuai Feng, Chaoyu Shao, Guangqi Wang, Hao Wang, Xiaotao Lei, Lei Xu, Lu Zhang, Chao Wang, Yasi Guo, Jin Sun, Yangfan Liu, Tianli Hao, Dejun Kinli, Furkan Özcan, Bariş Kiraç, Furkan Chung, Hyerin Lee, Nakyung Kwak, Sung Keun Conde, Marcos Elezabi, Omar Seizinger, Tim Vasluianu, Florin Hsieh, Chia-Hsuan Chen, Wei-Ting Yang, Hao-Hsiang Huang, Zhi-Kai Chang, Hua-En Chen, I-Hsiang Chen, Yi-Chung Chiang, Yuan-Chun Kuo, Sy-Yen Russia Gideon Brothers Croatia Swiss Federal Institute of Technology in Zurich Switzerland University of Milano - Bicocca Italy Zhejiang Dahua Technology China Xiaomi Inc. China Beijing Samsung Teleconmmunication R&D Center China Vision and Graphics Lab Özyeǧin University Turkey Fishency Innovation Norway T-Fashion Inc. Turkey CJ OliveNetworks AI Research Korea Republic of University of Wuerzburg Computer Vision Lab CAIDAS Germany ServiceNow United States Graduate Institute of Electronics Engineering National Taiwan University Taiwan Department of Electrical Engineering National Taiwan University Taiwan Graduate Institute of Communication Engineering National Taiwan University Taiwan University of Würzburg Germany

ISBN: (纸本)9798350302493

This paper presents a review of the NTIRE 2023 challenge on night photography rendering. The goal of the challenge was to find solutions that process raw camera images taken in nighttime conditions conditions, and thereby produce a photo-quality output images in the standard RGB (sRGB) space. Unlike the previous year's competition, participants were not provided with a large training dataset for the target sensor. Instead, this time they were given images of a color checker illuminated by a known light source. To evaluate the results, a sufficient number of viewers were asked to assess the visual quality of the proposed solutions, considering the subjective nature of the task. The highest ranking solutions were further ranked by Richard Collins, a renowned photographer. The top ranking participants' solutions effectively represent the state-of-the-art in nighttime photography rendering. More results can be found at https://***/ © 2023 IEEE.

关键词： Photography

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Flower Pollination Heuristics for Parameter Estimation of Electromagnetic Plane Waves

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computers, Materials & Continua 2021年第8期68卷 2529-2543页

作者： Sadiq Akbar Muhammad Asif Zahoor Raja Naveed Ishtiaq Chaudhary Fawad Zaman Hani Alquhayz Department of Electronics University of PeshawarPeshawar25120Pakistan Future Technology Research Center National Yunlin University of Science and TechnologyDouliou64002Taiwan Department of Electrical Engineering International Islamic UniversityIslamabad44000Pakistan Department of Electrical Engineering COMSATS University IslamabadIslamabad44000Pakistan Department of Computer Science and Information College of Science in ZulfiMajmaah UniversityAl-Majmaah11952Saudi Arabia

For the last few decades,the parameter estimation of electromagnetic plane waves i.e.,far field sources,impinging on antenna array geometries has attracted a lot of researchers due to their use in radar,sonar and under water acoustic *** this work,nature inspired heuristics based on the flower pollination algorithm(FPA)is designed for the estimation problem of amplitude and direction of arrival of far field sources impinging on uniform linear array(ULA).Using the approximation in mean squared error sense,a fitness function of the problem is developed and the strength of the FPA is utilized for optimization of the cost function representing scenarios for various number of sources non-coherent located in the far *** worth of the proposed FPA based nature inspired computing heuristic is established through assessment studies on fitness,histograms,cumulative distribution function and box plots *** other worthy perks of the proposed scheme include simplicity of concept,ease in the implementation,extendibility and wide range of applicability to solve complex optimization *** salient features make the proposed approach as an attractive alternative to be exploited for solving different parameter estimation problems arising in nonlinear systems,power signal modelling,image processing and fault diagnosis.

关键词： Direction of arrival flower pollination algorithm plane waves parameter estimation

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TS-CGNet: Temporal-Spatial Fusion Meets centerline-Guided Diffusion for BEV Mapping

arXiv

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arXiv 2025年

作者： Hong, Xinying Li, Siyu Zeng, Kang Shi, Hao Peng, Bomin Yang, Kailun Li, Zhiyong The College of Computer Science and Electronic Engineering Hunan University Changsha410082 China The School of Robotics The National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha410082 China The State Key Laboratory of Extreme Photonics and Instrumentation Zhejiang University Hangzhou310027 China

Bird’s Eye View (BEV) perception technology is crucial for autonomous driving, as it generates top-down 2D maps for environment perception, navigation, and decision-making. Nevertheless, the majority of current BEV map generation studies focusing on visual map generation lack depth-aware reasoning capabilities. They exhibit limited efficacy in managing occlusions and handling complex environments, with a notable decline in perceptual performance under adverse weather conditions or low-light scenarios. Therefore, this paper proposes TS-CGNet, which leverages Temporal-Spatial fusion with centerline-Guided diffusion. This visual framework, grounded in prior knowledge, is designed for integration into any existing network for building BEV maps. Specifically, this framework is decoupled into three parts: Local mapping system involves the initial generation of semantic maps using purely visual information;The Temporal-Spatial Aligner Module (TSAM) integrates historical information into mapping generation by applying transformation matrices;The centerline-Guided Diffusion Model (CGDM) is a prediction module based on the diffusion model. CGDM incorporates centerline information through spatial-attention mechanisms to enhance semantic segmentation reconstruction. We construct BEV semantic segmentation maps by our methods on the public nuScenes and the robustness benchmarks under various corruptions. Our method improves 1.90%, 1.73%, and 2.87% for perceived ranges of 60×30m, 120×60m, and 240×60m in the task of BEV HD mapping. TS-CGNet attains an improvement of 1.92% for perceived ranges of 100×100m in the task of BEV semantic mapping. Moreover, TS-CGNet achieves an average improvement of 2.92% in detection accuracy under varying weather conditions and sensor interferences in the perception range of 240×60m. The source code will be publicly available at https://***/krabs-H/TS-CGNet. Copyright © 2025, The Authors. All rights reserved.

关键词： Decision making

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Neuro-Evolutionary Framework for Design Optimization of Two-Phase Transducer with Genetic Algorithms

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Micromachines 2023年第9期14卷 1677-1677页

作者： Zameer, Aneela Naz, Sidra Raja, Muhammad Asif Zahoor Hafeez, Jehanzaib Ali, Nasir Department of Computer and Information Sciences Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Department of Electrical Engineering Pakistan Institute of Engineering and Applied Sciences Nilore Islamabad45650 Pakistan Future Technology Research Center National Yunlin University of Science and Technology 123 University Road Section 3 Yunlin Douliou64002 Taiwan

Multilayer piezocomposite transducers are widely used in many applications where broad bandwidth is required for tracking and detection purposes. However, it is difficult to operate these multilayer transducers efficiently under frequencies of 100 kHz. Therefore, this work presents the modeling and optimization of a five-layer piezocomposite transducer with ten variables of nonuniform layer thicknesses and different volume fractions by exploiting the strength of the genetic algorithm (GA) with a one-dimensional model (ODM). The ODM executes matrix manipulation by resolving wave equations and produces mechanical output in the form of pressure and electrical impedance. The product of gain and bandwidth is the required function to be maximized in this multi-objective and multivariate optimization problem, which is a challenging task having ten variables. Converting it into the minimization problem, the reciprocal of the gain-bandwidth product is considered. The total thickness is adjusted to keep the central frequency at approximately 50–60 kHz. Piezocomposite transducers with three active materials, PZT5h, PZT4d, PMN-PT, and CY1301 polymer, as passive materials were designed, simulated, and statistically evaluated. The results show significant improvement in gain bandwidth compared to previous existing techniques. © 2023 by the authors.

关键词： Genetic algorithms

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Comprehensive Feature Processing Based on Attention Mechanism for Co-Salient Object Detection

Comprehensive Feature Processing Based on Attention Mechanis...

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

作者： Guohua Lv Mao Yuan Zengbin Zhang Zhengyang Zhang Zhenhui Ding Guangxiao Ma Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Shandong Key Laboratory of Ubiquitous Intelligent Computing Jinan China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Co-salient object detection (CoSOD) aims to detect common salient objects across multiple related images. However, existing methods often struggle with limited attention coverage, missing some co-salient objects. To address this, we propose a two-stage feature processing module (FPM) comprising comprehensive feature extraction module (CFE) and feature enhancement module (FEM). CFE extracts comprehensive cosalient features while reducing background noise, and FEM enhances feature representation and adjusts attention weights for full object coverage. Additionally, we introduce an adversarial learning module (ALM) to improve prediction quality by reducing noise in the co-salient regions. Extensive experiments on three benchmark datasets—CoCA, CoSOD3k, and CoSal2015—demonstrate that our model significantly outperforms state-of-the-art methods. The source code is available at https://***/yaobaimiao/CFPAM.

关键词： Attention mechanisms Source coding Noise Object detection Benchmark testing Feature extraction Adversarial machine learning Finite element analysis Background noise Speech processing

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Automated Data Visualization from Natural Language via Large Language Models: An Exploratory Study

arXiv

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arXiv 2024年

作者： Wu, Yang Wan, Yao Zhang, Hongyu Sui, Yulei Wei, Wucai Zhao, Wei Xu, Guandong Jin, Hai Huazhong University of Science and Technology China Chongqing University China University of New South Wales Australia University of Technology Sydney Australia National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The Natural Language to Visualization (NL2Vis) task aims to transform natural-language descriptions into visual representations for a grounded table, enabling users to gain insights from vast amounts of data. Recently, many deep learning-based approaches have been developed for NL2Vis. Despite the considerable efforts made by these approaches, challenges persist in visualizing data sourced from unseen databases or spanning multiple tables. Taking inspiration from the remarkable generation capabilities of Large Language Models (LLMs), this paper conducts an empirical study to evaluate their potential in generating visualizations, and explore the effectiveness of in-context learning prompts for enhancing this task. In particular, we first explore the ways of transforming structured tabular data into sequential text prompts, as to feed them into LLMs and analyze which table content contributes most to the NL2Vis. Our findings suggest that transforming structured tabular data into programs is effective, and it is essential to consider the table schema when formulating prompts. Furthermore, we evaluate two types of LLMs: finetuned models (e.g., T5-Small) and inference-only models (e.g., GPT-3.5), against state-of-the-art methods, using the NL2Vis benchmarks (i.e., nvBench). The experimental results reveal that LLMs outperform baselines, with inference-only models consistently exhibiting performance improvements, at times even surpassing fine-tuned models when provided with certain few-shot demonstrations through in-context learning. Finally, we analyze when the LLMs fail in NL2Vis, and propose to iteratively update the results using strategies such as chain-of-thought, role-playing, and code-interpreter. The experimental results confirm the efficacy of iterative updates and hold great potential for future study. © 2024, CC BY.

关键词： Data visualization

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