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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Jin, Qiangguo Cui, Hui Sun, Changming Huang, Jiapeng Xuan, Ping Xu, Yiyue Wang, Linlin Cao, Leilei Wei, Leyi Su, Ran Northwestern Polytechnical University School of Software Shaanxi China Yangtze River Delta Research Institute of Northwestern Polytechnical University Taicang China La Trobe University Department of Computer Science and Information Technology Melbourne Australia Csiro Data61 Sydney Australia Shantou University School of Engineering Department of Computer Science Guangdong China Shandong First Medical University Shandong Academy of Medical Sciences Shandong Cancer Hospital and Institute Department of Radiation Oncology Shandong China Zhejiang University Innovation Center of Yangtze River Delta Zhejiang China Shandong University School of Software Shandong China Tianjin University School of Computer Software College of Intelligence and Computing Tianjin China

ISBN: (纸本)9798350337488

Accurate tumor segmentation is crucial for esophageal cancer radiotherapy treatment planning. The low contrast among the esophagus, tumors, and surrounding tissues, and irregular tumor shapes limit the performance of automatic segmentation methods. In this paper, we aim to exploit the irregular shapes of tumors to facilitate accurate segmentation. We propose a simple and pluggable shape-aware contrastive deep supervision network (SCDSNet) with shape-aware regularization and voxel-to-voxel contrastive deep supervision. Specifically, the shape-aware regularization with an uncertainty minimization strategy encourages the precise predictions of an additional shape-aware head. The voxel-to-voxel contrastive deep supervision enhances the multi-scale shape-tumor contrast for better voxel-to-voxel prediction of shapes. The proposed method is simple and highly pluggable, which can easily be extended to other frameworks. Further, we establish a large in-house dataset on esophageal cancer to validate the effectiveness of our proposed method. The quantitative and qualitative experimental results demonstrate the effectiveness of SCDSNet on the esophageal cancer dataset. © 2023 IEEE.

关键词： Deep supervision Esophagus tumor segmentation Shape-aware regularization Voxel-to-voxel contrastive learning

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A Multi-Head Convolution Network with Attention Consistency for Facial Expression Recognition

A Multi-Head Convolution Network with Attention Consistency ...

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Chinese Control Conference (CCC)

作者： Wenkang Liu Mingyi Sun Yang Li the Department of Automation Science and Electrical Engineering Beihang University Beijing China the Beijing Advanced Innovation Center for Big Data and Brain Computing Beijing China State Key Laboratory of Virtual Reality Technology and Systems Beihang University Beijing China Advanced Institute of Information Technology Peking University Hangzhou China

In recent years, the demand for facial expression recognition applications has increased rapidly, and its research has received extensive attention from researchers. However, the current recognition methods based on deep learning ignore the idea of multiple head attention and semantic consistency, resulting in the model only paying attention to the local area of the feature map. In addition, the model's inconsistent attention to the images before and after the flip, resulting the poor robustness, poor interpretation, and other shortcomings in the model. To address the above problems, we propose an Affinity Separation Loss (ASLoss), which improves the separability of samples through clustering. Moreover, a Separate Multi-head Attention block (SMA), and a Zonal Loss (ZLoss) are also designed to decentralize the model's attention. Experimental results demonstrate that our proposed MACNet method achieves competitive recognition performance on two public datasets RAF-DB and FERPlus.

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Multi-User Probabilistic Semantic Communication with Semantic Compression Ratio Optimization

Multi-User Probabilistic Semantic Communication with Semanti...

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IEEE International Conference on Communications Workshops, ICC

作者： Zhouxiang Zhao Zhaohui Yang Mingzhe Chen Changsheng You Qianqian Yang Wei Xu Zhaoyang Zhang College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China National Mobile Communications Research Laboratory Southeast University Nanjing China

ISBN: (数字)9798350304053

ISBN: (纸本)9798350304060

In this paper, the problem of joint transmission and computation resource allocation for a multi-user probabilistic semantic communication (PSC) network is investigated. In the considered model, users employ semantic information extraction techniques to compress their large-sized raw data before transmitting them to a multi-antenna base station (BS). Our model represents the raw data through comprehensive knowledge graphs, utilizing shared probability graphs between users and the BS for efficient semantic compression. The resource allocation problem is formulated as an optimization problem with the objective of maximizing the sum of equivalent rate of all users, considering total power budget constraint. This joint optimization problem inherently addresses the delicate balance between transmission efficiency and computational complexity. To address this optimization challenge, we present an iterative algorithm in which the optimal solution for the semantic compression ratio of a specific user is determined at each iteration. Numerical results validate the effectiveness of the proposed scheme.

关键词： Conferences Computational modeling Semantics Knowledge graphs Probabilistic logic information retrieval data models

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Auction Theory and Game Theory Based Pricing of Edge computing Resources: A Survey

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IEEE Internet of Things Journal 2025年

作者： Yu, Jiguo Liu, Shun Zou, Yifei Wang, Guijuan Hu, Chunqiang University of Electronic Science and Technology of China School of Information and Software Engineering Chengdu610054 China Qilu University of Technology Big Data Institute Jinan250353 China Qufu Normal University School of Computer Science Shandong Rizhao China Shandong University School of Computer Science and Technology Shandong Qingdao266237 China Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Jinan250353 China Shandong Fundamental Research Center for Computer Science Shandong Provincial Key Laboratory of Computer Networks Jinan250353 China Chongqing University School of Big Data and Software Engineering Chongqing400044 China

The proliferation of Internet of Things (IoT) devices and edge computing applications has heightened the demand for efficient resource allocation and pricing mechanisms. Effective pricing strategies play a crucial role in optimizing resource utilization, reducing latency, and ensuring fair resource distribution among users. Both auction theory and game theory have emerged as fundamental economic frameworks for designing pricing mechanisms in edge computing. This paper presents a comprehensive survey of pricing strategies that leverage both auction and game theory, systematically categorizing existing research based on auction mechanisms and game-theoretic models. Unlike previous surveys that focus solely on auction-based pricing, this work integrates game-theoretic perspectives, providing a holistic analysis of strategic interactions in edge computing resource allocation. Furthermore, we discuss key challenges, emerging trends, and future research directions aimed at enhancing efficiency, fairness, and privacy protection in cloud-edge collaborative computing environments. © 2014 IEEE.

关键词： Mobile edge computing

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Resource Allocation for Semantic Relay Aided Wireless Networks with Probability Graph

Resource Allocation for Semantic Relay Aided Wireless Networ...

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IEEE International Conference on Communications (ICC)

作者： Jiahui Zhao Ming Chen Zhaohui Yang Changsheng You Mingzhe Chen National Mobile Communications Research Laboratory Southeast University Nanjing China College of Information Science and Electronic Engineering Zhejiang University Hangzhou China Zhejiang Provincial Key Laboratory of Info. Proc. Commun. & Netw. (IPCAN) Hangzhou China Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen China Department of Electrical and Computer Engineering and Institute for Data Science and Computing University of Miami Coral Gables FL USA

ISBN: (数字)9781728190549

ISBN: (纸本)9781728190556

In this paper, we introduce a novel uplink semantic relay (SemRelay)-aided wireless communication system, catering to multiple users by leveraging a shared probability graph between the SemRelay and the base station (BS). In this system, users transmit text information to the SemRelay through conventional bit transmission, and the SemRelay compresses this information using a knowledge based characterized by probability graph before transmitting it to the BS through semantic communication. Then, the BS recovers the information based on the shared probability graph. While the semantic information compression incurs computational resource consumption, it significantly reduces communication resource usage. This paper addresses the challenge of minimizing overall system latency through jointly optimizing communication and computation re-source allocation, considering limited wireless resources and the system's energy budget. To address this problem, we introduce an efficient iterative algorithm, which employs block coordinate descent for communication resource allocation and exhaustive searching for determining the optimal data compression scheme. In particular, both power allocation subproblem and bandwidth allocation subproblem are proved to be convex. The complexity analysis of the proposed algorithm are also provided. Numerical results validate the effectiveness of the proposed algorithm and the superior performance of semantic communication compared to the conventional bit transmission.

关键词： Wireless networks Semantics Knowledge based systems data compression Search problems Complexity theory Resource management

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Unsupervised Feature Disentanglement and Augmentation Network for One-class Face Anti-spoofing

arXiv

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arXiv 2025年

作者： Huang, Pei-Kai Chong, Jun-Xiong Hsu, Ming-Tsung Hsu, Fang-Yu Lin, Yi-Ting Chien, Kai-Heng Shao, Hao-Chiang Hsu, Chiou-Ting Institute of Data Science and Information Computing National Chung Hsing University Taichung City402204 Taiwan Department of Computer Science National Tsing Hua University Taiwan

Face anti-spoofing (FAS) techniques aim to enhance the security of facial identity authentication by distinguishing authentic live faces from deceptive attempts. While two-class FAS methods risk overfitting to training attacks to achieve better performance, one-class FAS approaches handle unseen attacks well but are less robust to domain information entangled within the liveness features. To address this, we propose an Unsupervised Feature Disentanglement and Augmentation Network (UFDANet), a one-class FAS technique that enhances generalizability by augmenting face images via disentangled features. The UFDANet employs a novel unsupervised feature disentangling method to separate the liveness and domain features, facilitating discriminative feature learning. It integrates an out-of-distribution liveness feature augmentation scheme to synthesize new liveness features of unseen spoof classes, which deviate from the live class, thus enhancing the representability and discriminability of liveness features. Additionally, UFDANet incorporates a domain feature augmentation routine to synthesize unseen domain features, thereby achieving better generalizability. Extensive experiments demonstrate that the proposed UFDANet outperforms previous one-class FAS methods and achieves comparable performance to state-of-the-art two-class FAS methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Feature extraction

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Impurity explicit optimal Machine learning and statistical perfect construction for air quality prediction

Impurity explicit optimal Machine learning and statistical p...

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Networking and Communications (ICNWC), International Conference on

作者： Nareshkumar R R. Shalinikumari S. Sri Nandhini Kowsalya Vijay Anand P Ajmal Mohamed S. Gayathri Department of Networking and Communications School of Computing College of Engineering and Technology SRM Institute of Science and Technology Kattankulathur Tamilnadu India Department of Architecture Bharath Institute of Higher Education and Research Selaiyur Chennai Tamilnadu India Department of Information Technology Veltech Multitech Dr.Rangarajan Dr.Sakunthala Engineering College Chennai Tamilnadu India Department of Artificial Intelligence and Data Science CMR Institute of Technology Bengaluru India Department of Computer Science and Engineering Saveetha School of Engineering Chennai India Department of Computer Science and Engineering K. Ramakrishnan College of Technology Trichy Tamilnadu India

ISBN: (数字)9798350365269

ISBN: (纸本)9798350365276

The air quality index (AQI) is a metric used to report air quality levels. There has been a substantial rise in the level of air pollution in Indian cities. Multiple methodologies exist for formulating a mathematical equation to ascertain the air quality index. Multiple studies have established a correlation between exposure to air pollution and negative health effects in the general population. The objective of this study is to determine the optimal approach for predicting AQI in order to aid in climate regulation. Nine separate Machine learning (ML) algorithms have been employed in the suggested study to ascertain the AQI. The use of the SMOTE algorithm in the datasets in ML resulted in improved accuracy when comparing the outcomes. Furthermore, all of the implementations have been thoroughly documented using graphs and metrics, which clearly illustrate the differences in outcomes and aid in identifying the specific factors that led to the improvement in accuracy.

关键词： Measurement Machine learning algorithms Urban areas Sociology Machine learning Predictive models Air pollution

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Deep Learning Strategies for Addressing Anomalous Exposure in Image Processing: The FARDBUNet Approach 5

Deep Learning Strategies for Addressing Anomalous Exposure i...

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5th International Conference on High Performance Big data and Intelligent Systems, HDIS 2023

作者： Zhou, Qi Yang, Kai Ke, Zunwang Wang, Gang Zhang, Yugui Jia, Yizhen Cao, Fengcai Ma, Junxiao Liu, Changlin Zhang, Kaijie Wu, Min School of Semiconductor Science and Technology South China Normal University GuangZhou510631 China CGNPC Uranium Industry Development Co. Ltd. Beijing100020 China School of Software Xinjiang University Urumqi830046 China School of Computing and Data Engineering NingboTech University Ningbo315100 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Co. Ltd Beijing100176 China School of Information Beijing Forestry University Beijing100083 China

ISBN: (纸本)9798350394160

In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of imaging devices caused by their size and material constraints. This phenomenon gives rise to anomalous exposure problems and triggers the loss of crucial initial details, thereby becoming a significant obstacle to achieving high image quality. Despite numerous researchers’ integration of exposure compensation techniques into the imaging process, the anomalous exposure problem remains unsolved, hindering progress in image processing. Consequently, considerable research is valuable in implementing exposure calibration techniques on visual perception and multimedia devices. This dissertation proposes FARDBUNet, a novel end-to-end exposure calibration model designed to address the anomalous exposure problem. A key innovation of FARDBUNet is the incorporation of a fused attention mechanism, enabling the model to prioritize regions needing enhanced correction while accounting for the global exposure characteristics of the image. Additionally, to tackle the challenge of inadequate information availability in the localized areas due to exposure issues, we introduce an atrous residual dense block that efficiently captures local information by expanding the receptive field. Comprehensive tests validate the effectiveness of our suggested model, which performs on par with leading techniques on expansive exposure datasets. ©2023 IEEE.

关键词： Image enhancement

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Individual Tree Crown Delineation Based on Deep Learning for Arid Areas Using High-Resolution Satellite Imagery

Individual Tree Crown Delineation Based on Deep Learning for...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： Jinzhuang Shi Hui Li Yuanyuan Lin Linhai Jing Kongwen Zhang International Research Center of Big Data for Sustainable Development Goals Beijing China College of Mathematics and System Sciences Xinjiang University Xinjiang China Key Laboratory of Digital Earth Science Aerospace Information Research Institute Chinese Academy of Sciences Beijing C Hainan Key Laboratory of Earth Observation Hainan Aerospace Information Research Institute Wenchang China School of Earth Sciences and Resources China University of Geosciences Beijing China School of Computing University of the Fraser Valley Abbotsford Canada

ISBN: (数字)9798350360325

ISBN: (纸本)9798350360332

In this work, we explored the potential of the individual tree crown (ITC) delineation and individual tree species classification using deep learning using a Worldview-3 scene covering the Wushen Banner area, Ordos City, where is a typical arid region located in the hinterland of the Mu Us Desert. Combined with field survey data, an ITC delineation sample set and an individual tree species sample set were established. The Mask R-CNN model was employed for ITC delineation, and ResNet-18, GoogLeNet, and DenseNet-40 network models were considered for individual tree species classification. The results showed that the highest accuracy of ITC delineation using MASK R-CNN reached 78.9%. The training accuracies of individual tree species classification models were above 90%, and ResNet-18 achieved the highest classification accuracy. The trained ResNet-18 model was employed to classify tree species from the ITC delineation results of two testing images, and the overall accuracies were higher than 90%.

关键词： Deep learning Training Surveys Accuracy Satellites Urban areas Vegetation

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Joint Optimization of Sensing and Communications in Vehicular Networks: A Graph Neural Network-Based Approach

Joint Optimization of Sensing and Communications in Vehicula...

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IEEE International Conference on Communications (ICC)

作者： Xuefei Li Mingzhe Chen Zhilong Zhang Danpu Liu Yuchen Liu Shiwen Mao Beijing Laboratory of Advanced Information Network Beijing University of Posts and Telecommunications Beijing China Department of Electrical and Computer Engineering Institute for Data Science and Computing University of Miami Coral Gables FL USA Department of Computer Science North Carolina State University Raleigh NC USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA

In this paper, the problem of joint sensing and communications is studied over terahertz (THz) vehicular networks. In the studied model, a set of service provider vehicles provide either communication service or sensing service to communication target vehicles or sensing target vehicles, respectively. Therefore, it is necessary to determine the service mode (i.e., providing sensing or communication service) for each service provider vehicle and the subset of target vehicles that each service provider vehicle will serve. The problem is formulated as an optimization problem aiming to maximize the sum of the data rates of all communication target vehicles while satisfying the sensing service requirements of all sensing target vehicles by determining the service mode and the user association for each service provider vehicle. To solve this problem, a graph neural network (GNN) based algorithm with a heterogeneous graph representation is proposed. The proposed algorithm enables the central controller to extract each vehicle's graph information related to its location, connection, and communication interference. Using the extracted graph information, the joint service mode selection and user association strategy will be determined. Simulation results show that the proposed GNN-based scheme can achieve 94% of the sum rate produced by the optimal solution, and yield up to 3.95% and 36.16% improvements in sum rate, respectively, compared to a homogeneous GNN-based algorithm and the conventional optimization algorithm without using GNNs.

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