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7th International Conference on Big Data Computing and Communications, BigCom 2021

作者： Zhao, Peng Wang, Lei Tian, Yu Guo, Linlin Lu, Bingxian Dalian University of Technology School of Software Liaoning China Shandong Normal University School of Information Science and Engineering Shandong China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Liaoning China

ISBN: (纸本)9781665442527

The ubiquitous Global Positioning System (GPS) provides great convenience for people's outdoor activities but failed to fulfill the requirements of indoor positioning. Therefore, different technologies, such as WiFi and Bluetooth, are researched and applied to provide solutions for indoor scenarios. In this paper, we developed a WiFi-Bluetooth dual-modal indoor positioning system that uses WiFi for communication and Bluetooth Low Energy (BLE) for positioning. To balance accuracy and power consumption and mitigate the noise caused by the external environment, a range-based filter is proposed to enhance the multilateration result. Experiments are carried out in real environments and simulations based on ns-3 are also developed for repeatability. The result shows that our work gives 90% of accuracy for locating objects with 1-meter precision which can meet the requirements of indoor positioning. © 2021 IEEE.

关键词： Indoor positioning systems

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Multi-task learning modeling of attribute mutual association based on confidence and imputation of missing values 21

Multi-task learning modeling of attribute mutual association...

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2nd International Conference on Computing, networks and Internet of Things, CNIOT 2021

作者： Lai, Xiaochen Mao, Liwen Zhang, Zheng Zhang, Genglin School of Software Dalian University of Technology China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province China International School of Information Science and Engineering Dalian University of Technology China Dalian Chinacreative Technology Co. Ltd. China

ISBN: (纸本)9781450389693

Incomplete datasets with missing values increase the difficulty of data analysis. In this paper, the incomplete data are modeled based on the Auto-Association Neural network(AANN) and the missing values are imputation. In order to strengthen the correlation among the attributes of samples, we propose an Attribute Mutual Associative Multi-Task Learning Model Based on Confidence (AM-MTL). By optimizing the data transmission path of nodes in the output layer, we construct an MTL architecture in which the main imputation task is parallel to the secondary classification task, and use the missing rate of sample attributes as the initial sample confidence to weaken the influence of incomplete input samples on the network parameter adjustment. The missing values and confidence are used as variables for training, and they can be updated iteratively, thus continuously reduce the estimation error of missing values. Experiments demonstrate the effectiveness of the proposed method in missing value imputation. © 2021 ACM.

关键词： Learning systems

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Precise and Efficient Third-party Java Libraries Identification Tool for Collaborative software

Precise and Efficient Third-party Java Libraries Identificat...

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International Conference on Computer Supported Cooperative Work in Design

作者： Zhuo Wang Hongtu Zhang Jingdong Guo Laile Xi Sidy Tambadou Fang Zuo Hong Li Yan Hu School Of Software Henan University Kaifeng China Institute of Information Engineering Chinese Academy of Sciences Beijing China Henan International Joint Laboratory ofIntelligent Network Theory and Key Technology Henan University School of Computer & Communication Engineering University of Science and Technology Beijing Beijing China

ISBN: (数字)9798350349184

ISBN: (纸本)9798350349191

Collaborative systems frequently depend on various software components, like third-party libraries (TPLs), to execute their functions and expedite the development of the system. The security of an entire collaboration system can be compromised by a TPL that is vulnerable, particularly in an industrial setting. Unfortunately, current TPL detection tools encounter difficulties in precisely identifying version levels and exhibit inefficiency in detecting TPLs on a large *** address these challenges, we recommend JHunter, a precise and efficient tool for detecting TPL version details. Our approach involves introducing a novel concept called the attribute class dependency graph (ACDG) as a feature at the package level for TPLs. We then utilise a graph neural network-based method to compare the similarity of ACDGs and identify a list of candidate TPLs. Later, we use more detailed class-level features, such as Control Flow Graphs (CFGs), and constant features to determine version-specific information. We collected 19,095 different versions of TPLs from Maven to build our feature database. Our analysis demonstrates the effectiveness of JHunter on a real-world dataset, achieving F1 scores of 99.34% and 97.28% at the library and version levels, respectively, surpassing previous state-of-the-art (SOTA) results.

关键词： Java Federated learning Collaborative software Collaboration Reliability engineering Libraries software

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Contextual Graph Reconstruction and Emotional Variation Learning for Conversational Emotion Recognition

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IEEE Transactions on Affective Computing 2025年

作者： Rao, Yujing Cao, Min Ye, Mang Wuhan University National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan430072 China Soochow University School of Computer Science and Technology Suzhou215006 China

Conversational Emotion Recognition (CER) significantly benefits from the integration of multiple modalities. However, real-world scenarios are often plagued by hardware malfunctions and network failures that lead to missing modalities and incomplete emotional representations. Existing methods primarily focus on modeling inter-modal relationships within isolated utterances to generate missing data, so they do not adequately capture conversational context and dynamic emotional evolution. In conversations, emotional expressions are inherently context-dependent and dynamic. Insufficient modeling of these properties can substantially degrade performance. To address these challenges, we propose Contextual Graph Reconstruction and Emotional Variation Learning (CGR-EVL). Our approach flexibly constructs an utterance graph with diverse coverage based on speaker activity and modality availability, thereby capturing more contextual information. To enhance the handling of missing modalities, we further integrate temporal relations through a relational graph convolutional network to reconstruct missing features and introduce a speaker emotion-aware constraint to ensure emotional coherence. Additionally, we propose the concept of emotional entropy to quantify variation patterns and develop a novel loss function that aligns predicted and actual variations. Experiments on the IEMOCAP and MELD datasets show that CGR-EVL outperforms state-of-the-art methods, particularly under conditions of incomplete modalities. © 2010-2012 IEEE.

关键词： Graph neural networks

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Emotion Recognition Empowered Human-Computer Interaction with Domain Adaptation network

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IEEE Transactions on Consumer Electronics 2024年

作者： Xu, Xu Fu, Chong Chen, Junxin Northeastern University School of Computer Science and Engineering Shenyang110004 China Dalian University of Technology China The School of Software Dalian116621 China Northeastern University School of Computer Science and Engineering Shenyang110004 China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Shenyang110819 China Ministry of Education Engineering Research Center of Security Technology of Complex Network System Shenyang110004 China Dalian University of Technology School of Software Dalian116621 China

Multi-modal emotion recognition plays a vital role in the human-computer interaction (HCI) for consumer electronics. Nowadays, many studies have developed multi-modal fusion algorithms for this ***, two challenging issues remain unsolved, i.e., inefficient multi-modal feature fusion and unclear distance in feature *** this end, we develop a novel framework, namely LAFDA-Net, for cross-subject emotion recognition using EEG and eye movement *** is based on low-rank fusion and domain adaptation *** specifically, the multi-modal signals are input into the feature extraction branch in parallel to generate ***, these features are fused by the low-rank fusion branch, reducing complexity and avoiding ***, the fused features are flattened and sent to the classification branch to determine the emotion *** training, these features are input into the domain adaptive branch to bridge the gap between the source domain and target *** benchmark datasets, i.e., SEED, SEED-IV, and SEED-V, are employed for performance *** results demonstrate that the proposed LAFDA-Net is robust, effective, and has advantages over peer methods. © 1975-2011 IEEE.

关键词： Emotion Recognition

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Incentive Sharing: A Consumer-Data Privacy Trusted Sharing Paradigm Via Proximal Policy Optimization in Industry 5.0

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IEEE Transactions on Consumer Electronics 2024年

作者： Cai, Ting Hu, Ying Li, Xiang Chen, Wuhui Wu, Yuxin Ye, Zhiwei Zheng, Zibin Hubei University of Technology China China Hubei University of Technology School of Computer Science The Provincial Key Laboratory of Green Intelligent Computing Network Wuhan430068 China Sun Yat-sen University China Pengcheng Laboratory China Hubei University of Arts and Science School of Computer Engineering Xiangyang441053 China Sun Yat-sen University School of Software Engineering Zhuhai519000 China

Industry 5.0 emphasizes consumer data sharing to drive data-driven innovation and enhance user experiences in consumer electronics. Blockchain-integrated federated learning (blockchained FL) has recently emerged as a privacy-Trusted paradigm to facilitate sharing. However, most blockchained FL sharing paradigms cannot adapt to the complexity or variability of consumer data and network conditions such as: 1) inadequate balancing of the trade-off between data quality and training costs during device selection;2) incentives are designed to maximize instantaneous profit without considering the long-Term dynamics of electronic devices. These limitations lead to low-quality model training and security degradation. This paper proposes a novel blockchained FL incentive-sharing paradigm that dynamically selects consumer devices from a long-Term perspective while balancing data quality and training costs. Specifically, we first present a reputation strategy to enhance security by applying a predefined threshold to prevent malicious and low-quality consumers from participating in FL. Then, to optimize device selection under complex and dynamic consumer environments with high-dimensional system states, a Proximal Policy Optimization (PPO)-based incentive mechanism is proposed, which includes: 1) building an 'intelligent engine' to select the optimal consumer devices for each round of FL based on data quality and costs, and 2) implementing incentives to achieve long-Term maximization of the system's social welfare. Extensive simulations show that our sharing paradigm has, on average, 46.97% higher social welfare and 77.27% lower social costs than baselines and can guarantee high data quality in a trustless environment. © 1975-2011 IEEE.

关键词： Differential privacy

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Precise Timing over Beyond 5G networks for Intelligent Transport in the Smart City

Precise Timing over Beyond 5G Networks for Intelligent Trans...

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IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC)

作者： Hongxing Li Guochu Shou Yaqiong Liu Yihong Hu State Key Laboratory of Networking and Switching Technology School of Computer Science (National Pilot Software Engineering School) Beijing University of Posts and Telecommunications Beijing China Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

ISBN: (数字)9798350362244

ISBN: (纸本)9798350362251

The need for precise timing is increasing with the development of the smart city, and intelligent transport is a key application that often relies on the distribution of accurate timing. Existing time synchronization solutions cannot adequately meet the precise timing needs for practical applications in intelligent transportation. Global Navigation Satellite Systems (GNSS) signals, for instance, can be obstructed, weakened, or deflected in urban canyon environments. While certain sectors like telecommunications and smart grids have successfully adopted IEEE 1588 for high-precision time synchronization, its dependence on standalone networks and limited application within local area networks poses challenges when catering to the wide-ranging and mobile demands of intelligent transportation applications. This paper categorizes the time synchronization requirements of intelligent transport and proposes a time synchronization scheme over converged networks, which separates time transmission and precision compensation based on software-defined networking (SDN) principles, and makes it possible to provide precise timing for intelligent transport devices, including on-board units (OBUs) and roadside units (RSUs), over wired and wireless communication networks. Experimental results indicate that the proposed scheme achieves a relative time error of less than 1.2 microseconds over the converged networks.

关键词： Wireless communication Global navigation satellite system Accuracy Smart cities Transportation Telecommunications Smart grids Synchronization software defined networking Local area networks

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ASF-Net: Robust Video Deraining via Temporal Alignment and Online Adaptive Learning

arXiv

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arXiv 2023年

作者： Xue, Xinwei He, Jia Ma, Long Meng, Xiangyu Li, Wenlin Liu, Risheng The DUT-RU International School of Information Science & Engineering Dalian University of Technology China The Engineering Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian University of Technology Dalian116024 China The School of Software Technology Dalian University of Technology Dalian116024 China The DUT-RU International School of Information Science & Engineering Dalian University of Technology Dalian116024 China

—In recent times, learning-based methods for video deraining have demonstrated commendable results. However, there are two critical challenges that these methods are yet to address: exploiting temporal correlations among adjacent frames and ensuring adaptability to unknown real-world scenarios. To overcome these challenges, we explore video deraining from a paradigm design perspective to learning strategy construction. Specifically, we propose a new computational paradigm, Alignment-Shift-Fusion network (ASF-Net), which incorporates a temporal shift module. This module is novel to this field and provides deeper exploration of temporal information by facilitating the exchange of channel-level information within the feature space. To fully discharge the model’s characterization capability, we further construct a LArge-scale RAiny video dataset (LARA) which also supports the development of this community. On the basis of the newly-constructed dataset, we explore the parameters learning process by developing an innovative re-degraded learning strategy. This strategy bridges the gap between synthetic and real-world scenes, resulting in stronger scene adaptability. Our proposed approach exhibits superior performance in three benchmarks and compelling visual quality in real-world scenarios, underscoring its efficacy. The code is available at https://***/vis-opt-group/ASF-Net. Copyright © 2023, The Authors. All rights reserved.

关键词： Large dataset

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Continuous contrastive learning for long-tailed semi-supervised recognition 24

Continuous contrastive learning for long-tailed semi-supervi...

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Zi-Hao Zhou Siyuan Fang Zi-Jing Zhou Tong Wei Yuanyu Wan Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China Xiaomi Inc. China School of Software Technology Zhejiang University Ningbo China

ISBN: (纸本)9798331314385

Long-tailed semi-supervised learning poses a significant challenge in training models with limited labeled data exhibiting a long-tailed label distribution. Current state-of-the-art LTSSL approaches heavily rely on high-quality pseudo-labels for large-scale unlabeled data. However, these methods often neglect the impact of representations learned by the neural network and struggle with real-world unlabeled data, which typically follows a different distribution than labeled data. This paper introduces a novel probabilistic framework that unifies various recent proposals in long-tail learning. Our framework derives the class-balanced contrastive loss through Gaussian kernel density estimation. We introduce a continuous contrastive learning method, CCL, extending our framework to unlabeled data using reliable and smoothed pseudo-labels. By progressively estimating the underlying label distribution and optimizing its alignment with model predictions, we tackle the diverse distribution of unlabeled data in real-world scenarios. Extensive experiments across multiple datasets with varying unlabeled data distributions demonstrate that CCL consistently outperforms prior state-of-the-art methods, achieving over 4% improvement on the ImageNet-127 dataset. Our source code is available at https://***/zhouzihao11/CCL.

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Unsupervised MR to CT Image Translation Exploiting Multi-scale and Contextual Cues 10th

Unsupervised MR to CT Image Translation Exploiting Multi-sca...

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10th KES International Conference on Innovation in Medicine and Healthcare, KES-InMed 2022

作者： Xu, Rui Zhang, Yuening Ye, Xinchen Jin, Fu Tan, Xia Luo, Huanli DUT-RU International School of Information Science and Engineering Dalian University of Technology Dalian China DUT-RU Co-Research Center of Advanced ICT for Active Life Dalian China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian China College of Software Dalian University of Technology Dalian China Department of Radiation Oncology Chongqing University Cancer Hospital Chongqing China

ISBN: (纸本)9789811934391

Image-to-image translation is becoming an important frontier in the field of medical image analysis. Many unsupervised methods have been proposed for MR to CT image translation, but few of them have exploited the generator capability of synthesizing globally and locally coherent images with structure and context indistinguishable from real images. To address this issue, we propose a more powerful discriminator including a well-designed multi-scale architecture and a context loss function together with a structure-based slice selecting training strategy. The proposed discriminator can provide multiple feedbacks with different respective fields between pixel-level and image-level to the generator which encourages it to focus on both global structures and local details in a multi-scale view. Besides, our training strategy selects image slices with similar medical structure and constrain them with a context loss function based on greyscale histogram for more effective adversarial training on context details. We evaluate our proposed model using a series of metrics and experimental results have demonstrated that our approach achieves better results. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Computerized tomography

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