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15th IEEE International Conference on Wireless Communications and Signal Processing, WCSP 2023

作者： Chen, Zhiru Wang, Zhe Li, Jun Shi, Long Zhang, Yijin Nanjing University of Science and Technology The School of Electronic and Optical Engineering Nanjing China Nanjing University of Science and Technology The School of Computer Science and Engineering Nanjing China

ISBN: (纸本)9798350324662

In the unmanned aerial vehicle (UAV) assisted edge computing networks, the UAVs act as mobile edge nodes to provide flexible communication and computation services to user equipment (UE). To enable efficient resource trading between the UAV and UE, it is of paramount importance to design an incentive mechanism that compensates for the costs of the UAVs, while preventing the UAVs from falsely reporting their private resource information. To address this issue, we propose a contract-matching integrated incentive mechanism in a multi-UAV and multi-UE network, where the UEs are unaware of the UAVs' locations and computation frequencies. To deal with the asymmetric information, we employ contract theory to maximize each UE's expected utility while encouraging the UAVs to truthfully participate in the edge computing service. Then, we adopt the Gale-Shapley algorithm to establish the one-to-one association between UAVs and UEs. Finally, simulation results validate that our proposed mechanism can achieve individual rationality, incentive compatibility, and improve UEs' average utility compared with the benchmark algorithm. © 2023 IEEE.

关键词： contract theory incentive mechanism matching algorithm Mobile edge computing unmanned aerial vehicle (UAV)

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Decformer: A Multi-model Fusion Method Based on Decomposition and Transformer for Industrial Energy Consumption Prediction 16

Decformer: A Multi-model Fusion Method Based on Decompositio...

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16th International Conference on Wireless Communications and Signal Processing, WCSP 2024

作者： Li, Jun Li, Dagang Xia, Pengcheng Ni, Yiyang Qian, Yuwen School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing210094 China School of Computer Engineering Jiangsu Second Normal University Nanjing210013 China

ISBN: (纸本)9798350390643

As the industrial Internet of Things (IIoT) advances, the need for precise and effective long-term energy consumption forecasting grows more critical. Addressing this challenge relies on the technology of Time Sequence Forecast (TSF). Some ad-vanced deep learning methods have been used for TSF. However, existing work suffers from overly complex model structures and a lack of consideration for characteristics of the specific data. Therefore, we propose a time series prediction method called Decformer based on sequence decomposition and multi-model fusion. For components exhibiting strong periodicity, we use a simplified Transformer model with channel-independence processing to efficiently extract time-dimension feature information. Experimental results on multiple datasets demonstrate that the proposed method outperforms state-of-the-art time series prediction methods. © 2024 IEEE.

关键词： Prediction models

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Enhanced Data Mining Algorithms for Sybil Attack Prevention in Truth Discovery 24

Enhanced Data Mining Algorithms for Sybil Attack Prevention ...

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3rd International Conference on Algorithms, Data Mining, and Information technology, ADMIT 2024

作者： Shao, Yanjing Zhao, Wei Liu, Jianghua Xu, Lei Xu, Chungen School of Mathematics and Statistics Nanjing University of Science and Technology China National Key Laboratory of Security Communication China School of Computer Science and Engineering Nanjing University of Science and Technology China

ISBN: (纸本)9798400718120

In crowdsourcing systems, where substantial amounts of data from various contributors are aggregated to discern reliable information, privacy concerns are often managed through differential privacy techniques. However, these systems are vulnerable to Sybil attacks, where attackers create multiple fake contributors to gain undeserved rewards. The presence of differential privacy noise complicates the detection of these fraudulent contributors, thereby reducing the accuracy of truth discovery. To address this challenge, we introduce an enhanced data mining algorithm, Truth Discovery against Sybil Attacks (TDSA). This algorithm is specifically designed to improve truth discovery by effectively mitigating Sybil attacks and reducing transmission errors in data mining contexts, utilizing machine learning clustering algorithms and lattice-based linear coding techniques. TDSA optimizes the mining process to better handle malicious data contributions and reduces errors associated with data aggregation. Experimental results on real-world datasets demonstrate that TDSA improves accuracy by approximately 20% compared to conventional methods lacking Sybil attack defenses, highlighting its efficacy in advanced data mining applications. © 2024 Copyright held by the owner/author(s).

关键词： Data aggregation

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CeFET: Contrast-Enhanced Guided Facial Expression Translation 14th

CeFET: Contrast-Enhanced Guided Facial Expression Translatio...

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14th International Conference on computer engineering and Networks, CENet 2024

作者： Han, Linfeng Jin, Zhong Li, Yi-Chang Jia, Zhiyang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China Department of Computer School of Petroleum China University of Petroleum-Beijing at Karamay Karamay China

ISBN: (纸本)9789819640157

Advancements in deep learning have stimulated the creation of multiple techniques for facial expression translation. However, these methods frequently rely on detailed annotations of action units (AU) or 3D modelling techniques. In this paper, we introduce a novel Contrast-enhanced Guided Facial Expression Translation (CeFET) method. The model uses only facial images as input and extracts facial features from these images using an encoder model based on the StyleGAN prior. We propose a contrast-enhanced guidance technique aimed at minimizing the distance between the generated face and the input face, as well as the distance between the generated expression and the reference expression. This ensures that the generated face maintains identity consistency with the source face and expression consistency with the reference face. Extensive experimental results support the effectiveness of our method. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Contrastive Learning

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A Novel Deep Neural Network Architecture based GAN-DRANet for DOA Sensing With an Enhanced Performance in Low SNR

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IEEE Internet of Things Journal 2025年第13期12卷 22610-22622页

作者： Bai, Jiatong Shu, Feng Gao, Wei Wu, Guilu Yang, Weiwei Chen, Riqing Zhuang, Zhihong Hainan University School of Information and Communication Engineering Haikou570228 China Hainan University School of Information and Communication Engineering Collaborative Innovation Center of Information Technology Haikou570228 China Tibet University China School of Information Science and Technology Lhasa850000 China Nanjing University of Science and Technology School of Electronic and Optical Engineering Nanjing210094 China College of Communications Engineering Army Engineering University of PLA Nanjing China Fujian Agriculture and Forestry University School of Computer Science and Information Engineering Fuzhou350002 China

In extremely low signal-to-noise ratio (SNR) region, the useful features of the signal are weakened by higher-power noise, making it difficult for conventional direction-of-arrival (DOA) estimation methods to adequately exploit and extract the low-SNR signal features. Thus, a generative adversarial network (GAN) is presented to learn the underlying features and complex distributions of high-SNR covariance matrices. The introduced GAN establishes a mapping between low-SNR and high-SNR covariance matrices, thereby generating first-rate high-SNR covariance matrices that closely resemble real high-SNR matrices. Also, it effectively captures signal features that are overwhelmed by excessive noise power. Additionally, to improve the performance of convolutional neural network (CNN)-based DOA estimation models in medium-to-high SNR ranges, a deep residual attention network (DRANet) is designed to significantly enhance DOA estimation accuracy in such SNR region. By integrating residual and attention modules, the network effectively filters key features. This enhances feature learning and adaptability, allowing it to capture DOA-related features more proficiently. The experimental results indicate that the developed GAN-DRANet approach can approach the CRLB in the extremely-low SNR range and improves the estimation resolution limits of the other two DL-based methods, DNN and CNN, in medium to high SNR conditions. © 2014 IEEE.

关键词： Generative adversarial networks

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Reliable Entropy-Induced Anchor Learning for Incomplete Multi-View Subspace Clustering

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IEEE Transactions on Circuits and Systems for Video technology 2025年第6期35卷 5293-5306页

作者： Shen, Qiangqiang Guo, Zihou Wang, Hanzhang Xu, Yanhui Chen, Yongyong Wang, Shiqi Liang, Yongsheng City University of Hong Kong Department of Computer Science Hong Kong Harbin Institute of Technology School of Science Shenzhen518055 China Nanjing University of Posts and Telecommunications College of Electronic and Optical Engineering Nanjing210023 China School of Computer Science and Technology Shenzhen518055 China School of Electronics and Information Engineering Shenzhen518055 China Shenzhen Technology University College of Big Data and Internet Shenzhen518118 China

Under large-scale data with missing views, fast incomplete multi-view clustering (IMVC) with anchor learning is of critical importance due to its linear complexity O(n). However, existing anchor-based methods only explore the column orthogonality of anchor points, where their arbitrary column orthogonal basis vectors have weak constraint relationships with real samples and significant deviations from more representative anchors, thereby impeding the precise representation of sample similarities. To solve this issue, we propose a Reliable Entropy-induced anchor learning for incomplete Multi-view subspace Clustering (REMC), which performs an entropy approximation term to learn more representative anchors, and we prove that the information entropy minimization can be relaxed into the 2,1-norm paradigm. Specifically, the proposed REMC first integrates anchor learning and subspace clustering to produce multiple view-specific bipartite graphs and capture the high-order correlations by imposing these bipartite graphs with the tensor nuclear norm. Then, we fuse all the view-specific bipartite graphs to build a consensus bipartite graph with entropy approximation regularization, and hence the proposed REMC can produce a more discriminative similarity graph, preserving each non-zero element in its column close to 1, while the other elements are approaching 0. Besides, an efficient algorithm is designed to solve the proposed REMC. Numerous results show the superior performance of our method on both the complete and incomplete data. © 1991-2012 IEEE.

关键词： Graph theory

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Mining User Preferences from Online Reviews with the Genre-aware Personalized Neural Topic Model 25

Mining User Preferences from Online Reviews with the Genre-a...

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34th ACM Web Conference, WWW 2025

作者： Wang, Rui Lu, Jiahao Lv, Xincheng Chang, Shuyu Wu, Yansheng Yao, Yuanzhi Huang, Haiping Sun, Guozi School of Computer Science Nanjing University of Posts and Telecommunications Jiangsu Nanjing China School of Computer Science and Information Engineering Hefei University of Technology Anhui Hefei China

ISBN: (纸本)9798400712746

Customer-generated reviews on e-commerce websites often contain valuable insights into users’ interests in product genres and provide a rich source for mining user preferences. However, most existing neural topic models tend to generate meaningless topics that share low correlations with product genres. Furthermore, they often fail to mine user preferences and discover personalized topic profiles due to the absence of explicit user modeling. To address these limitations, we propose a novel Genre-aware Personalized neural Topic Model (GPTM), which incorporates product genre information into the topic modeling process to ensure the relevance between mined topics and product genres. Moreover, it could produce a personalized topic profile for each user by performing user preference modeling. Extensive experimental results on three publicly available Amazon review corpora validate the effectiveness of the proposed GPTM in genre-aware topic modeling. Furthermore, GPTM surpasses state-of-the-art baselines in user preference mining and generates high-quality personalized topic profiles. © 2025 Copyright held by the owner/author(s).

关键词： Text mining

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Towards Adaptive Adjusting and Efficient Scheduling Coflows Based on Deep Reinforcement Learning 29

Towards Adaptive Adjusting and Efficient Scheduling Coflows ...

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29th IEEE International Conference on Parallel and Distributed Systems, ICPADS 2023

作者： Wang, Zichao Yan, Kedong Chang, Guanxin Huang, Chanying Xiao, Shan Yang, Yuxin Nanjing University of Science and Technology School of Cyber Science and Engineering Jiangyin China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Fiberhome Communication Technology Co. LTD Fiberhome R & D Center Wuhan China Nanjing Univ. of Science & Technology School of Cyber Science & Engineering Jiangyin China

ISBN: (纸本)9798350330717

The rapid development of current data centers and Industrial Internet of Things has brought about the explosive growth of information, which leads to the need for better performance of cluster communication systems. Coflow scheduling has the potential to enhance communication performance among applications in data parallel clusters. However, current coflow scheduling techniques that lack preliminary knowledge often depend on a fixed set of threshold parameters within a multilevel feedback queue (MLFQ), disregarding network variability. Though manually tweaking threshold settings may support network flexibility, it negatively impacts real-time performance and increases workload. Furthermore, manual threshold adjustments often fail to react promptly and adaptively to network environmental changes. To address the issues highlighted above, this paper presents D-MLFQ, a novel approach that leverages deep reinforcement learning to dynamically and autonomously regulate the threshold of MLFQ. As a result, D-MLFQ offers enhanced scheduling and communication optimization capabilities. Furthermore, the study performs trace-driven simulations to assess the efficacy of D-MLFQ. Empirical data indicate that D-MLFQ outperforms Aalo, which utilizes fixed threshold, by up to 1.39× in terms of coflow's completion time. Compared to other common scheduling algorithms such as per-flow fairness, D-MLFQ achieves up to 2.13× faster completion time. © 2023 IEEE.

关键词： coflow scheduling deep reinforcement learning dynamic threshold efficiency MLFQ

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Research on Pulmonary Nodule Detection Method Based on YOLO Algorithm 5

Research on Pulmonary Nodule Detection Method Based on YOLO ...

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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Ma, Zewei Wu, Feier School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (纸本)9798350385557

This paper presents a method for pulmonary nodule detection in CT images based on an improved YOLOv8 framework. Against the backdrop of current deep learning technologies, automated detection and segmentation of medical images are crucial for enhancing diagnostic speed and accuracy. We combine the YOLOv8 object detection framework with Receptive Fields Block (RFB) modules to enhance feature extraction capabilities. Additionally, we introduce Inner-IoU as a loss function to optimize the detector's localization accuracy. Experimental results demonstrate significant performance improvement in pulmonary nodule detection tasks on CT images, thereby providing substantial support for the automation and intelligence of medical image processing. © 2024 IEEE.

关键词： Object detection

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A Novel Diversified API Recommendation for Power System Sensors 17

A Novel Diversified API Recommendation for Power System Sens...

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IEEE Congress on Cybermatics: 17th IEEE International Conference on Internet of Things, iThings 2024, 20th IEEE International Conference on Green Computing and Communications, GreenCom 2024, 17th IEEE International Conference on Cyber, Physical and Social Computing, CPSCom 2024, 10th IEEE International Conference on Smart Data, SmartData 2024

作者： Zhu, Minhao Gu, Huanhuan Che, Xun Chen, Jingfei Zhao, Qian Liu, Fan Zheng, Yu Nari Group Corporation Nanjing China Nanjing University of Science and Technology School of Cyber Science and Engineering Nanjing China Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing China Guodian Nanjing Automation Co. Ltd. Nanjing China

ISBN: (纸本)9798350351637

The collaborative interaction between power systems and the Internet of Things (IoT) is strengthening, with IoT devices facilitating real-time monitoring and governance of the power grid, pushing power systems to the next frontier: the smart grid. Nevertheless, the reliance of power systems on a vast array of IoT devices, each with its unique API, makes the development of a unified smart grid software solution extremely complex. Most existing research focuses on accuracy in recommendations, thus neglecting users' needs for functional diversity. To address this issue, we propose a diversified API recommendation approach that suggests a variety of functional APIs from power system sensors to users. We start by converting API labels into feature vectors based on a pre-trained language model and calculate the similarity between APIs through clustering. Subsequently, we construct an API graph to model the functional similarity relationships between APIs. Finally, we generate a minimum weighted tree to obtain a combination of APIs that meets the requirements and ensures diversity. We demonstrate the advantages of our proposed method in terms of diversity through a case study. © 2024 IEEE.

关键词： Power systems computer aided design

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