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2022 IEEE SmartWorld, 19th IEEE International Conference on Ubiquitous Intelligence and Computing, 2022 IEEE International Conference on Autonomous and Trusted Vehicles Conference, 22nd IEEE International Conference on Scalable Computing and Communications, 2022 IEEE International Conference on Digital Twin, 8th IEEE International Conference on Privacy Computing and 2022 IEEE International Conference on Metaverse, SmartWorld/UIC/ATC/ScalCom/DigitalTwin/PriComp/Metaverse 2022

作者： Huang, Weiyi Li, Peng Li, Bo Liu, Jing Nie, Lei Bao, Haizhou Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350346558

For the application of task assignment in crowdsourcing, previous research has focused on offline version or one-sided online version. Some of them draw extra attention to stability in task assignment. Our work extends a one-sided task assignment and its stable generalization. In particular, we present a two-sided online stable task assignment (TS-OSTA) problem in which any vertex from two sides can maintain an incomplete neighbor list and ties. We first give the fundamental definitions of task assignment in spatial crowdsourcing and stable matching. Then, we analyze and present the matching condition and formulate the TS-OSTA problem. As a warm-up, we investigate and analyze two classical baseline algorithms Greedy and Random Threshold. Then we present a bipartite ranking algorithm that maintains the efficiency of Greedy and effectiveness of Random Threshold. Through extensive experiments, we examine the performance of our algorithm in size, memory, and time. The results show that our algorithm performs better than the baselines. © 2022 IEEE.

关键词： Crowdsourcing

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Improving energy consumption forecasting using DLnet based on periodic modeling

Improving energy consumption forecasting using DLnet based o...

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2022 International Conference on Networks, Communications and information technology, NCIT 2022

作者： Liao, XueChao Huang, Xiang School of Computer Science and Technology Wuhan University of Science and Technology Hubei China Key Laboratory of Intelligent Information Processing and Real-Time Industrial Systems Hubei China

ISBN: (纸本)9783800760190

Improving the prediction accuracy of energy consumption in office buildings is necessary to achieve high energy efficiency in smart buildings. The existing forecasting methods rarely analyze the periodic characteristics of energy consumption independently. In this paper, a short-term office building energy consumption prediction model (DLnet) is proposed to address the problem of inefficiency in the utilization of periodic energy consumption ***, the period component of the energy consumption data is decomposed using STL, and the optimal period of the energy consumption data is searched for by a grid search algorithm, and then the Periodic block is constructed based on the optimal period;Secondly, the Time-series block data is constructed according to the data shape of the Periodic block;then the Time-series block data and the Periodic block data are trained and learned using LSTM;Finally, the prediction results of the Time-series block data and the Periodic block data are fused by linear *** four prediction accuracy indicators of the proposed model have been demonstrated to be 7%, 21%, 25%,and 26% higher than those of the LSTM model. © VDE VERLAG GMBH ∙ Berlin ∙ Offenbach.

关键词： Office buildings

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Low Energy Consumption and Time Deterministic Energy-saving Workflow Task Scheduling Algorithm based on DVFS 9

Low Energy Consumption and Time Deterministic Energy-saving ...

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9th IEEE International Conference on Cyber Security and Cloud Computing and 8th IEEE International Conference on Edge Computing and Scalable Cloud, CSCloud-EdgeCom 2022

作者： Liu, Xun Wuhan University of Science and Technology Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System College of Computer Science and Technology Wuhan China

ISBN: (数字)9781665480666

ISBN: (纸本)9781665480666

With the development of streaming media, soft real-time system in today's life could participate in the use of more extensive areas. The use frequency was also increasing. Consequently, modern processors were equipped with software control mechanisms such as DVFS (Dynamic Voltage Frequency Scaling) to allow operating systems to meet required performance while reducing power consumption. Therefore, we propose a task scheduling algorithm combined DVFS technology and time deterministic cyclic scheduling to achieve energy saving effect. First, the algorithm needed to minimize the preemption between tasks to reduce latency, so we created a buffer to save periodic tasks to avoid preemption. Second, to reduce the computational cost of the scheduling scheme, a scheduling template were designed to perform tasks. In this paper, the scheduling of periodic tasks, task scheduling would be designed when the task scheduling template would be fixed length. Besides, this algorithm supported that task could adopt appropriate voltage and frequency through DVFS technology in idle time under the condition of satisfying task dependence. Experimental analysis showed that the proposed algorithm could effectively reduce the system energy consumption while ensuring the completion of the task. © 2022 IEEE.

关键词： Scheduling

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GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning 39

GNN-Transformer Cooperative Architecture for Trustworthy Gra...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Liang, Jianqing Wei, Xinkai Chen, Min Wang, Zhiqiang Liang, Jiye Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan 030006 China

ISBN: (纸本)157735897X

Graph contrastive learning (GCL) has become a hot topic in the field of graph representation learning. In contrast to traditional supervised learning relying on a large number of labels, GCL exploits augmentation strategies to generate multiple views and positive/negative pairs, both of which greatly influence the performance. Unfortunately, commonly used random augmentations may disturb the underlying semantics of graphs. Moreover, traditional GNNs, a type of widely employed encoders in GCL, are inevitably confronted with over-smoothing and over-squashing problems. To address these issues, we propose GNN-Transformer Cooperative Architecture for Trustworthy Graph Contrastive Learning (GTCA), which inherits the advantages of both GNN and Transformer, incorporating graph topology to obtain comprehensive graph representations. Theoretical analysis verifies the trustworthiness of the proposed method. Extensive experiments on benchmark datasets demonstrate state-of-the-art empirical performance. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Contrastive Learning

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Influence Maximization With Graph Neural Network in Multi-Feature Social Network 24

Influence Maximization With Graph Neural Network in Multi-Fe...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and Systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data Systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Wang, Yuhao Li, Peng Huang, Weiyi School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

The classical influence maximization (IM) prob-lem aims to select a set of nodes as seed nodes for information propagation. Most related studies focus on one-dimensional information dissemination. However, the diffusion of information should be a multi-dimensional (or multi-feature) diffusion process. The user's judgment of information often depends on the combined judgment of multiple-dimensional information. Based on this, we consider a multi-feature diffusion model. In-formation will be decomposed into multi-dimensional features for diffusion. The user will receive these features and integrate them into a complete message. In addition, we consider seed node selection as a network node classification task. Consid-ering that social networks satisfy a graph structure, we use the node classification technique of graph neural networks. This technique classifies the nodes by the network topology to find the set of influential nodes. We propose a multi-feature influence (MFI) algorithm to improve the performance of the prediction. The set of candidate nodes obtained by classification is used as a benchmark to find the set of nodes with maximum influence. Our experiment results on real datasets show that the proposed algorithm has better algorithmic efficiency than the traditional greedy algorithm and better effectiveness than similar heuristic algorithms. © 2022 IEEE.

关键词： information dissemination

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Class Semantic Attribute Perception Guided Zero-Shot Learning 39

Class Semantic Attribute Perception Guided Zero-Shot Learnin...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Yue, Qin Cui, Junbiao Liang, Jianqing Bai, Liang Key Laboratory of Computational Intelligence and Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University Shanxi Taiyuan 030006 China

ISBN: (纸本)157735897X

Deep learning has achieved remarkable success in supervised image classification tasks, which relies on a large number of labeled samples for each class. Recently, zero-shot learning has garnered significant attention, which aims to recognize unseen classes using only training samples from seen classes. To bridge the gap between images and classes, class semantic attributes are introduced, making the alignment between image and class semantic attributes critical to zero-shot learning. However, existing methods often struggle to accurately focus on the image regions corresponding to individual class semantic attributes and tend to overlook the relations between different regions of an image, leading to poor alignment. To address these challenges, we propose a class semantic attribute perception guided zero-shot learning method. Specifically, we achieve coarse-grained perception of class semantic attributes across the entire image through contrastive semantic learning. Additionally, we attain fine-grained perception of individual class semantic attributes within image regions via region partitioning-based attribute alignment, which fully considers the relations between different regions of an image. By integrating these two processes into a unified network, we achieve multi-grained class semantic attribute perception, thereby enhancing the alignment between images and class semantic attributes. We validate the effectiveness of the proposed method on zero-shot learning benchmark data sets. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Zero-shot learning

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Dual-branch adjacent connection and channel mixing network for video crowd counting

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Pattern Recognition 2025年 167卷

作者： Ling, Miaogen Chen, Jixuan Liu, Yongwen Fang, Wei Geng, Xin School of Computer Science Nanjing University of Information Science and Technology Nanjing210044 China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China School of Computer Science and Engineering Southeast University Nanjing210096 China

This paper focuses on the problem of video crowd counting, which usually uses the spatial and temporal correlations of the consecutive frames to achieve better performance than the single-image crowd counting methods. However, most of the current video crowd counting methods either use only two or three frames for optical flow or frame-difference feature extraction or construct a single-branch network to extract spatiotemporal correlated features. The interactions of features for multiple adjacent frames, which can effectively prevent disturbances caused by background noise, are mostly overlooked. Considering the above problems, we propose a dual-branch adjacent connection and channel mixing network for multi-frame video crowd counting. For the upper branch, an adjacent layer connection method is proposed to capture the multi-scaled spatiotemporal correlations among multiple consecutive frames instead of the traditional dense connections in decomposed 3D convolutional blocks. It achieves better performance and low computation cost. For the lower branch, adaptive temporal channel mixing blocks are proposed to exchange partial channel information among the adjacent frames for feature interaction. The partial channel transpose operation is first proposed to exchange information. It is parameter-free and flexible to achieve interactions among features of any number of consecutive frames. The proposed method outperforms the current image-based and video-based crowd counting models, achieving state-of-the-art performance on six publicly available datasets. The code is available at: https://***/aaaabbbbcccccjxzxj/mfvcc. © 2025 Elsevier Ltd

关键词： Feature extraction

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Mining Alarm Association Rules in Optical Transmission Networks Using a Statistical Approach 24

Mining Alarm Association Rules in Optical Transmission Netwo...

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作者： Deng, Miao Li, Peng Liu, Yu Zhu, Rongjing Zhang, Yujie School of Computer Science and Technology Wuhan University of Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing and Real-time Industrial System China

ISBN: (纸本)9798350319934

Due to the interconnection between devices in the optical transmission network (OTN), when one device fails and generates alarm information, a large number of other invalid alarms are generated in OTN. How to mining association rules in alarm data is an important problem. In this paper, we investigate the association rules mining problem based on a statistical method. First, we remove the chattering alarm in preprocess to reduce data redundancy. Second, we propose a statistics-based association rule mining algorithm. We use a sliding time window to scan the alarm data, and we exploit the matrix to record the corresponding results. According to the matrix, we calculate the support and confidence of association rules. Compared with other traditional association rule mining algorithms, the proposed algorithm does not need to set the minimum support artificially, and the obtained association rules have strong causality. Through extensive experiments on syn-thetic and real OTN alarm datasets, we verify the effectiveness of our proposed algorithm and the correctness of the obtained association rules. © 2022 IEEE.

关键词： Association rules

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Duration-Aware Task Assignment for Heterogeneous Mobility Users in Crowdsensing 25

Duration-Aware Task Assignment for Heterogeneous Mobility Us...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Liu, Zhiyuan Li, Peng Huang, Weiyi Wang, Jiahu Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

This paper investigates the duration-aware task assignment problem for heterogeneous mobility users in MCS (DTAH problem). There are two types of users in MCS, vehicle users and pedestrian users. The vehicle user gets to the task fast, but the task duration is short. Pedestrian user meets the task duration limit, but it takes a long time to reach the task. Given a set of tasks and two types of users, each user, and each task has a duration limit. The DTAH problem is how to assign tasks to maximize the utility of the system. We prove that the DTAH problem is NP-hard by reducing the weighted maximum set coverage problem to the DTAH problem. Then, we solve the problem from a pedestrian perspective and a vehicle perspective. From the pedestrian user perspective, we propose a pedestrian user task assignment (PTA) algorithm based on the Kuhn-Munkres algorithm. From the vehicle user perspective, we propose a greedy vehicle user task assignment (VTA) algorithm. We prove that the VTA algorithm can obtain the approximate ratio of 1-1/e to the optimal value. Finally, we design the heterogeneous user task assignment (HTA) algorithm based on these PTA and VTA algorithms. Extensive experiments have proved that our proposed HTA algorithm achieves more efficient performance than other comparison algorithms. © 2022 IEEE.

关键词： Combinatorial optimization

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Task Priority Aware Incentive Mechanism with Reward Privacy-Preservation in Mobile Crowdsensing 25

Task Priority Aware Incentive Mechanism with Reward Privacy-...

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25th IEEE International Conference on computer Supported Cooperative Work in Design, CSCWD 2022

作者： Wang, Jiahu Li, Peng Huang, Weiyi Chen, Zeqiang Nie, Lei Wuhan University of Science and Technology School of Computer Science and Technology China Hubei Province Key Laboratory of Intelligent Information Processing Real-Time Industrial System China

ISBN: (纸本)9781665405270

In mobile crowdsensing, there are generally two types of tasks, popular tasks, and unpopular tasks. For popular tasks, many people can perform that task, and the budget is overallocated. For unpopular tasks, fewer or no one is willing to complete them. How to motivate users to complete different popularity tasks during their work time is a challenging problem. In this paper, we design a task priority-aware incentive mechanism to solve this problem. First, we use hierarchical clustering to classify tasks into different priorities by considering their budgets, deadlines, and density distribution. The higher the task priority, the higher the extra rewards and credits the participating users get. To motivate more users to perform unpopular tasks, we give high priority to unpopular tasks. Then, we propose a greedy algorithm that allows more users to do high-priority tasks. However, too many budget adjustments can cause most users to do unpopular tasks as users are obsessed with their income. To prevent users from all selecting high-priority tasks, we further propose a differential privacy-based algorithm to protect task priority and reduce users' attention to their income. This algorithm protects users' income and allows users to focus more on task characteristics, such as task distribution and task deadline. Through many experiments in the reality Roma dataset, we evaluate two proposed algorithms compared with other solutions. © 2022 IEEE.

关键词： Budget control

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