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18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021

作者： Wang, Ziyi Zhou, Yujie Li, Chun Shang, Lin Xue, Bing State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science and Technology Nanjing University Nanjing210023 China Victoria University of Wellington Wellington New Zealand

ISBN: (纸本)9783030891879

Classification of time series has attracted substantial interest over past decades. Methods based on Dynamic Time Warping (DTW), Symbolic Aggregate approXimation (SAX) and Shapelets are widely used and have achieved success in various real-world scenarios. However most existing time series classification methods either focus on global variation (e.g. DTW, SAX) or local variation (e.g. Shapelets). In this paper, we propose a Multi-Grained Ensemble Method for time series classification (MEGoT), which can make use of the variation of multi-grained data at the same time. In MEGoT, unstable base learners (Neural Networks) are assigned different weights to combine the ensemble. Different learners represent the learning features of different subsequences in time series, which can discover the discriminative regions, providing interpretability for classification. The training process of MGEoT is simpler and apt to parallel implementation. In the experiments, we conduct empirical evaluations and comparisons with various existing methods on 25 benchmark datasets. The final results show that dividing samples into smaller granularity is able to improve the diversity of ensemble, and MGEoT is competitive in accuracy under the Nemenyi test. Furthermore, MGEoT can discover the discriminative regions in time series, which may be neglected in the global methods. © 2021, Springer Nature Switzerland AG.

关键词： Time series

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Compromising Rechargeable Sensor Networks in Marine Environment

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IEEE Transactions on Mobile Computing 2025年

作者： Wang, Qiwei Lin, Chi Dai, Haipeng Obaidat, Mohammad S. Hsiao, Kuei-Fang Fan, Xin Dalian University of Technology School of Software Technology & DUT-RU International School of Information Science and Engineering China Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province Dalian116620 China Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China University of Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China SRM University Department of Computational Intelligence School of Computing SRM Nagar TN Kattankulathur603203 India The Amity University School of Engineering UP Noida201301 India The King Abdullah II School of Information Technology The University of Jordan Amman11942 Jordan

Marine Wireless Rechargeable Sensor Networks (MWRSNs), enhanced by recent Wireless Power Transfer (WPT) technology, present a significant advancement in extending network life. Traditional methods improve network performance through algorithm optimization, but neglect charging security, exposing networks to potential attacks. This paper addresses this problem from an adversarial view and develops a novel attack for MWRSN through Denying of Charge (DoC) to maximize network destructiveness. We start by establishing a generalized on-demand charging model, essential for developing DoC tactics. Subsequently, we unveil the Collaborative DoC (CoDoC) algorithm, capable of manipulating and falsifying charging requests. Central to CoDoC is the Request Prediction Method (RPM), which forecasts the initiation of charging requests and facilitates rapid request surges to enhance the attack's efficacy. CoDoC is able to disguise the presence of the attack, which is able to escape from being detected by the base station. Theoretical analyses are provided to explore the features of the proposed scheme. To demonstrate the outperformed features of the proposed schemes, extensive simulations and test-bed experiments are conducted. Our analysis and extensive simulations demonstrate that CoDoC increases sensor node failures by 20% to 142% compared to traditional methods, highlighting its effectiveness in marine environments. © 2002-2012 IEEE.

关键词： Charging stations

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Service offloading oriented edge server placement in smart farming

Service offloading oriented edge server placement in smart f...

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作者： Zhang, Jie Li, Xiang Zhang, Xuyun Xue, Yuan Srivastava, Gautam Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Department of Computing Macquarie University SydneyNSW Australia Department of Mathematics and Computer Science Brandon University BrandonMB Canada

Currently, smart farming has been established to realize agriculture automation by leveraging sensors to gather the growth and environmental data for crops, and realizing multiple intelligent controls, such as irrigation, fertilization, and so on, to increase the crop yields. To support real-time intelligent controls, edge computing is introduced to smart farming by endowing computing and storage capacities to edge devices nearby the geographically distributed sensors. However, the farmers are relatively willing to purchase and deploy a small quantity of edge servers (ESs) in the farm from the perspective of expenditure saving, thereby leading to a key challenge to guarantee the performance of the real-time controls and the overall edge services. In view of this challenge, a service offloading-oriented ES placement method for supporting smart farming, called SOP, is proposed to optimize the data transmission delay from sensors to ESs and the load balance among ESs. More precisely, the corresponding service range of a certain ES is ascertained according to the specific analysis of the farming service requirements. Subsequently, the layout policies for the trade-offs of the ES performance and service efficiency are acquired. Then the most balanced policy is determined as the final ES placement strategy. Eventually, we evaluate the performance of the whole ES system and the service execution efficiency with SOP. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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DivGCL: A Graph Contrastive Learning Model for Diverse Recommendation 39

DivGCL: A Graph Contrastive Learning Model for Diverse Recom...

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39th Annual AAAI Conference on Artificial Intelligence, AAAI 2025

作者： Gong, Wenwen Geng, Yangliao Zhang, Dan Zhu, Yifan Xu, Xiaolong Xiang, Haolong Beheshti, Amin Zhang, Xuyun Qi, Lianyong School of Information and Electrical Engineering China Agricultural University China School of Computer Science & Technology Beijing Jiaotong University China Department of Computer Science and Technology Tsinghua University China School of Computer Science Beijing University of Posts and Telecommunications China School of Software Nanjing University of Information Science and Technology China School of Computing Macquarie University Australia College of Computer Science and Technology China University of Petroleum (East China China State Key Lab. for Novel Software Technology Nanjing University China

ISBN: (纸本)157735897X

Graph Contrastive Learning (GCL), as a primary paradigm of graph self-supervised learning, spurs a fruitful line of research in tackling the data sparsity issue by maximizing the consistency of user/item embeddings between different augmented views with random perturbations. However, diversity, as a crucial metric for recommendation performance and user satisfaction, has received rather little attention. In fact, there exists a challenging dilemma in balancing accuracy and diversity. To address these issues, we propose a new Graph Contrastive Learning model for Diversifying recommendations (DivGCL). Inspired by the excellence of the determinant point process (DPP), DivGCL adopts a DPP likelihood-based loss function to achieve an ideal trade-off between diversity and accuracy, optimizing it jointly with the advanced Gaussian noise-augmented GCL objective. Extensive experiments on four popular datasets demonstrate that DivGCL surpasses existing approaches in balancing accuracy and diversity, with an improvement of 23.47% at T@20 (abbreviation for trade-off metric) on ML-1M. Copyright © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Self-supervised learning

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An Epidemic Model Based on Intra- and Inter-group Interactions

An Epidemic Model Based on Intra- and Inter-group Interactio...

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International Conference on computer Supported Cooperative Work in Design

作者： Wencong Geng Guijuan Zhang Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately.

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Singular Value Decomposition Based Pedestrian Trajectory Prediction

Singular Value Decomposition Based Pedestrian Trajectory Pre...

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International Conference on computer Supported Cooperative Work in Design

作者： Heng Wang Guijuan Zhang Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

Numerous cameras deployed in venues can collect video data, which can be analyzed to help evacuate people in emergency situations. Most surveillance videos can provide data support for pedestrian trajectory prediction. However, data-driven prediction method do not consider the impact of personalization on pedestrian trajectories, which results in ignoring individual personalization. How to accurately analyze the differences among pedestrians and yet accurately predict pedestrian trajectories is a challenging problem. To solve the above problems, we propose an singular value decomposition (SVD) based pedestrian trajectory prediction method, which introduces matrix decomposition to pedestrian trajectory prediction for the first time. Thus, This method analyzes the impact of pedestrian personalization on motion trajectories by mining the interaction patterns between pedestrians and environment. Firstly, we propose an information collection method based on environmental semantics, which can extract scene information from historical video data to construct an environmental information matrix. Secondly, we propose an SVD-based individual environmental feature preference method, which uses singular value decomposition methods to mine the data and analyze personalized pedestrian motion patterns to construct a personalized individual preference matrix. Finally, we built an personalized trajectory prediction method to predict pedestrian movement trajectories. The experimental results show that the method can not only analyze the effect of personalization on pedestrian movement but also accurately predict the pedestrian movement trajectory.

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Abnormal Crowd Behavior Detection Based on the Fusion of Macro and Micro Features

Abnormal Crowd Behavior Detection Based on the Fusion of Mac...

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International Conference on computer Supported Cooperative Work in Design

作者： Shourong Jiao Dianjie Lu Chuanhua Jia Hong Liu Guijuan Zhang School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

There are two types of research on abnormal crowd behavior detection: micro modeling methods (e.g., crowd pose) and macro modeling methods (e.g., optical flow methods). In crowd evacuation, however, micro methods fail to solve the occlusion problem, while macro methods fail to address issues such as poor real-time performance and non-adaptive motion target detection. To address these issues, we propose a method that combines macro and micro methods to detect abnormal crowd behavior. Firstly, we extract the two-dimensional poses of moving people using the human pose estimation algorithm (OpenPose) and get the corresponding micro motion features. Secondly, we create an optical flow map of the video using the dense optical flow algorithm (Farneback) and a scene activity map by comparing the optical flow sizes between two consecutive frames. Then we derive the entropy change curve, which represents the macro motion features. Finally, we fuse micro and macro motion features and put the fused hybrid feature vectors into our classification network to train our model, and then find the transition time from micro to macro abnormal states in crowd evacuation videos. The experimental findings demonstrate that our proposed method can more accurately and quickly identify the abnormal behaviors that appear in evacuation scenes.

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Spatial-temporal Consistency based Crowd Emotion Recognition

Spatial-temporal Consistency based Crowd Emotion Recognition

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International Conference on computer Supported Cooperative Work in Design

作者： Chuanmiao Zhao Guijuan Zhang Lei Lv Hong Liu Dianjie Lu School of Information Science and Engineering Shandong Normal University Jinan China Shandong Provincial Key Laboratory for Novel Distributed Computer Software Technology Jinan China State Key Laboratory of High-end Server & Storage Technology Jinan China

In crowd evacuation scenarios, it is an effective way to ensure evacuation safety by recognizing real crowd emotions and then taking measures such as emotional infection to reduce crowd panic. However, factors such as distance, exposure, angle, and occlusion can lead to incomplete face expression information collection, thus failing to accurately identify crowd emotions. Therefore, it is still a very challenging problem to recognize individual emotions in the case of incomplete face expression information collection, and thus accurately identify crowd emotions during evacuation. To solve this problem, we propose a spatial-temporal consistency-based crowd emotion recognition method to accurately identify the real emotions of the crowd. First, for video frames that can capture the complete facial expression information, we use the residual network to accurately identify the individual emotion values in each frame. For video frames that cannot capture the complete facial expression information, we propose an individual emotion calculation model based on spatial-temporal consistency to calculate the individual emotion values in each frame. Second, we define the crowd panic level and obtain the real crowd emotion by calculation. Finally, we implement an end-to-end crowd panic emotion recognition system to verify our method. The experimental results show that the method can accurately calculate the crowd panic level, which is important for guiding crowd evacuation.

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Dynamic Modeling of Strip Rolling Process Using Probabilistic Neural Network

Dynamic Modeling of Strip Rolling Process Using Probabilisti...

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18th IEEE International Conference on Advanced Motion Control, AMC 2024

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern University State Key Laboratory of Rolling and Automation Shenyang China Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342819

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model. © 2024 IEEE.

关键词： Deep neural networks

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Understanding Wireless Charger Networks: Concepts, Current Research, and Future Directions

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IEEE Communications Surveys and Tutorials 2024年

作者： Ren, Meixuan Dai, Haipeng Liu, Tang Deng, Xianjun Dou, Wanchun Yang, Yuanyuan Chen, Guihai Nanjing University State Key Laboratory for Novel Software Technology Nanjing210023 China Sichuan Normal University College of Computer Science Sichuan Chengdu610101 China Sichuan Normal University Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Sichuan Chengdu610068 China Huazhong University of Science and Technology School of Cyber Science and Engineering Wuhan430074 China Stony Brook University Department of Electrical and Computer Engineering NY11794 United States

Wireless Charger Network (WCN) emerges as a promising networking paradigm, employing wireless chargers with Wireless Power Transfer (WPT) technology to provide long-term and sustainable energy supply for future networks. Although extensive research has been conducted in this area over the last decade, there is currently no comprehensive survey to compile the latest literature and provide insights into future research directions. To fill this gap, our survey explores the recent developments in the active research area of WCNs. This paper starts by providing a framework of WCNs in detail, covering aspects of network architecture, various charging models, network design issues, and typical applications of WCNs. Then, we give an overview of charger deployment schemes, focusing on omnidirectional, directional, non-radiative, and heterogeneous charger deployments. We also provide an overview of charging scheduling schemes, encompassing power control, time allocation, energy beamforming, and multi-resource scheduling. Moreover, we explore communication optimization schemes, including Medium Access Control (MAC) protocols, routing protocols, broadcast transmission, and data collection. Finally, we highlight some future research directions and present corresponding open issues to advance the research on WCNs. © 1998-2012 IEEE.

关键词： Inductive power transmission

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