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Delay-Sensitive Goods Delivery and In-Situ Sensing Using A Multi-Task Drone

IEEE Transactions on Mobile Computing 2025年

作者： Liu, Bin Ni, Wei Liu, Ren Ping Guo, Y. Jay Zhu, Hongbo Nanjing University of Posts and Telecommunications Jiangsu Key Laboratory of Wireless Communications Nanjing 210003 China Nanjing University of Posts and Telecommunications Engineering Research Center of Health Service System Based on Ubiquitous Wireless Networks Ministry of Education Nanjing 210003 China University of Technology Sydney Global Big Data Technologies Centre Ultimo NSW Australia Barkhausen Institut Technische Universität Dresden Dresden Germany DATA61 CSIRO Marsfield 2122 NSW Australia

Drones are evolving into highly capable and adaptable devices, prompting the development of advanced control frameworks. This paper introduces a novel online control framework tailored for a multi-task drone, explicitly addressing the simultaneous execution of in-situ sensing and goods delivery. To tackle this complex scenario, a finite-horizon Markov decision process (FH-MDP) is formulated to ensure not only the prompt delivery of goods but also the minimization of energy consumption and the maximization of the drone's reward for in-situ sensing. A significant contribution lies in establishing the monotonicity and subadditivity of the FH-MDP. This mathematical foundation provides evidence for the existence of an optimal, monotone, deterministic Markovian policy. The crux of the optimal policy revolves around flight distance- and time-related thresholds, determining the precise points at which the drone should switch its optimal action. This unique feature empowers the multi-task drone to make real-time decisions, such as adjusting flight speed or engaging in in-situ sensing, by comparing its current state with these predefined thresholds. This process can be accomplished with a linear complexity, ensuring efficiency in decision-making. The optimality of our approach is rigorously demonstrated through numerical validation, where it is compared against a computationally expensive, dynamic programming-based alternative. Under the considered simulation settings, our approach reduces drone energy consumption by a substantial 19.8% compared to existing benchmarks. This not only highlights the practical effectiveness of the proposed framework but also underscores its potential for significant advancements in the field of drone operations and energy efficiency. © 2002-2012 IEEE.

关键词： delivery Drone finite-horizon Markov decision process in situ sensing multitask

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Research and analysis of psychological data based on machine learning methods

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International Journal of Wireless and Mobile Computing 2022年第1期22卷 1-8页

作者： Chen, Guangshun Lv, Wei Ma, Junwei Liang, Yanchun Zhuhai Lab. of Key Lab. of Symbol Computation and Knowledge Engineering of Ministry of Education Zhuhai College of Jilin University Jilin Zhuhai China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education College of Computer Science and Technology Jilin University Jilin Changchun China

The integration of psychology and computer science has become the mainstream contemporary research method on psychological data. Weibo, China's largest open platform for communication and information sharing between users, has many emotional contents hidden in its data. According to the current trend, the Weibo data are segmented by machine learning to obtain a psychological portrait of Weibo users. This design uses Long and Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNNs) to perform sentiment classification on Weibo data. The classification results are analysed using word frequency analysis and the Latent Dirichlet Allocation (LDA) model to obtain portraits of Weibo users' sentiment and an analysis of the results. The results are displayed in the form of word clouds. According to the clustering results of the word clouds, the main factors affecting different polar emotions can be analysed. © 2022 Inderscience Enterprises Ltd.. All rights reserved.

关键词： Statistics

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A randomized diversification strategy for solving satisfiability problem with long clauses

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Science China(Information Sciences) 2017年第9期60卷 121-131页

作者： Jian GAO Ruizhi LI Minghao YIN College of Computer Science Northeast Normal University College of Information Science and Technology Dalian Maritime University Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University

Satisfiability problem(SAT) is a central problem in artificial intelligence due to its computational complexity and usefulness in industrial applications. Stochastic local search(SLS) algorithms are powerful to solve hard instances of satisfiability problems, among which CScore SAT is proposed for solving SAT instances with long clauses by using greedy mode and diversification mode. In this paper, we present a randomized variable selection strategy to improve efficiency of the diversification mode, and thus propose a new SLS *** perform a number of experiments to evaluate the new algorithm comparing with the recently proposed algorithms, and show that our algorithm is comparative with others for solving random instances near the phase transition threshold.

关键词： SAT local search randomized diversification strategy phase transition long clause

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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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Task Offloading Optimization Based on Position Prediction in Pedestrian- Robot Mixed Traffic Flows 8

Task Offloading Optimization Based on Position Prediction in...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Zheng, Dawen Wang, Lusheng Kai, Caihong Peng, Min Key Laboratory of Knowledge Engineering with Big Data Ministry of Education School of Computer Science and Information Engineering Hefei University of Technology Hefei China

ISBN: (纸本)9781665450515

Service robots play an increasingly important role in people's daily life. The density of pedestrians is large and the movement is irregular in pedestrian-robot mixed traffic flows. Robots are prone to collision with pedestrians, and the tasks to be offloaded are closely related to pedestrians. How to analyze the tasks of robots and select the appropriate roadside unit is an important issue. In this paper, the social force model is used to predict the positions of pedestrians and robots, taking into account the influence of various forces to avoid collisions. A task offloading resource optimization algorithm with position prediction is proposed. According to the predicted information, the size and position distribution of all tasks in the scenario are obtained, and then the neural network trained beforehand based on deep Q-Iearning is used to generate a task offloading strategy. The simulation results show that the running time of the proposed algorithm is very short, and the resource allocation required for task offloading is completed in advance based on the predicted information before robots arriving the corresponding positions. Besides, the algorithm significantly reduces the task offloading delay. © 2022 IEEE.

关键词： Edge computing

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Mining Repetitive Negative Sequential Patterns with Gap Constraints

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ACM Transactions on knowledge Discovery from data 2025年第4期19卷

作者： Li, Yan Wang, Zhulin Liu, Jing Guo, Lei Fournier-Viger, Philippe Wu, Youxi Wu, Xindong School of Economics and Management Hebei University of Technology Tianjin China School of Artificial Intelligence Hebei University of Technology Tianjin China State Key Laboratory of Reliability and Intelligence of Electrical Equipment Hebei University of Technology Tianjin China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Hebei Key Laboratory of Big Data Computing Tianjin China Key Laboratory of Knowledge Engineering with Big Data (the Ministry of Education of China) Hefei University of Technology Hefei China

Sequential pattern mining (SPM) with gap constraints (or repetitive SPM or tandem repeat discovery in bioinformatics) can find frequent repetitive subsequences satisfying gap constraints, which are called positive sequential patterns with gap constraints (PSPGs). However, classical SPM with gap constraints cannot find the frequent missing items in the PSPGs. To tackle this issue, this article explores negative sequential patterns with gap constraints (NSPGs). We propose an efficient NSPG-Miner algorithm that can mine both frequent PSPGs and NSPGs simultaneously. To effectively reduce candidate patterns, we propose a pattern join strategy with negative patterns which can generate both positive and negative candidate patterns at the same time. To calculate the support (frequency of occurrence) of a pattern in each sequence, we explore a NegPair algorithm that employs a key-value pair array structure to deal with the gap constraints and the negative items simultaneously and can avoid redundant rescanning of the original sequence, thus improving the efficiency of the algorithm. To report the performance of NSPG-Miner, 11 competitive algorithms and 11 datasets are employed. The experimental results not only validate the effectiveness of the strategies adopted by NSPG-Miner but also verify that NSPG-Miner can discover more valuable information than the state-of-the-art algorithms. Copyright © 2025 held by the owner/author(s). Publication rights licensed to ACM.

关键词： gap constraint key-value pair negative sequential pattern sequential pattern mining

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Meta-path Enhanced knowledge Graph Convolutional Network for Recommender Systems 12

Meta-path Enhanced Knowledge Graph Convolutional Network for...

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12th IEEE International Conference on Big knowledge, ICBK 2021

作者： Wang, Ru Wu, Meng Ji, Shengwei Hefei University of Technology Key Laboratory of Knowledge Engineering With Big Data Ministry of Education School of Computer Science and Information Engineering Hefei China

ISBN: (纸本)9781665438582

knowledge Graph (KG) is a directed heterogeneous information network that contains a large number of entities and relations, which is widely used as effective side information in rec-ommender systems. Moreover, in recommender systems, the Graph Convolutional Network (GCN) model is introduced to mine the relatedness between entities in a KG because of its efficiency in extracting spatial features on topological graphs. The knowledge Graph Convolutional Network (KGCN) model up-dates the embedding of a currently positioned entity by aggregating the information of adjacent entities selected randomly. Never-theless, it has two limititations: 1) the information of neighbors se-lected randomly cannot accurately represent the current entity in the KG;2) the model is hard to converge as graph features (i.e. The spatial relation features and semantic information features of en-tities in the KG) grow. To solve these limitations, in this paper, a meta-path (i.e., a sequence of artificially constructed relationships) is introduced into the selection of neighbors in the KGCN model to enhance the representation of each entity. Furthermore, two construction methods of the meta-path - constructing a meta-path based on the same relation (KGCN-SP) and the characteris-tics of KG (KGCN-MP) -are proposed. The experiments based on three real-world datasets demonstrate that the neighbor selection based on the meta-path is able to collect more accurate infor-mation from a KG and improve the recommendation performance effectively. © 2021 IEEE.

关键词： knowledge graph

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DAMBLO: Improving arrhythmia classification with plug-and-play dual attention-based multiscale feature learning blocke

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Expert Systems with Applications 2025年 285卷

作者： Zhuang, Tianming Qin, Zhiguang You, Li Deng, Erqiang Ding, Yi Cao, Mingsheng Guo, Yingkun Network and Data Security Key Laboratory of Sichuan Province University of Electronic Science and Technology of China Sichuan Chengdu610054 China School of Information and Software Engineering University of Electronic Science and Technology of China Sichuan Chengdu610054 China Department of Molecular Genetics Erasmus University Medical Center Rotterdam Netherlands Department of Radiology Key Laboratory of Obstetric and Gynecologic and Pediatric Diseases and Birth Defects of Ministry of Education West China Second University Hospital Sichuan University Sichuan Chengdu610072 China

The electrocardiogram diagnosis played an important role in early arrhythmia prevention and cardiovascular disease detection. How to analysis and detect the electrocardiogram automatically becomes a challenging task in clinical practice. Convolutional neural network based approaches have been widely applied in the field of automatic arrhythmia detection and analysis. However, the existing methods fails to preserve the abundant and essential latent representatives from multiple dimensions. To address these problems, a dual spatial-channel aggregation module is proposed, which expects to concentrate on the prominent target information along spatial and channel dimension of electrocardiogram signals. Moreover, a multi-scale enhancement module with channel-wise gating mechanism is also proposed to enlarge the receptive resolution and further to improve the learning ability of long-range dependencies from the global information. Furthermore, these two proposed modules are integrated as a "plug-and-play" block that can be easily adopted as a drop-in replacement for any 3×3 convolution layers in convolutional neural networ based architectures. In order to evaluate the effectiveness and generalization of the proposed block, it is plugged in a series of convolutional neural network based frameworks and are evaluated on two benchmark datasets, MIT-BIH and PTB. The extensive experimental results show that the proposed "plug-and-play" block can significantly improve the arrhythmia classification performance. Moreover, compared to other state-of-the-art methods, the framework plugged with the proposed block can achieve a competitive classification performance, proving the feasibility and advancement of the proposed dual attention-based multiscale feature learning block. © 2025 Elsevier Ltd

关键词： Electrocardiography

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Recognizing the agent's goals incrementally:planning graph as a basis

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中国高等学校学术文摘·计算机科学 2007年第1期1卷 26-36页

作者： SUN Jigui YIN Minghao College of Computer Science Jilin UniversityChangchun 130012China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012China College of Computer Science Jilin UniversityChangchun 130012China Key Laboratory of Symbol Computation and Knowledge Engineering of Ministry of Education Changchun 130012China College of Computer Northeast Normal UniversityChangchun 130024China

Plan recognition,the inverse problem of plan synthesis,is important wherever a system is expected to produce a kind of cooperative or competitive *** plan recognizers,however,suffer the problem of acquisition and hand-coding a larger plan *** paper is aims to show that modern planning techniques can help build plan recognition systems without suffering such ***,we show that the planning graph,which is an important component of the classical planning system Graphplan,can be used as an implicit,dynamic planning library to represent actions,plans and *** also show that modern plan generating technology can be used to find valid plans in this *** this sense,this method can be regarded as a bridge that connects these two research *** and theoretical results also show that the method is efficient and scalable.

关键词： Graphplan plan recognition constraints automated planning

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Joint user profiling with hierarchical attention networks

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Frontiers of Computer Science 2023年第3期17卷 133-143页

作者： Xiaojian LIU Yi ZHU Xindong WU Key Laboratory of Knowledge Engineering with Big Data(Hefei University of Technology) Ministry of EducationHefei 230009China School of Computer Science and Information Engineering Hefei University of TechnologyHefei 230009China School of Information Engineering Yangzhou UniversityYangzhou 225009China Mininglamp Academy of Sciences MininglampBeijing 100193China

User profiling by inferring user personality traits,such as age and gender,plays an increasingly important role in many real-world *** existing methods for user profiling either use only one type of data or ignore handling the noisy information of ***,they usually consider this problem from only one *** this paper,we propose a joint user profiling model with hierarchical attention networks(JUHA)to learn informative user representations for user *** JUHA method does user profiling based on both inner-user and inter-user *** explore inner-user features from user behaviors(e.g.,purchased items and posted blogs),and inter-user features from a user-user graph(where similar users could be connected to each other).JUHA learns basic sentence and bag representations from multiple separate sources of data(user behaviors)as the first round of data *** this module,convolutional neural networks(CNNs)are introduced to capture word and sentence features of age and gender while the self-attention mechanism is exploited to weaken the noisy *** this,we build another bag which contains a user-user ***-user features are learned from this bag using propagation information between linked users in the *** acquire more robust data,inter-user features and other inner-user bag representations are joined into each sentence in the current bag to learn the final bag ***,all of the bag representations are integrated to lean comprehensive user representation by the self-attention *** experimental results demonstrate that our approach outperforms several state-of-the-art methods and improves prediction performance.

关键词： user profiling hierarchical attention joint learning inner-user feature inter-user feature

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