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SSRN 2022年

作者： Zuo, Dajie Liang, Qichen Zhan, Shuguang Huang, Wencheng Yang, Shenglan Wang, Mengyun School of Transportation and Logistics Southwest Jiaotong University Chengdu611756 China National and Local Joint Engineering Laboratory of Comprehensive Intelligent Transportation Southwest Jiaotong University Chengdu611756 China National Engineering Laboratory of Integrated Transportation Big Data Application Technology Southwest Jiaotong University Chengdu611756 China Key Laboratory of Comprehensive Transportation of Sichuan Province Southwest Jiaotong University Chengdu611756 China

China has set the goal of controlling the total energy consumption to 6 billion tonnes of standard coal by 2030 in an attempt to avoid an energy crisis. Generally, freight transportation is an energy-intensive industry. Controlling the energy consumption of freight transportation is crucial for achieving energy-saving goals in China. In this study, we establish the annual control model for freight transportation structure adjustment from the perspectives of the target year prediction and the intermediate year controlling to control energy consumption in the freight industry. We solve the model and obtain the annual control plan of freight transportation structure adjustment in China (2021-2030). As per the plan, energy consumption will reach its peak in 2029, with 145 million tonnes of standard coal. Lastly, we proposed a series of reasonable policy recommendations. This study will provide a reference for the sustainable development of transportation in China. © 2022, The Authors. All rights reserved.

关键词： Energy utilization

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Enhanced Particle Swarm Optimization for Workflow Scheduling in Clouds

Enhanced Particle Swarm Optimization for Workflow Scheduling...

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Symposium of Image, Signal Processing, and Artificial Vision (STSIVA)

作者： Chang Lu Dayu Feng Jie Zhu Haiping Huang Nanjing University of Posts & Telecommunications Nanjing China Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing China State Key Laboratory of Computer Architecture Institute of Computing Technology Chinese Academy of Sciences Beijing China Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks Nanjing China

ISBN: (纸本)9781665426565

As a NP-hard problem, it is always baffling to figure out a scheduling strategy to arrange the interconnected tasks of a workflow on the infinite number of resources in the cloud environment so that the workflow can be addressed efficiently and robustly. This paper focuses on scheduling the workflow’s tasks on the cloud resources with less rental cost of resources while the whole schedule length (makespan) will not exceed the given deadline. As one of the most popular evolutionary algorithms, particle swarm optimization (PSO) has been successfully applied for the workflow scheduling problem. Inspired by the idea of multiple groups and the distributed parallel computing, we develop an enhanced PSO algorithm for the workflow scheduling problem in clouds. Besides, a pretreatment strategy is adopted to simplify the workflow’s structure. The experimental results demonstrate that our proposal has good performance on improving the algorithm’s searching ability and finding better solutions.

关键词： Schedules Costs Processor scheduling NP-hard problem Parallel processing Scheduling Proposals

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A Diverse Biases Non-negative Latent Factorization of Tensors Model for Dynamic Network Link Prediction

A Diverse Biases Non-negative Latent Factorization of Tensor...

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2020 IEEE International Conference on Networking, Sensing and Control, ICNSC 2020

作者： Wu, Xuke Gou, Hang Wu, Hao Wang, Juan Chen, Minzhi Lai, Siyu Computer School China West Normal University Nanchong Sichuan637002 China Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China University of Chinese Academy of Sciences Beijing100049 China Department of Medical Imaging North Sichuan Medical College Nanchong Sichuan637000 China

ISBN: (纸本)9781728168531

Dynamic networks vary over time, making it vital to capture networks temporal patterns for predicting missing links with high accuracy. A biased non-negative latent factorization of tensors (BNLFT) model is very effective in extracting such patterns from dynamic data. However, a BNLFT model only integrates single bias, which cannot adequately represents the volatility of the dynamic data. To address this issue, this paper presents a Diverse Biases Non-negative Latent Factorization of Tensors (DBNT) model for accurate prediction of missing links in dynamic networks. Meanwhile, for further prediction accuracy improvement, the preprocessing bias is integrated into the DBNT model. Empirical studies on two dynamic networks datasets from real applications show that compared with state of the art predictors, a DBNT model achieves higher prediction accuracy. © 2020 IEEE.

关键词： Factorization

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A Traversal Multi-Target Path Planning Method for Multi-Unmanned Surface Vessels in Space-Varying Ocean Current

SSRN

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SSRN 2022年

作者： Yu, Jiabin Chen, Zhihao Zhao, Zhiyao Yao, Peng Xu, Jiping Artificial Intelligence Beijing Technology and Business University Beijing100048 China China and Beijing Laboratory for Intelligent Environmental Protection Beijing Technology and Business University Beijing100048 China Key Laboratory of Industrial Internet and Big Data China National Light Industry Beijing Technology and Business University Beijing100048 China College of Engineering Ocean University of China Qingdao266100 China

The multi-USVs cooperation is widely used. Multi-target traversing is an important aspect on the research on the multi-USVs cooperation in space-varying ocean current environment. Therefore, this paper proposes a traversal multi-target path planning method for multi-unmanned surface vessels in space-varying ocean current. The proposed algorithm is divided into two steps: First, a task allocation method for multi-USVs traveling multi-target based on improved Hungary algorithm is proposed for multi-target task allocation. The improved Hungary algorithm is used to allocate tasks of multi-target. Second, to solve the path planning problem, a virtual field RRT* (VF-RRT*) algorithm is proposed. The virtual field sampling algorithm and the ocean current constraint function are introduced in the RRT* algorithm. In order to verify the effectiveness of the algorithm, three groups of simulation experiments are implemented. The results show that the proposed algorithm has good effectiveness in path planning and task allocation. © 2022, The Authors. All rights reserved.

关键词： Motion planning

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Graph convolutional module for temporal action localization in videos

arXiv

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arXiv 2021年

作者： Zeng, Runhao Huang, Wenbing Tan, Mingkui Rong, Yu Zhao, Peilin Huang, Junzhou Gan, Chuang The School of Software Engineering South China University of Technology The Pazhou Laboratory Guangzhou China China The School of Software Engineering South China University of Technology The Key Laboratory of Big Data and Intelligent Robot South China University of Technology Ministry of Education China The Tencent AI Laboratory Shenzhen China The MIT-IBM Watson AI Lab CambridgeMA02142 United States

Temporal action localization, which requires a machine to recognize the location as well as the category of action instances in videos, has long been researched in computer vision. The main challenge of temporal action localization lies in that videos are usually long and untrimmed with diverse action contents involved. Existing state-of-the-art action localization methods divide each video into multiple action units (i.e., proposals in two-stage methods and segments in one-stage methods) and then perform action recognition/regression on each of them individually, without explicitly exploiting their relations during learning. In this paper, we claim that the relations between action units play an important role in action localization, and a more powerful action detector should not only capture the local content of each action unit but also allow a wider field of view on the context related to it. To this end, we propose a general graph convolutional module (GCM) that can be easily plugged into existing action localization methods, including two-stage and one-stage paradigms. To be specific, we first construct a graph, where each action unit is represented as a node and their relations between two action units as an edge. Here, we use two types of relations, one for capturing the temporal connections between different action units, and the other one for characterizing their semantic relationship. Particularly for the temporal connections in two-stage methods, we further explore two different kinds of edges, one connecting the overlapping action units and the other one connecting surrounding but disjointed units. Upon the graph we built, we then apply graph convolutional networks (GCNs) to model the relations among different action units, which is able to learn more informative representations to enhance action localization. Experimental results show that our GCM consistently improves the performance of existing action localization methods, including two-stage methods

关键词： Convolution

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Latent Group Recommendation based on Double Fuzzy Clustering and Matrix Tri-factorization

Latent Group Recommendation based on Double Fuzzy Clustering...

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IEEE International Conference on Web Services (ICWS)

作者： Haiyan Wang Jinxia Zhu Zhousheng Wang Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728187860

ISBN: (纸本)9781728187877

Group recommendation has received great attention owing to its practical value in real applications. However, group members are implicit and groups are formed occasionally in some scenarios. Existing solutions for latent group recommendation assumes a user belongs to a specific group, and totally ignore the possible correlation between the user' s preferences and other groups' preferences. In addition, existing methods cannot deal with new items cold-start problem effectively because they only focus on which items are favored by the group without considering the hidden related information between items. These weaknesses usually lead to poor performance of latent group recommendation. To address the problems above, this paper proposes a latent group recommendation method based on double fuzzy clustering and matrix tri-factorization (DFCMTF -LGR). Firstly, this method utilizes unsupervised learning to implement potential feature extraction and double fuzzy clustering for users and items. Secondly, a novel matrix tri-factorization method is presented to adjust the membership of user-to-group, item-to-item category, and the incidence of group-to-item category is obtained. Finally, latent groups are detected according to user-to-group membership, and group rating can be generated in accordance with group-to-item category incidence matrix and item-to-item category membership. Experimental results on real datasets demonstrate that our proposed DFCMTF-LGR has better performance compared with state-of-the art methods.

关键词： Correlation Art Web services Conferences Feature extraction Real-time systems Unsupervised learning

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Modeling Disease Progression via Weakly Supervised Temporal Multitask Matrix Completion

Modeling Disease Progression via Weakly Supervised Temporal ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Lingsheng Wang Lei Xu Ping Li Siming Zha Lei Chen Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing China

ISBN: (数字)9781728185262

ISBN: (纸本)9781728185279

Alzheimer's disease (AD) is one of the most common neurodegenerative diseases. Understanding AD progression can empower the patients in taking proactive care. Mini Mental State Examination (MMSE) and AD Assessment Scale Cognitive subscale (ADAS-Cog) are two prevailing clinical measures designed to evaluate the AD progression. In this paper, we propose a weakly supervised Temporal Multitask Matrix Completion (TMMC) framework, which combines a novel transductive multitask feature selection scheme, to simultaneously predict AD progression measured by MMSE and ADAS-Cog, and identify related biomarkers trackable of AD progression. Specifically, by treating the prediction of cognitive scores at each time point as a regression task, we first formulate AD progression problem as a standard Multitask Matrix Completion (MMC) model. Secondly, considering the limited number of samples available in this study, we introduce a transductive feature selection scheme to jointly select the task-shared features for multiple time points and the task-specific features for different time points, and thus alleviate the over-fitting defect caused by Small-Sample-Size issue. Thirdly, aiming at the small change of cognitive scores between successive time points for a patient, we employ a temporal regularization scheme to capture the temporal smoothness of cognitive scores. Furthermore, we design an efficient optimization algorithm based on Alternative Minimization and Difference of Convex Programming techniques to solve the proposed TMMC framework. Finally, the extensive experiments performed on real-world Alzheimer's disease dataset demonstrate the effectiveness of our TMMC framework.

关键词： Predictive models Biomarkers Programming Feature extraction Task analysis Standards Diseases

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Clustering data Stream with Rough Set 19

Clustering Data Stream with Rough Set

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Proceedings of the 2019 8th International Conference on Computing and Pattern Recognition

作者： Renxia Wan Yanyan Li Ningxia Key Laboratory of Intelligent Information and Big Data Processing North Minzu University Yinchuan Ningxia China

ISBN: (纸本)9781450376570

In this paper, the upper and lower approximations of rough set are introduced to describe the micro-cluster feature in the procedure of clustering uncertain data stream. The proposed algorithm employs presents the micro-cluster timestamp with the time decay and uses agglomerative clustering method to emerge new cluster in the buffer of outliers. Experimental results show that the proposed algorithm can generate natural clusters and outperforms the existing method in term of accuracy.

关键词： lower approximation cluster feature vector outlier buffer upper approximation time decay Rough set standard deviation

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Modeling Transition Matrix for a Collaborative Rating Prediction Recommendation System via Nonnegative Tensor Decomposition

Modeling Transition Matrix for a Collaborative Rating Predic...

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International Conference on Computer Science & Education (ICCSE)

作者： Zhehao Zhou Shenbao Yu Jiewen Guan Bilian Chen Langcai Cao Department of Automation Xiamen University Xiamen Key Laboratory of Big Data Intelligent Analysis and Decision Xiamen China

ISBN: (数字)9781728172675

ISBN: (纸本)9781728172682

We investigate the problem of user preferences changing over time for a collaborative rating prediction recommendation system. The rating data of a time-aware collaborative recommendation can be represented as a 3rd order tensor, each of which denotes users, items and time. We mainly focus on establishing a user preference transfer model according to the time latent factor matrice, which is computed by tensor decomposition. Subsequently, a novel nonnegative tensor decomposition with temporal similarity (TS-NTD) is put forward to predict rating values. The proposed temporal similarity measure succeeds in describing various changes in user preferences. We evaluate the TS-NTD on two real world datasets, and experimental results show that our model has higher accuracy than state-of-the-art models.

关键词： Matrix decomposition Tensile stress Collaboration Optimization Computational modeling Predictive models Mathematical model

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Chinese News Text Classification and Its Application Based on Combined-Convolutional Neural Network

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Journal of Computers (Taiwan) 2022年第4期33卷 1-14页

作者： Liu, Kai-Feng Zhang, Yu Zhang, Quan-Xin Wang, Yan-Ge Gao, Kai-Long Jiangsu Vocational College of Finance and Economics Huaian223003 China School of Electrical and Information Engineering Beijing Key Laboratory of Intelligent Processing for Building Big Data Beijing University of Civil Engineering and Architecture Beijing100044 China State Key Laboratory in China for GeoMechanics and Deep Underground Engineering China University of Mining & Technology Beijing100083 China School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China

A method based on combined-convolutional neural network (Combined-CNN) for Chinese news text classification is proposed. First of all, in order to solve the problem of a lack of special term set for Chinese news classification, a vocabulary suitable for Chinese long text classification is made by constructing a data index method. The Word2Vec pre-trained model was used to embed the text features word vectors. Second, by optimizing the structure of the classical convolutional neural network (CNN) model, a new idea of Combined-CNN model is proposed, which solves the problem of incomplete feature extraction of local text blocks and improves the accuracy rate of Chinese news text classification. Effective model regularization and RAdam optimization algorithm are designed in the model to enhance the model training effect. The experimental results show that the precision of the Combined-CNN model for Chinese news text classification reaches 93.69%. Compared with traditional machine learning methods and deep learning algorithms, the accuracy rate is improved by a maximum of 11.82% and 1.9%, respectively, and it is better than the comparison model in Recall and F-Measure. Finally, the Chinese news classification algorithm of the Combined-CNN is applied to realize a personalized recommendation system. © 2022 Computer Society of the Republic of China. All rights reserved.

关键词： Recommender systems

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