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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Shilong Bao Qianqian Xu Zhiyong Yang Yuan He Xiaochun Cao Qingming Huang State Key Laboratory of Information Security Institute of Information Engineering CAS and School of Cyber Security University of Chinese Academy of Sciences Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS School of Computer Science and Tech. University of Chinese Academy of Sciences Alibaba Group School of Cyber Science and Technology Shenzhen Campus Sun Yat-sen University Key Lab. of Intelligent Information Processing Institute of Computing Technology CAS and School of Computer Science and Tech. University of Chinese Academy of Sciences and Key Laboratory of Big Data Mining and Knowledge Management CAS and Peng Cheng Laboratory

ISBN: (纸本)9781713871088

Collaborative Metric Learning (CML) has recently emerged as a popular method in recommendation systems (RS), closing the gap between metric learning and Collaborative Filtering. Following the convention of RS, existing methods exploit unique user representation in their model design. This paper focuses on a challenging scenario where a user has multiple categories of interests. Under this setting, we argue that the unique user representation might induce preference bias, especially when the item category distribution is imbalanced. To address this issue, we propose a novel method called Diversity-Promoting Collaborative Metric Learning (DPCML), with the hope of considering the commonly ignored minority interest of the user. The key idea behind DPCML is to include a multiple set of representations for each user in the system. Based on this embedding paradigm, user preference toward an item is aggregated from different embeddings by taking the minimum item-user distance among the user embedding set. Furthermore, we observe that the diversity of the embeddings for the same user also plays an essential role in the model. To this end, we propose a Diversity Control Regularization Scheme (DCRS) to accommodate the multi-vector representation strategy better. Theoretically, we show that DPCML could generalize well to unseen test data by tackling the challenge of the annoying operation that comes from the minimum value. Experiments over a range of benchmark datasets speak to the efficacy of DPCML.

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Freight Transportation Structure Adjustment Control with Priority in Energy Consumption in China

SSRN

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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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Learning Active Camera for Multi-Object Navigation

arXiv

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arXiv 2022年

作者： Chen, Peihao Ji, Dongyu Lin, Kunyang Hu, Weiwen Huang, Wenbing Li, Thomas H. Tan, Mingkui Gan, Chuang South China University of Technology China Pazhou Laboratory China MIT-IBM Watson Ai Lab United States UMass Amherst United States Gaoling School of Artificial Intelligence Renmin University of China China Information Technology R&d Innovation Center of Peking University China Key Laboratory of Big Data and Intelligent Robot Ministry of Education China

Getting robots to navigate to multiple objects autonomously is essential yet difficult in robot applications. One of the key challenges is how to explore environments efficiently with camera sensors only. Existing navigation methods mainly focus on fixed cameras and few attempts have been made to navigate with active cameras. As a result, the agent may take a very long time to perceive the environment due to limited camera scope. In contrast, humans typically gain a larger field of view by looking around for a better perception of the environment. How to make robots perceive the environment as efficiently as humans is a fundamental problem in robotics. In this paper, we consider navigating to multiple objects more efficiently with active cameras. Specifically, we cast moving camera to a Markov Decision Process and reformulate the active camera problem as a reinforcement learning problem. However, we have to address two new challenges: 1) how to learn a good camera policy in complex environments and 2) how to coordinate it with the navigation policy. To address these, we carefully design a reward function to encourage the agent to explore more areas by moving camera actively. Moreover, we exploit human experience to infer a rule-based camera action to guide the learning process. Last, to better coordinate two kinds of policies, the camera policy takes navigation actions into account when making camera moving decisions. Experimental results show our camera policy consistently improves the performance of multi-object navigation over four baselines on two datasets. Copyright © 2022, The Authors. All rights reserved.

关键词： Cameras

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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 Vision-based Lane Departure Warning Framework

A Vision-based Lane Departure Warning Framework

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IEEE International Conference on e-Business Engineering (ICEBE)

作者： Jiaju Wu Pengshuai Yin Xin Shu Huichou Huang Fei Liu Qingyao Wu School of Software Engineering South China University of Technology China Key Laboratory of Big Data and Intelligent Robot Ministry of Education City University of Hong Kong Hong Kong China Pazhou Lab Guangzhou China

ISBN: (纸本)9781665444194

Lane departure warning is an essential function of driver assistance systems. In this paper, we propose a vision-based lane departure warning framework, which can determine whether the vehicle deviates from the lane only with images as input. Our framework combines the deep learning method and traditional line detection algorithm. Specifically, the framework consists of a Lane Boundaries Localization module, a Lane Boundaries Generator module, and a Departure Warning module. The Lane Boundaries Localization module is a deep neural network for finding the area containing lane Boundaries. Then the Lane Boundaries Generator module performs backbone detection and line detection to generate lane boundaries on the located area. After identifying the lane boundaries, the Departure Warning module can quickly determine whether the vehicle deviates from the lane. Additionally, we implemented and deployed our framework to an Android platform and tested its running speed.

关键词： Location awareness Deep learning Conferences Neural networks Transforms Generators Convolutional neural networks

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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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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 ...

引用

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

引用

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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