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arXiv 2020年

作者： Yang, Zhiyong Xu, Qianqian Cao, Xiaochun Huang, Qingming State Key Laboratory of Information Security Institute of Information Engineering Chinese Academy of Sciences Beijing100093 China School of Cyber Security University of Chinese Academy of Sciences Beijing100049 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China Cyberspace Security Research Center Peng Cheng Laboratory Shenzhen518055 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China University of Chinese Academy of Sciences Beijing101408 Peng Cheng Laboratory Shenzhen518055 China

As an effective learning paradigm against insufficient training samples, Multi-Task Learning (MTL) encourages knowledge sharing across multiple related tasks so as to improve the overall performance. In MTL, a major challenge springs from the phenomenon that sharing the knowledge with dissimilar and hard tasks, known as negative transfer, often results in a worsened performance. Though a substantial amount of studies have been carried out against the negative transfer, most of the existing methods only model the transfer relationship as task correlations, with the transfer across features and tasks left unconsidered. Different from the existing methods, our goal is to alleviate negative transfer collaboratively across features and tasks. To this end, we propose a novel multi-task learning method called Task-Feature Collaborative Learning (TFCL). Specifically, we first propose a base model with a heterogeneous block-diagonal structure regularizer to leverage the collaborative grouping of features and tasks and suppressing inter-group knowledge sharing. We then propose an optimization method for the model. Extensive theoretical analysis shows that our proposed method has the following benefits: (a) it enjoys the global convergence property and (b) it provides a block-diagonal structure recovery guarantee. As a practical extension, we extend the base model by allowing overlapping features and differentiating the hard tasks. We further apply it to the personalized attribute prediction problem with fine-grained modeling of user behaviors. Finally, experimental results on both simulated dataset and real-world datasets demonstrate the effectiveness of our proposed method. Copyright © 2020, The Authors. All rights reserved.

关键词： Knowledge management

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Analysis of Hotel Customer Satisfaction in Heilongjiang Province Based on OTA Data

Analysis of Hotel Customer Satisfaction in Heilongjiang Prov...

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International Conference on Artificial Intelligence and Advanced Manufacturing (AIAM)

作者： Yan Liu Xiaowei Han Zhijie Zhao Yanrong Zhang Jiayuan Sun School of Computer and Information Engineering Harbin University of Commerce Harbin China School of Computer and Information Engineering Harbin University of Commerce Heilongjiang Provincial Key Laboratory of Electronic Commerce and Information Processing Harbin University of Commerce Harbin China

In order to uphold the concept of high-quality service development and improve the service quality of hotel industry in Heilongjiang Province, on the basis of customer satisfaction theory and combined with the development status of hotel industry in Heilongjiang Province, this paper uses keyword extraction algorithm, AHP analytic hierarchy process and emotional analysis technology to extract 12000 Hotel commentary data on OTA platform from equipment and facilities, services, transportation location, catering, price and health care. Six levels and 20 indicators are used to construct the evaluation model of hotel customer satisfaction in Heilongjiang Province. It is found that the factors that have significant impact on high-star hotels are relatively not obvious. The analysis results show that the overall customer satisfaction of hotels in Heilongjiang Province is relatively high, but there are still some improvements. For high-star hotels, we should pay more attention to equipment, facilities and service factors. Middle and low star hotels should pay attention to traffic location and price factors. Finally, according to the conclusion of the model and local characteristics, the corresponding countermeasures and suggestions are proposed.

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Power Control for SISO Interference Channel Networks Based on Successive Convex Approximation

Research Square

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Research Square 2020年

作者： Mao, Ruina Yu, Jiguo Dong, Anming Wang, Yue Huang, Baogui School of Information Science and Engineering Qufu Normal University Yantai Road Shandong Rizhao276826 China School of Computer Science and Technology Qilu University of Technology Shandong Academy of Sciences University Road Shandong Jinan250353 China Shandong Provincial Key Laboratory of Computer Networks Shandong Computer Science Center National Supercomputer Center in Jinan Xinluo Street Shandong Jinan250014 China

Interference channel (IC) is a classical model used to characterize the effect of interference for many real-life communication systems. In this paper, we consider a sum rate maximization problem subject to maximum power restriction at each transmitter for a single-input single-output (SISO) IC network. The considered power control problem is typically a nonconvex problem which is hard to solve directly. We propose a solving algorithm for this power control problem based on successive convex approximation (SCA). Specifically, we first reformulate the original nonconvex objective function as the difference of two concave (D.C.) functions near a given point in the feasible region of the solution space. After that, we construct a convex substitute function for the D.C. objective by approximating its second concave function with the one-order Taylor expansion. The problem is further reformulated as an unconstrained problem by transforming the constraints as a barrier function. This unconstrained problem is then solved iteratively using Newton’s method. We update the substitute function given the newly obtained solution, near which the one-order Taylor expansion is performed again. This process is then repeated until a smooth point is reached. Simulation results show the effectiveness of the proposed SCA-based power control algorithm. © 2020, CC BY.

关键词： Power control

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A Dynamic Network Resource Demand Predicting Algorithm Based on Incremental Design of RBF

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Procedia computer Science 2019年 147卷 29-35页

作者： Xiancui Xiao Xiangwei Zheng School of Information Science and Engineering Shandong Normal University Jinan 250014 China Shandong Provincial Key Laboratory for Distributed Computer Software Novel Technology Jinan 250014 China

Nowadays, the research on network mapping is mostly limited to static resource allocation. In fact, the user’s demand of network resources changes dynamically over time. Therefore, how to predict the time-varying demand of users and allocate appropriate resources becomes an important way to improve resource utilization. As a fully connected artificial neural network (ANN), the RBFN (Radial Basis Function Network) has diagnostic, predictive and classification functions. However, due to the excessive use of hidden RBF units during training process, it suffers from expensive core inner product calculations and long training time. This paper proposes a dynamic network resource demand predicting algorithm based on RBF incremental design (GSO-INC-RBFDM). In the network mapping, the group search optimizer (GSO) is used to optimize the mapping scheme, and then the radial basis function (RBF) of the incremental construction is used to predict the time-varying demand of the user. GSO-INC-RBFDM based on incremental design of RBF can construct a compact neural network structure, which not only accelerates the training speed, but also improves the predictive accuracy. Simulation experiments show, compared with traditional algorithms and the original RBF, GSO-INC-RBFDM have lower cost, higher acceptance rate and network revenue.

关键词： Static resource allocation Dynamically Time-varying RBFN GSO-INC-RBFDM

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Multi-attribute group decision making based on bipolar-valued fuzzy multigranulation rough sets over two universes 4

Multi-attribute group decision making based on bipolar-value...

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4th International Conference on Fuzzy Systems and Data Mining, FSDM 2018

作者： Zhang, Chao Li, Deyu Key Laboratory of Computational Intelligence Chinese Information Processing of Ministry of Education School of Computer and Information Technology Shanxi University China

ISBN: (纸本)9781614999270

As a significant multi-granularity computing model in the rough set community, multigranulation rough sets (MGRSs) establish multi-dimension and multi-view problem solving methods through providing synthesis and analysis strategies for solution space in different granularity levels, which act as a powerful tool for coping with various complicated multi-attribute group decision making (MAGDM) problems. The main point of interest in this work is to apply the concept of bipolar-valued fuzzy sets (BVFSs) to MGRSs. Firstly, the definition of bipolar-valued fuzzy multigranulation rough sets (BVF MGRSs) over two universes is presented. Then, we construct a MAGDM method on the basis of BVF MGRSs over two universes. Finally, a case study is provided to show the validation and practicality of the newly constructed method. © 2018 The authors and IOS Press. All rights reserved.

关键词： Fuzzy sets

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Improving unsupervised domain adaptation by reducing bi-level feature redundancy

arXiv

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arXiv 2020年

作者： Wang, Mengzhu Zhang, Xiang Lan, Long Wang, Wei Tan, Huibin Luo, Zhigang Science and Technology on Parallel and Distributed Laboratory College of Computer National University of Defense Technology Changsha China Institute for Quantum State Key Laboratory of High Performance Computing National University of Defense Technology Changsha China DUT-RU International School of Information Science & Engineering Dalian University of Technology DalianLiaoning116000 China Department of Science and Technology on Parallel and Distributed Processing National University of Defense Technology Changsha China

Reducing feature redundancy has shown beneficial effects for improving the accuracy of deep learning models, thus it is also indispensable for the models of unsupervised domain adaptation (UDA). Nevertheless, most recent efforts in the field of UDA ignore this point. Moreover, main schemes realizing this in general independent of UDA purely involve a single domain, thus might not be effective for cross-domain tasks. In this paper, we emphasize the significance of reducing feature redundancy for improving UDA in a bi-level way. For the first level, we try to ensure compact domain-specific features with a transferable decorrelated normalization module, which preserves specific domain information whilst easing the side effect of feature redundancy on the sequel domain-invariance. In the second level, domain-invariant feature redundancy caused by domain-shared representation is further mitigated via an alternative brand orthogonality for better generalization. These two novel aspects can be easily plugged into any BN-based backbone neural networks. Specifically, simply applying them to ResNet50 has achieved competitive performance to the state-of-the-arts on five popular benchmarks. Our code will be available at https://***/dreamkily/gUDA. © 2020, CC BY.

关键词： Deep learning

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二维材料最新研究进展

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物理化学学报 2021年第12期37卷 1-151页

作者：常诚陈伟陈也陈永华陈雨丁峰樊春海范红金范战西龚成宫勇吉何其远洪勋胡晟胡伟达黄维黄元季威李德慧李连忠李强林立凌崇益刘鸣华刘楠刘庄 Kian Ping Loh 马建民缪峰彭海琳邵明飞宋礼苏邵孙硕谭超良唐智勇王定胜王欢王金兰王欣王欣然 Andrew T.S.Wee 魏钟鸣吴宇恩吴忠帅熊杰熊启华徐伟高尹鹏曾海波曾志远翟天佑张晗张辉张其春张铁锐张翔赵立东赵美廷赵伟杰赵运宣周凯歌周兴周喻朱宏伟张华刘忠范 Institute of Science and Technology Austria Am Campus 13400 KlosterneuburgAustria Department of Chemistry National University of Singapore3 Science Drive 3117543Singapore Department of Chemistry The Chinese University of Hong KongShatinNew TerritoriesHong KongChina Key Laboratory of Flexible Electronics and Institute of Advanced Materials Nanjing Tech UniversityNanjing 211816China School of Life Sciences Shanghai UniversityShanghai 200444China Centre for Multidimensional Carbon Materials Institute for Basic ScienceUlsan 44919Korea School of Chemistry and Chemical Engineering Shanghai Jiao Tong UniversityShanghai 200240China School of Physical and Mathematical Sciences Nanyang Technological UniversitySingapore 637371Singapore Department of Chemistry City University of Hong KongKowloonHong KongChina Department of Electrical and Computer Engineering and Quantum Technology Center University of MarylandCollege ParkMaryland 20742USA School of Materials Science and Engineering Beihang UniversityBeijing 100191China Department of Materials Science and Engineering City University of Hong KongKowloonHong KongChina Center of Advanced Nanocatalysis(CAN) Hefei National Laboratory for Physical Sciences at the MicroscaleDepartment of Applied ChemistryUniversity of Science and Technology of ChinaHefei 230026China College of Chemistry and Chemical Engineering State Key Laboratory of Physical Chemistry of Solid SurfacesCollaborative Innovation Center of Chemistry for Energy Materials(iChEM)Xiamen UniversityXiamen361005Fujian ProvinceChina State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of SciencesShanghai 200083China Advanced Research Institute of Multidisciplinary Science Beijing Institute of TechnologyBeijing100081China Beijing Key Laboratory of Optoelectronic Functional Materials&Micro-Nano Devices Department of PhysicsRenmin University of ChinaBeijing 100872China School of Optical and Electronic Information Wuhan National Laboratory

Research on two-dimensional(2D) materials has been explosively increasing in last seventeen years in varying subjects including condensed matter physics, electronic engineering, materials science, and chemistry since the mechanical exfoliation of graphene in 2004. Starting from graphene, 2D materials now have become a big family with numerous members and diverse categories. The unique structural features and physicochemical properties of 2D materials make them one class of the most appealing candidates for a wide range of potential applications. In particular, we have seen some major breakthroughs made in the field of 2D materials in last five years not only in developing novel synthetic methods and exploring new structures/properties but also in identifying innovative applications and pushing forward commercialisation. In this review, we provide a critical summary on the recent progress made in the field of 2D materials with a particular focus on last five years. After a brief backgroundintroduction, we first discuss the major synthetic methods for 2D materials, including the mechanical exfoliation, liquid exfoliation, vapor phase deposition, and wet-chemical synthesis as well as phase engineering of 2D materials belonging to the field of phase engineering of nanomaterials(PEN). We then introduce the superconducting/optical/magnetic properties and chirality of 2D materials along with newly emerging magic angle 2D superlattices. Following that, the promising applications of 2D materials in electronics, optoelectronics, catalysis, energy storage, solar cells, biomedicine, sensors, environments, etc. are described sequentially. Thereafter, we present the theoretic calculations and simulations of 2D materials. Finally, after concluding the current progress, we provide some personal discussions on the existing challenges and future outlooks in this rapidly developing field.

关键词： Two-dimensional materials Transition metal dichalcogenides Phase engineering of nanomaterials Electronics Optoelectronics Catalysis Energy storage and conversion

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Overcoming Classifier Imbalance for Long-Tail Object Detection With Balanced Group Softmax

Overcoming Classifier Imbalance for Long-Tail Object Detecti...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Yu Li Tao Wang Bingyi Kang Sheng Tang Chunfeng Wang Jintao Li Jiashi Feng Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China Department of Electrical and Computer Engineering National University of Singapore Singapore Institute of Data Science National University of Singapore Singapore

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Solving long-tail large vocabulary object detection with deep learning based models is a challenging and demanding task, which is however under-explored. In this work, we provide the first systematic analysis on the underperformance of state-of-the-art models in front of long-tail distribution. We find existing detection methods are unable to model few-shot classes when the dataset is extremely skewed, which can result in classifier imbalance in terms of parameter magnitude. Directly adapting long-tail classification models to detection frameworks can not solve this problem due to the intrinsic difference between detection and classification. In this work, we propose a novel balanced group softmax (BAGS) module for balancing the classifiers within the detection frameworks through group-wise training. It implicitly modulates the training process for the head and tail classes and ensures they are both sufficiently trained, without requiring any extra sampling for the instances from the tail classes. Extensive experiments on the very recent long-tail large vocabulary object recognition benchmark LVIS show that our proposed BAGS significantly improves the performance of detectors with various backbones and frameworks on both object detection and instance segmentation. It beats all state-of-the-art methods transferred from long-tail image classification and establishes new state-of-the-art. Code is available at https://***/FishYuLi/BalancedGroupSoftmax.

关键词： Training Object detection Proposals Adaptation models Feature extraction Computational modeling Detectors

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ANL: Anti-noise learning for cross-domain person re-identification

arXiv

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arXiv 2020年

作者： Zhang, Hongliang Han, Shoudong Pan, Xiaofeng Zhao, Jun National Key Laboratory of Science and Technology on Multispectral Information Processing School of Artificial Intelligence and Automation Huazhong University of Science and Technology 1037 Luoyu Road WuhanP.C: 430074 China School of Computer Science and Engineering Nanyang Technological University 50 Nanyang Avenue Singapore639798 Singapore

Due to the lack of labels and the domain diversities, it is a challenge to study person re-identification in the cross-domain setting. An admirable method is to optimize the target model by assigning pseudo-labels for unlabeled samples through clustering. Usually, attributed to the domain gaps, the pre-trained source domain model cannot extract appropriate target domain features, which will dramatically affect the clustering performance and the accuracy of pseudo-labels. Extensive label noise will lead to sub-optimal solutions doubtlessly. To solve these problems, we propose an Anti-Noise Learning (ANL) approach, which contains two modules. The Feature Distribution Alignment (FDA) module is designed to gather the id-related samples and disperse id-unrelated samples, through the camera-wise contrastive learning and adversarial adaptation. Creating a friendly cross-feature foundation for clustering that is to reduce clustering noise. Besides, the Reliable Sample Selection (RSS) module utilizes an Auxiliary Model to correct noisy labels and select reliable samples for the Main Model. In order to effectively utilize the outlier information generated by the clustering algorithm and RSS module, we train these samples at the instance-level. The experiments demonstrate that our proposed ANL framework can effectively reduce the domain conflicts and alleviate the influence of noisy samples, as well as superior performance compared with the state-of-the-art methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Clustering algorithms

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Wireless Communication Signal Strength Prediction Method Based on the K-nearest Neighbor Algorithm

Wireless Communication Signal Strength Prediction Method Bas...

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2019国际计算机前沿大会

作者： Zhao Chen Ning Xiong Yujue Wang Yong Ding Hengkui Xiang Chenjun Tang Lingang Liu Xiuqing Zou Decun Luo Key Lab on Cognitive Radio and Information Processing School of Information and CommunicationGuilin University of Electronic Technology Guangxi Key Laboratory of Cryptography and Information Security School of Computer Science and Information SecurityGuilin University of Electronic Technology

Existing interference protection systems lack automatic evaluation methods to provide scientific,objective and accurate assessment *** address this issue,this paper develops a layout scheme by geometrically modeling the actual scene,so that the hand-held full-band spectrum analyzer would be able to collect signal field strength values for indoor complex *** improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression was proposed to predict the signal field strengths for the whole plane before and after being *** the highest accuracy set of data could be picked out by *** experimental results show that the improved prediction algorithm based on the K-nearest neighbor non-parametric kernel regression can scientifically and objectively predict the indoor complex scenes' signal strength and evaluate the interference protection with high accuracy.

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