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arXiv 2022年

作者： Zhao, Chenwei Ye, Miao Xue, Xingsi Lv, Jianhui Jiang, Qiuxiang Wang, Yong School of Information and Communication Guilin University of Electronic Technology Guilin541004 China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin University of Electronic Technology Guilin541004 China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian Fuzhou350118 China Peng Cheng Lab. Guangdong Shenzhen518038 China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin541004 China

Traditional multicast routing methods have some problems in constructing a multicast tree, such as limited access to network state information, poor adaptability to dynamic and complex changes in the network, and inflexible data forwarding. To address these defects, the optimal multicast routing problem in software-defined networking (SDN) is tailored as a multi-objective optimization problem, and an intelligent multicast routing algorithm DRL-M4MR based on the deep Q network (DQN) deep reinforcement learning (DRL) method is designed to construct a multicast tree in SDN. First, the multicast tree state matrix, link bandwidth matrix, link delay matrix, and link packet loss rate matrix are designed as the state space of the DRL agent by combining the global view and control of the SDN. Second, the action space of the agent is all the links in the network, and the action selection strategy is designed to add the links to the current multicast tree under four cases. Third, single-step and final reward function forms are designed to guide the intelligence to make decisions to construct the optimal multicast tree. The experimental results show that, compared with existing algorithms, the multicast tree construct by DRL-M4MR can obtain better bandwidth, delay, and packet loss rate performance after training, and it can make more intelligent multicast routing decisions in a dynamic network environment. Copyright © 2022, The Authors. All rights reserved.

关键词： Reinforcement learning

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14th International Conference on Bio-inspired Computing: Theories and Applications, BIC-TA 2019

作者： Sun, Dengdi Sun, Haifeng Wu, Hang Liang, Huadong Ding, Zhuanlian Key Lab of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Anhui University Hefei230601 China iFlytek Co. Ltd. Hefei230088 China School of Internet Anhui University Hefei230039 China

ISBN: (纸本)9789811534140

Determining the functional roles of proteins is a vital task to understand life at molecular level and has great biomedical and pharmaceutical implications. With the development of novel high-throughput techniques, enormous amounts of protein-protein interaction (PPI) data are collected and provide an important and feasible way for studying protein function predictions. According to this, many approaches assign biological functions to all proteins using PPI networks directly. However, due to the extreme complexity of the topology structure of real PPI networks, it is very difficult and time consuming to seek the global optimization or clustering on the networks. In addition, biological functions are often highly correlated, which makes functions assigned to proteins are not independent. To address these challenges, in this paper we propose a two-stage function annotation method with robust feature selection. First, we transform the network into the low-dimensional representations of nodes via manifold learning. Then, we integrate the functional correlation into the framework of multi-label linear regression, and introduce robust sparse penalty to achieve the function assignment and representative feature selection simultaneously. For the optimization, we design an efficient algorithm to iteratively solve several subproblems with closed-form solutions. Extensive experiments against other baseline methods on Saccharomyces cerevisiae data demonstrate the effectiveness of the proposed approach. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Proteins

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Adaptive Fractional Dilated Convolution Network for Image Aesthetics Assessment

Adaptive Fractional Dilated Convolution Network for Image Ae...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Qiuyu Chen Wei Zhang Ning Zhou Peng Lei Yi Xu Yu Zheng Jianping Fan Department of Computer Science UNC Charlotte Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Amazon Lab126 School of Cyber Engineering Xidian University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

To leverage deep learning for image aesthetics assessment, one critical but unsolved issue is how to seamlessly incorporate the information of image aspect ratios to learn more robust models. In this paper, an adaptive fractional dilated convolution (AFDC), which is aspect-ratio-embedded, composition-preserving and parameter-free, is developed to tackle this issue natively in convolutional kernel level. Specifically, the fractional dilated kernel is adaptively constructed according to the image aspect ratios, where the interpolation of nearest two integer dilated kernels are used to cope with the misalignment of fractional sampling. Moreover, we provide a concise formulation for mini-batch training and utilize a grouping strategy to reduce computational overhead. As a result, it can be easily implemented by common deep learning libraries and plugged into popular CNN architectures in a computation-efficient manner. Our experimental results demonstrate that our proposed method achieves state-of-the-art performance on image aesthetics assessment over the AVA dataset.

关键词： Kernel Convolution Training Machine learning Interpolation Task analysis Libraries

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Adaptive fractional dilated convolution network for image aesthetics assessment

arXiv

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arXiv 2020年

作者： Chen, Qiuyu Zhang, Wei Zhou, Ning Lei, Peng Xu, Yi Zheng, Yu Fan, Jianping Department of Computer Science UNC Charlotte Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Amazon Lab126 School of Cyber Engineering Xidian University

关键词： Aspect ratio

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A Large-Scale Chinese Short-Text Conversation Dataset 1

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9th CCF International Conference on Natural Language processing and Chinese Computing, NLPCC 2020

作者： Wang, Yida Ke, Pei Zheng, Yinhe Huang, Kaili Jiang, Yong Zhu, Xiaoyan Huang, Minlie Tsinghua Shenzhen International Graduate School Tsinghua University Beijing China Institute for Artifical Intelligence State Key Lab of Intelligent Technology and Systems Beijing National Research Center for Information Science and Technology Department of Computer Science and Technology Tsinghua University Beijing China Beijing China

ISBN: (数字)9783030604509

ISBN: (纸本)9783030604493

The advancements of neural dialogue generation models show promising results on modeling short-text conversations. However, training such models usually needs a large-scale high-quality dialogue corpus, which is hard to access. In this paper, we present a large-scale cleaned Chinese conversation dataset LCCC, which contains a base version (6.8 million dialogues) and a large version (12.0 million dialogues). The quality of our dataset is ensured by a rigorous data cleaning pipeline, which is built based on a set of rules and a classifier that is trained on manually annotated 110K dialogue pairs. We also release pre-training dialogue models which are trained on LCCC-base and LCCC-large respectively. The cleaned dataset and the pre-training models will facilitate the research of short-text conversation modeling. All the models and datasets are available at https://***/thu-coai/CDial-GPT. © 2020, Springer Nature Switzerland AG.

关键词： Large dataset

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Sketch-BERT: Learning Sketch Bidirectional Encoder Representation From Transformers by Self-Supervised Learning of Sketch Gestalt

Sketch-BERT: Learning Sketch Bidirectional Encoder Represent...

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Conference on computer Vision and Pattern Recognition (CVPR)

作者： Hangyu Lin Yanwei Fu Xiangyang Xue Yu-Gang Jiang School of Data Science Fudan University School of Data Science MOE Frontiers Center for Brain Science Shanghai Key Lab of Intelligent Information Processing Fudan University School of Computer Science Fudan University

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Previous researches of sketches often considered sketches in pixel format and leveraged CNN based models in the sketch understanding. Fundamentally, a sketch is stored as a sequence of data points, a vector format representation, rather than the photo-realistic image of pixels. SketchRNN studied a generative neural representation for sketches of vector format by Long Short Term Memory networks (LSTM). Unfortunately, the representation learned by SketchRNN is primarily for the generation tasks, rather than the other tasks of recognition and retrieval of sketches. To this end and inspired by the recent BERT model, we present a model of learning Sketch Bidirectional Encoder Representation from Transformer (Sketch-BERT). We generalize BERT to sketch domain, with the novel proposed components and pre-training algorithms, including the newly designed sketch embedding networks, and the self-supervised learning of sketch gestalt. Particularly, towards the pre-training task, we present a novel Sketch Gestalt Model (SGM) to help train the Sketch-BERT. Experimentally, we show that the learned representation of Sketch-BERT can help and improve the performance of the downstream tasks of sketch recognition, sketch retrieval, and sketch gestalt.

关键词： Task analysis Bit error rate Shape Natural language processing Supervised learning Data science Two dimensional displays

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Customized Conversational Recommender Systems

arXiv

引用

arXiv 2022年

作者： Li, Shuokai Zhu, Yongchun Xie, Ruobing Tang, Zhenwei Zhang, Zhao Zhuang, Fuzhen He, Qing Xiong, Hui Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China University of Chinese Academy of Sciences Beijing100049 China WeChat Search Application Department Tencent China King Abdullah University of Science and Technology Thuwal Saudi Arabia Institute of Artificial Intelligence Beihang University Beijing100191 China Sklsde School of Computer Science Beihang University Beijing100191 China Artificial Intelligence Thrust The Hong Kong University of Science and Technology Guangzhou China The Key Lab of Intelligent Information Processing of Chinese Academy of Sciences China

Conversational recommender systems (CRS) aim to capture user's current intentions and provide recommendations through real-time multi-turn conversational interactions. As a human-machine interactive system, it is essential for CRS to improve the user experience. However, most CRS methods neglect the importance of user experience. In this paper, we propose two key points for CRS to improve the user experience: (1) Speaking like a human, human can speak with different styles according to the current dialogue context. (2) Identifying fine-grained intentions, even for the same utterance, different users have diverse finegrained intentions, which are related to users' inherent preference. Based on the observations, we propose a novel CRS model, coined Customized Conversational Recommender System (CCRS), which customizes CRS model for users from three perspectives. For human-like dialogue services, we propose multi-style dialogue response generator which selects context-aware speaking style for utterance generation. To provide personalized recommendations, we extract user's current fine-grained intentions from dialogue context with the guidance of user's inherent preferences. Finally, to customize the model parameters for each user, we train the model from the meta-learning perspective. Extensive experiments and a series of analyses have shown the superiority of our CCRS on both the recommendation and dialogue service. © 2022, CC BY-SA.

关键词： Recommender systems

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Local to global Tracker: A Siamese Network for Long-term Tracking

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Journal of Physics: Conference Series 2022年第1期2216卷

作者： Ya Yang Xinyu Zhou KaiXun Jiang XiaoNan Hou ChunLei Wang WenQiang Zhang The 21st Research Institute of China Electronics Technology Group Corporation Shanghai China Shanghai Key Laboratory of Intelligent Information Processing School of Computer Science Fudan University Shanghai China

Visual object tracking is a basic task in computer vision, which can be applied to different applications. Former methods of single object tracking problem demands to perform target state estimation according to object position of previous frame, but these methods are poorly capable of handling the fast movement of the target. In this paper, we introduce a Siamese network for single object tracking task. The network consists of two branches, one of which is the classification branch used to predict positive or negative samples, and the other is the regression branch used to predict the specific location of the object in sequence. In addition, we propose a local to global algorithm for long-term tracking to expand the search region when the target is out-of-view or full occlusion. Experiments show that our tracker achieves very promising results on VOT2018LT datasets while running at over 89 FPS.

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Adaptive temporal segmentation for action recognition

Adaptive temporal segmentation for action recognition

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2019 International Conference on Security, Pattern Analysis, and Cybernetics, SPAC 2019

作者： Chen, Zhiyu Gu, Yangwei Deng, Chunhua Zhu, Ziqi School of Computer Science and Technology Wuhan University of Science and Technology Wuhan China Hubei Key Lab of Intelligent Information Processing and Real-time Industrial System Wuhan China

ISBN: (纸本)9781728159287

Learning deep representations have been widely used in action recognition task. However, the features extracted by deep convolutional neural networks (CNNs) have many redundant information. This paper aims to discover the relevance between temporal features extracted by CNNs. Different fromTemporal Segment Networks (TSN) to randomly select video clips. Based on the matrix-based Rényi's α-entropy, we estimate the mutual information between temporal domain features. Through our experiments, we propose an adaptive temporal segmentation scheme to represent the entire videos. We also combine the features of RGB and optical flow frames extracted by 3D ConvNets to verify the complementary information between them. We show that the proposed approach achieves 94.4 and 72.8 percent accuracy, in the UCF- 101 and HMDB-51 datasets. © 2019 IEEE.

关键词： Deep neural networks

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Design of People Flow Monitoring System in Public Place based on MD-MCNN

Design of People Flow Monitoring System in Public Place base...

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2020 International Conference on 5G Mobile Communication and information science, MCIS-5G 2020

作者： Tianmin, Xiong Xiaochun, Lei Xingwen, Zheng Junyan, Chen Yizhou, Feng School of Computer and Information Security Guilin University of Electronic Technology Guilin541004 China Guangxi Colleges and Universities Key Lab. of Intelligent Processing of Computer Images and Graphics Guilin541004 China Guangxi Cooperative Innovation Center of Cloud Computing and Big Data Guilin University of Electronic Technology Guilin541004 China

Due to the limitation of hardware resources, the traditional people flow monitoring system based on computer vision in public places can't meet different crowd-scale scenarios. Therefore, a people flow monitoring system based on MD-MCNN algorithm is designed, which is an application system combining the improved SSD object detection algorithm MNSSD and MCNN density map regression algorithm. In the initial stage, the system uses MNSSD for accurate detection and counting. If the people flow gradually reaches a certain threshold, the system automatically uses MCNN to estimate people flow until the people flow falls below the threshold. Through the experimental verification, the system can realize the people flow statistics of low-density and high-density people in different scenarios, and can be applied on the existing embedded platform. This scheme can be extended to smart cities, smart scenic spots, smart transportation and other fields. © 2020 Published under licence by IOP Publishing Ltd.

关键词： Object detection

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