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Fine-grained attribute-aware analysis for person re-identification

Procedia Computer Science 2022年 199卷 276-283页

作者： Kunlong Bai Saiji Fu Linrui Yang Dalian Liu School of Computer Science & Technology University of Chinese Academy of Sciences Beijing 100049 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing 100190 China Sino-Danish College University of Chinese Academy of Sciences Beijing 100049 China Institute of Mathematics and Physics Beijing Union University Beijing 100101 China Institute of Fundamental and Interdisciplinary Sciences Beijing Union University Beijing 100101 China

How and where to learn the discriminative feature has always been a critical issue of person re-identification (re-ID). Most of the previous methods focus on extracting global representation from overall identity image and lack of high-level semantic attribute information. Based on the considerations above, we focus on looking for a way to make people’s attribute play a role in person re-identification task. In this paper, a novel multitask-like network, namely, Attribute-Identity Complementary Network (AICNet), is designed. It contains two branches to learn the identity and attribute feature separately, and a reciprocal interaction process is added to enrich the discrimination of the resulting feature. In addition, in order to better extract high-level semantic attribute information, multi-scale features are combined in the process of attribute extraction. With the help of this attribute-identity complementary strategy, the generated feature can be guided to learn most distinctive attribute feature that is most relevant to identity feature. The present paper carries out extensive experiments on two large-scale datasets, including Market-1501, and DukeMTMC-reID, after which it is found that our attribute-identity complementary framework significantly outperforms the baseline model and achieves competitive performance compared with the state-of-the-art person re-ID methods.

关键词： Person re-identification Attribute recognition Multi-task learning Deep neural network

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Improved ACD-based financial trade durations prediction leveraging LSTM networks and attention mechanism

arXiv

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arXiv 2021年

作者： Shi, Yong Dai, Wei Long, Wen Li, Bo School of Economics and Management University of Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing100190 China Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences No. 80 of Zhongguancun East Street Haidian District Beijing100190 China

The liquidity risk factor of security market plays an important role in the formulation of trading strategies. A more liquid stock market means that the securities can be bought or sold more easily. As a sound indicator of market liquidity, the transaction duration is the focus of this study. We concentrate on estimating the probability density function (+1|) where +1 represents the duration of the (i+1)-th transaction, represents the historical information at the time when the (i+1)-th transaction occurs. In this paper, we propose a new ultra-high-frequency (UHF) duration modelling framework by utilizing long short-term memory (LSTM) networks to extend the conditional mean equation of classic autoregressive conditional duration (ACD) model while retaining the probabilistic inference ability. And then the attention mechanism is leveraged to unveil the internal mechanism of the constructed model. In order to minimize the impact of manual parameter tuning, we adopt fixed hyperparameters during the training process. The experiments applied to a large-scale dataset prove the superiority of the proposed hybrid models. In the input sequence, the temporal positions which are more important for predicting the next duration can be efficiently highlighted via the added attention mechanism layer. Copyright © 2021, The Authors. All rights reserved.

关键词： Long short-term memory

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Corrigendum to “Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data” [Journal of Informetrics 13/4 (2019) 100979]

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Journal of Informetrics 2024年第3期18卷

作者： Lutz Bornmann Alexander Tekles Helena H. Zhang Fred Y. Ye Science Policy and Strategy Department Administrative Headquarters of the Max Planck Society Hofgartenstr. 8 80539 Munich Germany University of Passau Innstr. 41 94032 Passau Germany Jiangsu Key Laboratory of Data Engineering and Knowledge Service School of Information Management Nanjing University Nanjing 210023 China

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data-Driven Single Image Deraining: A Comprehensive Review and New Perspectives

TechRxiv

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TechRxiv 2021年

作者： Zhang, Zhao Wei, Yanyan Zhang, Haijun Yang, Yi Yan, Shuicheng Wang, Meng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Key Laboratory of Knowledge Engineering with Big Data Ministry of Education Hefei University of Technology Hefei230009 China Shenzhen China Centre for Artificial Intelligence University of Technology Sydney SydneyNSW Australia Department of Electrical and Computer Engineering National University of Singapore Singapore Hefei University of Technology China

Single Image Deraining task aims at recovering the rain-free background from an image degraded by rain streaks and rain accumulation. For the powerful fitting ability of deep neural networks and massive training data, data-driven deep SID methods obtained significant improvement over traditional ones. Current SID methods usually focus on improving the deraining performance by proposing different kinds of deraining networks, while neglecting the interpretation of the solving process. As a result, the generalization ability may still be limited in real-world scenarios, and the deraining results also cannot effectively improve the performance of subsequent high-level tasks (e.g., object detection). To explore these issues, we in this paper re-examine the three important factors (i.e., data, rain model and network architecture) for the SID problem, and specifically analyze them by proposing new and more reasonable criteria (i.e., general vs. specific, synthetical vs. mathematical, black-box vs. white-box). We also study the relationship of the three factors from a new perspective of data, and reveal two different solving paradigms (explicit vs. implicit) for the SID task. We further discuss the current mainstream data-driven SID methods from five aspects, i.e., training strategy, network pipeline, domain knowledge, data preprocessing, and objective function, and some useful conclusions are summarized by statistics. Besides, we profoundly studied one of the three factors, i.e., data, and measured the performance of current methods on different datasets through extensive experiments to reveal the effectiveness of SID data. Finally, with the comprehensive review and in-depth analysis, we draw some valuable conclusions and suggestions for future research. © 2021, CC BY.

关键词： Object detection

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A hybridized parallel bats algorithm for combinatorial problem of traveling salesman

A hybridized parallel bats algorithm for combinatorial probl...

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作者： Nguyen, Trong-The Qiao, Yu Pan, Jeng-Shyang Chu, Shu-Chuan Chang, Kuo-Chi Xue, Xingsi Dao, Thi-Kien Intelligent Information Processing Research Center Fujian University of Technology Fujian Fuzhou China Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology Fujian China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China Department of Information Technology University of Manage and Technology Haiphong Viet Nam

Metaheuristic algorithms have been applied widely for real-world problems in many fields, e.g., engineering, financial, healthcare. Bats algorithm (BA) is a recent metaheuristic algorithm with considering as a robust optimization method that can outperform existing algorithms. However, when dealing with complicated combinatorial problems such as traveling salesman problems (TSP), the BA can be fallen in a local optimum. This paper proposes a new hybridizing Parallel BA (HPBA) with a mutation in local-search to escape such its drawback scenario for TSP. A graph theory mutation method is used to embed for hybridizing BA with exploiting similarities among individuals. The proposed method is extensively evaluated in TSP with series instances of the benchmark from TSPLIB to test its performance. The compared experimental result with the previous method and the best-known solutions (B.K.S) in the literature shows that the proposed approach offers competitive results. © 2020 - IOS Press and the authors. All rights reserved.

关键词： Traveling salesman problem

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A Heterogeneous Spatiotemporal Network for Lightning Prediction

A Heterogeneous Spatiotemporal Network for Lightning Predict...

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IEEE International Conference on data mining (ICDM)

作者： Yangli-ao Geng Qingyong Li Tianyang Lin Jing Zhang Liangtao Xu Wen Yao Dong Zheng Weitao Lyu Heng Huang Beijing Key Lab of Traffic Data Analysis and Mining Beijing Jiaotong University State Key Laboratory of Severe Weather Chinese Academy of Meteorological Sciences Department of Electrical and Computer Engineering University of Pittsburgh

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Lightning prediction is a complicated and challenging task requiring meteorologists to integrate information from multiple data sources to make decisions. Although some data-driven models have been proposed to make prediction automatically, most of them are based on a single data source or several basically-homogeneous data sources, making them hard to adapt to complex and diverse data in practice. In this work, we propose a heterogeneous spatiotemporal network (HSTN) for lightning prediction, aiming at mining knowledge from several heterogeneous spatiotemporal (ST) data sources. Specifically, HSTN comprises three modules: Gaussian diffusion module, ST encoder and ST decoder. Noting that most of meteorological data can be formatted into either a dense ST tensor or a sparse ST tensor, the ST encoder, with the help of the Gaussian diffusion module, is designed to extract information from both two types of tensors. On the other hand, ST decoder is responsible for merging all information from the other modules and generate the final prediction. By organically combining the three modules, HSTN can handle complex input with heterogeneity in both space and time domains. We conduct experimental evaluations on a real-world lightning dataset. The results demonstrate that HSTN achieves state-of-the-art performance compared with several established baselines.

关键词： Tensors Lightning Predictive models data models Spatiotemporal phenomena Decoding data mining

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Deep Reinforcement Learning with Transformers for Text Adventure Games

Deep Reinforcement Learning with Transformers for Text Adven...

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IEEE Symposium on Computational Intelligence and Games, CIG

作者： Yunqiu Xu Ling Chen Meng Fang Yang Wang Chengqi Zhang Centre for Artificial Intelligence University of Technology Sydney Sydney Australia Tencent Robotics X Key Laboratory of Knowledge Engineering with Big Data (Ministry of Education) Hefei University of Technology China

ISBN: (数字)9781728145334

ISBN: (纸本)9781728145341

In this paper, we study transformers for text-based games. As a promising replacement of recurrent modules in Natural Language Processing (NLP) tasks, the transformer architecture could be treated as a powerful state representation generator for reinforcement learning. However, the vanilla transformer is neither effective nor efficient to learn with a huge amount of weight parameters. Unlike existing research that encodes states using LSTMs or GRUs, we develop a novel lightweight transformer-based representation generator featured with reordered layer normalization, weight sharing and block-wise aggregation. The experimental results show that our proposed model not only solves single games with much fewer interactions, but also achieves better generalization on a set of unseen games. Furthermore, our model outperforms state-of-the-art agents in a variety of man-made games.

关键词： Games Generators Task analysis Reinforcement learning Logic gates Buildings Computer architecture

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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Refined nonuniform embedding for coupling detection in multivariate time series

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Physical Review E 2020年第6期101卷 062113-062113页

作者： Ziyu Jia Youfang Lin Yunxiao Liu Zehui Jiao Jing Wang School of Computer and Information Technology Beijing Jiaotong University Beijing 100044 China Beijing Key Laboratory of Traffic Data Analysis and Mining Beijing 100044 China Key Laboratory of Intelligent Passenger Service of Civil Aviation CAAC Beijing 101318 China Beijing Laboratory of National Economic Security Early-warning Engineering Beijing Jiaotong University Beijing 100044 China

State-space reconstruction is essential to analyze the dynamics and internal interactions of complex systems. However, it is difficult to estimate high-dimensional conditional mutual information and select the optimal time delays in most existing nonuniform state-space reconstruction methods. Therefore, we propose a nonuniform embedding method framed in information theory for state-space reconstruction. We use a low-dimensional approximation of conditional mutual information criterion for time delay selection, which is effectively solved by the particle swarm optimization algorithm. The obtained embedded vector has relatively strong independence and low redundancy, which better characterizes multivariable complex systems and detects coupling within complex systems. In addition, the proposed nonuniform embedding method exhibits good performance in coupling detection of linear stochastic, nonlinear stochastic, chaotic systems. In the actual application, the importance of small airports that cause delay propagation has been demonstrated by constructing the delay propagation network.

关键词： Coupled oscillators Dynamical systems Information theory Time series analysis

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Genomic divergence in cotton germplasm related to maturity and heterosis

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Journal of Integrative Plant Biology 2019年第8期61卷 929-942页

作者： Shoupu He Gaofei Sun Longyu Huang Daigang Yang Panhong Dai Dayun Zhou Yuzhen Wu Xiongfeng Ma Xiongming Du Shoujun Wei Jun Peng Meng Kuang Research Base State Key Laboratory of Cotton BiologyAnyang Institute of TechnologyAnyang 455000China Institute of Cotton Research of the Chinese Academy of Agricultural Sciences Anyang 455000China Department of Computer Science and Information Engineering/Data Mining Institute Anyang Institute of TechnologyAnyang 455000China

Commercial varieties of upland cotton(Gossypium hirsutum)have undergone extensive breeding for agronomic traits,such as fiber quality,disease resistance,and *** breeding programs have widely used Chinese upland cotton source germplasm(CUCSG)with excellent agronomic traits.A better understanding of the genetic diversity and genomic characteristics of these accessions could accelerate the identification of desirable ***,we analyzed 10,522 high-quality singlenucleotide polymorphisms(SNP)with the CottonSNP63 K microarray in 137 cotton accessions(including 12 hybrids of upland cotton).These data were used to investigate the genetic diversity,population structure,and genomic characteristics of each population and the contribution of these loci to *** subgroups were identified,in agreement with their knownpedigrees,geographical distributions,and times since *** each group,we identified lineagespecific genomic divergence regions,which potentially harbor key alleles that determine the characteristics of each group,such as early maturity-related *** of the distribution of heterozygous loci,among 12 commercial cotton hybrids,revealed a potential role for these regions in *** study provides insight into the population structure of upland cotton ***,the overlap between lineagespecific regions and heterozygous loci,in the high-yield hybrids,suggests a role for these regions in cotton heterosis.

关键词： upland cotton fiber quality genetic diversity

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