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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Ziye Zhu Xiaoqian Liu Huakang Li Tao Li School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

intelligent Community Question Answering (CQA) system is a popular research topic in Natural Language Processing (NLP) area. Recently, the development of distributed data mining has significantly improved the efficiency of the searching and sorting operations in CQA database. However, problems are left unanswered such as how to choose the best answer among multiple candidates for a single question. To solve this issue, we design a CQA system called UB-CQA. First, in database refinement part, we construct an optimized and structured database by the best answer choosing method leveraging user attribute provided by the response provider. Second, in human-computer part, the UB-CQA is able to search and provide a more satisfying answer to users by similarity calculation and re-ranking method leveraging text categorization information. Empirical evaluations show that the best answer choosing and the candidate question re-ranking methods bring great improvements in accuracy and reliability.

关键词： databases Knowledge based systems Mathematical model Knowledge discovery Internet Computer science

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GARCH-LSSVM Coupled Predication Model and Its Application on Stock Index Forecasting

GARCH-LSSVM Coupled Predication Model and Its Application on...

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2017 International Conference on Mathematics, Modelling and Simulation Technologies and Applications（MMSTA 2017)

作者： Xiao-xu HU Meng-qi ZHANG Xin JIANG Key Laboratory of Mathematics Informatics and Behavioral ScienceMinistry of EducationBeihang University School of Mathematics and Systems Science Beihang University Beijing Advanced Innovation Center for Big Data and Brain Computing

It is usually a challenge for traditional time series prediction models to combine the linear and non-linear factors effectively,which might cause a problem that the trend forecast is not *** this paper we propose a new prediction model based on GARCH and *** GARCH model is used to deal with the heteroscedasticity of the residual series of the closing price of the stock index *** the same time,a number of technical indicators are constructed to train the LSSVM model and the corresponding predicted values and residuals are ***,the predicted value and the residual obtained respectively from GARCH and LSSVM are used as the training set to modify the LSSVM model,with the predicted value of the logarithmic closing price of the stock index *** coupled model not only contains the linear trend of historical information,but also integrates the non-linear features such as market volatility information which is closely related to the target *** experimental results show that the prediction accuracy of the model is 98.22% on the testing data *** model performs also better than the GARCH 97.84% and the LSSVM 98.04% respectively in accuracy deviation.

关键词： Generalized auto-regressive conditional heteroscedastic(GARCH) Least square support vector machine(LSSVM) Coupled prediction algorithm Stock index forecasting Technical indicators

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Cross-view Asymmetric Metric Learning for Unsupervised Person Re-identification

arXiv

引用

arXiv 2017年

作者： Yu, Hong-Xing Wu, Ancong Zheng, Wei-Shi School of Data and Computer Science Sun Yat-sen University China School of Electronics and Information Technology Sun Yat-sen University China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China Collaborative Innovation Center of High Performance Computing NUDT China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

While metric learning is important for Person reidentification (RE-ID), a significant problem in visual surveillance for cross-view pedestrian matching, existing metric models for RE-ID are mostly based on supervised learning that requires quantities of labeled samples in all pairs of camera views for training. However, this limits their scalabilities to realistic applications, in which a large amount of data over multiple disjoint camera views is available but not labelled. To overcome the problem, we propose unsupervised asymmetric metric learning for unsupervised RE-ID. Our model aims to learn an asymmetric metric, i.e., specific projection for each view, based on asymmetric clustering on cross-view person images. Our model finds a shared space where view-specific bias is alleviated and thus better matching performance can be achieved. Extensive experiments have been conducted on a baseline and five large-scale RE-ID datasets to demonstrate the effectiveness of the proposed model. Through the comparison, we show that our model works much more suitable for unsupervised RE-ID compared to classical unsupervised metric learning models. We also compare with existing unsupervised REID methods, and our model outperforms them with notable margins. Specifically, we report the results on large-scale unlabelled RE-ID dataset, which is important but unfortunately less concerned in literatures. Copyright © 2017, The Authors. All rights reserved.

关键词： Machine learning

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Named entity recognition for Chinese telecommunications field based on Char2Vec and Bi-LSTMs

Named entity recognition for Chinese telecommunications fiel...

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Yu Wang Bin Xia Zheng Liu Yun Li Tao Li School of Computer Science Jiangsu Key Laboratory of Big Data Security and Intelligent Processing Nanjing Jiangsu China School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China

Named Entity Recognition (NER) is a basic task in Natural Language Processing (NLP), which extracts the meaningful named entities from the text. Compared with the English NER, the Chinese NER is more challenge, since there is no tense in the Chinese language. Moreover, the omissions and the Internet catchwords in the Chinese corpus make the NER task more difficult. Traditional machine learning methods (e.g., CRFs) cannot address the Chinese NER effectively because they are hard to learn the complicated context in the Chinese language. To overcome the aforementioned problem, we propose a deep learning model Char2Vec+Bi-LSTMs for Chinese NER. We use the Chinese character instead of the Chinese word as the embedding unit, and the Bi-LSTMs is used to learn the complicated semantic dependency. To evaluate our proposed model, we construct the corpus from the China TELECOM FAQs. Experimental results show that our model achieves better performance than other baseline methods and the character embedding is more appropriate than the word embedding in the Chinese language.

关键词： Hidden Markov models Logic gates Computer architecture Machine learning Semantics Telecommunications

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Flame detection via quaternion discrete cosine transform based spectral saliency

International Journal of Control and Automation

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International Journal of Control and Automation 2016年第9期9卷 343-352页

作者： Zhang, Haiyan Gao, Shangbing Xue, Jianxun Zheng, Hao Faculty of computer and software Jiangsu Provincial Key Laboratory for advanced manufacturing technology Huaiyin Institute of Technology Huai'an China The Key Laboratory for traffic and transportation security of Jiangsu Province Huai'an China Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing China

To address the problem of detecting the specific object of flame, this paper proposes quaternion discrete cosine transform based video flame detection algorithm in conjunction with the bottom-up visual attention mechanisms and the flame features. According to the characteristics of the flame to be detected, the brightness, color, texture and motion information of the image are respectively defined and extracted to form a quaternion image, and then the flame saliency map is produced by Quaternion discrete cosine Transform with this quaternion image. Extensive experimental results verify the accuracy and robustness of the proposed method on flame detection. © 2016 SERSC.

关键词： Discrete cosine transforms

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Biomedical image analysis competitions: The state of current participation practice

arXiv

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arXiv 2022年

作者： Eisenmann, Matthias Reinke, Annika Weru, Vivienn Tizabi, Minu Dietlinde Isensee, Fabian Adler, Tim J. Godau, Patrick Cheplygina, Veronika Kozubek, Michal Maier-Hein, Klaus Jäger, Paul F. Kopp-Schneider, Annette Maier-Hein, Lena Ali, Sharib Gupta, Anubha Kybic, Jan Noble, Alison de Solórzano, Carlos Ortiz Pachade, Samiksha Petitjean, Caroline Sage, Daniel Wei, Donglai Wilden, Elizabeth Alapatt, Deepak Andrearczyk, Vincent Baid, Ujjwal Bakas, Spyridon Balu, Niranjan Bano, Sophia Bawa, Vivek Singh Bernal, Jorge Bodenstedt, Sebastian Casella, Alessandro Choi, Jinwook Commowick, Olivier Daum, Marie Depeursinge, Adrien Dorent, Reuben Egger, Jan Eichhorn, Hannah Engelhardt, Sandy Ganz, Melanie Girard, Gabriel Hansen, Lasse Heinrich, Mattias Heller, Nicholas Hering, Alessa Huaulmé, Arnaud Kim, Hyunjeong Li, Hongwei Bran Landman, Bennett Li, Jianning Ma, Jun Martel, Anne Martín-Isla, Carlos Menze, Bjoern Nwoye, Chinedu Innocent Oreiller, Valentin Padoy, Nicolas Pati, Sarthak Payette, Kelly Sudre, Carole van Wijnen, Kimberlin Vardazaryan, Armine Vercauteren, Tom Wagner, Martin Wang, Chuanbo Yap, Moi Hoon Yu, Zeyun Yuan, Chun Zenk, Maximilian Zia, Aneeq Zimmerer, David Bao, Rina Choi, Chanyeol Cohen, Andrew Dzyubachyk, Oleh Galdran, Adrian Gan, Tianyuan Guo, Tianqi Gupta, Pradyumna Haithami, Mahmood Ho, Edward Jang, Ikbeom Li, Zhili Luo, Zhengbo Lux, Filip Makrogiannis, Sokratis Müller, Dominik Oh, Young-Tack Pang, Subeen Pape, Constantin Polat, Gorkem Reed, Charlotte Rosalie Ryu, Kanghyun Scherr, Tim Thambawita, Vajira Wang, Haoyu Wang, Xinliang Xu, Kele Yeh, Hung Yeo, Doyeob Yuan, Yixuan Zeng, Yan Zhao, Xin Abbing, Julian Adam, Jannes Adluru, Nagesh Agethen, Niklas Ahmed, Salman Al Khalil, Yasmina Alenyà, Mireia Alhoniemi, Esa An, Chengyang Arega, Tewodros Weldebirhan Avisdris, Netanell Aydogan, Dogu Baran Bai, Yingbin Calisto, Maria Baldeon Basaran, Berke Doga Beetz, Marcel Bian, Hao Blansit, Kevin Bloch, Louise Bohnsack, Robert Bosticardo, Sara Breen, Jack Brudfors, Mikael Brüngel, Raphael Cabezas, Mariano Cacciola, Alb Heidelberg Division of Intelligent Medical Systems Germany Heidelberg HI Helmholtz Imaging Germany Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Biostatistics Germany Heidelberg Division of Medical Image Computing Germany Heidelberg HI Applied Vision Lab Germany IT University of Copenhagen Copenhagen Denmark Centre for Biomedical Image Analysis Masaryk University Brno Czech Republic Heidelberg Interactive Machine Learning Group Germany Faculty of Mathematics and Computer Science and Medical Faculty Heidelberg University Heidelberg Germany NCT Heidelberg DKFZ University Hospital Heidelberg Germany School of Computing University of Leeds Leeds United Kingdom SBILab Department of ECE IIIT-Delhi India Faculty of Electrical Engineering Czech Technical University Prague Czech Republic Institute of Biomedical Engineering University of Oxford United Kingdom Center for Applied Medical Research Pamplona Spain Shri Guru Gobind Singhji Institute of Engineering and Technology Maharashtra Nanded India Université de Rouen Normandie France Lausanne Switzerland School of Engineering and Applied Science Harvard University United States ICube University of Strasbourg CNRS France Institute of Informatics School of Management HES-SO Valais-Wallis University of Applied Sciences and Arts Western Switzerland Techno-Pôle 3 Sierre3960 Switzerland Department of Nuclear Medicine and Molecular Imaging Lausanne University Hospital Rue du Bugnon 46 LausanneCH-1011 Switzerland University of Pennsylvania PhiladelphiaPA United States Department of Radiology University of Washington United States Wellcome EPSRC Centre for Interventional and Surgical Sciences University College London London United Kingdom Visual Artificial Intelligence Lab Oxford Brookes University Oxford United Kingdom Universitat Autònoma de Barcelona & Computer Vision Center Spain Dresden Fetscherstraße 74 PF 64 Dresden01307 Germany

The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known about the common practice as well as bottlenecks faced by the community in tackling the research questions posed. To shed light on the status quo of algorithm development in the specific field of biomedical imaging analysis, we designed an international survey that was issued to all participants of challenges conducted in conjunction with the IEEE ISBI 2021 and MICCAI 2021 conferences (80 competitions in total). The survey covered participants' expertise and working environments, their chosen strategies, as well as algorithm characteristics. A median of 72% challenge participants took part in the survey. According to our results, knowledge exchange was the primary incentive (70%) for participation, while the reception of prize money played only a minor role (16%). While a median of 80 working hours was spent on method development, a large portion of participants stated that they did not have enough time for method development (32%). 25% perceived the infrastructure to be a bottleneck. Overall, 94% of all solutions were deep learning-based. Of these, 84% were based on standard architectures. 43% of the respondents reported that the data samples (e.g., images) were too large to be processed at once. This was most commonly addressed by patch-based training (69%), downsampling (37%), and solving 3D analysis tasks as a series of 2D tasks. K-fold cross-validation on the training set was performed by only 37% of the participants and only 50% of the participants performed ensembling based on multiple identical models (61%) or heterogeneous models (39%). 48% of the respondents applied postprocessing steps. © 2022, CC BY.

关键词： Benchmarking

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Texture synthesis and design based on elements distribution creation 1

Texture synthesis and design based on elements distribution ...

引用

1st EAI International Conference on Computer Science and Engineering, COMPSE 2016

作者： Gui, Yan Liu, Yang Li, Feng Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and Technology Changsha Hunan410114 China

ISBN: (纸本)9781631901362

The texture synthesis and design starts with an initial elements arrangement and expands it outward by using local and global growth, to obtain the new larger distribution of texture elements. There are two types of synthesized distributions, and their diversity consists in changing the layout of texture elements and decreasing or increasing the number of texture elements according to user's creation. Furthermore, we apply a set of deformation operations to locally change the shapes of texture elements when placing the extracted texture elements into the synthesized distribution, in order to guarantee structure consistency in final synthesized textures. Experimental results show that our method creates a large variety of textures from a given texture sample.

关键词： Computer graphics

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On Link Formation in Heterogeneous Information Networks: A View Based on Multi-Label Learning

On Link Formation in Heterogeneous Information Networks: A V...

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International Conference on Advances in Social Network Analysis and Mining, ASONAM

作者： Ke-Jia Chen Shijun Xue Yun Li Bin Liu Jiangsu Key Laboratory of Big Data Security & Intelligent Processing Nanjing University of Posts and Telecommunications Nanjing Jiangsu China College of Computer Science Nanjing University of Posts and Telecommunications Nanjing Jiangsu China

ISBN: (数字)9781450349932

ISBN: (纸本)9781450349932

This paper studies the problem of relationship prediction in heterogeneous information networks. Our goal is not only to predict links/relationships more accurately but also to provide more viable paths to facilitate the formation of new links/relationships. A relationship prediction method based on multi-label learning named ML 3 P is proposed. In ML 3 P, each meta-path between nodes is regarded as a type of relationship and is given a label. Under the framework of multi-label learning, any potential relationship including the target relationship can be predicted. The results of comparative experiments in DBLP and Twitter datasets show that ML 3 P better uses heterogeneous information in supervised learning process and thus achieves better performance. Moreover, our method can output the correlation between relationships.

关键词： Twitter Task analysis Feature extraction Training Measurement Correlation

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Enabling QoE-aware Mobile Cloud Video Recording over Roadside Vehicular Networks

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China Communications 2016年第8期13卷 63-73页

作者： Tongtong Jiang Zhaobin Deng Weiwei Huang Guoqing Zhang Department of Computer Science Sun Yat-sen University Guangdong Province Key Laboratory of Big Data Analysis and Processing Sun Yat-sen University Collaborative Innovation Center of High Performance Computing National University of Defense Technology Institute of Computing Technology Chinese Academy of Sciences

Most of previous video recording devices in mobile vehicles commonly store captured video contents locally. With the rapid development of 4G/Wi Fi networks, there emerges a new trend to equip video recording devices with wireless interfaces to enable video uploading to the cloud for video playback in a later time point. In this paper, we propose a QoE-aware mobile cloud video recording scheme in the roadside vehicular networks, which can adaptively select the proper wireless interface and video bitrate for video uploading to the cloud. To maximize the total utility, we need to design a control strategy to carefully balance the transmission cost and the achieved QoE for users. To this purpose, we investigate the tradeoff between cost incurred by uploading through cellular networks and the achieved QoE of users. We apply the optimization framework to solve the formulated problem and design an online scheduling algorithm. We also conduct extensive trace-driven simulations and our results show that our algorithm achieves a good balance between the transmission cost and user QoE.

关键词： mobile cloud video vehicular networks quality-of-experience

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Improved kernel and algorithm for claw and diamond free edge deletion based on refined observations

arXiv

引用

arXiv 2017年

作者： Li, Wenjun Peng, Huan Yang, Yongjie School Of Computer And Communication Engineering Hunan Provincial Key Laboratory Of Intelligent Processing Of Big Data On Transportation Changsha University Of Science And Technology Changsha China Economic Theory Saarland University Saarbrücken Germany

In the {CLAW, DIAMOND}-FREE EDGE DELETION problem (CDFED), we are given a graph G and an integer k > 0, and the question is whether there are at most k edges whose deletion results in a graph without claws and diamonds as induced subgraphs. Based on some refined observations, we propose a kernel of O(k3) vertices and O(k4) edges, significantly improving the previous kernel of O(k12) vertices and O(k24) edges. In addition, we derive an O∗(3.792k)-time algorithm for CDFED. Copyright © 2017, The Authors. All rights reserved.

关键词： Diamonds

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