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IEEE International Conference on Big Data

作者： Limeng Cui Jiawei Zhang Zhensong Chen Yong Shi Philip S. Yu School of Computer and Control Engineering University of Chinese Academy of Sciences Beijing China Department of Computer Science Florida State University FL USA Chinese Academy of Sciences Key Laboratory of Big Data Mining and Knowledge Management Beijing China Department of Computer Science University of Illinois at Chicago IL USA

In large-scale learning problem, the scalability of learning algorithms is usually the key factor affecting the algorithm practical performance, which is determined by both the time complexity of the learning algorithms and the amount of supervision information (i.e., labeled data). Learning with label proportions (LLP) is a new kind of machine learning problem which has drawn much attention in recent years. Different from the well-known supervised learning, LLP can estimate a classifier from groups of weakly labeled data, where only the positive/negative class proportions of each group are known. Due to its weak requirements for the input data, LLP presents a variety of real-world applications in almost all the fields involving anonymous data, like computer vision, fraud detection and spam filtering. However, even through the required labeled data is of a very small amount, LLP still suffers from the long execution time a lot due to the high time complexity of the learning algorithm itself. In this paper, we propose a very fast learning method based on inversing output scaling process and extreme learning machine, namely Inverse Extreme Learning Machine (IELM), to address the above issues. IELM can speed up the training process by order of magnitudes for large datasets, while achieving highly competitive classification accuracy with the existing methods at the same time. Extensive experiments demonstrate the significant speedup of the proposed method. We also demonstrate the feasibility of IELM with a case study in real-world setting: modeling image attributes based on ImageNet Object Attributes dataset.

关键词： Training Calibration Support vector machines Supervised learning Visualization Optimization

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A comparison study between Doane's and Freedman-Diaconis' binning rule in characterizing potential water resources availability

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Journal of Physics: Conference Series 2019年第1期1366卷

作者： Zun Liang Chuan Wan Nur Syahidah Wan Yusoff Mohd Khairul Bazli Mohd Aziz Azlyna Senawi Tan Lit Ken Centre for Mathematical Sciences Universiti Malaysia Pahang Lebuhraya Tun Razak 26300 Gambang Kuantan Pahang Malaysia Takasago Thermal/Environmental Systems Laboratory Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Kuala Lumpur Malaysia Department of Computer Science Faculty of Knowledge Engineering Tokyo City University 1-28-1 Tamazutsumi Setagaya-ku Tokyo 158-8557 Japan

One of the primary constraints for development and management of water resources is the spatial and temporal uncertainty of rainfall. This is due to the stability and reliability of water supply is dynamically associated with the spatial and temporal uncertainty of rainfall. However, this spatial and temporal uncertainty can be assessed using the intensity entropy (IE) and apportionment entropy (AE). The main objective of this study is to investigate the implications of the use of Doane's and Freedman-Diaconis' binning rule in characterizing potential water resource availability (PWRA), which the PWRA is assessed via the standardized intensity entropy (IE') against the standardized apportionment entropy (AE') scatter diagram. To pursue the objective of this study, the daily rainfall data recorded ranging from January 2008 to December 2016 at four rainfall monitoring stations located Coastal region of Kuantan District Pahang are analyzed. The analysis results illustrated that the use of Doane's binning rule is more appropriate than Freedman-Diaconis' binning rule. This is due to the resulted PWRA characteristics using Doane's binning rule is relatively consistent with practical climate such that the study region is experiencing poor-in-water zone with less amount and high uncertainty of rainfall during the Southwest Monsoon, while abundant and perennial rainfall during the Northeast Monsoon. Furthermore, the use of Doane's binning rule is more advantages compared to the Freedman-Diaconis' binning rule with the abstraction of computational cost and time.

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A Comparative Study on Latest Substring Association Rule Mining and Hidden Markov Model

A Comparative Study on Latest Substring Association Rule Min...

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International Conference on computer, Electrical & Communication engineering (ICCECE)

作者： Rudra Prasad Chatterjee Chhanda Ray Rajib Bag Department of Computer Science & Engineering Supreme Knowledge Foundation Group Of Institutions Mankundu West Bengal INDIA State Council of Educational Research Training Govt. of West Bengal Kolkata INDIA

The Web usage mining techniques are used to scrutinize the web usage patterns for a web site. Web page prediction plays a vital role by predicting next set of web pages that a user may visit based on the knowledge of the previously visited pages. Web page prediction is the focus of attention of many researchers in recent times and different web page prediction frameworks have been proposed. In this paper, a comparative analysis between two different approaches of web page prediction, namely, Latest Substring Association Rule mining (LSA) and Hidden Markov Model (HMM) has been represented. Web page prediction is implemented by using both the approaches and the experimental results are provided. Finally, an improved approach for web page prediction is proposed at the end of the paper.

关键词： Web pages Hidden Markov models Predictive models Markov processes Clustering algorithms Prediction algorithms Data mining

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Relation extraction from clinical texts using domain invariant convolutional neural network 15

Relation extraction from clinical texts using domain invaria...

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15th Workshop on Biomedical Natural Language Processing, BioNLP 2016

作者： Sahu, Sunil Kumar Anand, Ashish Oruganty, Krishnadev Gattu, Mahanandeeshwar Department of Computer Science and Engineering Iit Guwahati Assam India Excelra Knowledge Solutions Pvt Ltd Telangana Hyderabad India

ISBN: (纸本)9781945626128

In recent years extracting relevant information from biomedical and clinical texts such as research articles, discharge summaries, or electronic health records have been a subject of many research efforts and shared challenges. Relation extraction is the process of detecting and classifying the semantic relation among entities in a given piece of texts. Existing models for this task in biomedical domain use either manually engineered features or kernel methods to create feature vector. These features are then fed to classifier for the prediction of the correct class. It turns out that the results of these methods are highly dependent on quality of user designed features and also suffer from curse of dimensionality. In this work we focus on extracting relations from clinical discharge summaries. Our main objective is to exploit the power of convolution neural network (CNN) to learn features automatically and thus reduce the dependency on manual feature engineering. We evaluate performance of the proposed model on i2b2-2010 clinical relation extraction challenge dataset. Our results indicate that convolution neural network can be a good model for relation exaction in clinical text without being dependent on expert's knowledge on defining quality features. © BioNLP 2016. All rights reserved.

关键词： Extraction

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Human behavior analysis for library and information science

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Library Hi Tech 2017年第4期35卷 442-444页

作者： Mu-Yen Chen Edwin David Lughofer Neil Y. Yen Chia-Chen Chen Department of Information Management National Taichung University of Science and Technology Taichung Taiwan Department of Knowledge-Based Mathematical Systems Johannes Kepler University Linz Linz Austria School of Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan Department of Management Information Systems National Chung Hsing University Taichung City Taiwan

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Semantics and computation of the evolution of hybrid systems with ariadne 14

Semantics and computation of the evolution of hybrid systems...

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14th International Conference on Computability and Complexity in Analysis, CCA 2017

作者： Collins, Pieter Bresolin, Davide Gonzalez, Sanja Živanovic Geretti, Luca Villa, Tiziano Department of Data Science and Knowledge Engineering Maastricht University Bouillonstraat 8-10 Postbus 616 6200MD Netherlands Dipartimento di Informatica Università di Bologna Mura Anteo Zamboni 7 Bologna40126 Italy Department of Mathematics and Computer Science Barry University 11300 NE 2nd Avenue Miami ShoresFL33161-6695 United States Dipartimento di Informatica Università Degli Studi di Verona Strada le Grazie 15 Verona37134 Italy

In this talk we will present material on the semantics, computability, and algorithms for the evolution of hybrid dynamical systems, and an overview of the tool Ariadne for verification of hybrid systems [1]. Hybrid systems are characterised by undergoing continuous evolution interspersed by discrete jumps. They exhibit all the complexities of finite automata, nonlinear dynamic systems and differential equations, and are extremely difficult to analyze. We will consider hybrid systems in which the continuous dynamics is given by a differential equation x = f(x), with discrete jumps x' = ri(x) which occur as soon as a guard condition gi(x) ≥ 0 is activated. It is clear that the evolution of a hybrid system undergoes discontinuities, but since only continuous functions are computable, it is not clear to what extent, if any, it is possible to perform a rigorous analysis of a hybrid system. We will first show that we can define lower and upper semantics of evolution under which it is possible to compute reachable sets, and that away from discontinuity points (such as grazing or corner collision points), these semantics agree [2]. In order to perform reachability analysis, it is necessary to define the evolution over bounded initial sets of states. We show that this can be done using the operations of range, compose, flow and solve operations on functions. We will see that constrained image sets of the form {f(x) | x ∈ D | g(x) ∈ C}, are sufficient to express the evolution exactly, except for the case of degenerate (non-transverse) cross- ings [3]. The flow operation is the most computationally demanding, and we will give some details of the implementation and efficiency considerations [4]. We will give examples of reachability analysis in Ariadne, including electrical power converters and heating systems. Finally, we will outline some areas of active research, including differential inclusions [5] and modular reasoning. © 2017 14th International Conference on Computabili

关键词： Hybrid systems

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Accuracy estimation of link-based similarity measures and its application

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Frontiers of computer science 2016年第1期10卷 113-123页

作者： Yinglong ZHANG Cuiping LI Chengwang XIE Hong CHEN School of Software East China Jiaotong University Nanchang 330045 China Key Laboratory of Data Engineering and Knowledge Engineering of Ministry of Education and Department of Computer Science Renmin University of China Beijing 100872 China Intelligent Optimization and Information Processing Laboratory East China Jiaotong University Nanchang 330013 China

Link-based similarity measures play a significant role in many graph based applications. Consequently, mea- suring node similarity in a graph is a fundamental problem of graph data mining. Personalized PageRank （PPR） and Sim- Rank （SR） have emerged as the most popular and influen- tial link-based similarity measures. Recently, a novel link- based similarity measure, penetrating rank （P-Rank）, which enriches SR, was proposed. In practice, PPR, SR and P-Rank scores are calculated by iterative methods. As the number of iterations increases so does the overhead of the calcula- tion. The ideal solution is that computing similarity within the minimum number of iterations is sufficient to guaran- tee a desired accuracy. However, the existing upper bounds are too coarse to be useful in general. Therefore, we focus on designing an accurate and tight upper bounds for PPR, SR, and P-Rank in the paper. Our upper bounds are designed based on the following intuition： the smaller the difference between the two consecutive iteration steps is, the smaller the difference between the theoretical and iterative similar- ity scores becomes. Furthermore, we demonstrate the effec- tiveness of our upper bounds in the scenario of top-k similar nodes queries, where our upper bounds helps accelerate the speed of the query. We also run a comprehensive set of exper- iments on real world data sets to verify the effectiveness and efficiency of our upper bounds.

关键词： personalized PageRank SimRank P-Rank up-per bound

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Group sparse Bayesian learning for active surveillance on epidemic dynamics

arXiv

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arXiv 2017年

作者： Pei, Hongbin Yang, Bo Liu, Jiming Dong, Lei College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation Knowledge Engineering of Ministry of Education China Department of Computer Science Hong Kong Baptist University Hong Kong Institute of Remote Sensing and Geographical Information Systems Peking University Beijing China

Predicting epidemic dynamics is of great value in understanding and controlling diffusion processes, such as infectious disease spread and information propagation. This task is intractable, especially when surveillance resources are very limited. To address the challenge, we study the problem of active surveillance, i.e., how to identify a small portion of system components as sentinels to effect monitoring, such that the epidemic dynamics of an entire system can be readily predicted from the partial data collected by such sentinels. We propose a novel measure, the γ value, to identify the sentinels by modeling a sentinel network with row sparsity structure. We design a flexible group sparse Bayesian learning algorithm to mine the sentinel network suitable for handling both linear and non-linear dynamical systems by using the expectation maximization method and variational approximation. The efficacy of the proposed algorithm is theoretically analyzed and empirically validated using both synthetic and real-world data. Copyright © 2017, The Authors. All rights reserved.

关键词： Monitoring

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Testing the application of warranting theory to online third party marketplaces: The effects of information uniqueness and product type 16

Testing the application of warranting theory to online third...

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7th International Conference on Social Media and Society, SMSociety 2016

作者： Sanders, W. Scott Nutakki, Gopi Chand Nasraoui, Olfa Department of Communication University of Louisville United States Knowledge Discovery and Web Mining Lab Dept. of Computer Engineering and Computer Science Speed School of Engineering University of Louisville United States

ISBN: (纸本)9781450339384

Online third party marketplaces link buyers and sellers by providing a neutral platform for exchange. However, this requires buyers to assess the quality of goods without being able to handle or sample them. Recent research has proposed extending the warranting principle, an emerging theory of online interpersonal impression formation, to the judgements consumer make about products online. The warranting principle holds that information that is more difficult to manufacture or manipulate, such as an individualized photo of an auction item, should be more informative to a consumers judgement of the product than information which is easy to falsify or manipulate. While this theoretical extension assumes that all online goods are capable of being assessed in the same way, some goods (i.e. experience goods) are more difficult to assess in online marketplaces because they must be experienced before their true quality and characteristics can be known. The current study examines 2,401 completed auctions from eBay revealing that the relationship between warranting cues and price discounts is moderated by the type of good being sold. The theoretical contributions and limitations of the study are discussed. © Copyright is held by the owner/author(s). Publication rights licensed to ACM.

关键词： Electronic commerce

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作者： Nguyen, Linh Anh Nguyen, Thi Hong Khanh Nguyen, Ngoc-Thanh Ha, Quang-Thuy Division of Knowledge and System Engineering for ICT Faculty of Information Technology Ton Duc Thang University Ho Chi Minh City Viet Nam Institute of Informatics University of Warsaw Warsaw Poland Electric Power University Hoang Quoc Viet Tu Liem Hanoi Viet Nam Department of Information Systems Faculty of Computer Science and Management Wroclaw University of Science and Technology Str. Wyb. Wyspianskiego 27 Wroclaw50-370 Poland Faculty of Information Technology VNU University of Engineering and Technology Xuan Thuy Hanoi Viet Nam

We introduce comparisons w.r.t. information between interpretations in paraconsistent description logics and use them to define bisimilarity for such logics. This notion is useful for concept learning in description logics when inconsistencies occur. We give preservation results and the Hennessy-Milner property for comparisons w.r.t. information in paraconsistent description logics. As consequences, we obtain also invariance results and the Hennessy-Milner property for bisimilarity in paraconsistent description logics. © 2017 - IOS Press and the authors. All rights reserved.

关键词： Data description

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