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Exploring anti-community structure in networks with application to incompatibility of traditional Chinese medicine

PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS 2017年 486卷 31-43页

作者： Zhu, Jiajing Liu, Yongguo Zhang, Yun Liu, Xiaofeng Xiao, Yonghua Wang, Shidong Wu, Xindong [a]Knowledge and Data Engineering Laboratory of Chinese Medicine School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu 610054 PR China [b]School of Basic Medical Science Beijing University of Chinese Medicine Beijing 100029 PR China [c]Dongzhimen Hospital Beijing University of Chinese Medicine Beijing 100700 PR China [d]School of Computing and Informatics University of Louisiana at Lafayette Lafayette LA 70503 USA

Community structure is one of the most important properties in networks, in which a node shares its most connections with the others in the same community. On the contrary, the anti-community structure means the nodes in the same group have few or no connections with each other. In Traditional Chinese Medicine (TCM), the incompatibility problem of herbs is a challenge to the clinical medication safety. In this paper, we propose a new anti community detection algorithm, Random non-nEighboring nOde expansioN (REON), to find anti-communities in networks, in which a new evaluation criterion, anti-modularity, is designed to measure the quality of the obtained anti-community structure. In order to establish anti-communities in REON, we expand the node set by non-neighboring node expansion and regard the node set with the highest anti-modularity as an anti-community. Inspired by the phenomenon that the node with higher degree has greater contribution to the anti-modularity, an improved algorithm called REONI is developed by expanding node set by the non-neighboring node with the maximum degree, which greatly enhances the efficiency of REON. Experiments on synthetic and real-world networks demonstrate the superiority of the proposed algorithms over the existing methods. In addition, by applying REONI to the herb network, we find that it can discover incompatible herb combinations. (C) 2017 Elsevier B.V. All rights reserved.

关键词： Complex networks Anti-community detection Traditional Chinese medicine Incompatibility of herbs

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Max-min fairness user scheduling and power allocation in full-duplex OFDMA systems

arXiv

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arXiv 2018年

作者： Zhang, Xiaozhou Chang, Tsung-Hui Liu, Ya-Feng Shen, Chao Zhu, Gang State Key Laboratory of Rail Traffic Control and Safety Beijing Jiaotong University Beijing China School of Science and Engineering Chinese University of Hong Kong Shenzhen China Shenzhen Research Institute of Big Data Shenzhen China State Key Laboratory of Scientific and Engineering Computing Institute of Computational Mathematics and Scientific/Engineering Computing Academy of Mathematics and Systems Science Chinese Academy of Sciences Beijing China

In a full-duplex (FD) multi-user network, the system performance is not only limited by the self-interference but also by the co-channel interference due to the simultaneous uplink and downlink transmissions. Joint design of the uplink/downlink transmission direction of users and the power allocation is crucial for achieving high system performance in the FD multi-user network. In this paper, we investigate the joint uplink/downlink transmission direction assignment (TDA), user paring (UP) and power allocation problem for maximizing the system max-min fairness (MMF) rate in a FD multi-user orthogonal frequency division multiple access (OFDMA) system. The problem is formulated with a two-time-scale structure where the TDA and the UP variables are for optimizing a long-term MMF rate while the power allocation is for optimizing an instantaneous MMF rate during each channel coherence interval. We show that the studied joint MMF rate maximization problem is NP-hard in general. To obtain high-quality suboptimal solutions, we propose efficient methods based on simple relaxation and greedy rounding techniques. Simulation results are presented to show that the proposed algorithms are effective and achieve higher MMF rates than the existing heuristic methods. Copyright © 2018, The Authors. All rights reserved.

关键词： Optimization

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Predicting news values from headline text and emotions 2

Predicting news values from headline text and emotions

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EMNLP 2017 2nd Workshop on Natural Language Processing Meets Journal., NLPmJ 2017

作者： Di Buono, Maria Pia Šnajder, Jan Bašic, Bojana Dalbelo Glavaš, Goran Tutek, Martin Milic-Frayling, Natasa TakeLab Faculty of Electrical Engineering and Computing University of Zagreb Croatia Data and Web Science Group University of Mannheim Germany School of Computer Science University of Nottingham United Kingdom

ISBN: (纸本)9781945626883

We present a preliminary study on predicting news values from headline text and emotions. We perform a multivariate analysis on a dataset manually annotated with news values and emotions, discovering interesting correlations among them. We then train two competitive machine learning models - an SVM and a CNN - to predict news values from headline text and emotions as features. We find that, while both models yield a satisfactory performance, some news values are more difficult to detect than others, while some profit more from including emotion information. © EMNLP *** right reserved.

关键词： Forecasting

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Deep Multi-Metric Learning for Person Re-Identification

Deep Multi-Metric Learning for Person Re-Identification

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Yongxin Ge Xinqian Gu Min Chen Hongxing Wang Dan Yang Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education (Chongqing University) Chongqing China Key Laboratory of Intelligent Information Processing of Chinese Academy of Sciences (CAS) Institute of Computing Technology CAS Beijing China School of Big Data & Software Engineering Chongqing University Chongqing China

In this paper, to exploit more discriminative information of the global-body and body-parts features, we present a novel deep multi-metric learning (DMML) network for person re-identification under the triplet framework. The main novelty of our learning framework lies in two aspects: 1) Unlike most existing metric learning-based approaches, which learn only one distance metric for comparison, our DMM-L method aims to learn different metrics for the global-body and body-parts features respectively by using convolutional neural network (CNN); 2) A new multi-metric loss function is proposed to train the DMML network, under which the distance of each negative pair is greater than that of each positive pair by a predefined margin, and the correlations of different metrics are maximized. Compared with the previous person re-identification methods that have shown state-of-the-art performances, our DMML approach can achieve competitive results on the challenging CUHK03, CUHKOl, VIPeR and iLIDS datasets.

关键词： Measurement Image color analysis Feature extraction Training Machine learning Kernel Learning systems

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On the relationship between energy complexity and other boolean function measures

arXiv

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arXiv 2018年

作者： Sun, Xiaoming Sun, Yuan Wu, Kewen Xia, Zhiyu CAS Key Lab of Network Data Science and Technology Institute of Computing Technology Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China School of Electronics Engineering and Computer Science Peking University China

In this work we investigate energy complexity, a Boolean function measure related to circuit complexity. Given a circuit C over the standard basis {∨2, ∧2, ¬}, the energy complexity of C, denoted by EC(C), is the maximum number of its activated inner gates over all inputs. The energy complexity of a Boolean function f, denoted by EC(f), is the minimum of EC(C) over all circuits C computing f. Recently, K. Dinesh et al. [3] gave EC(f) an upper bound in terms of the decision tree complexity, EC(f) = O(D(f)3). They also showed that EC(f) ≤ 3n−1, where n is the input size. For the lower bound, they show that EC(f) ≥ 13 psens(f), where psens(f) is the positive sensitivity. They asked whether EC(f) can be lower bounded by a polynomial of D(f). We improve both the upper and lower bounds in this paper. For upper bounds, We show that EC(f) ≤ min{ 12 D(f)2 + O(D(f)), n + 2 D(f) − 2}. For the lower bound, we answer Dinesh et al.'s question by proving that EC(f) = Ω(pD(f)). For non-degenerated functions, we also give another lower bound EC(f) = Ω(log n) where n is the input size. These two lower bounds are incomparable to each other. Besides, we examine the energy complexity of OR functions and ADDRESS functions, which implies the tightness of our two lower bounds respectively. In addition, the former one answers another open question in [3] asking for non-trivial lower bound for energy complexity of OR functions. Copyright © 2018, The Authors. All rights reserved.

关键词： Decision trees

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The development of data science: implications for education, employment, research, and the data revolution for sustainable development

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Big data and Cognitive computing 2018年第2期2卷 1-16页

作者： Murtagh, Fionn Devlin, Keith Centre of Mathematics and Data Science School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom H-STAR Institute Stanford University Ventura Hall 220 Panama Street StanfordCA94305-4101 United States

In data Science, we are concerned with the integration of relevant sciences in observed and empirical contexts. This results in the unification of analytical methodologies, and of observed and empirical data contexts. Given the dynamic nature of convergence, the origins and many evolutions of the data Science theme are described. The following are covered in this article: the rapidly growing post-graduate university course provisioning for data Science;a preliminary study of employability requirements, and how past eminent work in the social sciences and other areas, certainly mathematics, can be of immediate and direct relevance and benefit for innovative methodology, and for facing and addressing the ethical aspect of Big data analytics, relating to data aggregation and scale effects. Associated also with data Science is how direct and indirect outcomes and consequences of data Science include decision support and policy making, and both qualitative as well as quantitative outcomes. For such reasons, the importance is noted of how data Science builds collaboratively on other domains, potentially with innovative methodologies and practice. Further sections point towards some of the most major current research issues. © 2018 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： data Analytics

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Attentive relational networks for mapping images to scene graphs

arXiv

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arXiv 2018年

作者： Qi, Mengshi Li, Weijian Yang, Zhengyuan Wang, Yunhong Luo, Jiebo State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and Engineering Beihang University China Beijing Advanced Innovation Center for Big Data and Brain Computing Department of Computer Science University of Rochester United States

Scene graph generation refers to the task of automatically mapping an image into a semantic structural graph, which requires correctly labeling each extracted object and their interaction relationships. Despite the recent success in object detection using deep learning techniques, inferring complex contextual relationships and structured graph representations from visual data remains a challenging topic. In this study, we propose a novel Attentive Relational Network that consists of two key modules with an object detection backbone to approach this problem. The first module is a semantic transformation module utilized to capture semantic embedded relation features, by translating visual features and linguistic features into a common semantic space. The other module is a graph self-attention module introduced to embed a joint graph representation through assigning various importance weights to neighboring nodes. Finally, accurate scene graphs are produced by the relation inference module to recognize all entities and the corresponding relations. We evaluate our proposed method on the widely-adopted Visual Genome dataset, and the results demonstrate the effectiveness and superiority of our model. Copyright © 2018, The Authors. All rights reserved.

关键词： Semantics

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Sysevr: A framework for using deep learning to detect software vulnerabilities

arXiv

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arXiv 2018年

作者： Li, Zhen Zou, Deqing Xu, Shouhuai Jin, Hai Zhu, Yawei Chen, Zhaoxuan Services Computing Technology and System Lab Cluster and Grid Computing Lab Big Data Security Engineering Research Center School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China Department of Computer Science University of Texas at San Antonio San AntonioTX78249 United States

The detection of software vulnerabilities (or vulnerabilities for short) is an important problem that has yet to be tackled, as manifested by many vulnerabilities reported on a daily basis. This calls for machine learning methods to automate vulnerability detection. Deep learning is attractive for this purpose because it does not require human experts to manually define features. Despite the tremendous success of deep learning in other domains, its applicability to vulnerability detection is not systematically understood. In order to fill this void, we propose the first systematic framework for using deep learning to detect vulnerabilities. The framework, dubbed Syntax-based, Semantics-based, and Vector Representations (SySeVR), focuses on obtaining program representations that can accommodate syntax and semantic information pertinent to vulnerabilities. Our experiments with 4 software products demonstrate the usefulness of the framework: we detect 15 vulnerabilities that are not reported in the National Vulnerability database. Among these 15 vulnerabilities, 7 are unknown and have been reported to the vendors, and the other 8 have been "silently" patched by the vendors when releasing newer versions of the products. Copyright © 2018, The Authors. All rights reserved.

关键词： Syntactics

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Has “Intelligent Manufacturing” Promoted the Productivity of Manufacturing Sector?--Evidence from China’s Listed Firms

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Procedia Computer Science 2018年 139卷 299-305页

作者： Yi Qu Yong Shi Kun Guo Yuanchun Zheng School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha NE 68182 USA School of Computing and Control Engineering University of Chinese Academy of Sciences Beijing 100190 China

Intelligent Manufacturing has attracted global and continuous attention recent years, with more and more intelligent devices and systems applied in production. In this paper, we take China’s manufacturing listed firms to investigate the productivity difference between intelligent and general manufacturing firms. By the Cobb-Douglas production function, we built a Coefficient-varying Model and used Seemingly Unrelated Regression (SUR) to estimate the time-varying trend of productivity from 2011 to 2017. The empirical results show that “Intelligent Manufacturing” has obviously promoted the Labor factor utilization efficiency and the Total Factor Productivity (TFP) through the advances in technology. But it doesn’t have universally enhancing effect of all industries. The impact of “Intelligent Manufacturing” still remains to be observed overtime.

关键词： Intelligent Manufacturing Cobb-Douglas Production Function Total Factor Productivity (TFP) Coefficient-varying Model Seemingly Unrelated Regression (SUR)

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Robust, discriminative and comprehensive dictionary learning for face recognition

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Pattern Recognition 2018年 81卷 341-356页

作者： Guojun Lin Meng Yang Jian Yang Linlin Shen Weicheng Xie School of Data and Computer Science Sun Yat-Sen University Guangzhou China School of Automation and Electric Information Sichuan University of Science and Engineer Zigong China Key Laboratory of Machine Intelligence and Advanced Computing Sun Yat-Sen University Ministry of Education China School of Computer Science and Technology Nanjing University of Science and Technology Nanjing China School of Computer Science and Software Engineering Shenzhen University Shenzhen China

For sparse representation or sparse coding based image classification, the dictionary, which is required to faithfully and robustly represent query images, plays an important role on its success. Learning dictionaries from the training data for sparse coding has shown state-of-the-art results in image classification and face recognition. However, for face recognition, conventional dictionary learning methods cannot well learn a reliable and robust dictionary due to suffering from the small-sample-size problem. The other significant issue is that current dictionary learning do not completely cover the important components of signal representation (e.g., commonality, particularity, and disturbance), which limit their performance. In order to solve the above issues, in this paper, we propose a novel robust, discriminative and comprehensive dictionary learning (RDCDL) method, in which a robust dictionary is learned from comprehensive training sample diversities generated by extracting and generating facial variations. Especially, to completely represent the commonality, particularity and disturbance, class-shared, class-specific and disturbance dictionary atoms are learned to represent the data from different classes. Discriminative regularizations on the dictionary and the representation coefficients are used to exploit discriminative information, which effectively improves the classification capability of the dictionary. The proposed RDCDL method is extensively evaluated on benchmark face image databases, and it shows superior performance to many state-of-the-art dictionary learning methods for face recognition.

关键词： Dictionary learning Face recognition Sparse representation

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