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International Conference on Tools with Artificial Intelligence

作者： Lan Jiang Hengyang Lu Ming Xu Chongjun Wang National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University

ISBN: (纸本)9781509044603

In the past few years, we have witnessed a rapid development of online social media, from which we can access various short texts. Understanding the topic patterns of these short text is significant. Traditional topic models, like LDA, are not suitable when applied to short text topic analysis due to data sparsity. A lot of efforts have been made to solve this problem. However, there is still significant space to improve the effectiveness of these short text specific methods. In this paper, we proposed a novel word co-occurrence network based method, referred to as biterm pseudo document topic model (BPDTM), which extended the previous biterm topic model (BTM) for short text. We utilized the word co-occurrence network to construct biterm pseudo documents. The proposed model is promising since it represents words with their semantic adjacent biterms and is able to model the corpus-level semantic relation between two words. Besides, BPDTM naturally lengthens the documents, which alleviate the influence for performance exerted by data sparsity. Experiments demonstrated that our model outperformed two baselines, i.e. LDA and BTM, which proved its effectiveness on short text topic modeling task.

关键词： texts laser Doppler anemometry Anterior interventricular branch of left coronary artery Left-sided displacement of abomasum Pseudo Word

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Image Segmentation Based on Prototypes Extraction and Merging of Clusters in Multiple Spaces

Image Segmentation Based on Prototypes Extraction and Mergin...

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International Joint Conference on Neural Networks

作者： Xiaofeng Shi Furao Shen Jinxi Zhao The National Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University

ISBN: (纸本)9781509006212

Pixel clustering is one of the basic methods for image segmentation. A critical problem of pixel clustering is how to measure the similarity between colors of the pixels on human visual perception. In this paper, we propose an adaptive clustering method for image segmentation, namely Prototypes Extraction and Merging (PEM) method. We first build a prototype network based on the Hebbian learning rule to represent the image. Then we use the Density Peaks Based Clustering (DPBC) method on the prototypes rather than the pixels for clustering in multiple color spaces, in which we choose a tiny Euclidean distance to approximate the similarity of the neighboring prototypes and find the density peaks. Finally we conduct the Multi-Space Merging (MSM) method to merge the color regions in multiple color spaces and get the final segmentation. In PEM the similarity measuring of colors is achieved adaptively according to the complexity and local contrast ratio of the image. Thus, it is extremely close to the discriminability of human visual system.

关键词： image segmentation merger pixel prototype network color spaces PROTOTYPE Hebbian principle similar measure IMAGE CONTRAST Color Clustering methods Cluster Analysis Visual Perception

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A Fast Manifold Learning Algorithm for Dimensionality Reduction

A Fast Manifold Learning Algorithm for Dimensionality Reduct...

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International Conference on Tools with Artificial Intelligence

作者： Yu Liang Furao Shen Jinxi Zhao Yi Yang National Key Laboratory for Novel Software Technology and Department of Computer Science and Technology Nanjing University

ISBN: (纸本)9781509044603

This paper proposes a new manifold learning method called "Soinnmanifold". Traditional manifold learning method needs a lot of computation and appropriate priori parameters. This has somewhat restricted the domains in which manifold learning can potentially be applied. However, with the high-dimensional inputs, our method can generate a low-dimensional manifold in the high-dimensional space and determine the intrinsic dimension automatically. Then we will use this manifold to do dimensionality reduction quickly. Experiments demonstrate that our method can get promising results with less time and memory.

关键词： Manifold learning Intrinsic Dimension Dimensionality Reduction

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Learning with only multiple instance positive bags

Learning with only multiple instance positive bags

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Wang, Zhigang Zhao, Zengshun Zhang, Changshui Computer and Communication Engineering School Tianjin University of Technology Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology China Key Laboratory of Computer Vision and System Tianjin University of Technology Ministry of Education China College of Electronics Communication and Physics Shandong University of Science and Technology China Automation Department Tsinghua University State Key Laboratory of Intelligent Technology and Systems Tsinghua National Laboratory for Information Science and Technology China

ISBN: (纸本)9781509006199

In traditional multiple instance learning (MIL), both positive and negative bags are required to learn a prediction function. However, a high human cost is needed to know the label of each bag - positive or negative. Only positive bags contain our focus (positive instances) while negative bags consist of noise or background (negative instances). So we do not expect to spend too much to label the negative bags. Contrary to our expectation, nearly all existing MIL methods require enough negative bags besides positive ones. In this paper we propose an algorithm called 'Positive Multiple Instance' (PMI), which learns a classifier given only a set of positive bags. So the annotation of negative bags becomes unnecessary in our method. PMI is constructed based on the assumption that the unknown positive instances in positive bags be similar each other and constitute one compact cluster in feature space and the negative instances locate outside this cluster. The experimental results demonstrate that PMI achieves the performances close to or a little worse than those of the traditional MIL algorithms on benchmark and real data sets. However, the number of training bags in PMI is reduced significantly compared with traditional MIL algorithms. © 2016 IEEE.

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A cloud-based taxi trace mining framework for smart city

A cloud-based taxi trace mining framework for smart city

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作者： Liu, Jin Yu, Xiao Xu, Zheng Choo, Kim-Kwang Raymond Hong, Liang Cui, Xiaohui State Key Lab. of Software Engineering Computer School Wuhan University Wuhan China Third Research Institute of Ministry of Public Security Shanghai China Department of Information Systems and Cyber Security University of Texas at San Antonio San AntonioTX United States School of Information Technology and Mathematical Science University of South Australia AdelaideSA Australia School of Information Management Wuhan University Wuhan China International School of Software Wuhan University Wuhan China

As a well-known field of big data applications, smart city takes advantage of massive data analysis to achieve efficient management and sustainable development in the current worldwide urbanization process. An important problem in smart city is how to discover frequent trajectory sequence pattern and cluster trajectory. To solve this problem, this paper proposes a cloud-based taxi trajectory pattern mining and trajectory clustering framework for smart city. Our work mainly includes (1) preprocessing raw Global Positioning System trace by calling the Baidu API Geocoding;(2) proposing a distributed trajectory pattern mining (DTPM) algorithm based on Spark;and (3) proposing a distributed trajectory clustering (DTC) algorithm based on Spark. The proposed DTPM algorithm and DTC algorithm can overcome the high input/output overhead and communication overhead by adopting in-memory computation. In addition, the proposed DTPM algorithm can avoid generating redundant local trajectory patterns to significantly improve the overall performance. The proposed DTC algorithm can enhance the performance of trajectory similarity computation by transforming the trajectory similarity calculation into AND and OR operators. Experimental results indicate that DTPM algorithm and DTC algorithm can significantly improve the overall performance and scalability of trajectory pattern mining and trajectory clustering on massive taxi trace data. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

关键词： Clustering algorithms

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3D panorama reconstruction based on sitemap joining 41

3D panorama reconstruction based on sitemap joining

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41st IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2016

作者： Wang, Huixuan Guo, Yanwen Do, Minh N. Zhang, Caiming Tu, Changhe School of Computer Science and Technology Shandong University Jinan Shandong250101 China National Key Lab for Novel Software Technology Nanjing University Nanjing Jiangsu210023 China Coordinated Science Laboratory University of Illinois at Urbana-Champaign UrbanaIL61801 United States

ISBN: (纸本)9781479999880

We present a new approach for constructing the 3D panorama for an indoor environment by joining together aligned submaps. For each submap, the trajectory of the moving camera is estimated based on the Kanade-Lucas-Tomasi (KLT) features. Our method can update the feature set status by adding new features and removing expiring ones adaptively to accommodate scene changes. The accuracy of the estimated poses is further improved through sparse bundle adjustment. Furthermore, we utilize a linear optimization framework to align all submaps to obtain a consistently extended 3D panorama and to refine the visual odometry at the same time. We evaluated our approach on publicly available benchmark datasets. The experiments demonstrate that the proposed method achieves low translational drift and is robust even when the camera moves very fast. © 2016 IEEE.

关键词： linear optimization RGB-D camera SLAM visual odometry

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Finding persistent items in data streams 43

Finding persistent items in data streams

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43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Dai, Haipeng Shahzad, Muhammad Liu, Alex X. Zhong, Yuankun State Key Laboratory for Novel Software Technology Nanjing University Nanjing Jiangsu China Department of Computer Science North Carolina State University RaleighNC United States

Frequent item mining, which deals with finding items that occur frequently in a given data stream over a period of time, is one of the heavily studied problems in data stream mining. A generalized version of frequent item mining is the persistent item mining, where a persistent item, unlike a frequent item, does not necessarily occur more frequently compared to other items over a short period of time, rather persists and occurs more frequently over a long period of time. To the best of our knowledge, there is no prior work on mining persistent items in a data stream. In this paper, we address the fundamental problem of finding persistent items in a given data stream during a given period of time at any given observation point. We propose a novel scheme, PIE, that can accurately identify each persistent item with a probability greater than any desired false negative rate (FNR) while using a very small amount of memory. The key idea of PIE is that it uses Raptor codes to encode the ID of each item that appears at the observation point during a measurement period and stores only a few bits of the encoded ID in the memory of that observation point during that measurement period. The item that is persistent occurs in enough measurement periods that enough encoded bits for the ID can be retrieved from the observation point to decode them correctly and get the ID of the persistent item. We implemented and extensively evaluated PIE using three real network traffic traces and compared its performance with two prior adapted schemes. Our results show that not only PIE achieves the desired FNR in every scenario, its FNR, on average, is 19.5 times smaller than the FNR of the best adapted prior art.

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Online stochastic linear optimization under one-bit feedback 33

Online stochastic linear optimization under one-bit feedback

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33rd International Conference on Machine Learning, ICML 2016

作者： Zhang, Lijun Yang, Tianbao Jin, Rong Xiao, Yichi Zhou, Zhi-Hua National Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China Department of Computer Science University of Iowa Iowa CityIA52242 United States Alibaba Group Seattle United States

ISBN: (纸本)9781510829008

In this paper, we study a special bandit setting of online stochastic linear optimization, where only one-bit of information is revealed to the learner at each round. This problem has found many applications including online advertisement and online recommendation. We assume the binary feedback is a random variable generated from the logit model, and aim to minimize the regret defined by the unknown linear function. Although the existing method for generalized linear bandit can be applied to our problem, the high computational cost makes it impractical for real-world applications. To address this challenge, we develop an efficient online learning algorithm by exploiting particular structures of the observation model. Specifically, we adopt online Newton step to estimate the unknown parameter and derive a tight confidence region based on the exponential concavity of the logistic loss. Our analysis shows that the jjroposed algorithm achieves a regret bound of O(d√T), which matches the optimal result of stochastic linear bandits.

关键词： Linear programming

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Register automata with linear arithmetic

arXiv

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arXiv 2017年

作者： Chen, Yu-Fang Lengal, Ondrej Tan, Tony Wu, Zhilin Institute of Information Science Academia Sinica Taiwan FIT Brno University of Technology IT4Innovations Centre of Excellence Czech Republic Department of Computer Science and Information Engineering National Taiwan University Taiwan State Key Laboratory of Computer Science Institute of Software Chinese Academy of Sciences China

We propose a novel automata model over the alphabet of rational numbers, which we call register automata over the rationals (RAQ). It reads a sequence of rational numbers and outputs another rational number. RAQ is an extension of the well-known register automata (RA) over infinite alphabets, which are finite automata equipped with a finite number of registers/variables for storing values. Like in the standard RA, the RAQ model allows both equality and ordering tests between values. It, moreover, allows to perform linear arithmetic between certain variables. The model is quite expressive: In addition to the standard RA, it also generalizes other well-known models such as affine programs and arithmetic circuits. The main feature of RAQ is that despite the use of linear arithmetic, the so-called invariant problem-a generalization of the standard non-emptiness problem-is decidable. We also investigate other natural decision problems, namely, commutativity, equivalence, and reachability. For deterministic RAQ, commutativity and equivalence are polynomial-time inter-reducible with the invariant problem. Copyright © 2017, The Authors. All rights reserved.

关键词： Polynomial approximation

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Combined Cloud： A Mixture of Voluntary Cloud and Reserved Instance Marketplace

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Journal of computer science & technology 2016年第6期31卷 1110-1123页

作者： Wei Shen Wan-Chun Dou Fan Wu Shaojie Tang Qiang Ni State Key Laboratory for Novel Software Technology Nanjing University Nanjing 210046 China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai 200240 China Department of Information Systems University of Texas at Dallas Texas TX 75080 U.S.A. School of Computing and Communications Lancaster University Lancaster LA1 4WA U.K.

Voluntary cloud is a new paradigm of cloud computing. It provides an alternative selection along with some well-provisioned clouds. However, for the uncertain time span that participants share their computing resources in voluntary cloud, there are some challenging issues, i.e., fluctuation, under-capacity and low-benefit. In this paper, an architecture is first proposed based on Bittorrent protocol. In this architecture, resources could be reserved or requested from Reserved Instance Marketplace and could be accessed with a lower price in a short circle. Actually, these resources could replenish the inadequate resource pool and relieve the fluctuation and under-capacity issue in voluntary cloud. Then, the fault rate of each node is used to evaluate the uncertainty of its sharing time. By leveraging a linear prediction model, it is enabled by a distribution function which is used for evaluating the computing capacity of the system. Moreover, the cost optimization problem is investigated and a computational method is presented to solve the low-benefit issue in voluntary cloud. At last, the system performance is validated by two sets of simulations. And the experimental results show the effectiveness of our computational method for resource reservation optimization.

关键词： voluntary cloud system architecture resource reservation

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