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International Conference on Audio, Language and Image Processing, ICALIP

作者： Chengchao Yu Qingshi Tang Zheng Liu Bin Dong Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Shanghai Zhuoji Information Technology ltd. Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

With the development of ordering platform, an increasing number of people are paying their attention to design a suitable recommender system. Most of the traditional recommender systems are based on the abundant rating information of users. However, Only historical order data can be provided to the recommender system in ordering platform as training data. This paper proposes an improved Collaborative Filtering algorithm based on historical order data of restaurants. The recommender system includes two parts: 1) rule generation module, we define a new method for measuring the similarity between dishes. Furthermore, we incorporate an incremental learning method in this module. 2) recommendation module, we design user interest vector and propose a noise filtering method. Experimental results demonstrate that the proposed algorithm can effectively improve the performance of recommendation in terms of the accuracy and coverage ratio. Moreover, our recommender system has been successfully put into service.

关键词： Recommender systems Collaboration Training Measurement Filtering algorithms Training data Learning systems

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An Event Timeline Extraction Method Based on News Corpus 2

An Event Timeline Extraction Method Based on News Corpus

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2017 IEEE 2nd International Conference on Big Data Analysis（ICBDA 2017）

作者： Yaguang Wu Haichun Sun Chungang Yan Department of Computer Science and Technology Tongji University The Key Laboratory of Embedded System and Service Computing Tongji University College of Police Information Engineering People's Public Security University of China

ISBN: (纸本)9781509036202

Event extraction is an important research point in information extraction area, and news event extraction has a greater practical significance. The existing methods of extracting news event, which starts from the time element, is to identify the date sentences hold by Natural Language Processing and extract the event on the date by Text Clustering. However, they only process the news that holds a time-tag and gives up the news that has no accurate date, which easily leads an extraction deviation of significant events and reduces the accuracy of ordering the significant events. In this paper, improvement for this defect is to calculate the similarities between sentences to put part of the non-time-tag sentences into the right date container, thereby the accuracy of ordering the significant event is *** experiments and compared with the existing methods,the accuracy of ordering the significant events in this paper is improved by 14.6%.

关键词： event extraction text information mining sentence similarity timeline

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An Improved Convolutional Neural Network Algorithm for Multi-Label Classification

An Improved Convolutional Neural Network Algorithm for Multi...

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International Conference on Audio, Language and Image Processing, ICALIP

作者： Xinsheng Wang Lijun Sun Zhihua Wei Department of Computer Science and Technology Tongji University Shanghai China Research Center of Big Data and Network Security Tongji University Shanghai China Key Laboratory of Embedded System and Service Computing Tongji University Shanghai China

Recent years conventional neural network(CNN) has been applied to different natural language processing(NLP) tasks such as sentence classification, sentence modeling, etc. Some researchers use CNN to do multi-label classification but their work mainly focus on image rather than text. In this paper, we propose an improved CNN via hierarchical dirichlet process(HDP) model to deal with the multi-label classification problem in NLP. We first apply an HDP model to discard some words which are less important semantically. Then we use word embedding methods to transform words to vectors. Finally, we train CNN based on word vectors. Experimental results demonstrate that our method is superior to most traditional multi-label classification methods and TextCNN in terms of performance.

关键词： Feature extraction Training Convolutional neural networks Natural language processing Vocabulary Transforms

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Semi-Supervised Learning in Large Scale Text Categorization

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Journal of Shanghai Jiaotong university(Science) 2017年第3期22卷 291-302页

作者：许泽文李建强刘博毕敬李蓉毛睿 School of Software Engineering Beijing University of Technology Beijing Engineering Research Center for Io T Software and Systems Beijing University of Technology Guangdong Key Laboratory of Popular High Performance Computers Shenzhen University Shenzhen Key Laboratory of Service Computing and Applications Shenzhen University

The rapid development of the Internet brings a variety of original information including text information, audio information, etc. However, it is difficult to find the most useful knowledge rapidly and accurately because of its huge number. Automatic text classification technology based on machine learning can classify a large number of natural language documents into the corresponding subject categories according to its correct semantics. It is helpful to grasp the text information directly. By learning from a set of hand-labeled documents,we obtain the traditional supervised classifier for text categorization(TC). However, labeling all data by human is labor intensive and time consuming. To solve this problem, some scholars proposed a semi-supervised learning method to train classifier, but it is unfeasible for various kinds and great number of Web data since it still needs a part of hand-labeled data. In 2012, Li et al. invented a fully automatic categorization approach for text(FACT)based on supervised learning, where no manual labeling efforts are required. But automatically labeling all data can bring noise into experiment and cause the fact that the result cannot meet the accuracy requirement. We put forward a new idea that part of data with high accuracy can be automatically tagged based on the semantic of category name, then a semi-supervised way is taken to train classifier with both labeled and unlabeled data,and ultimately a precise classification of massive text data can be achieved. The empirical experiments show that the method outperforms the supervised support vector machine(SVM) in terms of both F1 performance and classification accuracy in most cases. It proves the effectiveness of the semi-supervised algorithm in automatic TC.

关键词： text data mining semi-supervised automatic tagging classifier

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Spatial sparse subspace clustering for compressive spectral imaging

arXiv

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arXiv 2019年

作者： Zhu, Jianchen Zhang, Tong Zhao, Shengjie Hinojosa, Carlos Liu, Zengli Arce, Gonzalo R. Key Laboratory of Embedded System and Service Computing Ministry of Education School of Software Engineering Tongji University Shanghai201804 China School of Electronic and Information Engineering Tongji University Shanghai201804 China Department of System Engineering and Informatics Universidad Industrial de Santander Bucaramanga680002 Colombia Institute of information engineering and automation Computer Center Kunming University of Science and Technology Kunming650500 China Department of Electrical and Computer Engineering University of Delaware NewarkDE19716 United States

This paper aims at developing a clustering approach with spectral images directly from CASSI compressive measurements. The proposed clustering method first assumes that compressed measurements lie in the union of multiple low-dimensional subspaces. Therefore, sparse subspace clustering (SSC) is an unsupervised method that assigns compressed measurements to their respective subspaces. In addition, a 3D spatial regularizer is added into the SSC problem, thus taking full advantages of the spatial information contained in spectral images. The performance of the proposed spectral image clustering approach is improved by taking optimal CASSI measurements obtained when optimal coded apertures are used in CASSI system. Simulation with one real dataset illustrates the accuracy of the proposed spectral image clustering approach. Copyright © 2019, The Authors. All rights reserved.

关键词： Spectroscopy

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Vision-based Semantic Mapping and Localization for Autonomous Indoor Parking

arXiv

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arXiv 2018年

作者： Huang, Yewei Zhao, Junqiao He, Xudong Zhang, Shaoming Feng, Tiantian Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai Department of Computer Science and Technology School of Electronics and Information Engineering Tongji University Shanghai School of Surveying and Geo-Informatics Tongji University Shanghai China

In this paper, we proposed a novel and practical solution for the real-time indoor localization of autonomous driving in parking lots. High-level landmarks, the parking slots, are extracted and enriched with labels to avoid the aliasing of low-level visual features. We then proposed a robust method for detecting incorrect data associations between parking slots and further extended the optimization framework by dynamically eliminating suboptimal data associations. Visual fiducial markers are introduced to improve the overall precision. As a result, a semantic map of the parking lot can be established fully automatically and robustly. We experimented the performance of real-time localization based on the map using our autonomous driving platform TiEV, and the average accuracy of 0.3m track tracing can be achieved at a speed of 10kph. Copyright © 2018, The Authors. All rights reserved.

关键词： Indoor positioning systems

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Implicit cooperative caching based on information popularity for vehicular networks 12

Implicit cooperative caching based on information popularity...

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12th Workshop on Challenged Networks, CHANTS 2017

作者： Li, Yuhong Wang, Ji Lindgren, Anders Su, Xiang Riekki, Jukka State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China RISE SICS Sweden Luleå University of Technology Sweden Centre for Ubiquitous Computing University of Oulu Finland

ISBN: (纸本)9781450351447

Information-centric networking (ICN) technology is becoming a popular research topic in vehicular networks due to the connectionless and lightweight characteristics of this networking paradigm. Caching plays an essential role in information-centric networks, but current caching techniques for ICN are not ideal for the dynamic and wireless vehicular networks. This paper presents a caching approach for ICN-based vehicular networks that takes into account both the dynamicity of vehicular networks and the popularity of the information being distributed. We introduce an interval metric for selective caching. With this metric and estimates of information popularity and vehicle density, cooperative caching can be realized without exchanging cache management information among the vehicular nodes. Simulation results show that the proposed approach can increase the storage space utilization and has low data response time for vehicular networks. © 2017 Association for computing Machinery.

关键词： Digital storage

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A novel visible-infrared image fusion framework for smart city

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International Journal of Simulation and Process Modelling 2018年第2期13卷 144-155页

作者： Zhu, Zhiqin Qi, Guanqiu Chai, Yi Yin, Hongpeng Sun, Jian School of Automation Chongqing University Chongqing400044 China State Key Laboratory of Power Transmission Equipment and System Security and New Technology College of Automation Chongqing University Chongqing400044 China School of Computing Informatics and Decision Systems Engineering Arizona State University TempeAZ85287 United States Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education Chongqing400030 China

Image fusion technology is widely used in different areas and can integrate complementary and relevant information of source images captured by multiple sensors into a unitary synthetic image. Image fusion technology as an efficient way to integrate information from multiple images plays a more and more important role in smart city. The quality of fused image affects the accuracy, efficiency, and robustness of the related applications. Existing sparse representation-based image fusion methods consist of overly complete and redundant dictionaries learning and sparse coding. However, overly complete and redundant dictionary does not consider the discriminative ability of dictionaries that may seriously affect the image fusion. A good dictionary is the key to a successful image fusion technique. To construct a discriminative dictionary, a novel framework that integrates an image-patches clustering and online dictionary learning methods is proposed for visible-infrared image fusion. The comparison experiments with existing solutions are used to validate and demonstrate the effectiveness of the proposed solution for image fusion. Copyright © 2018 Inderscience Enterprises Ltd.

关键词： Image fusion

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Differential Private Preservation Multi-core DBScan Clustering for Network User Data

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Procedia Computer Science 2018年 129卷 257-262页

作者： Lina Ni Chao Li Haoran Liu Anu G. Bourgeois Jiguo Yu College of Computer Science and Engineering Shandong University of Science and Technology Qingdao China The Key Laboratory of Embedded System and Service Computing Ministry of Education Tongji University Shanghai China Department of Computer Science Georgia State University University Plaza Atlanta GA 30303 USA School of Information Science and Engineering Qufu Normal University Shandong China

Mining rich network user data to extract valuable information becomes the focus and commercial interest of researchers. Differential privacy protection is a new paradigm based on data distortion, which protects sensitive data while maintains a certain statistical properties by adding random noise, and makes up for the shortcomings that traditional schemas require knowledge background assumptions and cannot analyze quantitatively. In this paper, we propose a DP-MCDBScan schema based on the powerful differential privacy. We perform simulation to evaluate our schema, whose results show that our schema has better efficiency, accuracy, and privacy protection effect than previous schemas.

关键词： Private preserving differential privacy MCDBScan clustering data mining Laplace noise

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Full-Duplex Millimeter-Wave Communication

arXiv

引用

arXiv 2017年

作者： Xiao, Zhenyu Xia, Pengfei Xia, Xiang-Gen Beihang University Key Laboratory of Embedded System and Service Computing Tongji University University of Delaware

The potential of doubling the spectrum efficiency of full-duplex (FD) transmission motivates us to investigate FD millimeter-wave (FD-mmWave) communication. To realize FD transmission in the mmWave band, we first introduce possible antenna configurations for FD-mmWave transmission. It is shown that, different from the cases in micro-wave band FD communications, the configuration with separate Tx/Rx antenna arrays appears more flexible in self-interference (SI) suppression while it may increase some cost and area versus that with the same array. We then model the mmWave SI channel with separate Tx/Rx arrays, where a near-field propagation model is adopted for the line-of-sight (LOS) path, and it is found that the established LOS-SI channel with separate Tx/Rx arrays also shows spatial sparsity. Based on the SI channel, we further explore approaches to mitigate SI by signal processing, and we focus on a new cancellation approach in FD-mmWave communication, i.e., beamforming cancellation. Centered on the constant-amplitude (CA) constraint of the beamforming vectors, we propose several candidate solutions. Lastly, we consider an FD-mmWave multi-user scenario, and show that even if there are no FD users in an FD-mmWave cellular system, the FD benefit can still be exploited in the FD base station. Candidate solutions are also discussed to mitigate both SI and multi-user interference (MUI) simultaneously. Copyright © 2017, The Authors. All rights reserved.

关键词： Beamforming

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