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arXiv 2019年

作者： Xing, Bowen Liao, Lejian Song, Dandan Wang, Jingang Zhang, Fuzhen Wang, Zhongyuan Huang, Heyan Lab of High Volume Language Information Processing & Cloud Computing Beijing Lab of Intelligent Information Technology School of Computer Science & Technology Beijing Institute of Technology Meituan-Dianping Group

Aspect-based sentiment analysis (ABSA) aims to predict fine-grained sentiments of comments with respect to given aspect terms or categories. In previous ABSA methods, the importance of aspect has been realized and verified. Most existing LSTM-based models take aspect into account via the attention mechanism, where the attention weights are calculated after the context is modeled in the form of contextual vectors. However, aspect-related information may be already discarded and aspect-irrelevant information may be retained in classic LSTM cells in the context modeling process, which can be improved to generate more effective context representations. This paper proposes a novel variant of LSTM, termed as aspect-aware LSTM (AA-LSTM), which incorporates aspect information into LSTM cells in the context modeling stage before the attention mechanism. Therefore, our AA-LSTM can dynamically produce aspect-aware contextual representations. We experiment with several representative LSTM-based models by replacing the classic LSTM cells with the AA-LSTM cells. Experimental results on SemEval-2014 Datasets demonstrate the effectiveness of AA-LSTM. Copyright © 2019, The Authors. All rights reserved.

关键词： Sentiment analysis

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Robust unsupervised feature selection by nonnegative sparse subspace learning

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Neurocomputing 2019年 334卷 156-171页

作者： Wei Zheng Hui Yan Jian Yang School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing PR China School of Computer Engineering Jinling Institute of Technology Nanjing PR China PCA Lab Key Lab of Intelligent Perception and Systems for High-Dimensional Information of Ministry of Education and Jiangsu Key Lab of Image and Video Understanding for Social Security School of Computer Science and Engineering Nanjing University of Science and Technology Key Laboratory of Trusted Cloud Computing and Big Data Analysis Nanjing Xiaozhuang University Nanjing PR China

Sparse subspace learning has been demonstrated to be effective in data mining and machine learning. In this paper, we cast the unsupervised feature selection scenario as a matrix factorization problem from the viewpoint of sparse subspace learning. By minimizing the reconstruction residual, the learned feature weight matrix with the l 2,1 -norm and the non-negative constraints not only removes the irrelevant features, but also captures the underlying low dimensional structure of the data points. Meanwhile in order to enhance the model's robustness, l 1 -norm error function is used to resistant to outliers and sparse noise. An efficient iterative algorithm is introduced to optimize this non-convex and non-smooth objective function and the proof of its convergence is given. Although, there is a subtraction item in our multiplicative update rule, we validate its non-negativity. The superiority of our model is demonstrated by comparative experiments on various original datasets with and without malicious pollution.

关键词： Subspace learning Non-negative matrix factorization Unsupervised feature selection

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Sequence-to-sequence Prediction of Personal Computer Software by Recurrent Neural Network

Sequence-to-sequence Prediction of Personal Computer Softwar...

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International Joint Conference on Neural Networks

作者： Qichuan Yang Zhiqiang He Fujiang Ge Yang Zhang School of Computer Science and Engineering Beihang University Lenovo Group Cloud and Intelligent Computing Lab Lenovo (Beijing) Ltd. Lenovo (Beijing) Ltd.

ISBN: (纸本)9781509061839

Sequence to sequence (seq2seq) prediction is a key to many tasks of machine learning. Personal computer software sequence, as one of these tasks, was regarded as stochastic and unpredictable in the past. However, the deep neural networks (DNNs) have achieved excellent performance recently in sequence to sequence tasks, especially in the field of natural language process (NLP) such as language model, machine translation and dialogue systems. This paper examines the most popular DNNs approaches: LSTM, Encoder-Decoder network and Memory network in sequence prediction field to handle the software sequence learning and prediction task. Then three modified approaches based on these state of the art models are proposed to deal with additional information in sequence. These approaches focus on three aspects: adding information to enrich embedding input of Long-Short Term Memory, adding classifier to encoder-decoder neural network as an assistive model and processing data to be structured for memory unit in memory network. Experimental results based in real user data sets show that these proposed approaches outperform their corresponding standard DNNs and additional information can benefit the sequence neural network in different phases while constructing models. Experiments in different users shown that these modified strategies are robust and can be applied widely.

关键词： Sequence to sequence Software prediction Neural networks User pattern learning Neural network recurrent neural nets state of the art Religious Missions Personal computers memory unit extra message Natural Language Processing computer software computer programs

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Quantitative composite decision-theoretic rough set

Quantitative composite decision-theoretic rough set

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International Conference on intelligent System and Knowledge Engineering, ISKE

作者： Linna Wang Ling Liu Xin Yang Pan Zhuo School of Electronic and Information Engineering Sichuan Technology and Business University Chengdu China Key Lab of Cloud Computing and Intelligent Information Processing Sichuan Technology and Business University Chengdu China

In practical decision-making, we prefer to characterize the uncertain problems with the hybrid data, which consists of various types of data, e.g., categorical, numerical, set-valued and interval-valued. The extended rough sets can deal with single types of data based on specific binary relation, including the equivalence relation, neighborhood relation, partial order relation, tolerance relation, etc. However, the fusion of these relations is a significant challenge task in such composite information table. To tackle this issue, this paper proposes the intersection and union composite relation, and further introduces a quantitative composite decision-theoretic rough set model. Moreover, we present a novel matrix-based approach to compute the upper and lower approximations in proposed model. Finally, an numerical example is conducted to illustrate the efficiency of proposed method.

关键词： Rough sets Numerical models Mathematical model Computational modeling Data models Probabilistic logic Business

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A Time Localization System in Smart Home Using Hierarchical Structure and Dynamic Frequency

A Time Localization System in Smart Home Using Hierarchical ...

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IEEE International Conference on High Performance computing and Communications

作者： Qichuan Yang Zhiqiang He Kai Zhao Tian Gao School of Computer Science and Engineering Beihang University Beijing China Lenovo Group Beijing China Cloud and Intelligent Computing Lab Lenovo (Beijing) Ltd. Beijing China Lenovo (Beijing) Ltd. Beijing China

ISBN: (纸本)9781509042982

Both GPS and WiFi based localization have been exploited in recent years, yet most researches focus on localizing at home without environment context. Besides, the near home or workplace area is complex and has little attention in smart home or IOT. Therefore, after exploring the realistic route in and out of building, we conducted a time localization system (TLS) based on off-the-shelf smart phones with WiFi identification. TLS can identify the received signal strength indication (RSSI) of home and construct radio map of users' time route without site survey. As designed to service the smart devices in home, TLS applies the time interval as the distance of positions and as the variables of WiFi environment to mark time points. Experimental results with real users show that TLS as a service system for timeline localization achieves a median accuracy of 70 seconds and is more robust compared with nearest neighbor localization approach.

关键词： smart home time localization RSSI mobile computing

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QACtools: A Quality Assessment and Quality Control Tool for Next-Generation Sequencing Data

QACtools: A Quality Assessment and Quality Control Tool for ...

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2015年中国智能自动化学术会议

作者： Dandan Song Ning Li Lejian Liao Beijing Lab of Intelligent Information Technology School of Computer Science and Technology Beijing Engineering Research Center of High Volume Language Information Processing and Cloud Computing Applications Beijing Institute of Technology

In recent years, next-generation sequencing technologies have been widely used, and are rapidly changing the landscape of genetics with unimaginable speed. However, the preliminary quality control regulations including trimming adaptor sequences, quality statistics, and depth and coverage statistics need to be applied to raw reads before further analysis. QACtools implements various utilities for processing raw reads in the FASTQ format and alignments in the BAM *** analyzes some relevant properties of next-generation sequencing data such as average read length, base quality scores, and so on. Additionally, to our knowledge,none of the other tools support depth and coverage statistic that have been provided in our tool for analysis of large DNA sequencing data. It is crucial for overcoming errors in base calling and assembly. We suppose tool is useful for the quality control of NGS data to perform further analysis. This software is an open source application freely available at https://***/projects/qactools/.

关键词： NGS data analysis Quality control FASTQ format BAM format

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Prediction analysis of the railway track state based on PCA-RBF neural network 7

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7th International Conference on intelligent Systems and Knowledge Engineering, ISKE 2012 with 1st International Conference on Cognitive Systems and Information Processing, CSIP 2012

作者： Yang, Yan Lu, Xuyao Dai, Qi Wang, Hongjun School of Information Science and Technology Provincial Key Lab of Cloud Computing and Intelligent Technology Southwest Jiaotong University Chengdu China

ISBN: (纸本)9783642378287

With the development of high-speed railway, its security guarantee has received more and more attention. The railway track state is safety-critical and affected by many factors. The proposed approach focuses on establishing a trackstate prediction model by monitoring data and analyzing the deformation trends in track geometric dimensions. Radial basis function (RBF) neural network is widely used in many industrial prediction domains, and principal component analysis (PCA) is a kind of methods to reduce dimensions. In this paper, we present an approach for predicting track irregularity index using PCA-RBF model. It is benefit for periodical maintenance of railway systems and safeguarding of transportation. The experiments show that the proposed model is effective. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Principal component analysis

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A multi-channel SSVEP-based brain-computer interface using a canonical correlation analysis in the frequency domain

Advances in Intelligent Systems and Computing

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Advances in intelligent Systems and computing 2014年 215卷 603-616页

作者： Chen, Guang Song, Dandan Liao, Lejian Beijing Eng. Research Center of High Volume Language Inf. Processing and Cloud Computing App. Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology Beijing China

Brain-computer interface (BCI) is a new way for man-machine interaction with wide applications, in which steady-state visual evoked potentials (SSVEP) is a promising option. However, many characteristics of SSVEP show great user variation. So parameter optimization and channel selection for each subject were applied to improve the performance of BCI. These optimizations limit the practical applicability of the SSVEP-based BCI. The use of a canonical correlation analysis (CCA) method for multi-channel SSVEP in the time domain detection showed highly increased detection accuracy, but it is sensitive to the noise when the stimulate frequency is low. In this paper, a method of CCA in the frequency domain is presented for classifying multi-channel SSVEPs. First overlapping average is conducted on the original training signals. Then fast Fourier transform (FFT) is used to transform the signals from time domain to frequency domain to produce the reference data. Finally, according to the correlation coefficients of the new data and the references in the frequency domain, the SSVEP is classified. The experimental results show the enhanced accuracy of our method when applied to low stimulate frequencies. © Springer-Verlag Berlin Heidelberg 2014.

关键词： Brain computer interface

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Using hybrid Neural Network to address Chinese Named Entity Recognition

Using hybrid Neural Network to address Chinese Named Entity ...

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IEEE International Conference on cloud computing and Intelligence Systems (CCIS)

作者： Guoyu Wang Yongquan Cai Fujiang Ge Beijing University of Technology Beijing China Lenovo Cloud/Big Data and Intelligent Computing Lab China

Many machine learning methods have been applied on Named Entity Recognition (NER). Such methods generally build on a large manually-annotated training set. However, the training set is usually limited as human labeling is costly and time consuming. Compare to the training set, the unlabeled corpus is usually much bigger and contains rich information about language. In this paper, a hybrid Deep Neural Network (DNN) is proposed to take advantage of the implicit information embedded in the un-labeled corpus. The experiments show that F1-score is improved from 85% to 90% (person name), from 75% to 81% (location name), and from 74% to 78% (organization name), compared with Conditional Random Fields (CRFs).

关键词： Training Neurons Neural networks Semantics Computational modeling Hidden Markov models Dictionaries

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A novel approach for localization of an indoor autonomous mobile surveillance robot 11

A novel approach for localization of an indoor autonomous mo...

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2014 11th World Congress on intelligent Control and Automation, WCICA 2014

作者： Jiang, Guolai Syed, Muhammad Umer Liu, Letian Ou, Yongsheng Xu, Yangsheng Malik, Muhammad Sarfaraz Chandio, Aftab Ahmed Center for Intelligent and Biomimetic Systems Institute of Advanced Integration Technology Chinese Academy of Sciences Guangdong China Department of Mechanical and Automation Engineering Chinese University of Hong Kong Hong Kong Lab of Simulation and Intelligent Control University of Science and Technology of China Heife China Center for Cloud Computing Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China

ISBN: (纸本)9781479958252

Localization of an autonomous mobile robot has always been a topic of interest in Robotics community. The constraints of working under different environments are constantly a challenge in designing an efficient localization system. We propose a localization algorithm for a mobile robot, which will be used for home security. The objective is to perform indoor localization efficiently and keeping the cost as low as possible so as to make it commercializable. Here, we introduce an approach based on recognizing a pattern generated by laser pointers on the wall, which is captured by an infrared camera. The image of this pattern is used to locate the position of a robot in the room. The problem of working under black ceiling has been overcomed because the projection is on the wall and is moving which also increases the range of operation. As the pattern is unique, the interference of strong sunlight has also been fixed to some extent. Experimental results show that the proposed approach is effective in real environment and opens new doors for future research. © 2014 IEEE.

关键词： Indoor positioning systems

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