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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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Analyzing closeness of code dependencies for improving IR-based Traceability Recovery

Analyzing closeness of code dependencies for improving IR-ba...

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IEEE International Conference on software Analysis, Evolution and Reengineering (SANER)

作者： Hongyu Kuang Jia Nie Hao Hu Patrick Rempel Jian Lü Alexander Egyed Patrick Mäder State Key Lab for Novel Software Technology Nanjing University Nanjing Jiangsu China Institute for Software Systems Engineering Johannes Kepler University Linz Austria Fakultät für Informatik und Automatisierung Technische Universität Ilmenau Ilmenau Germany

Information Retrieval (IR) identifies trace links based on textual similarities among software artifacts. However, the vocabulary mismatch problem between different artifacts hinders the performance of IR-based approaches. A growing body of work addresses this issue by combining IR techniques with code dependency analysis such as method calls. However, so far the performance of combined approaches is highly dependent to the correctness of IR techniques and does not take full advantage of the code dependency analysis. In this paper, we combine IR techniques with closeness analysis to improve IR-based traceability recovery. Specifically, we quantify and utilize the “closeness” for each call and data dependency between two classes to improve rankings of traceability candidate lists. An empirical evaluation based on three real-world systems suggests that our approach outperforms three baseline approaches.

关键词： Monitoring software Electronic mail Biomedical monitoring Vocabulary Context

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Improving Learning Algorithm Performance for Spiking Neural Networks

Improving Learning Algorithm Performance for Spiking Neural ...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Qiang Fu Yuling Luo Junxiu Liu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

This paper proposes three methods to improve the learning algorithm for spiking neural networks（SNNs）. The aim is to improve learning performance in SNNs where neurons are allowed to fire multiple times. The performance is analyzed based on the convergence rate, the concussion condition in the training period and the error between actual output and desired output. The exclusive-or（XOR） and Wisconsin breast cancer（WBC） classification tasks are employed to validate the proposed optimized methods. Experimental results demonstrate that compared to original learning algorithm, all three methods have less iterations, higher accuracy, and more stable in the training period.

关键词： spiking neural network optimization method learning performance

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Chemical Substance Classification Using Long Short-Term Memory Recurrent Neural Network

Chemical Substance Classification Using Long Short-Term Memo...

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2017 17th IEEE International Conference on Communication Technology (ICCT 2017)

作者： Jinlei Zhang Junxiu Liu Yuling Luo Qiang Fu Jinjie Bi Senhui Qiu Yi Cao Xuemei Ding Guangxi Key Lab of Multi-Source Information Mining & Security Faculty of Electronic Engineering Guangxi Normal University Guangxi Experiment Centre of Information Science Department of Business Transformation and Sustainable Enterprise Surrey Business SchoolUniversity of Surrey Faculty of Software Fujian Normal University School of Computing and Intelligent Systems Faculty of Computing and Engineering Ulster University

This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks(LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.

关键词： recurrent neural networks chemical substances long short-term memory feed forward neural networks

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Global recursive based node importance evaluation 12th

Global recursive based node importance evaluation

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12th International Conference on Advanced Data Mining and Applications, ADMA 2016

作者： Zhao, Lu Xiong, Li Xue, Shan School of Management Shanghai University Shanghai200444 China Lab of Decision Systems and e-Service Intelligence Faculty of Engineering and Information Technology School of Software University of Technology Sydney Sydney2007 Australia

ISBN: (纸本)9783319495859

The world city network (WCN) research has been promoting the evaluation algorithms in complex networks. The study of urban network focuses on the measurements of city position in the WCN. In previous study, a set of algorithms such as centricity evaluation, power evaluation and their recursive power are proposed to support the WCN research. In this paper, we propose a novel global recursive based node importance evaluation (GRNIE) algorithm for WCN, which improves the performance in evaluating the network centricity and power by being applied to the Friedmann Basics Network. The results of the experiment show that the proposed GRNIE outperforms the previous algorithms (i.e., degree, recursive centricity, recursive power) in task of classification with the improvements of 72 %, 32 % and 20 % respectively in accuracy. © Springer International Publishing AG 2016.

关键词： Complex networks

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A network-based approach to detect spammer groups

A network-based approach to detect spammer groups

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2016 International Joint Conference on Neural Networks, IJCNN 2016

作者： Do, Quynh Ngoc Thuy Zhilin, Alexey Junior, Caibre Zordan Pio Wang, Gaoxiang Hussain, Farookh Khadeer Decision Support and e-Service Intelligence Lab Centre for Quantum Computation and Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney Australia School of Software University of Technology Sydney Sydney Australia

ISBN: (纸本)9781509006199

Online reviews nowadays are an important source of information for consumers to evaluate online services and products before deciding which product and which provider to choose. Therefore, online reviews have significant power to influence consumers' purchase decisions. Being aware of this, an increasing number of companies have organized spammer review campaigns, in order to promote their products and gain an advantage over their competitors by manipulating and misleading consumers. To make sure the Internet remains a reliable source of information, we propose a method to identify both individual and group spamming reviews by assigning a suspicion score to each user. The proposed method is a network-based approach combining clustering techniques. We demonstrate the efficiency and effectiveness of our approach on a real-world and manipulated dataset that contains over 8000 restaurants and 600,000 restaurant reviews from TripAdvisor website. We tested our method in three testing scenarios. The method was able to detect all spammers in two testing scenarios, however it did not detect all in the last scenario. © 2016 IEEE.

关键词： Graph mining K-means clustering Network method Spammer groups

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Classification and retrieval of digital pathology scans: A new dataset

arXiv

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arXiv 2017年

作者： Babaie, Morteza Kalra, Shivam Sriram, Aditya Mitcheltree, Christopher Zhu, Shujin Khatami, Amin Rahnamayan, Shahryar Tizhoosh, H.R. KIMIA Lab University of Waterloo Canada Mathematics and Computer Science Amirkabir University Tehran Iran Electrical and Computer Engineering University of Waterloo Canada School of Electronic & Optical Eng. Nanjing University of Science & Technology Jiangsu China Institute for Intelligent Systems Research and Innovation Deakin University Australia Electrical Computer and Software Engineering University of Ontario Institute of Technology Oshawa Canada

In this paper, we introduce a new dataset, Kimia Path24, for image classification and retrieval in digital pathology. We use the whole scan images of 24 different tissue textures to generate 1,325 test patches of size 1000×1000 (0.5mm×0.5mm). Training data can be generated according to preferences of algorithm designer and can range from approximately 27,000 to over 50,000 patches if the preset parameters are adopted. We propose a compound patch- and-scan accuracy measurement that makes achieving high accuracies quite challenging. In addition, we set the benchmarking line by applying LBP, dictionary approach and convolutional neural nets (CNNs) and report their results. The highest accuracy was 41.80% for CNN. Copyright © 2017, The Authors. All rights reserved.

关键词： Classification (of information)

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7.3: Compare Variation of User's EEG Owing to Parallax Cue

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SID Symposium Digest of Technical Papers 2018年第S1期49卷

作者： Haichen Hu Danli Wang Kang Yue Xinpan Yang Steve C. Chiu Beijing Key Lab of Human-Computer Interaction Institute of Software Chinese Academy of Sciences Beijing China University of Chinese Academy of Sciences Beijing China The State Key Laboratory of Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China College of Science and Engineering Idaho State University Pocatello America

3D display has become increasingly popular in many fields. It can provide incredible immersion compared with 2D display. There are many researches referring to the change of user about no matter subjective experience or objective physiology signal while watching 3D content. Similarly, in this paper, we study the variation of user’s EEG when the user watched 3D content. However, we only focus on parallax cue and avoid other cues of 3D content by using Random Dot Stereogram(RDS) as visual stimulus. We designed an experiment which asked the subject to watch RDS with nine kinds of parallaxes(0,±0.5,±1.1,±1.3,±1.7). The users watched one RDS for 1 minute and then closed eyes and rest for 1 minute every time. During watching, EEG signal with 30-channel was recorded. The result suggests that significant variation of the user’s EEG data occurred in the Alpha (8–13Hz) and Beta(14–30Hz) frequency band by different parallax’s comparison. And we found not only parallax but also electrode position can impact the user’s EEG data significantly. Accurately, the EEG data has a significant change in six electrodes position in both frequency bands including CZ, PZ, OZ, O2 in international 10–20 system. It means those electrode positions we detect can be regarded as typical positions where we record EEG signal. It will simplify the process of EEG signal acquisition when we need to study the relationship between parallax cue of 3D display and the user further.

关键词： EEG Parallax RDS 3D

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Temporal pattern discovery for accurate sepsis diagnosis in ICU patients

arXiv

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arXiv 2017年

作者： Sheetrit, Eitam Nissim, Nir Klimov, Denis Fuchs, Lior Elovici, Yuval Shahar, Yuval Department of Software and Information Systems Engineering Ben-Gurion University of the Negev Beer-Sheva Israel Malware Lab Cyber Security Research Center Ben-Gurion University of the Negev Beer-Sheva Israel Medical Informatics Research Center Ben-Gurion University of the Negev Beer-Sheva Israel Soroka University Medical Center Beer-Sheva Israel

Sepsis is a condition caused by the body's overwhelming and life-threatening response to infection, which can lead to tissue damage, organ failure, and finally death. Common signs and symptoms include fever, increased heart rate, increased breathing rate, and confusion. Sepsis is difficult to predict, diagnose, and treat. Patients who develop sepsis have an increased risk of complications and death and face higher health care costs and longer hospitalization. Today, sepsis is one of the leading causes of mortality among populations in intensive care units (ICUs). In this paper, we look at the problem of early detection of sepsis by using temporal data mining. We focus on the use of knowledge-based temporal abstraction to create meaningful interval-based abstractions, and on time-interval mining to discover frequent interval-based patterns. We used 2,560 cases derived from the MIMIC-III database. We found that the distribution of the temporal patterns whose frequency is above 10% discovered in the records of septic patients during the last 6 and 12 hours before onset of sepsis is significantly different from that distribution within a similar period, during an equivalent time window during hospitalization, in the records of non-septic patients. This discovery is encouraging for the purpose of performing an early diagnosis of sepsis using the discovered patterns as constructed features. Copyright © 2017, The Authors. All rights reserved.

关键词： Intensive care units

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Activity Recognition of a Badminton Game Through Accelerometer and Gyroscope

Activity Recognition of a Badminton Game Through Acceleromet...

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International Conference on Computer and Information Technology

作者： Md. Ariful Islam Anik Mehedi Hassan Hasan Mahmud Md. Kamrul Hasan Systems and Software Lab (SSL) Department of Computer Science and Engineering (CSE) Islamic University of Technology (IUT) Gazipur Bangladesh

ISBN: (纸本)9781509040919

The scope for doing physical exercises in daily life is declining day by day. But, the importance of human physical exercise for a healthy life, remains the same. It is necessary to generate a solution to simulate the outdoor experience of physical exercises and sports inside our home. In this paper, we propose an idea of recognizing the activities of a badminton game which has the potential to be useful in simulating the Badminton Sport. We have used motion sensors (e.g. Accelerometer, Gyroscope) to recognize different activities like, serve, smash, backhand, forehand, return etc. We have collected data from a large set of users and labeled their data over several instances. We have applied the K-Nearest Neighbors (k-NN) and Support Vector Machines (SVM) classifiers to recognize those activities. Existing approaches (e.g. Microsoft Xbox 360) used vision based techniques to recognize activities and use it in simulated games but we are using sensor based approach. Vision based approaches have some limitations such as slow rate of data, illumination constraints, occluded backgrounds etc. Our approach gives a low cost solution with a classification technique which is faster. The experimental result shows a decent recognition rate.

关键词： Badminton Sport Activity Recognition Accelerometer Gyroscope k-NN SVM

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