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10th Iberian Conference on Information Systems and Technologies, CISTI 2015

作者： Faria, Ana Raquel Almeida, Ana Martins, Constantino Gonçalves, Ramiro Computer Science Department GECAD-Knowledge Engineering and Decision Support Group Institute of Engineering Polytechnic of Porto Porto Portugal Universidade de Trás-os-Montes e Alto Douro Vila Real Portugal

ISBN: (纸本)9789899843455

The purpose of this paper is to propose new model for emotional interaction that uses learning styles and student emotional state to adapt the user interface and learning path. This aims to reduce the difficulty and emotional stain that students encounter while interacting with learning platforms. To this end will be used techniques of Affective computer that can capture the student emotional state and base on that change the course parameters (flow, organization or difficulty) or even an emotional interaction in order to recapture the student attention. © 2015 AISTI.

关键词： Students

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“When 'bad' is 'good'”: Identifying personal communication and sentiment in drug-related tweets

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JMIR Public Health and Surveillance 2016年第2期2卷 e162页

作者： Daniulaityte, Raminta Chen, Lu Lamy, Francois R. Carlson, Robert G. Thirunarayan, Krishnaprasad Sheth, Amit Center for Interventions Treatment and Addictions Research Department of Population and Public Health Sciences Boonshoft School of Medicine Wright State University Kettering OH United States Ohio Center of Excellence in Knowledge-enabled Computing Department of Computer Science and Engineering Wright State University Dayton OH United States

Background: To harness the full potential of social media for epidemiological surveillance of drug abuse trends, the field needs a greater level of automation in processing and analyzing social media content. Objectives: The objective of the study is to describe the development of supervised machine-learning techniques for the eDrugTrends platform to automatically classify tweets by type/source of communication (personal, official/media, retail) and sentiment (positive, negative, neutral) expressed in cannabis- and synthetic cannabinoid-related tweets. Methods: Tweets were collected using Twitter streaming Application Programming Interface and filtered through the eDrugTrends platform using keywords related to cannabis, marijuana edibles, marijuana concentrates, and synthetic cannabinoids. After creating coding rules and assessing intercoder reliability, a manually labeled data set (N=4000) was developed by coding several batches of randomly selected subsets of tweets extracted from the pool of 15,623,869 collected by eDrugTrends (May-November 2015). Out of 4000 tweets, 25% (1000/4000) were used to build source classifiers and 75% (3000/4000) were used for sentiment classifiers. Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machines (SVM) were used to train the classifiers. Source classification (n=1000) tested Approach 1 that used short URLs, and Approach 2 where URLs were expanded and included into the bag-of-words analysis. For sentiment classification, Approach 1 used all tweets, regardless of their source/type (n=3000), while Approach 2 applied sentiment classification to personal communication tweets only (2633/3000, 88%). Multiclass and binary classification tasks were examined, and machine-learning sentiment classifier performance was compared with Valence Aware Dictionary for sEntiment Reasoning (VADER), a lexicon and rule-based method. The performance of each classifier was assessed using 5-fold cross validation that calculated average F-s

关键词： Cannabis EDrugTrends Machine learning Sentiment analysis Social media Synthetic cannabinoids Twitter

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A Method for QRS Delineation Based on STFT Using Adaptive Threshold

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Procedia computer science 2015年 54卷 646-653页

作者： Basheeruddin Shah Shaik G.V.S.S.K.R. Naganjaneyulu T. Chandrasheker A.V. Narasimhadhan Department of Computational Engineering Rajiv Gandhi University of Knowledge Technologies Krishna India Department of Electronics and Communication Engineering Rajiv Gandhi University of Knowledge Technologies Krishna India Department of Computer Science and Engineering Rajiv Gandhi University of Knowledge Technologies Krishna India Department of Electronics and Communication Engineering National Institute of Technology Surathkal India

Electrocardiogram (ECG) is the electrical manifestation of the contractile activity of the heart. In this work, it is proposed to utilize an adaptive threshold technique on spectrogram computed using Short Time Fourier Transform (STFT) for QRS complex detection in electrocardiogram (ECG) signal. The algorithm consists of preprocessing the raw ECG signal to remove the power-line interference, computing the STFT, applying adaptive thresholding technique and followed by identifying QRS peaks. Sensitivity, Specificity and Detection error rate are calculated on MIT-BIH database using the proposed method, which yields a competitive results when compared with the state of art in QRS detection.

关键词： Adaptive threshold Electrocardiogram QRS detection Short time fourier transform Time-frequency analysis.

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TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay 9

TKLBLIIR: Detecting Twitter Paraphrases with TweetingJay

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9th International Workshop on Semantic Evaluation, SemEval 2015

作者： Karan, Mladen Glavaš, Goran Šnajder, Jan Bašić, Bojana Dalbelo Vulić, Ivan Moens, Marie-Francine University of Zagreb Faculty of Electrical Engineering and Computing Text Analysis and Knowledge Engineering Lab Unska 3 Zagreb10000 Croatia KU Leuven Department of Computer Science Language Intelligence Information Retrieval Group Celestijnenlaan 200A Leuven Belgium

ISBN: (纸本)9781941643402

When tweeting on a topic, Twitter users often post messages that convey the same or similar meaning. We describe TweetingJay, a system for detecting paraphrases and semantic similarity of tweets, with which we participated in Task 1 of SemEval 2015. TweetingJay uses a supervised model that combines semantic overlap and word alignment features, previously shown to be effective for detecting semantic textual similarity. TweetingJay reaches 65.9% F1-score and ranked fourth among the 18 participating systems. We additionally provide an analysis of the dataset and point to some peculiarities of the evaluation setup. © 2015 Association for Computational Linguistics

关键词： Semantics

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A demand based unicast data dissemination scheme in VANET

A demand based unicast data dissemination scheme in VANET

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IEEE India Conference (INDICON)

作者： Bidisha Bhabani Atanu Mondal Sulata Mitra Department of Computer Science and Engineering Supreme Knowledge Foundation Group of Institutions Kolkata India Department of Computer Science and Engineering Camellia Institute of Technology Kolkata India Department of Computer Science and Technology Indian Institute of Engineering Science and Technology Howrah India

ISBN: (纸本)9781467365413

Vehicular ad hoc network performs crucial function in road safety, detection of traffic accidents and reduction of traffic congestions by disseminating messages among vehicles. The periodic broadcasting is an efficient approach to serve the requests of many vehicles without selecting any route between source and destination. But it degrades the network performance due to hidden node problem and broadcasting storm problem. The present work is an on demand type of unicast pull based approach of data dissemination in vehicular ad hoc network. It disseminates data in the form of response message after receiving any query message from vehicle. The performance of the proposed scheme is evaluated in terms of block of service, loss of query messages due to time out, average required time of sending response messages to the vehicles and throughput.

关键词： Vehicles Vehicular ad hoc networks Broadcasting Unicast Authentication Databases

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Integration of scanning and image processing algorithms for lane detection based on fuzzy method

Integration of scanning and image processing algorithms for ...

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作者： Zhang, Jindong Jia, Xiaoyan Li, Jinfeng College of Computer Science and Technology Jilin University Changchun China Department of Hand Surgery First Hospital of Jilin University Changchun130021 China Key Laboratory of Symbol Computation and Knowledge Engineering of the Ministry of Education Changchun China

In order to detect lane rapidly and accurately, the integration of scanning and image processing algorithms (SIP) based on the fuzzy method is proposed. Further, combination of the proposed algorithm with an adaptive threshold value for image binarization, the least-square method and Bessel curve algorithm are proposed for detection and to fit the lane. The proposed SIP algorithm was evaluated by various tests. The experimental results indicate that the average time consumed for the detection of lane in each frame is 16.7639ms, and the accuracy of lane detection is 95. The proposed algorithm demonstrates good robustness, and can be used as the core algorithm for further application in lane departure warning systems. © 2015 - IOS Press and the authors. All rights reserved.

关键词： computer vision

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Geometric intuition and algorithms for Eυ-SVM

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Journal of Machine Learning Research 2015年 16卷 323-369页

作者： Barbero, Álvaro Takeda, Akiko López, Jorge Department of Computer Science Knowledge-Engineering Institute Autonomous University of Madrid Madrid Spain Department of Mathematical Informatics University of Tokyo Tokyo Japan

In this work we address the Eυ-SVM model proposed by Pérez-Cruz et al. as an extension of the traditional υ support vector classification model (υ-SVM). Through an enhancement of the range of admissible values for the regularization parameter υ, the Eυ-SVM has been shown to be able to produce a wider variety of decision functions, giving rise to a better adaptability to the data. However, while a clear and intuitive geometric interpretation can be given for the υ-SVM model as a nearest-point problem in reduced convex hulls (RCH-NPP), no previous work has been made in developing such intuition for the Eυ-SVM model. In this paper we show how Eυ-SVM can be reformulated as a geometrical problem that generalizes RCH-NPP, providing new insights into this model. Under this novel point of view, we propose the RAPMINOS algorithm, able to solve Eυ-SVM more efficiently than the current methods. Furthermore, we show how RAPMINOS is able to address the Eυ-SVM model for any choice of regularization norm p≥1 seamlessly, which further extends the SVM model flexibility beyond the usual Eυ-SVM models. ©2015 Álvaro Barbero, Akiko Takeda and Jorge López.

关键词： Computational geometry

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Learning platform: Emotional learning

Learning platform: Emotional learning

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Iberian Conference on Information Systems and Technologies (CISTI)

作者： Ana Raquel Faria Ana Almeida Constantino Martins Ramiro Gonçalves Computer Science Department GECAD-Knowledge Engineering and Decision Support Group Institute of Engineering - Polytechnic of Porto Porto Portugal

关键词： Prototypes Adaptation models Education Data models Computational modeling Gaussian distribution computers

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Pathological brain detection based on online sequential extreme learning machine

Pathological brain detection based on online sequential extr...

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IEEE International Conference on Progress in Informatics and Computing (PIC)

作者： Siyuan Lu Hainan Wang Xueyan Wu Shuihua Wang School of Computer Science and Technology Nanjing Normal University Nanjing Jiangsu China State Key Lab of CAD & CG Zhejiang University Hangzhou Zhejiang China Key laboratory of symbolic computation and knowledge engineering of ministry of education Jilin University Changchun Jilin China Key Laboratory of Statistical information technology and data mining State Statistics Bureau Chengdu Sichuan China Department of Electrical Engineering CUNY New York NY USA

ISBN: (纸本)9781509034857

Magnetic resonance imaging (MRI) is a kind of imaging modality, which offers clearer images of soft tissues than computed tomography (CT). It is especially suitable for brain disease detection. It is beneficial to detect diseases automatically and accurately. We proposed a pathological brain detection method based on brain MR images and online sequential extreme learning machine. First, seven wavelet entropies (WE) were extracted from each brain MR image to form the feature vector. Then, an online sequential extreme learning machine (OS-ELM) was trained to differentiate pathological brains from the healthy controls. The experiment results over 132 brain MRIs showed that the proposed approach achieved a sensitivity of 93.51%, a specificity of 92.22%, and an overall accuracy of 93.33%, which suggested that our method is effective.

关键词： Entropy Discrete wavelet transforms Feature extraction Pathology Training Magnetic resonance imaging Diseases

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Robust routing in urban public transportation: Evaluating strategies that learn from the past 15

Robust routing in urban public transportation: Evaluating st...

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15th Workshop on Algorithmic Approaches for Transportation Modelling, Optimization, and Systems, ATMOS 2015

作者： Böhmová, Katerina Mihalák, Matú Neubert, Peggy Pröger, Tobias Widmayer, Peter Department of Computer Science ETH Zürich Universitätstrasse 6 Zürich8092 Switzerland Department of Knowledge Engineering Maastricht University Postbus 616 Maastricht6200MD Netherlands Verkehrsbetriebe Zürich Luggwegstrasse 65 Zürich8048 Switzerland

ISBN: (纸本)9783939897996

Given an urban public transportation network and historic delay information, we consider the problem of computing reliable journeys. We propose new algorithms based on our recently presented solution concept (Böhmová et al., ATMOS 2013), and perform an experimental evaluation using real-world delay data from Zürich, Switzerland. We compare these methods to natural approaches as well as to our recently proposed method which can also be used to measure typicality of past observations. Moreover, we demonstrate how this measure relates to the predictive quality of the individual methods. In particular, if the past observations are typical, then the learning-based methods are able to produce solutions that perform well on typical days, even in the presence of large delays.

关键词： Urban transportation

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