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Deep learning approaches for facial emotion recognition: A case study on FER-2013 6th

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6th international Workshop on Combinations of intelligent Methods and Applications, CIMA 2016 held in conjunction with the 22nd European conference on Artificial Intelligence, ECAI 2016

作者： Giannopoulos, Panagiotis Perikos, Isidoros Hatzilygeroudis, Ioannis Department of Computer Engineering and Informatics University of Patras Patras Greece Technological Educational Institute of Western Greece Patras Greece

ISBN: (纸本)9783319667898

Emotions constitute an innate and important aspect of human behavior that colors the way of human communication. The accurate analysis and interpretation of the emotional content of human facial expressions is essential for the deeper understanding of human behavior. Although a human can detect and interpret faces and facial expressions naturally, with little or no effort, accurate and robust facial expression recognition by computer systems is still a great challenge. The analysis of human face characteristics and the recognition of its emotional states are considered to be very challenging and difficult tasks. The main difficulties come from the non-uniform nature of human face and variations in conditions such as lighting, shadows, facial pose and orientation. Deep learning approaches have been examined as a stream of methods to achieve robustness and provide the necessary scalability on new type of data. In this work, we examine the performance of two known deep learning approaches (GoogLeNet and AlexNet) on facial expression recognition, more specifically the recognition of the existence of emotional content, and on the recognition of the exact emotional content of facial expressions. The results collected from the study are quite interesting. © 2018, Springer international Publishing AG.

关键词： Emotion Recognition

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LSTM Based Approach for Diabetic Symptomatic Activity Recognition Using Smartphone Sensors

LSTM Based Approach for Diabetic Symptomatic Activity Recogn...

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international conference on Computer and Information Technology (ICCIT)

作者： Erfanul Hoque Bahadur Abdul Kadar Muhammad Masum Arnab Barua Md. Golam Rabiul Alam Md. Akib Uz Zaman Chowdhury Mohammad Robiul Alam Dept. of Computer Science and Engineering International Islamic University Chittagong Bangladesh Dept. of Computer Science and Engineering International Islamic University Chittagong Chittagong Bangladesh Dept. of Computer Science and Engineering BRAC University Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

Being concerned about the rising rate of the usage level of smartphone for last few years, researchers are striving to let out the disguised assets of smartphones. Regarding this phrase, the embedding of a variety of sensors such as accelerometer sensor, gyroscope sensor, humidity sensor etc. has attained the considerable attention of researchers for facilitating the human activity recognition task employing the sensor's applications. In this paper, we practiced the Long Short-Term Memory a.k.a. LSTM deep learning model with an aim to recognize thirteen human activities stated as walking, walking upstairs, walking downstairs, sitting, standing, jogging, squatting in the toilet, fallen down, lying, cycling, drinking, eating and genital itching. We opted for these activities with an intention of early diagnosing of diabetes in near future. We amassed data from four sensors stated accelerometer sensor, gyroscope sensor, humidity sensor and temperature sensor subjecting ten volunteers implying a frequency of 10Hz. The data was attained using an android application which was developed by us for the purpose of accumulation of sensor data from the smartphone.

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Spark-based data analytics of sequence motifs in large omics data 22

Spark-based data analytics of sequence motifs in large omics...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Sarumi, Oluwafemi A. Leung, Carson K. Adetunmbi, Adebayo O. FUTA Akure 704 Nigeria Univ Manitoba Winnipeg MB R3T 2N2 Canada

data explosion in bioinformatics in recent years has led to new challenges for researchers to develop novel techniques to discover new knowledge from the avalanche of omics data (e.g., genomics, proteomics, transcriptomics). These data are embedded with a wealth of information including frequently repeated patterns (i.e., sequence motifs). In genomics, deoxyribonucleic acid (DNA) sequence motifs are short repeated contiguous frequent subsequences located in the prompter region. Due to the high volume and various degrees of veracity of these DNA datasets generated by the next-generation sequencing techniques, sequence motif mining from DNA sequences poised a major challenge in bioinformatics. In this article, we present a distributed sequential algorithm-which uses the MapReduce programming model on a cluster of homogeneous distributed-memory system running on an Apache Spark computing framework-for DNA sequence motif mining. Experimental results show the effectiveness of our algorithm in Spark-based data analytics of sequence motifs in large omics data. (C) 2018 The Authors. Published by Elseiver Ltd.

关键词： Bioinformatics Spark MapReduce deoxyribonucleic acid (DNA) genomics sequence motifs

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On the influence of categorical features in ranking anomalies using mixed data 22

On the influence of categorical features in ranking anomalie...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Garchery, Mathieu Granitzer, Michael Atos Informat Technol GmbH Otto Hahn Ring 6 D-81739 Munich Germany Univ Passau Innstr 41 D-94032 Passau Germany

Most unsupervised anomaly ranking approaches are compatible with numeric data only, leading to categorical features often being ignored in practice. Even though some methods address this issue, few support mixed data and the influence of excluding or including categorical attributes has not been studied well yet. In this paper, we take a first step towards considering categorical and numeric attributes jointly for unsupervised anomaly ranking by benchmarking selected methods. We introduce three new approaches: two entropy-based methods based on individual and collective entropy contribution, as well as an extension of Isolation Forest supporting mixed data, and benchmark them against SPAD, a state-of-the-art probabilistic anomaly ranker. We observe that our entropy methods detect very similar anomalies in practice, and these anomalies are mostly globally isolated observations. Both entropy methods are also closely related to SPAD. Our empirical study additionally shows that categorical features can have high impact on anomaly ranking performance and thus should not be blindly ignored. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： anomaly detection anomaly ranking unsupervised categorical nominal mixed data entropy

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Improving Programming Education Quality with Automatic Grading System

Improving Programming Education Quality with Automatic Gradi...

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2nd international conference on Innovative Technologies and learning, ICITL 2019

作者： Cai, Yun-Zhan Tsai, Meng-Hsun Department of Computer Science and Information Engineering National Cheng Kung University Tainan Taiwan

ISBN: (纸本)9783030353421

As the rapid growth of information technology, the demand for proficiency in software programming skyrockets. Compared to teaching with slides traditionally, hands-on programming training is more beneficial and practical. However, it is exhausting and time-consuming for educators to grade all assignments in person. Besides, students may not get feedback immediately to correct their wrong conceptions. Therefore, an automatic grading system is required to grade and send feedback to students. Based on an existing continuous integration system, which checks whether new programs behave as expected, we develop a set of course management tools and deploy an automatic grading system in this paper. Our system requires a server to run and test the programs. However, the server is susceptible to being compromised by hackers. Therefore, how we protect sensitive data and prevent malicious network traffic are demonstrated in this paper as well. The tools were applied in an Android application development course with 140 students enrolled. Around 72% of the students indicate the automatic grading system is beneficial to their learning. © Springer Nature Switzerland AG 2019.

关键词： Students

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Potentials of Digital Business Models Empirical investigation of data driven impacts in industry 22

Potentials of Digital Business Models Empirical investigatio...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Ralf-Christian-Haerting Reichstein, Christopher Schad, Matthias Aalen Univ Appl Sci Business Adm D-73430 Aalen Germany Aalen Univ Appl Sci Competence Ctr Informat Syst D-73430 Aalen Germany

An elaborate business model is the fundament of every firm as it describes the manner of functioning. In the course of the new developments of digitization technologies such as Big data and data Science, business models and processes have changed tremendously and new business models pop up as never before. Although theory and practice show increasingly interest in the potentials of new business models through digitization, prior research has not demonstrated yet the main driver of its significantly use. To close this research gap, the authors implemented an empirical study based on a theoretical foundation and found four main driver (with five items each) regarding potentials of digital business models: Key Performance Indicators, Individualization, Efficiency and Communication. The results of the structural equation model (SEM) approach show that all four determinants do have a positive as well as significant influence on the potentials of digital business models. Interestingly, the main impact on the potentials of digital business models comes from Key Performance Indicators. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Potentials Digital Business Models European Experts Quantitative Study Empirical Results

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Employing Machine learning techniques on Sentiment Analysis of Google Play Store Bangla Reviews

Employing Machine Learning techniques on Sentiment Analysis ...

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international conference on Computer and Information Technology (ICCIT)

作者： Md Muhtasim Jawad Soumik Syed Salvi Md Farhavi Farzana Eva Tonmoy Sinha Mohammad Shafiul Alam dept. of Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh dept. Computer Science and Engineering Ahsanullah University of Science and Technology Dhaka Bangladesh

ISBN: (数字)9781728158426

ISBN: (纸本)9781728158433

This article offers an in-depth insight on a number of existing methodologies to perform sentiment analysis using text classification on Bangla dataset. Although the rapidly developing machine learning algorithms are showing promising results, the viability of those methods for non-English languages such as Bangla is yet to be fully explored. This research aims to fill in some of those existing research gaps through proper implementation of machine learning techniques where words are converted into feature vectors via implementation of TF-IDF algorithm on data crawled from Google play store, the largest Android application market. Many significant algorithms staring linear algorithms like Naïve Bayes, Linear Support Vector Machine (SVM) are implemented. An in-depth comparison is also made among the results of various existing algorithms. The experimental results indicate that even the base-line algorithms, after proper pre-processing, can show promising results on our Bangla dataset. Naïve Bayes, Support Vector Machine and Logistic Regression has shown very promising results (accuracy score of 0.75 on average) even with the data limitation. An Ensemble method is also proposed with Adaptive Boosting technique showing an accuracy score of 0.7639 with five-fold applied. SVM has the best accuracy score of 0.7648 among all the algorithms when five-fold is applied and Gradient Boosting has the best accuracy score of 0.7695 when five-fold is not applied.

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A System for Japanese Listening Training Support with Watching Japanese Anime Scenes 22

A System for Japanese Listening Training Support with Watchi...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Junjie, Shan Nishihara, Yoko Yamanishi, Ryosuke Ritsumeikan Univ Grad Sch Informat Sci & Engn Kyoto Japan Ritsumeikan Univ Coll Informat Sci & Engn Kyoto Japan

As the widespread of Japanese entertainments and pop-cultures around the world, more and more people choose to learn Japanese as a foreign language (JFL). With the increasing of JFL learners, according to the survey of Japan Foundation, traditional materials are becoming inadequate for learners' demands. For foreign language learning, the communication skills in listening and speaking would be more important and difficult at learning process, so are the TEL learners [1]. This research proposed a method using Japanese animation, which is called "Anime" in the world, as a learning support material for training TEL learners' Japanese listening skill. Anime has a huge amount of scenes in which the characters speak usually with the standard and clear pronunciations. These dialogue scenes could be useful examples of Japanese conversation for TEL learners to practice their speaking and listening. The proposed system classifies the dialogue scenes in Anime depending on the dialogues' degrees of difficulty. We firstly subdivided the dialogue scenes from each Anime episode. The system analyzes Japanese words and expressions used in the Anime dialogues and compares those with the scripts of listening tests from the previous Japanese Language Proficiency Tests (JLPT), like TOEIC or TOEFL for English. The system uses the cosine similarity of Japanese words and expressions frequency to estimate the corresponding degree level for each Anime scene and provides the appropriate scenes to TEL learners for listening training. The experimental results showed that the proposed system worked successfully and improved the correct answer rate of TEL learners more than 10% in listening tests. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Anime Japanese language learning support Listening training Words and expressions in Anime Conversational skills

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ACM international conference proceeding series

ACM international conference proceeding series

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22nd international database engineering and Applications Symposium, IDEAS 2018

ISBN: (纸本)9781450365277

The proceedings contain 39 papers. The topics discussed include: personalized keyword search on large RDF graphs based on pattern graph similarity;a useful four-valued database logic;quality awareness for a successful big data exploitation;a step forward for spatial skyline queries for a group of users: semantic in the evidence theory setting;secure reverse k-nearest neighbors search over encrypted multi-dimensional databases;efficient big data clustering;tensor based data model for storage and analysis of social network data;the inverse tree-OLAP problem: definitions, models, complexity analysis, and a possible solution;a predictive learning framework for monitoring aggregated performance indicators over business process events;and computing extensions of dynamic abstract argumentation frameworks with second-order attacks.

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GPU Cuda JSEG Segmentation Algorithm associated with Deep learning Classifier for Electrical Network Images Identification 22

GPU Cuda JSEG Segmentation Algorithm associated with Deep Le...

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22nd international conference on Knowledge-Based and intelligent Information and engineering Systems (KES)

作者： Fambrini, Francisco Iano, Yuzo Caetano, Diogo Gara Rodriguez, Abel A. D. Moya, Clodoaldo Carrara, Eduardo Arthur, Rangel Cabello, Frank C. Von Zubem, Joao Del Val Cura, Luis Mariano Destro-Filho, Joao Batista Campos, Jose Roberto Saito, Jose Hiroki Univ Fed Sao Carlos BR-13565905 Sao Carlos Brazil Univ Estadual Campinas UNICAMP BR-13083970 Campinas Brazil UNIFACCAMP Campo Limpo Paulista Brazil Univ Fed Uberlandia BR-38400902 Uberlandia MG Brazil

An automatic recognizer system based in Artificial Intelligence for thermographic images of the electric power distribution network is proposed in this article. The infrared thermography is usually used to conduct inspections in electrical power distribution lines, assisted by a human operator, which is usually responsible for operating all the equipment, selecting the hottest spots in the image (corresponding to the places needing maintenance), making reports and calling the technical team, which will do the repairs. The proposed automatic diagnosis system aims to replace the manual inspection operation using image processing algorithms. An old method of segmentation for thermal images known as JSEG is implemented and tested and a Deep learning Neural Network is responsible to recognize the segmented elements. A comparison between the exclusive Deep learning based image recognition with the same method preceded by the JSEG segmentation algorithm is done in this article, showing better performance with this previous segmentation of the thermographic images. (C) 2018 The Authors. Published by Elsevier Ltd.

关键词： Thermography Image Processing Deep learning CUDA JSEG Segmentation Preventive Maintenance Infrared Photo

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