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Evidence-Based Systematic Literature Reviews in the Cloud 19th

Evidence-Based Systematic Literature Reviews in the Cloud

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Ruiz-Rube, Ivan Person, Tatiana Miguel Mota, Jose Manuel Dodero, Juan Rafael Gonzalez-Toro, Angel Univ Cadiz ESI Dept Comp Engn Puerto Real Cadiz Spain

ISBN: (纸本)9783030034962;9783030034955

Systematic literature reviews and mapping studies are useful research methods used to lay the foundations of further research. these methods are widely used in the Health Sciences and, more recently, also in Computer Science. Despite existing tool support for systematic reviews, more automation is required to conduct the complete process. this paper describes CloudSERA, a web-based app to support the evidence-based systematic review of scientific literature. the tool supports researchers to carry out studies by additional facilities as collaboration, usability, parallel searches and search integration with other systems, the flexible data scheme of the tool enables the integration of bibliographic databases of common use in Computer Science and can be easily extended to support additional sources. It can be used as a service in a cloud environment or as on-premises software.

关键词： Systematic literature review Mapping study Information retrieval Bibliographic data

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Steganalysis of RGB Images Using Merged Statistical Features of Color Channels 11

Steganalysis of RGB Images Using Merged Statistical Features...

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11th international conference on Developments in eSystems engineering (DeSE)

作者： Rasool, Zaid I. Al-Jarrah, Mudhafar M. Amin, Saad Al Mustaqbal Univ Coll Dept Comp Technol Engn Babel Iraq Middle East Univ Dept Comp Sci Amman Jordan Al Khawarizmi Int Coll Dept Informat Technol Abu Dhabi U Arab Emirates

ISBN: (纸本)9781538667125

this paper presents a steganalysis model that uses an enhanced grayscale statistical feature set, in the detection of data hiding in uncompressed RGB color images. A dataset of 3000 RGB images is created, using natural images from public sources, in TIFF and JPEG formats, that are converted to BMP format and resized to 512x512 pixels. the clean images are embedded with secret image data, using two payload schemes, 2 bits per channel (bpc) and 4 bits per channel. the selected feature set consists of 24 features per color channel, 72 features per image, which includes the Gray Level Co-Occurrence Matrix (GLCM) features, Entropy features, and statistical measures of variation. the feature set elements are calculated for individual channels, combined into image features vector. the steganalysis process is based on supervised machine learning, utilizing the Support Vector Machine (SVM) binary classifier's implementation in MATLAB. the results show very high detection accuracy for the two cases of 2-bpc and 4-bpc embedding schemes. Also, there are no noticeable differences in the detection accuracy between the two sources of images, even though un-compression of the JPEG images has reduced their noise contents. the paper ends with a conclusion and suggestions for future work.

关键词： steganalysis feature set GLCM entropy machine learning SVM classifier detection accuracy RGB bit per channel

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On detecting chronic obstructive pulmonary disease (COPD) cough using audio signals recorded from smart-phones 11

On detecting chronic obstructive pulmonary disease (COPD) co...

引用

11th international conference on Health Informatics, HEALthINF 2018 - Part of 11th international Joint conference on Biomedical engineering Systems and Technologies, BIOSTEC 2018

作者： Windmon, Anthony Minakshi, Mona Chellappan, Sriram Athilingam, Ponrathi R. Johansson, Marcia Jenkins, Bradlee A. Department of Computer Science and Engineering University of South Florida TampaFL United States College of Nursing University of South Florida TampaFL United States

ISBN: (纸本)9789897582813

Chronic Obstructive Pulmonary Disease (COPD) is a lung disease that makes breathing a strenuous task with chronic cough. Millions of adults, worldwide, suffer from COPD, and in many cases, they are not diagnosed at all. In this paper, we present the feasibility of leveraging cough samples recorded using a smart-phone’s microphone, and processing the associated audio signals via machine learning algorithms, to detect cough patterns indicative of COPD. Using 39 adult cough samples evenly spread across both genders, that included 23 subjects infected with COPD and 16 Controls, not infected with COPD, our system, using Random Forest classification techniques, yielded a detection accuracy of 85.4% with very good Precision, Recall and F-Measures. To the best of our knowledge, this is the first work that designs a smart-phone based learning technique for detecting COPD via processing cough. Copyright © 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved.

关键词： Machine learning

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Temporal-Spatial Prediction of Trip Demand Using Neural Networks and Points of Interest

Temporal-Spatial Prediction of Trip Demand Using Neural Netw...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Xingxing Hu Qimei Cui Kwang-Cheng Chen National Engineering Lab for Mobile Network Technologies Beijing University of Posts and Telecommunications Beijing China University of South Florida Tampa FL USA

One of the major challenges in intelligent transportation and smart city is efficient assignment of vehicles to satisfy trip demands, and thus to achieve road utilization and energy efficiency. Traditional order-driven dispatches often result in instability of resource utilization toward unsatisfactory user experience and waste in scheduling, due to lacking of knowledge in future trip demands. Accurate and immediate prediction of trip demand of each region is important for coordinating regional autonomous fleet allocation and improving service efficiency. this paper proposes a temporal-spatial prediction model for trip demand based on points of interest (POI) and multivariate long short term memory (MLSTM) model. Parameters of models are trained using heterogeneous data sets. Influence of meteorology information on the reliability of historical trip demand data is considered in temporal prediction, while the regional imbalance caused by POI is considered in spatial prediction. the prediction accuracy of MLSTM is improved when compared with the univariate LSTM. A Beijing taxi trajectory dataset is used to verify the temporal-spatial prediction method.

关键词： Meteorology data models Predictive models Time series analysis Logic gates Public transportation Trajectory

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Novel and Classic Metaheuristics for Tunning a Recommender System for Predicting Student Performance in Online Campus 1

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Gomez-Pulido, Juan A. Cortes-Toro, Enrique Duran-Dominguez, Arturo Crawford, Broderick Soto, Ricardo Univ Extremadura Caceres 10003 Spain Univ Playa Ancha Valparaiso 2340000 Chile Pontificia Univ Catolica Valparaiso Valparaiso 2374631 Chile

ISBN: (数字)9783030034931

ISBN: (纸本)9783030034931;9783030034924

the prediction of the students' performance allows to improve the learning process using the online campus tools. In this context, recommender systems are useful for prediction purposes. this collaborative filtering tool, predicts the unknown performances analyzing the database that contains the performance of the students for particular tasks, considering matrix factorization and stochastic gradient descent. If we consider a fixed number of latent factors, the prediction error is mainly influenced by two parameters: learning rate and regularization factor. the best settings for these parameters is an optimization problem that can be tackled by soft computing techniques. In this work, we analyze three solving methods to select the optimal values of both parameters: a simple direct search, a classic evolutionary algorithm, and a novel metaheuristic. the results show the advantages of using metaheuristics instead of direct search in accuracy and computing effort terms.

关键词： Metaheuristics Genetic algorithms Vapour-liquid equilibrium Recommender systems Prediction Matrix factorization Stochastic gradient descent learning rate Regularization factor

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Detecting Intrusive Malware with a Hybrid Generative Deep learning Model 19th

Detecting Intrusive Malware with a Hybrid Generative Deep Le...

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19th international conference on intelligent data engineering and automated learning (IDEAL)

作者： Kim, Jin-Young Cho, Sung-Bae Yonsei Univ Dept Comp Sci Seoul South Korea

ISBN: (纸本)9783030034931;9783030034924

A small amount of unknown malware can be analyzed manually, but it is generated with extremely more and more so that automatic detection of them is needed. Malware is usually generated with different features from those of existing ones (e.g., code exchange, null value insertion, or reorganization of subroutines) to avoid detection of antivirus systems. To detect malware with obfuscation, this paper proposes a method called latent semantic controlling generative adversarial networks (LSC-GAN) that learns to generate malware data with i-feature from a specific Gaussian distribution which represents ifeature and distinguish it from the real. Variational autoencoder (VAE) projects data to latent space for feature extraction and is transferred to generator (G) of LSC-GAN to train it stably. G generates data from Gaussian distribution, so it produces similar data but not identical to the actual data: it includes modified features compared with the real. the detector is inherited with transfer learning in a encoder that learns various malware features using real and modified data generated by the LSC-GAN based on a LSC-VAE. We show that LSC-GAN achieves detection accuracy of 96.97% on average that is higher than those of other conventional models. We demonstrate statistical significance of the performance of the proposed model using t-test. the result of detection is analyzed with confusion matrix and F1-score.

关键词： Malicious software Malware detection Generative adversarial networks Variational autoencoder Transfer learning

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Human Gait Patterns Classification based on MEMS data using Unsupervised and Supervised learning Algorithms

Human Gait Patterns Classification based on MEMS Data using ...

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international conference on Knowledge and Systems engineering (KSE)

作者： My N. Nguyen John Kar-Kin Zao thanh Hai Nguyen College of ICT Can Tho University Can Tho Vietnam Pervasive Embedded System Lab National Chiao Tung University Hsinchu Taiwan

With the proliferation of smartphones and wearable devices having Micro-Electro-Mechanical Systems (MEMS) sensors built in, data samples of linear acceleration and angular velocity can be collected almost anytime anywhere. these motion data can be used to identify various types of human motions and to detect the anomaly of individuals movements. this work presents attempts to use the unsupervised Affinity Propagation (AP) clustering algorithm and the supervised Support Vector Machine (SVM) classification algorithm to identify four types of human gait motions: walking, jogging, climbing upstairs and downstairs. Features of three-dimensional linear acceleration that can enable the algorithms to identify these motion types correctly were selected by analyzing the variation of the feature values among different motion types. Efficacy of Affinity Propagation (AP), Linear and Non-linear Support Vector Machine (SVM) algorithms were also studied by comparing their ratios of correct, false positive, false negative and F1 score classification. this preliminary study demonstrated Linear SVM achieved the best performance, followed by Affinity Propagation. Quite surprisingly, Non-linear SVM appeared to be inferior to the other two algorithms.

关键词： Feature extraction Support vector machines Clustering algorithms Frequency selective surfaces Task analysis Sensors Radio frequency

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Energy-Efficient Resource Allocation in Uplink NOMA Systems with Deep Reinforcement learning

Energy-Efficient Resource Allocation in Uplink NOMA Systems ...

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international conference on Wireless Communications and Signal Processing (WCSP)

作者： Yuhan Zhang Xiaoming Wang Youyun Xu National Engineering Research Center of Communications & Networking Nanjing University of Posts and Telecommunications Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China

Non-orthogonal multiple access (NOMA) is regarded as a promising technology to satisfy the huge access demand and data rate requirements of the next generation network. In this paper, we investigate the joint subcarrier assignment and power allocation problem in an uplink multi-user NOMA system to maximize the energy efficiency (EE) while ensuring the quality-of-service (QoS) of all users. Different from conventional resource allocation methods, we propose a two-step deep reinforcement learning (DRL) based algorithm to solve this non-convex and dynamic optimization problem. In particular, with the current channel conditions as input, we design a deep q-network (DQN) to output the optimum subcarrier assignment policy, then use a deep deterministic policy gradient (DDPG) network to dynamically output the transmit power of all users, and finally adjust the entire resource allocation policy by updating the weights of neural networks according to the feedback of the system. Simulation results show that our DRL based algorithm can provide better EE under various transmit power limitations compared with other approaches.

关键词： Resource management NOMA Uplink Heuristic algorithms Dynamic scheduling Machine learning Neural networks

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An Exploratory Analysis on data Features and Analysis Techniques in Social Networks.

An Exploratory Analysis on Data Features and Analysis Techni...

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international Communication Systems and Networks and Workshops, COMSNETS

作者： U. Tanuja. H.L. Gururaj V. Janhavi Vidyavardhaka College of Engineering Mysuru Karnataka India

Now a days the growth of social networking sites gained much popularity around the world. Social networks provide a means for users to interact or communicate over the internet. Some of the trending social medias include Facebook, twitter, what's app, LinkedIn, Instagram, google+ and so on. these networks help to share their ideas, information, opinions in daily activities. It helps the users to create their individual platforms to connect with each other through online networks. Social networks also help to share multimedia data like audios, videos, stories and animations. Users can access these social media services via web-based technologies through desktop, laptops, smart phones and tablets. Social networks is a highly interactive platforms through which individuals, communities and organizations can share, co-create, discuss and modify the data through internet. In this paper the preliminary work concerning with several types of data features of social networks and also it discuss about the detailed data analysis on social networks.

关键词： Sentiment analysis Market research Machine learning Media Statistical analysis Facebook

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learning to Transform Vietnamese Natural Language Queries into SQL Commands

Learning to Transform Vietnamese Natural Language Queries in...

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international conference on Knowledge and Systems engineering

作者： thi-Hai-Yen Vuong Le-Minh Nguyen thi-thu-Trang Nguyen Nhu-thuat Tran Xuan-Hieu Phan University of Engineering & Technology Vietnam National University Hanoi Vietnam

In the field of data management, users traditionally manipulates their data using structured query language (SQL). However, this method requires an understanding of relational database, data schema, and SQL syntax as well as the way it works. database manipulation using natural language, therefore, is much more convenient since any normal user can interact with their data without a background of database and SQL. this is, however, really tough because transforming natural language commands into SQL queries is a challenging task in natural language processing and understanding. In this paper, we propose a novel two-phase approach to automatically analyzing and converting natural language queries into the corresponding SQL forms. In our approach, the first phase is component segmentation which identifies primary clauses in SQL such as SELECT, FROM, WHERE, ORDER BY, etc. the second phase is slot-filling that helps extract sub-components for each primary clause such as SELECT column(s), SELECT aggregation operation, etc. We carefully conducted an empirical evaluation for our method using conditional random fields (CRFs) on a medium-sized corpus of natural language queries in Vietnamese, and have achieved promising results with an average accuracy of more than 90%.

关键词： Structured Query Language Hidden Markov models Natural languages databases Task analysis Analytical models Semantics

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