Reviewerly/exHarmony huggingface dataset:
Big Data Based Decision-Making Support System Design for Efficient Analysis of the Performance of Software Education

This study is aimed to analyze the performance of software education that has been implemented thus far centering on the opinion mining visualization analysis technique based on big data related to software education collected over the last five years such as data from portal site news, SNS service, Internet café, and other performance data and develop a big data based decision-making support system for Korean style software education in order to derive agendas that will be discussed hereafter. In addition, through the prediction of more advanced future-oriented education systems and analysis of performance factors based on the foregoing, solutions for big data based decision-making support prediction analysis can be prepared hereafter in the field of education and measures for cultivation of creative convergence talents and promotion of software education can be sought so that the directions of mid/long-term development of Korean style software education can be accurately set.
Arabic Word stemming Based on Pattern Affixes Removal

Arabic word term stem became an essential part of any text processing algorithms and information retrieval. The big challenge is how to distinguish between affixes and original characters in Arabic term, where some characters become affixes in Arabic terms and become original in other Arabic terms. The goal of this research is to discover what extent depends on affix stripping to find Arabic term stem. The contribution consists of two parts, starting with removing all kinds of affixes from Arabic term, it has been done by constructing affixes hash tables. The second part is producing 24 possible stems for the same Arabic term by using 24 stripping orders.The experiments proved that there is at least one correct stem out of 24 possible stems. The conclusion is that the most efficient stripping orders are those that begin by removing prefixes followed by removing infixes, and then removing suffixes. The dataset that is used for testing consists of four different subject documents with 2000 Arabic words. The final results after using stripping orders has reached up to 86% of correctness which is the highest percentage comparing to other stripping orders.
Learning Influence Diagram Utility Function by Observing Behavior

This paper considers the task of learning utility functions of certain influence diagram based on the decision maker’s past decisions. We assume that the influence diagram structure and the probability distribution it assigns to random events are known, so that we need only infer the utility function u for its. We also assume that the decision maker is rational. In particular, the decision maker’s past decisions can be viewed as constraints on u. So, if we have a prior probability distribution p(u) over u, we can then condition on these constraints to obtain u. In this paper, an approach for learning utility functions from decision maker’s behavior was proposed. We also show that it is effective.
A Novel Genetic Algorithm Approach to Improving Error Correction Output Coding

This paper proposes a genetic algorithm (GA) for error correcting output codes (ECOC). In our GA framework, the individual structure represents a solution for the multiclass problem, consisting of a codematrix and filtered feature subsets. Besides, the selection of base classifier is injected to form the second type of individual structure. A novel mutation operator is proposed to produce ECOC-compatible children in the evolutionary process, along with a set of legality checking schemes guaranteeing the legality of individuals. In addition, a local improvement function is implemented to optimize individuals, with the goal of further promoting their fitness value. To verify the performances of our algorithm, experiments are carried out with deploying some state-of-art ECOC algorithms. Results show that our GA with two different individual structures may perform diversely, but they can both lead to better results compared with other algorithms in most cases. Besides, the base learner selection embedded in GA leads to higher performance across different data sets.
Assessment of spatial presence model for image-based virtual reality

Image-based virtual reality refers to photo-based images, stitched together to develop a realistic panoramic virtual environment. Despite limited interaction function in IBVR, it is expected that users still can feel-being located in the panoramic virtual environments. However, lack of research investigating spatial presence experience in IBVR application has been found. Developing the right IBVR characteristics in IBVR application is important, if not, users will not feel user’s spatial presence experience. Accordingly, spatial presence theoretical model is adapted to this research in purpose to confirm that IBVR can produce spatial presence. An initial model namely spatial presence model for image-based virtual reality (SPM4IBVR) is developed and validated. This research used PLS-SEM for validating this model because in the initial model a few items are combined to measure a variable. Following the steps of validation in PLS-SEM, six steps are conducted which are data collection and examination, the proposed model is transformed into the PLS-SEM conceptual model using software namely Smartpls3, assess the result for the proposed model from the reflective measurement model and assess the result for the proposed model from the structural model. The results from model validation shows that IBVR can enhance user’s spatial presence. The results from those steps are analyzed and discussed.
Supportability Evaluation of Radar Equipment Based on Cloud Model and Improved RS-G1

In the context that factors affecting the supportability of radar equipment are numerous and of diverse dimensions, the existing index systems of supportability evaluation of radar equipment can hardly satisfy the continuously improving requirements of radar equipment support. First, aiming at the problem of transformation between quantitative description and qualitative concept, this paper converts the evaluation index set into an evaluation cloud model based on the cloud theory, generates the digital characteristics of the cloud model of the expert scoring by using the reverse cloud generator and the supportable comprehensive evaluation cloud drop chart of radar equipment by using forward cloud generator. Second, to make up for the shortcomings of the existing single weighting method, the Rough Set (RS) weighting method is improved with the introduction of the Order Relationship Analysis method (G1). Then this paper proposes the RS-G1 synthetic weighting method, and designs radar equipment supportability cloud evaluation based on this method. In the end, example shows that this method can evaluate the supportability level of radar equipment scientifically and effectively.
Self-adaptation modeling for service evolution on the Internet of Things (IoT)

Abstract One of the most important concepts of the internet of things is related to software services, where the system must be able to provide real-time data collected from various environments. So that software services represent a variety of physical and virtual real-world objects that can grow very fast. This condition is related to the ability of self-adaptation software services. However, the existing model of self-adaptation is generally not paying attention to the requirements for service evolution. The objective of this paper is to introduce self-adaptation modeling techniques which consist of, first, domain modeling of the internet of things to represent real-world context; second, developing an inference engine for context inference. As a form of evaluation, this model is applied to the patient monitoring system that will relate to the concept of self-adaptation of various systems, devices, actors, and environment. The case study results show the system’s ability to anticipate changes in context and growth needs.
Hybrid Saliency-SVM Method Implementation for Automatic Data Training Selection in Image Segmentation

Abstract Image segmentation is one of the most important step in computer vision and image processing, which later will be used in image retrieval, object identifying and data classification. Image segmentation can be seen as classification problem, namely by marking each pixels according to certain characteristics. Support Vector Machine (SVM) is a classification method included in supervised learning. Supervised learning is a method which requires training and testing. Training sample used in training process isn’t always exist in few cases, especially in the image segmentation case. This particular research implemented SVM-based method which is Saliency-SVM for automatic data training selection in image segmentation. This method generates data training using SVM-based visual saliency detection where there is pre-segmentation step and trimap formation based on saliency information from visual saliency detection, HSV color space quantitation, histogram analysis and local homogeneity threshold. Data training produced is pixel belonging to positive (object) and negative (background). The step before segmentation done with SVM is feature extraction to create input vector in SVM. Object segmentation on the image is done by SVM based on SVM Trained Model. Test result from Saliency-SVM for this image segmentation has average accuracy value up to 94,84 % compared to the image ground truth
An inquiry on image enhancement methods of remote sensing images

Remote sensing image enhancement algorithm, which can enhance the contrast of image without changing its information, is crucial for the acquisition and analysis of remote sensing information. In this paper, we compare the performance of several state-of-the-art enhancement algorithms in processing remote sensing images. We classify and refine the main idea of each algorithm, and evaluate the enhancement effect with subjective evaluation and objective metrics such as entropy, brightness, contrast and mean gradient. In order to avoid color-enhanced interference, this paper took experiments both on RGB pictures and grayscale pictures. The significance and compared results were shown in the following paper.
Mitigating Data Integrity Attacks in Building Automation Systems using Denoising Autoencoders

Building automation systems (BAS) are a class of cyber-physical systems that aim to improve the efficiency of buildings through intelligent control. Typically, sensor measurements are used to compute for the optimal control action for equipment that minimizes the energy consumption while maintaining the comfort of occupants. Given the heavy reliance on sensor data, ensuring their validity is an essential concern in BAS. Several work have explored sensor validation, mostly for the purpose of automatic fault detection and diagnostics (AFDD). However, recent studies show BAS vulnerable to adversarial attacks - an area that has lacked consideration in the early design of BAS. In this work, we propose a sensor correction model, based on deep learning framework, for mitigating against data integrity attacks in BAS. We test our approach on real-world data obtained from a retrofitted air conditioning (AC) control testbed, with injected data simulating different attacker types and number of attacked sensors. We show that the model is capable of mitigating attacks by comparing with a baseline where no model is employed.
Ensemble Belief Rule-Based Model for Ecological Security of Land Resource Evaluation

Ecological security of land resource (ESLR) evaluation is a significant part in ecological environment protection and sustainable development. In order to consider the multiple influencing factors comprehensively, obtain a traceable and interpretable evaluation result, an evaluation process of ESLR based on the Ensemble Belief Rule-Based (Ensemble-BRB) model is proposed in this paper. The Ensemble-BRB model is a combination of several weak BRB models, each of which evaluates the ESLR from part of the influencing factors. Weighted averaging is introduced as the combination method to integrate the evaluation results of different weak BRB models. An ESLR evaluation case in Hunan Province, China is investigated to validate the feasibility and efficiency of the proposed method. The evaluation results can support the decision makers to develop targeted protection measures of land ecological.
Semantics of Opinion Transitions in Multi-Agent Forum Argumentation

There are online forums such as changemyview where a user may submit his/her views on a subject matter, against which other users argue to try to change the opinions of his/hers. To measure the quality of such discussion, one useful criterion is how influential a given topic is to participating users’ opinion changes, as may be measured by the change (if any) in the proportion of supporting-objecting-mixed opinions by users. In this work, we incorporate the notion of agency into a previously proposed argumentation framework for issue-based information systems, QuAD, and formulate semantics of opinion transitions by newly considering agent-wise evaluation of QuAD initial scores.
Design and construction of a low cost prototype for lecture and processing of electromyographic signals using multichannel techniques and spatial filtering

The electromyographic (EMG) signals analysis is imperative to understand the muscles behaviour under several diseases or fatigue studies. Although there are required sophisticated devices and algorithms, it is possible to obtain information using simpler techniques. The prototype that was developed has features for sEMG recording, such as high common mode reject (CMR), high coupling impedance, high gain and 2 stages of active filters. Besides, the analog to digital conversion is performed with an ADC Delta-Sigma converter. Besides, we propose a decomposition algorithm with 5 stages: pre-processing, segmentation, clustering, classification and a spatial filtering stage. We obtained successfully sEMG signals and we detect Motor Unit Action Potentials (MUAPs) of the same Motor Unit (MU) with a similarity over 80% between them.
A Study on the Factors Affecting the Effectiveness of English Hybrid Teaching Based on Web Platform

In order to better study the effect of blended English teaching, this paper proposes an evaluation method of influencing factors of blended English teaching effect based on network platform. Through collecting and analyzing the common influencing factors in the process of English teaching, this paper divides the influencing degree into grades. This paper calculates the influencing parameters of different levels of teaching effect with analytic hierarchy process, evaluates the influencing factors of different levels according to the calculation results, and finally evaluates the influencing factors of mixed English teaching effect. Finally, the experiment proves that the evaluation of influencing factors of mixed English teaching based on network platform has higher accuracy and timeliness.
Writing to the Hopfield Memory via Training a Recurrent Network

We consider the problem of writing on a Hopfield network. We cast the problem as a supervised learning problem by observing a simple link between the update equations of Hopfield network and recurrent neural networks. We compare the new writing protocol to existing ones and experimentally verify its effectiveness. Our method not only has a better ability of noise recovery, but also has a bigger capacity compared to the other existing writing protocols.
Prior-Knowledge-Embedded LDA with Word2vec – for Detecting Specific Topics in Documents

This paper proposes a method to apply prior knowledge about topics of interest to Latent Dirichlet Allocation (LDA). The conventional LDA sometimes fails to detect specific topics of interest. Therefore, our approach uses word2vec to acquire linkages between words related to specific topics. The extracted linkages are used as prior knowledge about the topics in the subsequent LDA process. The extracted linkages can also be used to annotate words in a consistent manner. Such consistent annotations cannot be realized using conventional LDA, which relies on bag-of-words–based clustering. We examine our approach by applying it to travelers’ reviews, to detect topics related to Japanese shrines. The experimental results show that our approach is effective in the following three aspects: (1) The average coherence of our approach, i.e., the semantic consistencies among words, outperforms that of the conventional LDA. (2) Words in each sentence are annotated such that the annotations reflect the topic of the sentence. The conventional LDA sometimes makes confusing/mixed annotations to the words in a single sentence. Our approach, on the contrary, can make annotations that reflect the topic of the sentence in a consistent manner. (3) Our approach enables to detect very specific topics complying with users’ interests.
Applicability of Text-representing Centroids for Thai Language Documents

Text-representing centroids are investigated method recently used to categorize and compare documents written in European languages. As it will be shown, Asian languages and in particular Thai exhibit completely other language structures. Nevertheless, a strong justification will be given that the methodology of the text-representing centroids can be successfully applied to Thai documents, too. For the experiments, a corpus which contained 100 randomly selected articles from an offline Thai Wikipedia was used. The obtained centroids well reflect the topic of those documents as in the original publication. In addition, the centroids are quite suitable to compare any two files.
Software Developer Performance Measurement Based on Code Smells in Distributed Version Control System

Effectively staffing a softwareJongprasit, Natach development team for a software project is important to the development of software in the project. Performance of software developers can be measured by the quality of the produced software, and the number of code smells is one factor that indicates software quality. However, modern software development uses distributed version control systems in whichSenivongse, Twittie different software developers collaborate to develop software. The number of code smells in the software is hence the result of the aggregate performance of the whole team. This makes it difficult to measure the performance of individual developers. This paper proposes a method and a supporting tool for measuring the performance of individual software developers in a Git project based on code smells. Bayesian average rating is adopted to rate the performance of each developer in a project by taking into account his/her level of contribution to the project, i.e. the number of source code commits, as well as his/her effort to produce clean code. An experiment on C# projects shows that there is a strong positive correlation between ranking of developer performance in a project by the proposed method and that by human evaluators.
Quantitative methods for HIV/AIDS researchCliburn Chan, Michael G. Hudgens, Shein‐Chung Chow, CRC Press

BiometricsVolume 75, Issue 2 p. 710-711 BOOK REVIEW Quantitative methods for HIV/AIDS research , Cliburn Chan, Michael G. Hudgens, Shein-Chung Chow, CRC Press W. Scott Comulada, Corresponding Author W. Scott Comulada wcomulada@mednet.ucla.edu Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CaliforniaSearch for more papers by this author W. Scott Comulada, Corresponding Author W. Scott Comulada wcomulada@mednet.ucla.edu Department of Psychiatry & Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CaliforniaSearch for more papers by this author First published: 22 August 2019 https://doi.org/10.1111/biom.13080Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume75, Issue2June 2019Pages 710-711 RelatedInformation
Evaluation of Pseudo-Relevance Feedback using Wikipedia

Users have specific information needs which are expressed in short queries to information retrieval systems. The queries are unstructured, and they tend to be short and ambiguous in most cases. Using the shallow language statistics including probabilistic or language models such as BM25 or Indri respectively can enhance the retrieval system metrics like Mean Average Precision (MAP). However, such methods depend on query terms and their presence in the retrieved document to define relevance. Query expansion is a technique that can be used to overcome this problem by expanding the query with terms from an initial top few relevant documents. The question that we try to answer is whether the quality of the corpus used for expansion produce a significant improvement MAP and precision at top 30 retrieved documents. We show that the quality and the selection criteria of expansion documents are important factors in query expansion performance.
Deep Feature Translation Network Guided by Combined Loss for Single Image Super-Resolution

Single image super-resolution (SISR) which aims to infer a high-resolution (HR) image from a single low-resolution (LR) image has wide applications such as surveillance and medical image processing. However, existing methods which aiming at minimizing the mean squared error (MSE) always get high objective quality, i.e., peak signal-to-noise ratios (PSNR), but their results are blurry which lacks high-frequency details thus are perceptually unsatisfying. Some recently proposed Generative Adversarial Networks enhance the perceptual quality greatly, but their objective quality is very low, which means their generated texture details are not faithful to the real image. In this paper, we adopt a multi-scale HR construction process to generate HR images gradually to achieve large upscaling factors. For each level, the generation of HR difference features from LR features is taken as a feature translation process, and deep image feature translation network (DFTN) is designed. To recover finer texture details, we combine three loss functions: content loss, a novel fine-grained texture loss and adversarial loss in our model optimization. We desire that the content loss ensures the LR results faithful to the original image, and the other two losses push our model to capture the manifold of natural images. Experiments confirm that our model can achieve the state-of-the-art results in different evaluating metrics, including both objective and perceptual quality evaluations. Therefore, our method can generate HR images with fine texture details and faithful to original images.
Decentralized Autonomous Corporations

This document is intended to provide an overview on decentralized autonomous corporations (DACs) based on the blockchain technology. We provide definitions of (1) DACs, (2) secure multiparty computation – a secure multiparty protocol for authorizing transactions, and (3) autonomous agents – a set of computer programs that carry out some set of operations on behalf of users. We conclude the document with an application for financial-portfolio management.