Reviewerly/exHarmony huggingface dataset:
Capturing Software Security Practices using CBR: Three Case Studies

Generally, software security can be regarded as one of the most important issues in software engineering field since it may affect the software product effectiveness due to the various technological vulnerabilities and menaces.Most traditional software security approaches provide security activities through the software development lifecycle (SDLC) from requirements to design, implementation, testing and deployment.This paper focuses on embedding security concerns in the software development lifecycle (SDLC) using a bottom-up approach that is based on case based reasoning (CBR) paradigm.Thus, we study three high security-focusing cases for software projects, namely "e-shop", "Mobiling" and "intranet" using a structured case study method.Then, we populate these three cases in the proposed framework that is an excerpt of the case project base.Furthermore, this paper identifies the specificity of each case, discusses completeness of the proposed framework and proposes suggestions for improvement.Finally, usages scenarios are defined sustaining the use of the proposed framework.
Educación mediática en México: de la vulnerabilidad y riesgos entre usuarios de redes sociales

The fragility and vulnerability caused by some practices and habits in the face of media consumption and the use of technologies by Mexicans lead to discuss the absence of educational policies aimed at media education and digital literacy: an issue that has historically been pending in the public agenda and among academic communities in the past few years. This article starts from an experience lived in the state of Veracruz, which triggered a series of events unveiling the vulnerability in the consumption and use of ICTs that affects its citizens. Thus, some theoretical aspects that contribute to seize the importance of media education and the responsible use of technologies in a country like Mexico are recovered, since events related to such expe­rience still occur nowadays with poor results.
Deep CMST Framework for the Autonomous Recognition of Heavily Occluded and Cluttered Baggage Items from Multivendor Security Radiographs.

In the last two decades, luggage scanning has globally become one of the prime aviation security concerns. Manual screening of the baggage items is a cumbersome, subjective and inefficient process. Hence, many researchers have developed Xray imagery-based autonomous systems to address these shortcomings. However, to the best of our knowledge, there is no framework, up to now, that can recognize heavily occluded and cluttered baggage items from multi-vendor X-ray scans. This paper presents a cascaded structure tensor framework which can automatically extract and recognize suspicious items irrespective of their position and orientation in the multi-vendor X-ray scans. The proposed framework is unique, as it intelligently extracts each object by iteratively picking contour based transitional information from different orientations and uses only a single feedforward convolutional neural network for the recognition. The proposed framework has been rigorously tested on publicly available GDXray and SIXray datasets containing a total of 1,067,381 X-ray scans where it significantly outperformed the state-of-the-art solutions by achieving the mean average precision score of 0.9343 and 0.9595 for extracting and recognizing suspicious items from GDXray and SIXray scans, respectively. Furthermore, the proposed framework has achieved 15.78% better time
Budget and Cost-Aware Resources Selection Strategy in Cloud Computing Environments

In this work, we introduce algorithms for resource selection in heterogeneous cloud computing environments. Cloud resources are represented as virtual machine instances ready to start with characteristics including performance, RAM, storage, bandwidth, and usage price. User request contains requirements that can be satisfied by different bundles of the virtual machines. We propose and analyze algorithms and scenarios for efficient resources selection and compare them with known approaches. The novelty of the proposed approach is in multiobjective selection of cloud resource bundles according to the specified limited budget.
Implementasi Teknik Search Engine Optimization Dalam Meningkatkan Trafik Website Bima Utomo Waterpark

Today's business people have generally used information technology for business activities, one of which is the use of a website as a medium to facilitate the promotion of business activities to a wider market. There are many ways that business people can use to promote the website pages they have for potential customers, the most common methods used include using email blast, advertising using third party services, the use of social network media and media blog. One very effective way is to utilize search engine optimization techniques, this is because internet users often search for information through search engines like Google. Search Engine Optimization (SEO) is a series of processes carried out systematically and aims to increase the volume and quality of traffic visited by search engines to certain websites by utilizing the search engine algorithm, which is called PageRank. The purpose of SEO is to place a website on the top position of search results based on certain keywords targeted. This research will apply SEO techniques to increase the number of visitors to the Bima Utomo Waterpark website through the Google search engine. The results of this study are expected to increase the popularity of the Bima Utomo Waterpark website. Keywords:  SEO, Google, Website, Waterpark
RETRACTED: Virtual scene dynamic interactive image segmentation technology based on OpenGL module

A virtual scene dynamic interactive image segmentation technology based on OpenGL sky module is proposed to improve the performance of image dynamic interactive recognition algorithm and the recognition effect of image segmentation. First, an automatic dynamic interactive image segmentation algorithm is proposed, a probability segmentation model of Gauss mixture fracture based on multi-scale crack enhancement filter is established, and expectation maximization algorithm is used to determine the parameters of GMM (Gaussian Mixture Model) and the underlying segmentation; second, the drawing function of OpenGL is used to draw the graph, and the drawing method needs to be calculated. That is to say, a mathematical model is established, which converts the graph into the calculation of vertex coordinates for drawing. It uses an extended cost function to guide dynamic interactive image segmentation using a set of cracks from the population to be segmented. Finally, the validity of the algorithm is verified by experimental simulation.
Evaluasi laporan pelaksanaan tugas tenaga pengajar BIPA wilayah ASEAN 2 masa tugas 2015-2018

BIPA program is a program designed for foreigners to study the Indonesian language as well as the Indonesian culture. This study attempts to analyze the quality of the final report of BIPA teachers from 20152018 in ASEAN 2 (Timor Leste, Vietnam, and the Philippines). The author uses a descriptive qualitative research method to evaluate the 23 final reports of BIPA teachers based on the checklists. The instruments were adapted from the theories and the guidelines of BIPA reports made by PPSDk. The results of the study indicate that the quality of the teachers’ reports is already good, however, the reporting guidelines for the implementation of the BIPA teaching workforce need to be refined by adding the intercultural understanding concepts included in the report introduction section. Besides, to provide a good service, PPSDK should give an intercultural understanding for the teachers in special training. It is expected that this study will be significant to the field of intercultural communication and education in Indonesia.
Cross-lingual embeddings for hate speech detection in comments

With the recent explosion of social media content, the amount of online hate speech is increasing, making it impossible to filter it manually.For automatic hate speech detection, a lot of annotated data is needed, which is mostly available for high-resource languages.In spite of data scarcity in low-resource languages, we want to detect hate speech in those languages.We use cross-lingual embeddings to achieve an acceptable performance in hate speech detection in a target language, using data from another language.We use hate speech comments from English, German, and Croatian.We use fastText word embeddings, align them with the RCSLS method, and achieve reasonable performance in 2 out of 6 language pairs.With Multilingual BERT, we improve upon this method, and achieve acceptable performance in 3 out of 6 language pairs.
Cross-Domain User Profile Construction by Log Analysis

This paper presents a user profile construction approach by analyzing user action log data obtained from cross-domain platforms. The concept of Semantic Activity Space was proposed to represent user characteristics from multiple dimensions, where the basic attributes and values are calculated and integrated from the logs. Non-parametric methods are used to classify and visualize the differences in level and type of activity preferences of users. Log items are integrated from heterogeneous modules of information management systems in manufacturing enterprises. The experiment results indicate that by extracting and analyzing key features from semantic activity space, the characters of a typical user can be properly profiled.
The evaluation of academic website using eye tracker and UEQ: a case study in a website of xyz

Abstract The current study evaluated the academic website using eye tracking and User Experience Questionnaire (UEQ). A total of 46 participants had been recruited for this study but then the data were reduced to 33 participants due to random response errors of an item in UEQ followed by reducing in eye tracker’s data. Eye tracker had a gaze plot (scan path) data and task completion time as time to first view area of interest (AOI) where these data were used to evaluate important gaze plot and whether the provision of information in website had been clearly done. The gaze plot gave insight where important link, information and picture should be put in the website. Furthermore, Kruskal Wallis and Mann Whitney U test were also applied to see the difference between each task which were used to improve sub menu’s name. At the same time, UEQ data showed that only novelty should be improved to be more creative, inventive, leading edge and innovative. Thus, this recommendation provides more input in designing website for academic use.
Testbed versus simulation approach on RF communication with AAβ asymmetric encryption scheme on internet of things devices

&lt;p&gt;The revolution of the Internet of Things (IoT) has given a better way of monitoring things including anything that could gather data and share the information over the internet. Most of the connected things are using Device to Device (D2D) connection to make it available on the internet such as client to a broker or client to a server. However, when IoT devices such as embedded devices and sensors that are connected to the internet, it becomes an open path for attackers to acquire the data and data vulnerably will become an issue. Thus, data integrity might become an issue, or the attackers could temper the data and could cause a disastrous domino effect to the interconnected IoT devices. Therefore, the data security collected from the sensors is substantial even though it could be a single character transmitted. However, IoT sensors are low powered devices in term of CPU, storage, memory and batteries. Securing the devices such as integrating the encryption algorithm computations might give overhead to the sensors and draining the batteries even faster than it is predicted. Alternatively, this paper attempts to explore the capabilities of the asymmetric scheme on resource constrained devices for its communications. Thus, this paper presents an RF communication analysis of a low consumption asymmetric encryption, the AA&lt;sub&gt;β&lt;/sub&gt; (AA-Beta) especially on encryption section that is likely to be feasible on IoT devices to preserve the data integrity. The design of RF transmission has been considered to suit the RF transceiver capability to prevent data losses and error from occurring. The result shows that 2.35 times of RF transmits runtime increased compared to RF simulation runtime. Meanwhile, at the receiver side, the runtime increases 60% compared to the simulation.&lt;/p&gt;
Personal Legacy Information Management System for Muslim in Malaysia: A Preliminary Design

There is an alarming amount of frozen and unclaimed assets in Malaysia.Among the factors that contribute to this situation is the failure of the decedent to leave a will to the heirs of the estate or to leave the important documents necessary to initiate the distribution of the estate.This lack of preparation will inimitably put the heirs in a difficult position because they will have to spend their time and money to search and collect all related information and documentation related to the estate to assist them in liquefying and distributing the assets.With the advancement of information technology, there are information systems that had been built to manage and plan the estate of individuals.However, these systems have limitations and are unsuitable to be implemented in Malaysia because of different laws and procedures.Therefore, this paper proposes a personal legacy information system that will allow the owner to manage information such as assets, debts, wills and personal secrets, in a single platform.In addition, the owner can keep related documents to the information that are required for asset planning and distribution, especially in the Malaysian context.The owner can appoint a trustee(s) through the system and all information will be released by the system to the trustee when the owner dies.By applying encrypted communication, the legacy data will be kept secure.Static analysis is described at the requirement level for specifying the functional requirements of the system, using Z Specification.We hope that the proposed system will help mitigate the issues surrounding the problem of frozen and unclaimed assets.
Web-based System for Decision Support on Surface Irrigation Modernization

The purpose of this paper is to describe a web-based system to assist on-farm design and management of surface irrigation systems. This application was designed to be an appropriate tool for generating design alternatives associated with attributes of technical, economic, and environmental nature, and handling and evaluating a large number of input and output data. It also allows the evaluation and ranking of design alternatives using multicriteria analysis where criteria are weighted according to the priorities and perception of the designer and users, and provides an appropriate dialogue between the designer and the user, with an effective help support with information about equipments and irrigation practices. The application has tools for the resolution of specific problems, such as land leveling, pipe sizing and economic calculation. Built with a simple user friendly interface, with several optional languages and online help for technical aspects, this tool will contribute to support the dissemination of knowledge, design procedures and field practices of surface irrigation. Tests and demonstrations are being done on Hetao Irrigation District, China.
A eficiencia da auriculoterapia no tratamento de estresse

Introduction: Stress is characterized as a natural reaction of the organism that occurs when the individual experiences situations of danger or threat, where alert or alarm mechanisms are triggered, causing physical and emotional changes.The resultant are symptoms such as fear, discomfort; physical wear and tear; arterial hypertension; gastritis among others.When chronic it can trigger other more serious illnesses.Due to the proportion of this harm today, the effectiveness of auriculotherapy for the treatment of stress is questioned.Objectives: To verify the effectiveness of auriculotherapy therapy for stress treatment.Methods: The study was based on bibliographic research, exploratory and qualitative.Data were collected from academic and scientific theses and dissertations, such as PudMed and Lilacs, among others, as well as from the CAPES, SCielo and Similar Journals Portal, with their text preferably in Portuguese and English.Results: We initially selected 20 articles that specifically addressed the scope of the study, of which eight were disregarded by the exclusion criteria.Priority was given to studies with an integrative (bibliographic) review methodology and clinical trials, since one provides review of concepts established in the literature and the other provides data obtained from applied studies.In this sense, studies that encompass two methodological aspects were examined: literature review and clinical trials.Thus, we obtained for data analysis of 806 participants submitted to auriculotherapy and 4 literary reviews.Conclusion: The data obtained, especially in clinical trial articles, auriculotherapy showed a high level of efficiency in all studies.
Hybrid Feature Extraction Technique on Brain MRI Images for Content-Based Image Retrieval of Alzheimer’s Disease

Alzheimer is a prominent cause of death, ranked sixth in the list. Timely diagnosis of such abnormalities may help to temporarily minimize its worsening. Computer-aided techniques are adapted to the brain MR images for diagnosing and retrieval of Alzheimer disease images. An immense amount of work has been carried on the generic image retrieval systems using content-based information. These image retrieval schemes have their own merits and demerits in their retrieval performance. So, it is required to develop an efficient content-based image retrieval (CBIR) system in the medical field which is still a challenging task. Therefore, a hybrid features extraction technique has been proposed for CBIR wherein contrast feature, texture-based features and morphological operated features of brain MRI images are extracted and these features are hybridized by applying fusion technique for better Alzheimer disease detection. The proposed approach of feature extraction techniques is evaluated using support vector machine (SVM) and decision tree (DT) classification scheme for pattern learning and classification. Based on the results of feature extraction techniques, SVM and DT achieve an overall accuracy of 91.25 and 86.66% with better precision, recall, sensitivity and specificity.
Modeling of the Global Solar Radiation Series as a Function of Probability Distribution

The use of probability density functions (pdf) is directly linked to the nature of the data to which they relate. Some have good estimation capacity for small number of data, others require a large number of observations. In this study, the most probability distribution function for modeling the global solar radiation in Campo Grande, MS (Brazil) was determined. The global solar radiation data used for the analysis consists of daily average global solar radiation collected from University of Mato Grosso do Sul which span over the period of one year from January 2016 to December 2016. Various distribution functions were tested in this study and the most suitable one is determined using four different goodness of fit tests. The tested distributions used are Weibull, Rayleigh, Gamma, Lognormal, Rician and Frechet distributions. Four performance indicators; Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (R 2) were calculated to evaluate the adequacy criteria of the chosen distributions. The best distribution that fits well the global solar radiation observations in Compo Grande region was the Frechet distribution, followed by Weibull and Rician distributions. The worst distributions are given by Rayleigh and Lognormal. This paper is useful as first-hand information in the prediction of future global solar radiation for Campo Grande having known the past behavior and for fixing the missing data.
Perancangan Sistem Informasi Penjualan Obat Apotek Amanah 24 Berbasis Web

This study aims to create an information system that helps facilitate pharmacy owners and pharmacy employees in managing transaction data and making reports on drug sales transactions. In the Amanah 24 pharmacy there are still many problems in the process of managing drug stock data and recording transactions that still use the manual method by writing in a book so that it has an impact on delays in the delivery of an information data search. To overcome these problems, the drug sales information system design is made using PHP programming language, the server uses xampp and for its database uses MySql. The method used is the waterfall method that is requirements analysis, design, implementation and system testing. The drug sales information system is tested by the blackbox method and user testing which results in an average percentage of 3 respondents about this system is 85%. The results of this study have features such as data searches for monthly or weekly drug sales transaction reports, drug stock data and printing sales transaction reports. Pharmacy owners and employees have tried this sales information system and the result is that this system runs well so that it helps the operation of pharmacies work more effectively and efficiently.
Application of association rules learning for studying the store history of a large retail chain

Abstract In this article the problem of goods clustering according to the probability of their joint presence in the transactions of the sale point was considered. The problem was solved by applying the algorithms for the formation of association rules based on real retail store transactions. The applicability of the algorithms of this class was assessed, the Apriori algorithm proposed by R. Agrawal was selected and implemented. Using this algorithm, the possibility of cutting off “random” goods in a cluster to create a consumer basket of the corresponding outlet was studied. It is shown that the most resistant to “random” associations are clusters with a small number of products in the cluster, and the higher the cluster size, the higher the likelihood of noisy search results with “random” goods with a high individual probability of their acquisition (‘support’ indicator). Further development of the proposed method may consist in its distribution to all transactions of the entire trading network and the transition from the tasks of organizing goods within the store (merchandising) to the tasks of forming assortment matrices for stores.
How We Achieved a Production Ready Slot Filling Deep Neural Network Without Initial Natural Language Data

Training deep networks requires large volumes of data. However, for many companies developing new products, those data may not be available and public data-sets may not be adapted to their particular use-case. In this paper, we explain how we achieved a production ready slot filling deep neural network for our new single-field search engine without initial natural language data. First, we implemented a baseline by using recurrent neural networks trained on expert defined templates with parameters extracted from our knowledge databases. Then, we collected actual natural language data by deploying this baseline in production on a small part of our traffic. Finally, we improved our algorithm by adding a knowledge vector as input of the deep learning model and training it on pseudo-labeled production data. We provide detailed experimental reports and show the impact of hyper-parameters and algorithm modifications in our use-case.