Reviewerly/exHarmony huggingface dataset:
Towards Software Product Lines Optimization Using Evolutionary Algorithms

Software product line (SPL) engineering methodology assist to create a range of software products within less time and cost but with high quality by the reuse of core software assets, which has been tested. Thus, testing is crucial for successfully deploying SPL. As the product features increases, testing process can be time-consuming. Testing in SPL is regarded as a combinatorial optimization problem. Evolutionary algorithms were reported to provide good results in such class of problems. This research provides a framework to compare different multi-objective Evolutionary Algorithms performance regarding software product line context. We report on the problem encoding, variation operators and different types of algorithms: Indicator Based Evolutionary Algorithm (IBEA), Strength Pareto Evolutionary algorithm II (SPEA-II), Multi-Objective Evolutionary Algorithms based on Decomposition (MOEA/D) and Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The framework will provide preliminary results on different Feature Models (FMs) to measure their feasibility to optimize SPL testing.
Data Curation Profiling to Assess Data Management Training Needs and Practices to Inform a Toolkit

The purpose of this paper is to explore current data management training needs and practices for Belmont Forum member agencies and researchers to inform a Toolkit. Fourteen Belmont Forum affiliated individuals were interviewed following a predetermined set of questions to create data curation profiles of their funded work. The data curation profile questionnaire includes questions related to data management, storage, stakeholders, costs, training, and credentials. The interview findings highlight gaps in existing knowledge of data management theory and practice that could impact data re-use. Results of these interviews were used to populate a Toolkit of data management training and effective practice resources specifically developed to train Belmont Forum grant awardees. The results also highlight some attitudes and behaviours of current scientists, researchers, and agency representatives, and the impact of the implementation of data management plans on the open science movement.
Applying Artificial Neural Networks In Construction

Currently, artificial neural networks (ANN) are used to solve the following complex problems: pattern recognition, speech recognition, complex forecasts and others. The main applications of ANN are decision making, pattern recognition, optimization, forecasting, data analysis. This paper presents an overview of applications of ANN in construction industry, including energy efficiency and energy consumption, structural analysis, construction materials, smart city and BIM technologies, structural design and optimization, application forecasting, construction engineering and soil mechanics.
An Analysis of Sindhi Annotated Corpus using Supervised Machine Learning Methods

The linguistic corpus of Sindhi language is significant for computational linguistics process, machine learning process, language features identification and analysis, semantic and sentiment analysis, information retrieval and so on. There is little computational linguistics work done on Sindhi text whereas, English, Arabic, Urdu and some other languages are fully resourced computationally. The grammar and morphemes of these languages are analyzed properly using dissimilar machine learning methods. The development and research work regarding computational linguistics are in progress on Sindhi language at this time. This study is planned to develop the Sindhi annotated corpus using universal POS (Part of Speech) tag set and Sindhi POS tag set for the purpose of language features and variation analysis. The features are extracted using TF-IDF (Term Frequency and Inverse Document Frequency) technique. The supervised machine learning model is developed to assess the annotated corpus to know the grammatical annotation of Sindhi language. The model is trained with 80% of annotated corpus and tested with 20% of test set. The cross-validation technique with 10-folds is utilized to evaluate and validate the model. The results of model show the better performance of model as well as confirm the proper annotation to Sindhi corpus. This study described a number of research gaps to work more on topic modeling, language variation, sentiment and semantic analysis of Sindhi language.
An Explicit Local and Global Representation Disentanglement Framework with Applications in Deep Clustering and Unsupervised Object Detection

Visual data can be understood at different levels of granularity, where global features correspond to semantic-level information and local features correspond to texture patterns. In this work, we propose a framework, called SPLIT, which allows us to disentangle local and global information into two separate sets of latent variables within the variational autoencoder (VAE) framework. Our framework adds generative assumption to the VAE by requiring a subset of the latent variables to generate an auxiliary set of observable data. This additional generative assumption primes the latent variables to local information and encourages the other latent variables to represent global information. We examine three different flavours of VAEs with different generative assumptions. We show that the framework can effectively disentangle local and global information within these models leads to improved representation, with better clustering and unsupervised object detection benchmarks. Finally, we establish connections between SPLIT and recent research in cognitive neuroscience regarding the disentanglement in human visual perception. The code for our experiments is at https://github.com/51616/split-vae .
COLLECTIONS DEVELOPMENT TRENDS IN LIBRARIES OF THE SIBERIAN BRANCH OF THE RUSSIAN ACADEMY OF SCIENCES UNDER THE CONDITIONS OF SCIENTIFIC INSTITUTIONS SYSTEM OF MANAGEMENT TRANSFORMATION

Academic libraries in the region are organized according to the territorial and departmental principles as components of variously directed in their activities scientific centers of the Siberian Branch of the Russian Academy of Sciences (SB RAS). In the context of transformation of institutional management system, it is important to proceed with information and methodical support, rendered by SPSL SB RAS to SB RAS libraries. Besides, information support of educational and scientific interests of scholars and specialists needs higher level of integration of resources and service technologies for academic, university and regional scientific libraries, and this direction requires comprehension on the part of the library community.
Adaptive threshold for moving objects detection using gaussian mixture model

Moving object detection becomes the important task in the video surveilance system. Defining the threshold automatically is challenging to differentiate the moving object from the background within a video. This study proposes gaussian mixture model (GMM) as a threshold strategy in moving object detection. The performance of the proposed method is compared to the Otsu algorithm and gray threshold as the baseline method using mean square error (MSE) and Peak Signal Noise Ratio (PSNR). The performance comparison of the methods is evaluated on human video dataset. The average result of MSE value GMM is 257.18, Otsu is 595.36 and Gray is 645.39, so the MSE value is lower than Otsu and Gray threshold. The average result of PSNR value GMM is 24.71, Otsu is 20.66 and Gray is 19.35, so the PSNR value is higher than Otsu and Gray threshold. The performance of the proposed method outperforms the baseline method in term of error detection.
Indoor positioning system using BLE beacon to improve knowledge about museum visitors

Generally, a museum has many locations and artifacts collection that display for visitors. Museum manager often have difficulty in obtaining information on visitors behavior such as, is there are particular locations/artifacts in the museum that are frequently/rarely visit by museum visitors, how long visitors spend their time in particular locations/artifacts, etc. The purpose of this study is try to build a suitable system in order to improve knowledge about the behavior of museum visitors by identifying the position of visitors in the museum. This study uses Bluetooth Low Energy (BLE) Beacon that place around the museum. The visitor mobile phone will detect BLE beacon signal, then the mobile phone application will calculated the visitor’s mobile phone position using the signal strength from the BLE beacons that are detected. The application then sends it to the computer server to display it in as museum visitor heat map. From this information, the museum manager could find out the visitors behavior movement and know which areas/artifacts that frequently/rarely visit by museum visitors. According to distance error testing which compare real location and position of the calculation, it is show that the average of distance error is around 140 cm. So, it can be concluded that the information obtained is sufficient enough to represent the position of museum visitors.
Understanding researcher needs and raising the profile of library research support

Researchers at North Carolina State University expect little to no difficulty in discerning how their Library can support their work. At the same time, librarians repeatedly find that researchers are unaware of what our Library has to offer. Within this context, we embarked on a two-year study to help inform the development of outreach strategies to enable new research engagement opportunities that will scale and, at the same time, help us transform our model of research support strategies and engagement. We interviewed both librarians and researchers to gain an understanding of researcher needs from both perspectives. The results of the interviews provided a solid grounding for building our awareness of researchers’ behaviors, expectations and workflows as well as presenting a unique picture of both unmet and unarticulated needs. In this article we summarize our results with a specific focus on findings from the researcher interviews. We share our recommendations for evolving library research support and enhancing outreach strategies to provide an easier starting point for different types of researchers to discover relevant research assets provided by libraries such as ours.
Forensic intelligence and the analytical process

Abstract A review was undertaken of the developments made with integrating forensic evidence into the analytical process to support police investigations. Evidence such as DNA, fingerprints, fibers, accelerants, tyre marks, and so forth, can support to differing degrees the various working theories or hypotheses about the nature of the alleged crime, the persons of interest and the modus operandi. Investigators however, either forensic or detective, bring various biases to evidence capture and analysis, biases which are better understood in the intelligence community. Structured analytical techniques have a long history in intelligence analysis, for example analysis of competing hypotheses, which serves several purposes: information sharing, clarity of communication, and to highlight the common forms of bias brought to bear in an investigation. We illustrate the representation of links based on traces and intelligence, and how these can be stored in databases permitting better “reasoning” with evidence. We also present some recommendations for integration of forensic intelligence into the investigative analytic process and review information systems in this area. This article is categorized under: Fundamental Concepts of Data and Knowledge &gt; Human Centricity and User Interaction Application Areas &gt; Society and Culture Fundamental Concepts of Data and Knowledge &gt; Knowledge Representation
Expert System for Stroke Classification Using Naive Bayes Classifier and Certainty Factor as Diagnosis Supporting Device

Abstract Stroke is a disease that has high mortality rate in Indonesia and worldwide. This disease is dangerous if not immediately treated because the brain circulatory disorder can cause a permanent disability or death. Diagnosis process of stroke disease can be assisted by an expert system using Naive Bayes Classifier and Certainty Factor. Decision making by Naive Bayes Classifier uses total probability value of all the diagnostic criteria on the existing database. Certainty Factor uses a weight value combination from measure of believe provided by expert. This expert system research aims to assist in the early diagnosis of a neurologist in diagnosing potential stroke patient from several diagnostic criteria as well as determine the accuracy of the expert system program created. The sample used in this study are the hospital medical records of 130 patients from Dr. Soetomo Hospital of Surabaya consists of 80 stroke patients data and 50 non-stroke patients data. Expert system program uses 12 category for diagnosis as an input and two statement outputs between stroke or non-stroke. Based on the analysis from the 25 testing data of 105 training data we obtained the accuracy of the method Naive Bayes classifier by 96% and of the method of Certainty Factor of 84%. Expert system program using Naive Bayes Classifier and Certainty Factor can be a device for stroke diagnosis.
Simple Black-Box Adversarial Examples Generation with Very Few Queries

Research on adversarial examples for machine learning has received much attention in recent years. Most of previous approaches are white-box attacks; this means the attacker needs to obtain before-hand internal parameters of a target classifier to generate adversarial examples for it. This condition is hard to satisfy in practice. There is also research on black-box attacks, in which the attacker can only obtain partial information about target classifiers; however, it seems we can prevent these attacks, since they need to issue many suspicious queries to the target classifier. In this paper, we show that a naive defense strategy based on surveillance of number query will not suffice. More concretely, we propose to generate not pixel-wise but block-wise adversarial perturbations to reduce the number of queries. Our experiments show that such rough perturbations can confuse the target classifier. We succeed in reducing the number of queries to generate adversarial examples in most cases. Our simple method is an untargeted attack and may have low success rates compared to previous results of other black-box attacks, but needs in average fewer queries. Surprisingly, the minimum number of queries (one and three in MNIST and CIFAR-10 dataset, respectively) is enough to generate adversarial examples in some cases. Moreover, based on these results, we propose a detailed classification for black-box attackers and discuss countermeasures against the above attacks.
Classification of Binary Fracture Using CNN

One of the major problems faced by any living organism since infancy are Musculoskeletal Injuries. To keep it quite simple musculoskeletal injuries are a range of disorders involving muscles, bones, tendons, blood vessels, nerves and other soft tissues. However one of the most common forms of musculoskeletal injuries are Fractures. Fractures are one of the most prevalent sores that are faced by any living organism. They are also easily overlooked by the best of physicians. Even with the help of an X-Ray they are one of the hardest symptoms to diagnose. We believe that we can provide a solution to this problem by implementing Convolutional Neural Networks (CNN) Image Processing Algorithms into the field of Medicine.We have designed a model using three layers of architecture which has been properly trained to identify the X-Ray images that have Fractures. To accomplish this we used large datasets that consist of 200 images of human hands, ribs, legs, and neck. These large datasets are clearly segregated to identify those images which contain fractures from those images which are perfectly fine. The Results gave us accurate predictions using some graphical representations as well as epochs of the various patients.
The National Project “Culture” as the New Stage of Development of Libraries (based on the Materials of the Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia)

On October 22—23, 2019, the Russian State Library hosted the Annual Meeting of the Heads of Federal and Central Regional Libraries of Russia. The Forum, focused on the analysis of the current state of the library sector and the development of priority areas for its further development, was held for the 26th time. The Meeting was organized by the Ministry of Culture of the Russian Federation, the Russian State Library and the National Library of Russia. 294 participants from 57 regions of Russia, including employees of the Ministry of Culture and the Ministry of Science and Higher Education of the Russian Federation, the Main Information Computing Centre (GIVC) of the Ministry of Culture of the Russian Federation, regional authorities, heads and specialists of libraries of various levels, universities, publishing houses and corporations attended the Meeting. The broadcasting of the meetings on the website of the Russian Association of Electronic Libraries significantly expanded the number of participants — there were recorded 609 connections.The main theme of the 2019 Meeting is “The National Project “Culture” as the New Stage of Development of Libraries”. The Meeting considered the issues of formation of the Concept of librarianship development in the Russian Federation, the results and prospects of creation of model municipal libraries within the framework of the Federal project “Cultural Environment”, the development of the National Electronic Library (NEL), the formation of the register of book monuments, including the issues of their digitization and inclusion in the NEL in the framework of the Federal project “Digital Culture”, etc.There were announced the results of two competitions: The Third All-Russian Competition of Library Innovations and the Sixth All-Russian Competition “Library Analytics 2019”. The Meeting included sessions of the jury of the All-Russian Competition “Librarian 2019” and the Board of the Russian Library Association. There were organized two Round tables: “The new mission of libraries — new competences of staff: How the system of professional education of library-information workers should change in the new reality” and “Subject fields statistics (6-NC Form) and Indicators of the National project “Culture””. The outcome document of the Meeting reflects the results of discussions.
Application of Waterfall Model In Development of Work Training Acceptance System

Unemployment became a social problem for the community, so they could not meet their life needs. One cause because they do not have special skills, so it is difficult to get a job. Therefore, in every local government through the UPTD Balai Tenaga Kerja, it often conducts work training for those who need it. However, the system applied in the UPTD Bogor area is still conventional. Causing problems such as data readings are less obvious; form storage and documents are not neatly arranged, so some documents are missing and lost. It resulted in a loss for the participants who would participate in job training. So it takes the development of the system to solve the problem. To improve the existing system, that is from conventional to computerized. For the development of this system, it is necessary to take steps such as those in the waterfall model. The result of the development of this system is the design of a web-based work training acceptance system that can facilitate participants to access online in the selection of the desired work exercises, and the storage of documents will be stored neatly in a database.
The Impact of Listening Phonological Errors on Speaking : A Case Study on English Education

Phonological errors in communicating will let the listener gain a different meaning and communication becomes impeded. The students’ errors are due to their lack of understanding of the sound system of language, as their second-language acquisition. The problem in this research is what type of phonological errors are made by students when they are listening to the short story and will impact on their speaking. The data is conducted in the language laboratory of The National University in Jakarta by second-semester students in the subject of Spoken English. in the Faculty of Letters, English Department. The purpose of this research study is to learnthe difficulties of students when learning Basic Spoken English. The method used is descriptive with content analysis techniques. The result showed the most the biggest of surface structure taxonomy of phonological errors created by the students when they are listening to the short story and has impacted on speaking is global errors in diphthong (61,29%). This is because in Indonesian there is no sound of a diphthong. These global errors will have an effect on good communication. The students should study the phonological system in the target language, especially diphthong sound, in addition to vowels and consonants. It is expected that the lecturer often guides students’ pronunciation based on the point and the manner of articulation and improve if they are not able to.
Reading on paper or reading digitally? Reflections and implications of ePIRLS 2016 in South Africa

participated in the electronic version of the Progress in International Reading Literacy Study (ePIRLS) in 2016 but faced many challenges during implementation.Accurate databases on information and communication technologies (ICT) capacity of schools were not available for sampling in Gauteng, many schools had old and/or non-functional hardware and half of the schools had not used their computer laboratories in the last 3 years.Consequently, South Africa was excluded from the international report as the study requirements could not be met.In this paper we examine the implications of the problems experienced in the ePIRLS multiple case study, conducted in 9 schools (n = 277) in Gauteng.Multilevel models were built using data from the nationally representative Grade 4 Progress in International Reading Literacy Study (PIRLS) data from 2011 (n = 15,744) and 2016 (n = 12,810).In the 2016 national study, principals and teachers reported fewer computers and libraries being available for learners than were reported in 2011.Computers and paper-based libraries being available were not significant predictors of reading literacy.Instead, the medium of instruction in the Foundation Phase, school location, gender, and socioeconomic composition of the school predicted reading literacy achievement.The ePIRLS results show no significant difference between paper-based and online reading.While issues of poverty, gender inequality, and historical disadvantage persist, Grade 4 learners may lack adequate opportunities to acquire paper and digital reading skills.We conclude that the most disadvantaged learners have increasingly insufficient opportunities and resources available to attain basic reading skills and this will have negative long-term consequences for South Africa's educational sector and economy.