Reviewerly/exHarmony huggingface dataset:
ML@SE: What do we know about how Machine Learning impact Software Engineering practice?

Machine learning (ML) based approaches provide efficient solutions successfully applied to different domains. In Software Engineering (SE) domain, ML is improving and automating various development activities, such as requirement classification, refactoring, defects prediction, and effort estimation. We investigated whether and how ML techniques currently support software development tasks and improve the way practitioners do their job. To achieve this objective, we performed a literature review and snowballing, obtaining a set of 209 articles. As a result, we present an organized approach among the first ten areas of SWEBOK, which guides possible ML solutions used in the tasks of the SE.
Life in Second Life through the eyes of the residents

More than a virtual world, Second Life (SL) is characterized by being an online metaverse in which the user can, in fact, create a second life. Since Second Life is a metaverse that simulates reality, users or residents can explore the world, meet other people, participate in activities, and thus simulate real world routines [1]. Based on the LOCUS project, this article intends, through the vision of the residents, to understand their preferences and what they value most in Second Life. The focus is on the space and the places they most like and what they value; on communication, that is, how are the communicational interactions between residents and preferences; and, on playability (if there is playability and if there is interest in this topic). In this way, the respondents' answers will be a trigger to build the interaction and playability of the village of Amiais in Second Life. Through this feedback, Amiais will continue to be developed with the goal of being an interesting environment that arouses curiosity, the desire to play and interact with other residents.
BYOD : Impact in Architecture and Information Security Corporate Policy

BYOD is a new business trend where employees are using their own devices for work purposes. This phenomenon has created new challenges in information security. Thus, organizations should adapt information security corporate strategy in order to address this new reality. This paper tries to identify and explore the optimal way to do this rearrangement, evaluating BYOD environment and measuring all relevant factors.
Impact of disruptive education on the academic performance of university students : Quantitative study in the students of Business Administration of the Private Technical University of Loja – Ecuador

The present study focuses on determining the impact of this educational disruption on the academic performance of students of the business administration career at the Universidad Técnica Particular de Loja (UTPL) face-to-face modality.The methodological approach used in this work is quantitative. The information of 298 students and the Results show that academic performance improved in times of pandemic in this student population, which means that the strategies applied were appropriate and positively influenced the learning of professionals in training.
A Non-linear Scenario Plotting to Guide Strategists : A Hybrid Model between Foresight and Computational Intelligence methods

The aim of this article is to integrate a typical scenario plot of the Schwartz approach [25], [30] with the FCM method [3], [6]–[7] mediated by stakeholder Participatory Modeling (PM) processes, to strengthen the system thinking approach during a strategic decision-making process guided by strategic foresight. The combination of these approaches provides a better understanding of the scenario alternatives available to decision makers. This is, recognizing the context of the focal question with creative scenario plots that show the interaction between its key drivers. Therefore, the hybrid model, should be useful to unify the strategic point of view by recognizing the most plausible scenario, while reducing the uncertainty due to ambiguous value judgments by Key Decision Makers (KDMs).The work presented in this article aims to develop hybrid model by combining qualitative and quantitative approaches to engage a new non-linear form of systems thinking about the future. The article includes a conceptual model and an experimental case that develops a case for rethinking the ideas of a research system for a local organization.The article ends with a reflection on outcomes and suggests ways for future research.
Secure File Systems for the Development of a Data Leak Protection (DLP) Tool Against Internal Threats

Data leakage by employees is a matter of concern for companies and organizations today. Previous studies have shown that existing Data Leakage Protection (DLP) systems on the market, the more secure they are, the more intrusive and tedious they are to work with. This paper proposes and assesses the implementation of four technologies that enable the development of secure file systems for insider threat-focused, low-intrusive and user-transparent DLP tools. Two of these technologies are configurable features of the Windows operating system (Minifilters and Server Message Block), the other two are virtual file systems (VFS) Dokan and WinFsp, which mirror the real file system (RFS) allowing it to incorporate security techniques. In the assessment of the technologies, it was found that the implementation of VFS was very efficient and simple. WinFsp and Dokan presented a performance of 51% and 20% respectively, with respect to the performance of the operations in the RFS. This result may seem relatively low, but it should be taken into account that the calculation includes read and write encryption and decryption operations as appropriate for each prototype. Server Message Block (SMB) presented a low performance (3%) so it is not considered viable for a solution like this, while Minifilters present the best performance but require high programming knowledge for its evolution. The prototype presented in this paper and its strategy provides an acceptable level of comfort for the user, and a high level of security.
Fuzzy Expert System for Constraint Handling in Real World Optimization

Optimization techniques have increasingly been applied in industrial environments over the past years. Even so, requirements and restrictions of real world problems, like technical standards and including physical impossibilities, are not so easy to include. Often, these constraints are hard coded in the computer program and the user cannot update them without the help of a programmer. In most cases, they are used as must regions in the search space. However, in real world problems the restrictions usually are not so strict. Expert systems have a knowledge base that resides outside of the computer program and relies on a set of rules that can be easily updated by the user. Fuzzy logic can make flexible the restrictions provided by specialists’ knowledge about the problem. This paper aims to propose a Fuzzy Expert System (FES) to better handle the constraints in real life engineering and business optimization problems. The proposed system was tested for optimizing an engineering problem comparing two known swarm algorithms, PSO and wFSS. The latter one is multimodal and suggest multiple feasible solutions, which is even better for supporting decisions. The results of the study show that the proposed method can achieve good and feasible solutions on the given problems and, consequently, increase competitiveness.
Training Transformers for Question Generation Task in Intelligent Tutoring Systems

Over the last few years, natural language processing (NLP) technologies have largely evolved, allowing their application in new scenarios with much more significant results. With the introduction of NLP into intelligent tutoring systems, several automation techniques could be used to improve the teaching process, among them, question generation, which allows the automated creation of interpretative questions from textual sources. This work explores the application of Transformers neural networks in the Question Generation task, developing several models and comparing their initial results.
Business-Driven Technical Debt Prioritization: A Replication Study

Context: Technical debt is a problem software development companies face, where teams create workarounds to achieve short-term benefits. Problem: organizations find it challenging to prioritize paying technical debt and align this decision to business objectives. Objective: To contribute to aligning the business perspectives concerning the prioritization of technical debt. Method: We applied the Tracy prioritization approach in an industrial case study in a software startup. Results: We used the approach in a new setting; we also identified the degree of agreement of the result of the framework with an expectation between the business and IT areas. Conclusion: This research analyzed the impacts of using the business-driven prioritization approach in a startup.
Distributed Ledger Technology to Enable Secure Management of IT Infrastructures : Development and evaluation of a Proof-of-concept tool using Hyperledger Fabric

IT Infrastructures have grown in both size and complexity. To help administrators to manage their infrastructure, several Infrastructure Management (IM) Tools have been created. However, none of them implements a secure and traceable log of changes that can bring accountability to the management of such infrastructures. On the other hand, recent research and development in blockchain technologies has allowed for the creation of Distributed Ledgers that can provide secure, immutable and traceable ledgers. These technologies have the potential to solve the problem by dynamically registering the changes the infrastructure management tools apply to IT infrastructures. For that purpose, a Proof-of-Concept tool was developed that incorporates a Permissioned Distributed Ledger, based on Hyperledger Fabric, as a middle layer to infrastructure management tools such as Ansible and Terraform, to prove the suitability of these technologies to provide a secure and immutable resource inventory and log of changes that enables for traceability and accountability of all modifications to the IT infrastructure, while also providing user identity management and control.
Web-based application for generation of video-interview summaries using neural networks

In this work is shown a preliminary development of a web application that generate summaries from video interviews. For the transcription, in addition to the SpeechRecognition library to separate by silences, the Google SpeechToText API is used to separate by speakers. For summarization we use BERT2BERT, a BETO model pre-trained on news summaries from the Hugging Face Transformers library.
Collaborative Filtering Enhanced by Demographic Information for Tourist Sites Recommendations

Recommender systems are very applicable in the tourism domain to alleviate the information overload problem. These systems are being developed to help tourists find sites and services that are of interest to them. To promote tourism in a city, this paper presents an enhanced user-based collaborative filtering approach with demographic information for recommending touristic sites, which provides precise recommendations. The collaborative filtering approach recommends touristic sites considering two user types: new tourist and registered tourist. In this way, the recommendations of tourist sites are generated according to the user's historical rating or based on the user's demographic information. The approach is evaluated on a tourist sites dataset extracted from the TripAdvisor platform that contains historical ratings, and demographic information of the tourist. To measure the performance of the proposed approach, RMSE is used to evaluate predictions accuracy, and Precision and Recall measures to evaluate the quality of recommendations. The results showed an improvement in the prediction accuracy and a significant performance with precision and recall, especially in the presence of the cold-start problem.
Proposal of a lightweight, offline, full-text search engine for an mHealth app

A patient’s ability to recall and retrieve health information contributes to a better health management. HealthTalks was developed to address these issues by recording a summary of a medical appointment, uttered by the physician, and transcribing it. For each appointment, the user can also take free-text notes. Nowadays, search engines have become a ubiquitous part of everyone’s life and are expected on most applications. Here, we describe the development of a search engine for HealthTalks. The app’s characteristics demand a lightweight and offline engine, which requires a specific solution rather than an existing library or service. Our approach combines SQLite’s Full-Text Search 4 module, which includes n-gram indexing, with traditional information retrieval techniques to rank the documents. We created a test collection with summaries of clinical appointments (our documents), information needs, search queries, and relevance assessments for an initial search engine evaluation. Using this test collection, we assessed performance using NDCG@10, the first rank position of a totally relevant result, and query latency. Results are promising, with an average NDCG of 0.97. The median rank position of the first relevant result varies between 1.9 and 1.95, depending on the use of 4-gram character tokenization, an aspect that did not significantly affect the results. We expect this work to be useful for future developments of full-text search engines in mobile environments.
Automatic regions detection in CT images based on Haralick textures

The identification of anomalies (such as bone fractures or tendonitis in muscles and soft tissues) through image processing and analysis techniques in Computed Tomography (CT) images is today of great importance to assist doctors and health professionals in making accurate diagnoses. The extraction of relevant information from the CT image is characterized by the calculation of gray level input image attributes. Statistical moments (SM) are calculated using the gray level distribution of an image and are therefore generally calculated from that image's histogram. These characteristics provide a statistical description of the relationship between different gray levels in the CT image. Haralick proposed a methodology for describing textures based on second order statistics, where characteristics are derived from co-occurrence matrices, which are constructed by counting different combinations of gray levels in an image according to certain directions. In this work, it is intended to automatically identify and extract regions in CT images based on textures as an aid for a quick and accurate diagnosis. CT images are first pre-processed for noise reduction and image enhancement, followed by the application of Haralick textures to segment and detect zones of interest. Classifiers trained on the Haralick invariant features showed good accuracy and performance. Despite the presence of low contrast and noise in some images, the proposed algorithms present promising results in the segmentation and automatic identification of regions of tomographic images, being an important contribution to support health professionals in the characterization of anomalies and their extension. Good results are expected for the next step of this work in the detection and segmentation of anomalies in CT images.
Aspect-Oriented Webassembly Transformation

There are scenarios where it can be useful or necessary to directly transform and instrument compiled code, rather than resorting to source code changes with subsequent compilation. These transformations can be motivated by several reasons, such as: immediate repair of problems encountered in production, neutralization of potentially malicious code, performance improvements, instrumentation for profiling and inspection purposes, fault injection, or unavailability of source code. While tools are available for conducting this kind of transformations for many different software ecosystems and languages, there is a limited set of options for doing so for WebAssembly applications. In this paper, we present a novel tool and language, the WasmManipulator/WmrLang, for manipulating WebAssembly code, which allows you to perform code transformations, using an aspect-oriented approach for specifying code locations, and code insertion, replacement, or deletion to be executed at those locations. In addition, because WebAssembly routines can be heavily interdependent on the JavaScript code that uses them, the tool has certain features that allow you to take advantage of and exploit this dependency. This includes defining additional types in WASM code, and interpreting/executing expressions at runtime.
Analyzing serious games and literacy

Technology allows access to a wealth of resources that facilitate learning, in the case of literacy offers audiovisual resources that streamline cognitive skills. However, pedagogical strategies are required to implement technologies and achieve meaningful learning. Serious games arise as a proposal that seeks the development of learning through the development of activities of the game adjusting to the main way in which it is learned: through what is said and done. In this context, the objective of the research is to establish the use of serious games aimed at reinforcing literacy skills that strengthen the learning of second-year students of EGB. The focus of the research is qualitative with a descriptive scope, in which interviews were conducted with the actors of the educational process. The results show that children fail to develop their literacy skills, due to scarce didactic resources, ignorance of methodologies, lack of practice exercises and poor integration of innovative strategies that stimulate collaborative learning in the learning environment. The development of a strategy based on serious games, from mobile applications for the strengthening of literacy skills, with positive reinforcement and constant feedback, will allow students to maintain interest and develop these skills that are indispensable to continue in the educational process.
Isolation Forest-based semi-supervised Anomaly Detection of multiple classes

Defining rules for finding all the uncountable patterns of events of interest, in complex and critical applications, is infeasible, if not impossible. Anomaly Detection (AD) methods are techniques well-suited for tackling this category of problem, namely, finding previously unknown deviant data due to their ability for identifying behavior that significantly went astray from an expected pattern. Isolation Forest (IF) is a state-of-the-art technique in the unsupervised Anomaly Detection area, mainly because of efficacy and low computational cost. However, unsupervised Anomaly Detection many times suffers from high false-positive rate and high false-negative rate. Semi-supervised techniques, e.g. Hybrid Isolation Forest (HIF)l can significantly improve the unsupervised algorithms with low human effort, and can also aggregate semantic knowledge into the models. HIF, however, can only aggregate known anomalies in a single class. This paper proposes a Hybrid Isolation Forest-based model capable of aggregating known anomalies in many classes, without loss of performance. Our proposed multiple anomaly classes semi-supervised model showed better performance in some known datasets, as well as showed the ability to significantly improve the underlying unsupervised algorithm.
Is there a Need for Automated Code Review to be Used in Teaching? : From the perspective of students

The aim of this study is to analyze the typical process of the practical part of software development courses at universities and to evaluate whether the current process meets the expectations of students or whether the needs of students would be better met by the benefits of automated code feedback. A semi-automated survey was conducted in German, involving bachelor students from different universities who had attended an introductory programming class within the last 6 semesters. The results clearly show that the students would like to have individual assignments instead of the due to time constraints, usual group exercises as they see more advantages for their learning progress. The use of automated code feedback could not only solve this time problem but would also bring other benefits.
Individual Convolution of Ankle, Hip, and Wrist Data for Activities-of-Daily-Living Classification

The Activities of Daily Living (ADL) include activities such as brushing teeth, sweeping, and walking that are critical to on-going health, especially in older adults. Activities may be determined using recorded video and 2D-CNNs, however video recordings present privacy and coverage challenges in personal spaces. Smartphones and newer wristworn devices that record motion data can also be used for activity recognition tasks. Ankle or shoe-based devices such as the retired Nike+ sensor are less common, however ear-based devices which may record head movement are gaining popularity. In this work we use accelerometer data from a recently released dataset using devices placed on the ankle, hip, and wrist. First, we evaluate a simple 1D-CNNs ability to classify the 17 included activities in subject-dependent and subject-independent analysis. Then we process the accelerometer data from the three sensors individually to evaluate each location’s ability to predict activities. Finally, we develop a functional model which independently executes a 1D-CNN for each sensor’s data and combines the results using Global Average Pooling. The functional model achieves a subject-independent accuracy of 70.7%.
Doki: A Multi-sensation Interaction Device that Communicates Emotions

During the pandemic, many people have found themselves physically isolated from each other for long periods of time, so online chatting tools have become the main path of communication. However, texts and other chatting tools do not properly transmit the complex emotions hidden behind them. However, texts and other visual information have created an overload of information and made people ignore the complex emotions hidden behind them. Without the stimulation of sensations from face-to-face communications, people, especially lovers, lose their ability to observe their beloved ones’ emotions and feelings effectively.In this work, we propose a device to improve the efficiency of emotional communication - a multi-sensation interaction installation called Doki, which utilizes light, digital display, vibrations, and tactile interaction to transfer emotions. In addition, this device is comfortable to touch and enjoyable to play with. When used in conjunction with its texting applications, this product will help people express emotions over long distances and alleviate feelings of isolation.
Object Detection Accuracy Improvements of Mobility Equipments through Substitution Augmentation of Similar Objects

A vast amount of labeled data is required for deep neural network training. A typical strategy to improve the performance of a neural network given a training data set is to use data augmentation technique. The goal of this work is to offer a novel image augmentation method for improving object detection accuracy. An object in an image is removed, and a similar object from the training data set is placed in its area. An in-painting algorithm fills the space that is eliminated but not filled by a similar object. Our technique shows at most 2.32 percent improvements on mAP in our testing on a military vehicle dataset using the YOLOv4 object detector.