Reviewerly/exHarmony huggingface dataset:
Work Like Ants! Atta 2.0: Dealing with Self-Organized Teams

Traditional software engineering approaches were developed based on the old manufacturing process, in terms of roles, responsibilities, tasks, and hierarchy. Modern approaches, such as, Agile approaches, are based on a fusion of these traditional approaches and the Agile Manifesto, which changes the perspective of main software process objectives. These approaches are designed to encourage self-management, which may be challenging even in big or very small software companies. This paper proposes a software engineering complementary framework called Atta 2.0 (Atta 2), which is an approach inspired by a nature organization and aims to deal with the difficulties of self-organization issues. We performed a case study on a start-up company, that follows Scrum guidelines, has a very small team, and maintains several projects at the same time. As the outcome, we have success running the Atta 2 and obtained gains in terms of self-organization, structure, and visibility. Based on this we advocate that Atta 2 improved Sprint planning and execution, tasks creation and distribution, and slightly the quality of delivered artifacts. We also focus that Atta 2 helped to integrate new team members and to extract the best from the new ones and old team members. We advocate that Atta 2, may have value to the community, once it is a complementary option to aggregates value to well-known agile or traditional approaches. Thus, we concluded that Atta 2 can be used jointly with other management approaches, in order to promote the best-aggregated value.
Advance Formation of Cross-Cutting Competencies of Students in a Cross-Multidimensional Educational Process

The article declares the author's concept of the cross-multidimensional organization of the educational process. From the standpoint of a multi-media approach in education, the author considers the formation and development of cross-cutting competencies among students in the system of general, professional, higher and additional education. The grouping of cross-cutting competences, a set of measures in education, contributing to their formation are presented. In the aspects of solving the problems of the formation and development of cross-cutting competencies, a characteristic of the advancing effects of a cross-multidimensional educational environment is given.
Learning Health Systems: An Anonymous Network Routing Protocol

Learning Healthcare Systems are an emerging approach to healthcare research as translated into practice. For this purpose, a strong interconnection comes to be a necessity when dealing with healthcare services, research and knowledge transfer all at once. Practically, these connections imply that a routing protocol should guarantee anonymity to entities in compliance with both laws and ethical requirements while restricting the quantity of information obtainable had an entity been compromised. In order to bring more protection and meet all the requirements, a new message routing protocol is offered to allow the use of data access paths and to resist traffic analysis security threats. The protocol protects the addresses and roles pertaining to entities from any lurking malevolent minds by implementing proxies into a mix-network. Moreover, flows of synthetic datasets and contents identifiers are handled separately so as to curb any risk of re-identification. A model of this protocol is provided in the form of a multi-objective optimization problem, natively integrating objectives of minimizing both latency and entropy of the information exchanged. The assessment of this model shows that the constrained separation of data flows has a minimal impact on delay times, which not only reveals to be an acceptable compromise but also significantly increases security in data access.
Review of neural approaches for conditional text generation

The article is devoted to the review of conditional test generation, one of the most promising fields of natural language processing and artificial intelligence. Specifically, we explore monolingual local sequence transduction tasks: paraphrase generation, grammatical and spelling errors correction, text simplification. To give a better understanding of the considered tasks, we show examples of good rewrites. Then we take a deep look at such key aspects as publicly available datasets with the splits (training, validation, and testing), quality metrics for proper evaluation, and modern solutions based primarily on modern neural networks. For each task, we analyze its main characteristics and how they influence the state-of-the-art models. Eventually, we investigate the most significant shared features for the whole group of tasks in general and for approaches that provide solutions for them.
Optimization of Human Body Attitude Detection Based on Mask RCNN

Mask RCNN has the advantage of higher accuracy than other human body attitude detection models, and has been widely used for multi-person human body attitude detection. However, Mask RCNN has a low detection speed. Therefore, we propose a MobileNet-based Mask RCNN optimization model to accelerate the inference calculation speed of Mask RCNN. At the same time, in order to further improve the detection accuracy of Mask RCNN, we propose a method by using pixel segmentation results to assist the detection of key points in the human body, which improves the speed of reasoning and reduces the rate of false detection to some extent.
Feature-based insight for forks in social coding platforms

Recently, fork-based development has shown to be an easy and straightforward technique to reuse the source code of existing projects (upstream projects and their forks) in open source communities (for example, GitHub) and industry. This technique allows developers to tailor the existing forks to build their applications and thus reduce the development’s burden. However, when the number of forks of a given repository increases, it is difficult to manually maintain and keep track of the development activities across all existing forks. Consequently, this leads to redundant development activities and to lose the efforts of the developers and maintainers. In this article, an automatic approach is proposed to overcome the above-mentioned problems. The proposed approach incorporates a mathematical research technique called formal concept analysis with other proposed algorithms. To evaluate the effectiveness of the proposed approach, it is applied on four software projects from different domains and sizes. The results show that the proposed approach gives promising results according to well-known metrics in the subject. Also, it significantly outperforms the existing state-of-the-art and gives developers in open source communities and industry a development overview about forks of a given repository.
Language mapping functions: Improving softmax estimation and word embedding quality

Summary One of the best methods for estimating the softmax layer in neural network language models is the noise‐contrastive estimation (NCE) method. However, this method is not proper for word embedding applications compared with some other robust methods such as the negative sampling (NEG) method. The NEG method implements the pointwise mutual information (PMI) relation between the word‐context space in the neural network, and the NCE method implements conditional probability. Both the NCE and NEG methods use dot‐product‐based mapping to map words and contexts vector to the probabilities. This article presents the parametric objective function, which uses the mapping function as the parameter. Also, we obtained a parametric relation between word‐context space according to the mapping parameter. Using the parametric objective function, we identify conditions for a mapping that make it a proper selection for both softmax estimation and word embedding. The article also presents two specific mapping functions with the required conditions, and we compared their performance with that of the dot‐product mapping function. The performance of the new mapping functions is also reported over common word embedding and language models' benchmarks.
Universal Semantics for the Stochastic λ-Calculus

We define sound and adequate denotational and operational semantics for the stochastic lambda calculus. These two semantic approaches build on previous work that used an explicit source of randomness to reason about higher-order probabilistic programs.
Deep Learning-Based Image Retrieval in the JPEG Compressed Domain

Content-based image retrieval (CBIR) systems on pixel domain use low-level features, such as colour, texture and shape, to retrieve images. In this context, two types of image representations, i.e. local and global image features have been studied in the literature. Extracting these features from pixel images and comparing them with images from the database are very time consuming. Therefore, in recent years, there has been some effort to accomplish image analysis directly in the compressed domain with lesser computations. Furthermore, most of the images in our daily transactions are stored in the JPEG compressed format. Therefore, it would be ideal if we could retrieve features directly from the partially decoded or compressed data and use them for retrieval. Here, we propose a unified model for image retrieval which takes DCT coefficients as input and efficiently extracts global and local features directly in the JPEG compressed domain for accurate image retrieval. The experimental findings indicate that our proposed model performed similarly to the current DELG model which takes RGB features as an input with reference to mean average precision while having a faster training and retrieval speed.
Market Power and Cost Efficiency in the African Banking Industry

Purpose - In this study, we test the so-called ‘Quiet Life Hypothesis’ (QLH) which postulates that banks with market power are less efficient.Design/methodology/approach - We employ instrumental variable Ordinary Least Squares, Fixed Effects, Tobit and Logistic regressions. The empirical evidence is based on a panel of 162 banks consisting of 42 African countries for the period 2001-2011. There is a two-step analytical procedure. First, we estimate Lerner indices and cost efficiency scores. Then, we regress cost efficiency scores on Lerner indices contingent on bank characteristics, market features and the unobserved heterogeneity.Findings - The empirical evidence does not support the QLH because market power is positively associated with cost efficiency. Originality/value - Owing to data availability constraints, this is one of the few studies to test the QLH in African banking.
Animation Qr-Codes for Facial Biometry Tasks and Its Applications

The article shows the history of the development of badges as an image accessory designed to represent and/or identify people at a service checkpoint, in a conference or other mass event related to a meeting and contacts of people. This story covers: simple badge cards with printed information about a person, electronic badges with scrolling text, badges with controlled display of QR codes and graphic portraits of people who carry these badges, smart identification and contact control badges. At the same time, it is noted that the latest ideas in the development of badges support the technical side of the creation of “Electronic Citizen” and, while not entirely perfect, are focused on more local use. One of the alternative solutions is the use of color animated biometric QR codes that store facial biometric information about a particular person - his color graphic portrait, QR codes with documentary and biometric information about him.
Blockchain-Based Healthcare Data Management: Current Practices and Opportunities Ahead

This paper studies the types of data stored in electronic health records (EHRs) and the associated data management practices. In addition, it explores blockchain as a technology that could facilitate trusted and privacy-preserving patient data sharing and access control management as demonstrated in several EHRs projects applying blockchain. Moreover, the paper discusses the perspective of a future adoption of a blockchain-based self-sovereign identity (SSI) in EHRs data management that could be the road ahead to ensure a patient-centric trusted data management.
Development of Porting Analyzer to Search Cross-Language Code Clones Using Levenshtein Distance

Software forking is the process of creating a variant of the existing system by reusing more reliable algorithms and the design of the legacy systems. The software porting team converts the legacy system to the new system by adding or deleting of features and retaining the more reliable algorithm and design. A cross-language clone detector can significantly improve the performance of the software forking team by helping them to locate the code clones of legacy software. This paper proposes a technique to detect functionally equivalent code clones across C, C++, and Java codes by employing the Levenshtein distance algorithm to build a porting analyzer that helps the software forking team with three features like (i) locating all functionally similar cross-language clones among the C, C++, and Java project repositories. (ii) Given an input code for searching, it fetches the functionally equivalent code clone types from the respective repositories. (iii) Displays bug fixes to convert the code from one language to another language. The proposed cross-language clone detector outperforms the accuracy of existing works in terms of precision and can enhance the speed of the software forking process. Results prove that keyword-based filtering using Levenshtein distance detects the cross-language type 1 (Exact) clones and type 2 (Renamed) clones with 100% precision and detects type 3 (Near miss) clones and type 4 (Semantic) clones with 98% and 95% precision, respectively, which is found to be the best accuracy in finding cross-language code clones so far.
A Glimpse into the Methodology of Developing Shell-Based Control Systems for UGVs

The evolution of robotics resulted in many different methodologies of developing control systems. Yet many robots are still developed intuitively, without any formalization. Existing methodologies rarely offer a complete end-to-end solutions. This article proposes a novel methodology to provide a control system for an Unmanned Ground Vehicle using knowledge-based approach. The methodology adopts the concept of a control system shell and different knowledge representations to provide a flexible yet easily configurable control system. A historical introduction describes the most important methodologies that inspired this paper. Then, the principles of a control system shell and its practical usage are discussed. Conclusions and future remarks finalize the article.
Development of a Checklist and Analysis on the Case of Design Thinking Activity in University

The purposes of this study were to develop a checklist, which guides the process of design thinking for the students, and to analyze the case of A university, so as to facilitate the non-subject educational activities in university.&lt;/br/&gt;From the results of the literature review, we derived the preliminary items included in the checklist. After verifying the content validity, a Delphi survey by 15 experts was conducted. Corresponding to the five steps of design thinking, a total of 22 items, including 3 items for ‘empathize’, 6 items for ‘define’, 5 items for ‘idea’, 3 items for ‘prototype’, and 5 items for ‘test’ were finally selected for the students’ checklist.&lt;br/&gt;With regard to their problem solving abilities, the design thinking based program might be beneficial for enhancing students’ competence when it comes to problem clarification and plan execution. Furthermore, the developed design thinking checklists, along with the presented case of the design thinking program, are expected to encourage students to more actively apply the design thinking process to their non-subject educational activities.
TECHNICAL MODELING IN DESIGN AND TECHNOLOGICAL ACTIVITY OF SCHOOLCHILDREN

The article considers the possibilities of purposeful influence on the development of schoolchildren creative potential, their involvement in various activities, self practical work in the field of education “Technology”. They can be effectively implemented through the use of modern pedagogical and technological systems, which are based on the principles of design and technological activities, which provides simultaneous development, training and education of students by involving them in active creative work.&#x0D; It is established that the creative process is characterized by the unity of theoretical knowledge and practical experience and the theory is tested in practice, but in practice the issues appear that require a theoretical solution. For technical creative activity the theoretical preparation of schoolboys consists in providing them with a system of knowledge, methods and ways of designing, receptions of the decision of creative problems and technical knowledge. Experience of practical work appears on the basis of the formation of skills and abilities to work with instruments and perform technological operations.&#x0D; It is described that the continuity of this process in creative activity is of great importance, and the episodic nature is ineffective. The education of constant interest in creative work is carried out continuously, systematically during all years of schooling by creative activities. The effectiveness of creative work has a significant impact on the education of creative personality traits. The result is that the student has more opportunities to implement in their intellectual and practical activities the necessary corrections to the concepts and images of technical objects and processes, which result in fundamentally new solutions. &#x0D; It is shown that project-technological activity as the main didactic unit promotes: formation of skills, creative system thinking, technological culture and ethics, strengthening of imagination. It is a powerful stimulus for the emergence of new creative ideas in students, the search for alternative solutions, their analysis and synthesis, and in the future provides a basis for innovative thinking and action. Implementation of personality-oriented paradigm of labor training of students; ensures the unity of education and upbringing of students, preparing them for professional self-determination, the desire for self-education.&#x0D; It is proved that the lessons of technical work open wide opportunities for improving the creativity of students. The use of methods of teaching students technical modeling in the process of organizing design and technological activities in the lessons of labor training contributes to this. It is important for the teacher to make the most of all opportunities for the formation of a technologically advanced personality, the level of development of which is not possible without proper creative abilities obtained in technical modeling in the process of organizing design and technological activities of students.
Annotation for Object Detection

Computer vision is an important, new area of research. It requires large datasets for training; such datasets are often inaccessible due to financial reasons or do not exist for specialized needs. This paper discusses an annotation tool designed for convenient data annotation. The aim is to enable easy manual annotation for image. Also, annotation accuracy has been compared under a case study for detection by humans and detection by YOLO9000.
Secure Crime Case Summary in Police Station Using Block Chain Technology

The data owner may adapt attribute-based encryption to encrypt the stored information for attaining access control and keeping data secure, in the cloud. As a solution to this, an encryption-based algorithm with delegation can be used. Therefore, AES Rijndael algorithm is adapted to encrypt and decrypt the data using the same key with the digitization of traditional records, police stations encounter difficult problems, such as crime case summary storage and access. Managing department, spends considerable time querying the required data when accessing crime case summary. On this basis, this study proposes a case summary sharing scheme which uses ciphertext-based encryption to ensure data confidentiality and access control of crime case summary The officer may encrypt the stored information for attaining access control and keeping data secure. Therefore, AES Rijndael algorithm is used for encryption. This algorithm ensures security of information and enables Privacy.
Rien que des mots: Counteracting Homophobic Speech in European and U.S. Law

Adopting a comparative perspective, this article examines legal means and practices of challenging homophobic speech in European and U.S. law. This exercise revolves around the study of major cases concerning homophobic speech from the law of the European Court of Human Rights (and broader legal framework within the Council of Europe (the CoE), the Court of Justice of the European Union, as well as the United States Supreme Court (along with a broader scrutiny of U.S. law in comparative perspective with European CoE and EU law) in recent years. The article concludes that the concepts of (1) hate speech (in constitutional, administrative and criminal settings) (2) direct discrimination and (3) harassment (in labour and anti-discrimination law) are central in the strategic litigation of LGBT organizations seeking to redress the climate of homophobia via various legal avenues in both Europe and the U.S. While in the settings of European law, all three concepts – depending on the context – can benefit victims of homophobia in their judicial redress, U.S. law offers coherent protection in its employment law framework, even though this remains in need of further strengthening.
Bayesian Dynamic Feature Partitioning in High-Dimensional Regression With Big Data

Bayesian computation of high-dimensional linear regression models using Markov chain Monte Carlo (MCMC) or its variants can be extremely slow or completely prohibitive since these methods perform costly computations at each iteration of the sampling chain. Furthermore, this computational cost cannot usually be efficiently divided across a parallel architecture. These problems are aggravated if the data size is large or data arrive sequentially over time (streaming or online settings). This article proposes a novel dynamic feature partitioned regression (DFP) for efficient online inference for high-dimensional linear regressions with large or streaming data. DFP constructs a pseudo posterior density of the parameters at every time point, and quickly updates the pseudo posterior when a new block of data (data shard) arrives. DFP updates the pseudo posterior at every time point suitably and partitions the set of parameters to exploit parallelization for efficient posterior computation. The proposed approach is applied to high-dimensional linear regression models with Gaussian scale mixture priors and spike-and-slab priors on large parameter spaces, along with large data, and is found to yield state-of-the-art inferential performance. The algorithm enjoys theoretical support with pseudoposterior densities over time being arbitrarily close to the full posterior as the data size grows, as shown in the supplementary material. Supplementary material also contains details of the DFP algorithm applied to different priors. Package to implement DFP is available in https://github.com/Rene-Gutierrez/DynParRegReg. The dataset is available in https://github.com/Rene-Gutierrez/DynParRegReg\_Implementation.