Reviewerly/exHarmony huggingface dataset:
Combining Two Modelling Approaches: GQM and KAOS in an Open Source Project

GQM is a software metrics method that produces meaningful and appropriate measurement metrics based on the specific goals of an organisation. KAOS goal modelling is a software engineering approach that allows to identify high level goals of an organisation, refine and expand them into lower-level more concrete goals and assign them to specific system agents. The two approaches can be combined, such that the goal modelling is used to derive specific goals rooted in the organisational context, policies and strategies of an organisation. The GQM, in turn, utilises the derived specific goals to produce appropriate and meaningful metrics that can be used to obtain valuable insights and track the state and progress of the formulated goals. Moreover, both models can be presented within a single diagram, thus providing an expressive and concise overview of the systems goals, actors and measurements. The suggested combination is then applied in the context of an open source project, followed by the discussion on potential benefits and drawbacks of using the two approaches.
Estimating Time to Progression of Chronic Obstructive Pulmonary Disease With Tolerance

We defined tolerance range as the distance of observing similar disease conditions or functional status from the upper to the lower boundaries of a specified time interval. A tolerance range was identified for linear regression and support vector machines to optimize the improvement rate (defined as IR) on accuracy in predicting mortality risk in patients with chronic obstructive pulmonary disease using clinical notes. The corpus includes pulmonary, cardiology, and radiology reports of 15,500 patients who died between 2011 and 2017. Their performance was compared against a long short-term memory recurrent neural network. The results demonstrate an overall improvement by those basic machine learning approaches after considering an optimal tolerance range: the average IR of linear regression was 90.1% and the maximum IR of support vector machines was 66.2%. There was a similitude between the time segments produced by our tolerance algorithms and those produced by the long short-term memory.
The impact of income diversification on liquidity creation and financial performance of Vietnamese Commercial Banks

This research is conducted to investigate the impact of income diversification on bank liquidity creation and the financial performance of Vietnamese commercial banks from 2007 to 2017.Data were collected from 21 Vietnamese commercial banks.Panel OLS with fixed effects and GMM estimations were employed to process data.The results show that diversification had a negative impact on both bank liquidity creation and bank profitability, and thus support the view that Vietnamese commercial banks should remain focusing on their traditional lines of business rather than diversifying towards nontraditional activities since this may lead to both a lower level of liquidity creation and profitability.The findings of this study could draw broad inferences for researchers, policy makers, and bank managers towards more focusing strategies to ensure the health of the banking system.
An Efficient Integrity Verification and Authentication Scheme over the Remote Data in the Public Clouds for Mobile Users

The digitalization of the modern world and its applications seem to be integrated more with the mobile phones than with any other communication devices. Since the mobile phones have become ubiquitous with applications for nearly all users, they have become a preferred choice for uploading the sensitive information to the cloud servers. Though the drive for data storage in cloud servers is implicit due to its pay-per-use policies, the manipulation of the data present in the cloud servers by hackers and hardware failure incidents, as happened in Amazon cloud servers in 2011, necessitates the demand for data verification at regular intervals over the data stored in the remote servers. In this line, modern day researchers have proposed many novel schemes for ensuring the remote data integrity, but they suffer from attacks or overheads due to computation and communication. This research paper provides solutions in three dimensions. Firstly, a novel scheme is introduced to verify the integrity of the data stored in the remote cloud servers in the context of mobile users. The second dimension is that of reducing the computational and communication overheads during the auditing process than the previous works. The third dimension securely authenticates the mobile user during the auditing process and the dynamic data operations such as block modification, insertion, and deletion. Moreover, the proposed protocol is provably secure exhibiting soundness, completeness, and data privacy making it an ideal scheme for implementation in the real-world applications.
Inferring sequences produced by elliptic curve generators using Coppersmith's methods

We analyze the security of two number-theoretic pseudo-random generators based on elliptic curves: the elliptic curve linear congruential generator and the elliptic curve power generator. We show that these recursive generators are insecure if sufficiently many bits are output at each iteration (improving notably the prior cryptanalysis of Gutierrez and Ibeas from 2007). We present several theoretical attacks based on Coppersmith's techniques for finding small roots on polynomial equations. Our results confirm that these generators are not appropriate for cryptographic purposes.
Transformational Leadership and Psychological Needs of Employees

Transformational leadership theory is an essential topic in management research. This style of leadership requires a leader's ability to identify and observe the psychological needs and values of followers, as well as to stimulate work motivation. Transformational leadership is characterized by four groups of factors: individualized consideration, intellectual stimulation, inspirational motivation and charisma or idealized influence. The role of a transformational leader is motivational in nature. Within the theory of self-determination, the authors emphasize the importance of the basic psychological need for relatedness with others, the feeling of personal competence and autonomy in performing a given behavior. Recent research has shown that perceived transformational leadership contributes to meeting the basic psychological needs of employees. Satisfying each of these needs directly impacts positive outcomes and achievement of organizational goals. The most commonly reported positive outcomes include trust of employees in the organization, positive feedback from managers, belief in one’s own ability to contribute to the organization, recognizing opportunities for progress, feeling of security, job satisfaction and a high level of enthusiasm for work. Transformational leadership facilitates the adoption of common motivational principles in the organization, empowers managers and gains the support of followers in their efforts to build a strong and stable organizational culture.
Digitalization of Ethno Constructivism Based Module for Elementary School Students

This research develops e-module teaching materials by using a digitalized book application based on ethno constructivism and also discerns students' responses to the e-modules. We employed the ADDIE model (Analysis, Design, Develop, Implement, and Evaluate) to create the books. The ADDIE model consists of five steps, namely (1) introduction, (2) planning, (3) development, (4) implementation, and (5) evaluation. The subjects of this study were 21 fifth-grade students of elementary schools 112/I. The re-sults of this study captured that the product was favored by most of the students with percentages of interest (72.73%), motivation (77.27%), and perception (63.6%).
Foreign Language Enjoyment and Anxiety among The Northern Borders University EFL Students: Links to Gender and Majors

This study aims to investigate the differences between the level of academic anxiety (LAA) and the enjoyment of learning English as a foreign language (ELEFL) among the students of Northern Borders University. The data was collected using a survey questionnaire based on 5-likert scales administered to 163(93, male and 70, female) respondents. The analysis was carried out using Statistical Package for Social Science (SPSS) version 25 to determine the independent t-test between the scores of the variables. The statistical analyses revealed that there were no statistically significant differences in the level of academic anxiety and the enjoyment of learning English as a foreign language due to the gender variable. However, there were significant differences in the level of academic anxiety for the humanity colleges due to college variable. On the other hand, there was a significant difference in the level of enjoyment of the foreign language for scientific colleges due to the college variable. In addition to that, the results of this study revealed that there was a negative relationship between academic anxiety and the enjoyment of learning English as a foreign language. Finally, the results suggested that there were other reasons for academic anxiety and level of enjoyment of learning English as a foreign language, which included among others, poor background, lack of interest, and methods of learning English
Formation of Professional Competence of the Future Computer Engineer in a Technical University

The paper considers the relevance of the formation of professional competence of future specialists associated with the features of engineering activities of computer profile.The aim of the study is to analyse of psychological and pedagogical literature and current educational standards, an attempt is made to understand the expected results of training first-and second-year students of engineering study programs, taking into account current trends in the engineering industry.The article attempts to clarify the concepts "professional competence of an engineer".The study demonstrates positive dynamics of development in most indicators.There is a qualitative growth in the following indicators: projective and prognostic skills; organizational skills; information skills; analytical skills and efficiency of knowledge; and the greatest development is obtained during training in the Bachelor's degree.The general conclusion is that the Russian technical university lays a positive dynamic in the development of professional competence.A technical university provides ample opportunities to encourage the development of professional and personal potential student, creating conditions for professional development, reflected in the successful implementation of professional functions, as well as contributing to excellence, by bringing the personal contribution of the engineer, which enriches the experience of the profession in the engineering field.
Improving Recommendation Performance with Clustering and Missing Value Prediction

High percentage of missing values in rating matrix results in low recommendation performance, which includes poor recommendation accuracy and slow recommendation generation. This paper proposes an integrated method with clustering algorithm and missing value prediction in a Collaborative Filtering Recommendation system to address the missing value issue in the rating matrix. The proposed method is validated using the MovieLens dataset. Experimental results show that the proposed method improves recommendation quality and online scalability while reducing recommendation generation time.
Evaluation of Feature Extraction and Recognition for Human Activity using Smartphone based Accelerometer data

Fall is one of the major life threatening problem faced by elder, because falls are dangerous and may result in death sometimes due to lack of help after fall. In the majority of fall events, automatic Ambient Assistive Living provides essential support to avoid major consequences. This may reduce the response time and significantly improves the prognosis of fall detection. This paper presents a fall detection system using Statistical and Higher order statistics features evaluated through Recursive Feature Elimination and classified with J48 and k-Nearest Neighbor algorithms for the tri-axial accelerometer data. The accelerometer data has been used from the popular smartphone based fall detection dataset MobiAct. The proposed system performs better with minimal number of features and depends only on accelerometer data rather state-of-the-art technique depends on more data such as Accelerometer, gyroscope, orientation with more features for better accuracy.
Multilingual Zero-shot Constituency Parsing.

Zero-shot constituency parsing aims to extract parse trees from neural models such as pre-trained language models (PLMs) without further training or the need to train an additional parser. This paper improves upon existing zero-shot parsing paradigms by introducing a novel chart-based parsing method, showing gains in zero-shot parsing performance. Furthermore, we attempt to broaden the range of zero-shot parsing applications by examining languages other than English and by utilizing multilingual models, demonstrating that it is feasible to generate parse tree-like structures for sentences in eight other languages using our method.
TurboDL: Improving the CNN Training on GPU With Fine-Grained Multi-Streaming Scheduling

Graphics Processing Units (GPUs) have evolved as powerful co-processors for the CNN training. Many new features have been introduced into GPUs such as concurrent kernel execution and hyper-Q technology. It is challenging to orchestrate concurrency for CNN (convolutional neural networks) training on GPUs since it may introduce synchronization overhead and poor resource utilization. Unlike previous research which mainly focuses on single layer or coarse-grained optimization, we introduce a critical-path based, asynchronous parallelization mechanism, and propose the optimization technique for the CNN training that takes into account global network architecture and GPU resource usage together. The proposed methods can effectively overlap the synchronization and the computation in different streams. As a result, the training process of CNN is accelerated. We have integrated our methods into Caffe. The experimental results show that the Caffe integrated with our methods can achieve 1.30X performance speedup on average compared with Caffe+cuDNN, and even higher performance speedup can be achieved for deeper, wider, and more complicated networks.
Digital Flipbook Empowerment as A Development Means for History Learning Media

The purpose of this research was to help empower the atmosphere and climate of learning students who had different learning styles with the prior generation by using a digital-based instructional media such as digital flipbook. The research methods used were Research and Development, ADDIE model. Data collection obtained through interviews, questionnaires or questionnaires with direct observation. The data analysis technique used was an Independent sample test of hypothesis testing via SPSS version 26. The objects of the research were 11th grade students of Public High School 1 Dagangan, academic year 2019/2020. The results of the research development of digital flipbook learning media on the effectiveness test stage through post-test of the control class and the experimental class showed the mean difference or the average grade of the experimental class (XI Social Sciences 2) 91.6 and grade control (XI Social Sciences 1) 83.5. Thus, digital flipbook learning media that have been developed have succeeded in gaining a significant increase in students' grades as well as succeeding as delivering information media to students in the classroom.
AUTHENTIC MEDIA RESOURCES AS MEANS OF FORMATION OF INTERCULTURAL COMMUNICATIVE COMPETENCE FOR PROSPECTIVE FRENCH LANGUAGE TEACHERS / LECTURERS

The monograph presents European and regional development trends in innovative scientific researches in pedagogical sciences. The general issues of the history of pedagogy, theory and methodology of teaching, correctional pedagogy, theory and methodology of vocational education and management of education, information and communication technologies in education, etc. are considered. The publication is intended for scholars, teachers, postgraduate students, and students of pedagogical specialties, as well as a wide readership interested in pedagogy. Innovative scientific researches: European development trends and regional aspect. Collective monograph. – 1st ed. – Riga, Latvia : “Baltija Publishing”, 2020. – 492 p.
A Zynq-Based Robotic System for Treatment of Contagious Diseases in Hospital Isolated Environment

The rapid evolution of smart assisted living operations in combination with the blooming of commercial robots calls for the use of robotic based systems. Specifically, certain circumstances such as the handling of critical, contagious virus outbreaks like the recent novel Coronavirus epidemic can be benefited by an assisting mobile robot system controlled remotely, complementing measures like the isolation of patients from medical stuff. Within this context, the robotic-based solution to be employed needs to be easy to deploy, able to manufacture with low cost, and able to operate with ease by non-trained personnel. Also, to address the needs of existing hospitals, traditional or smart ones, as well as the temporary risk management facilities in, for example, quarantined cities, ease of integration in terms of size and infrastructure requirements is a must. In this work, the design and implementation of a robotic chassis bearing an arm manipulator is presented, addressing all these needs efficiently. Special attention has been given to the ease of teleoperation with minimal need for equipment and expertise, utilizing a Leap Motion virtual reality sensor which outweighs Microsoft’s Kinect capabilities. Furthermore, a reconfigurable hardware and software integrated system has been used to control the communication, algorithm processing and motion control utilizing a Xilinx Zynq system on chip (SoC).
Facial landmark detection and tracking with dynamically adaptive matched filters

A reliable method for the detection and tracking of facial landmarks in image sequences is presented. Given a set of prespecified facial landmarks in a reference face image, a bank of composite matched filters is constructed for reliable detection and accurate location of the landmarks in an input image sequence. The filter bank is dynamically adapted to each captured frame by learning from current and past landmark detections and considering geometrical modifications of the landmarks. The detected landmarks are accurately tracked using a kinematic motion model that predicts their coordinates in future frames. The performance of facial landmark detection and tracking obtained with the proposed method is tested by processing real-life image sequences. The obtained results are analyzed and discussed in terms of objective measures.
Cloud Computing Placement Optimization Under Ubiquitous Power Internet of Things Background

With the development of power system and the introduction of the Energy Internet, the implementation of Ubiquitous Power Internet of Things (UPIoT) is necessary for power utilities to meet the demands of Integrated Energy Applications. Massive heterogeneous data from various devices surge into power system via UPIoT, which puts heavy burden on data processing capabilities of power system. Cloud computing is an effective measure to provide big data processing capabilities and the establishment of cloud computing for power system is of great significance. Firstly, the architecture of UPIoT and the cloud computing system based on UPIoT background are analyzed. Considering the characteristics of power system, a distributed cloud computing architecture for power system is proposed. A coordinated placement optimization strategy based on minimum cost and satisfaction of quality of service for the proposed architecture is formulated. Based on a given case, the placement optimization simulations are studied. The simulation results prove that the proposed architecture is cost-efficient and the proposed optimization strategy is effective and efficient.
Generating Images from Arabic Story-Text using Scene Graph

In this paper, we propose a new approach to generate descriptive images from simple Arabic text of stories for children to build a flexible and rich multimedia repository. To make it successful, we need to transform the actors and objects and their semantic relationships from the text in compact and structured entities. We use the scene graph representation that considers objects together with their attributes and relationships. In fact, this representation has been proven to be useful across a variety of vision and language applications. We extend, thereby the notion of image generation to our approach as the task of constructing new scenes based on a set of toolboxes and a set of generated images for common textual descriptions. Our preliminary results show that the image repository is very efficient, saving then huge storage space. Besides, the composed scenes using the generated images could be customized providing a fair understanding of the main actors/objects of the stories as well as a coherent visual layout of the scenes.
Efficiently Testing Digital Convexity and Recognizing Digital Convex Polygons

A set $$S \subset \mathbb {Z}^2$$ of integer points is digital convex if $${{\,\mathrm{conv}\,}}(S) \cap \mathbb {Z}^2 = S$$ , where $${{\,\mathrm{conv}\,}}(S)$$ denotes the convex hull of S. In this paper, we consider the following two problems. The first one is to test whether a given set S of n lattice points is digital convex. If the answer to the first problem is positive, then the second problem is to find a polygon $$P\subset \mathbb {Z}^2$$ with minimum number of edges and whose intersection with the lattice $$P\cap \mathbb {Z}^2$$ is exactly S. We provide linear-time algorithms for these two problems. The algorithm is based on the well-known quickhull algorithm. The time to solve both problems is $$O(n + h \log r) = O(n + n^{1/3} \log r)$$ , where $$h = \min (|{{\,\mathrm{conv}\,}}(S)|, n^{1/3})$$ and r is the diameter of S.
KomNET: Face Image Dataset from Various Media for Face Recognition

KomNet is a face image dataset originated from three media sources which can be used to recognize faces. KomNET contains face images which were collected from three different media sources, i.e. mobile phone camera, digital camera, and media social. The collected face dataset was frontal face image or facing the camera. The face dataset originated from the three media were collected without certain conditions such as lighting, background, haircut, mustache and beard, head cover, glasses, and differences of expression. KomNet dataset were collected from 50 clusters in which each of them consisted of 24 face images. To increase the number of training data, the face images were propagated with augmentation image technique, in which ten augmentations were used such as Rotate, Flip, Gaussian Blur, Gamma Contrast, Sigmoid Contrast, Sharpen, Emboss, Histogram Equalization, Hue and Saturation, Average Blur so the face images became 240 face images per cluster. The author trained the dataset by using CNN-based transfer learning VGGface. KomNET dataset are freely available on https://data.mendeley.com/datasets/hsv83m5zbb/2.