Reviewerly/exHarmony huggingface dataset:
A Productivity Optimising Model for Improving Software Effort Estimation

The estimation of software development effort is a critical task for the effective management of any software industry. Despite the fact that it has been under development for a long time - along with many contributions from many authors seeking to improve the accuracy of software effort estimation, it is still of great interest to many researchers. This study proposed an improved effort estimation model, named the Productivity Optimising Model. This model was designed, based on the Function Points Measurement method and the Multiple Linear Regression model. The Multiple Linear Regression model was built based on the research of historical datasets in order to provide an estimation model so that one can determine the optimising productivity, and then it is easy to calculate the effort. The effort result of this model was compared to the others that were calculated by the Mean Value of Productivity of the tested dataset, and the Capers Jones method. It proved that proposed method gives better accuracy results than the other models.
A Scenario-based Modeling Method for Crossover Services

With the continuous emergence of large companies, businesses in different domains have been amalgamated, which leads to a prevalence of crossover services. Different from service choreographies and collaborations, the crossover services involve multiple domains and are highly integrated in terms of processes, entities, abilities, and resources. In a crossover service, the elements of independent service are disrupted and reorganized, which leads to the increase of the complexity of traditional modeling methods. This new form of service brings challenges to business analysis and modeling, such as semantic ambiguity, business coupling, and test explosion. In this work, a crossover service-oriented modeling method, namely CoSM, is proposed to help with the construction, reuse, and analysis of crossover services. CoSM is designed based on scenarios, which can be used to slice the processes and route to each other through the triggers. Besides, a prototype platform is built and concepts and processes can be constructed through that. We verify CoSM in a real case and design experiments to compare with pertinent models. Experimental results show that our model can express the same process with fewer connectors.
Weighted Cluster-Range Loss and Criticality-Enhancement Loss for Speaker Recognition

While traditional i-vector based methods are popular in the field of speaker recognition, deep learning has recently found more and more applications to the end-to-end models due to its attractive performance. One effective practice is the integration of attention mechanism into the Convolution Neural Networks (CNNs). In this work, a light-weight dual-path attention block is proposed by combining the self-attention and Convolutional Block Attention Module (CBAM), which helps to capture more multi-source features with neglectable extra time expense. Additionally, a Weighted Cluster-Range Loss (WCRL) is proposed to enhance the identification performance of Cluster-Range Loss (CRL) on indecisive samples. Besides, to address the low efficiency in the initial training stage of CRL, a novel Criticality-Enhancement Loss (CEL) is also presented. Both of the proposed loss functions could significantly promote the training efficiency and globally improve the recognition performance. Experimental results are presented to show the effectiveness of the proposed scheme, which achieves a competitive top-1 accuracy of 92.0%, top-5 accuracy of 97.6%, and Equal Error Rate (EER) of 3.5% on the VoxCeleb1 dataset.
Regulating distance to the screen while engaging in difficult tasks

Regulation of distance to the screen (i.e., head-to-screen distance, fluctuation of head-to-screen distance) has been proved to reflect the cognitive engagement of the reader. However, it is still not clear (a) whether regulation of distance to the screen can be a potential parameter to infer high cognitive load and (b) whether it can predict the upcoming answer accuracy. Configuring tablets or other learning devices in a way that distance to the screen can be analyzed by the learning software is in close reach. The software might use the measure as a person-specific indicator of need for extra scaffolding. In order to better gauge this potential, we analyzed eye-tracking data of children (N = 144, Mage = 13 years, SD = 3.2 years) engaging in multimedia learning, as distance to the screen is estimated as a by-product of eye tracking. Children were told to maintain a still seated posture while reading and answering questions at three difficulty levels (i.e., easy vs. medium vs. difficult). Results yielded that task difficulty influences how well the distance to the screen can be regulated, supporting that regulation of distance to the screen is a promising measure. Closer head-to-screen distance and larger fluctuation of head-to-screen distance can reflect that participants are engaging in a challenging task. Only large fluctuation of head-to-screen distance can predict the future incorrect answers. The link between distance to the screen and processing of cognitive task can obtrusively embody reader’s cognitive states during system usage, which can support adaptive learning and testing.
Satellite image inpainting with deep generative adversarial neural networks

This work addresses the problem of recovering lost or damaged satellite image pixels (gaps) caused by sensor processing errors or by natural phenomena like cloud presence. Such errors decrease our ability to monitor regions of interest and significantly increase the average revisit time for all satellites. This paper presents a novel neural system based on conditional deep generative adversarial networks (cGAN) optimized to fill satellite imagery gaps using surrounding pixel values and static high-resolution visual priors. Experimental results show that the proposed system outperforms traditional and neural network baselines. It achieves a normalized least absolute deviations error of ( &amp;amp; decrease in error compared with the two baselines) and a mean squared error loss of ( &amp;amp; decrease in error) over the test set. The model can be deployed within a remote sensing data pipeline to reconstruct missing pixel measurements for near-real-time monitoring and inference purposes, thus empowering policymakers and users to make environmentally informed decisions.
How to Develop a Risk Prediction Smartphone App

Purpose. Powered by big data, predictive models provide individualized risk stratification to inform clinical decision-making and mitigate long-term morbidity. We describe how to transform a large institutional dataset into a real-time, interactive clinical decision support mobile user interface for risk prediction. Methods. A clinical decision point ideal for risk stratification and modification was identified. Demographics, medical comorbidities, and operative characteristics were abstracted from the electronic medical record (EMR) using ICD-9 codes. Surgery-specific predictive models were generated using regression modeling and corroborated with internal validation. A clinical support interface was designed in partnership with an app developer, followed by subsequent beta testing and clinical implementation of the final tool. Results. Individual, specialty-specific, and preoperatively actionable models incorporating clustered procedural codes were created. Using longitudinal inpatient, outpatient, and office-based data from a large multicenter health system, all patient and operative variables were weighted according to ß-coefficients. The individual risk model parameters were incorporated into specialty-specific modules and implemented into an accessible iOS/Android compatible mobile application. Conclusions. As proof of concept, we provide a framework for developing a clinical decision support mobile user interface, through the use of clinical and administrative longitudinal data. Point-of-care applications, particularly ones designed with implementation and actionability in mind, have the potential to aid clinicians in identifying and optimizing risk factors that impact the outcome of interest's occurrence, thereby enabling clinicians to take targeted risk-reduction actions. In addition, such applications may help facilitate counseling, informed consent, and shared decision-making, leading to improved patient-centered care.
Assessment of Heavy Metal Contamination in Roadside Soils along Irbid-Amman Highway, Jordan by ICP-OES

In this study, the concentrations of selected heavy metals (Al, Cd, Cr, Cu, Mn, Pb, Co, Fe, Ni, V and Zn) in roadside soil samples were determined by Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) after microwave digestion. A total of sixty roadside soil samples were collected during July 2018 from seventeen sampling sites (5 km apart) from both sides along Irbid-Amman highway, Jordan. The average concentrations (±SD) of the investigated metals in the analyzed soil samples were found to be 18400 (± 11200), 6.0 (± 4.7), 132 (± 47), 49 (± 26), 695 (± 310), 96 (± 53), 78 (± 32), 31800 (± 12600), 116 (± 67), 141 (± 74) and 129 (± 112) µg/g for Al, Cd, Cr, Cu, Mn, Pb, Co, Fe, Ni, V and Zn, respectively. With exception of Mn, the enrichment factors for the investigated metals in roadside soils were found to be more than 10, indicating anthropogenic sources such as automobile traffic. In the absence of any industrial activities in the sampling sites, the high concentrations of the investigated metals suggest that automobile emissions are the major source of roadside soil pollution. The results obtained in this study showed that metal concentrations in the analyzed soil samples are strongly influenced by the wind direction and traffic density in the investigated area. The higher metal concentrations on the west side of the road were due to the easterly prevailing wind in the studied area. As expected, the concentrations of heavy metals decreased with increasing distance from the edge of the road. The results obtained in this work were compared with the literature values.
The Adaptation of Gray Wolf Optimizer to Data Clustering

Data Clustering stands for a group of methods classifying patterns into groups and retrieving similarities or dissimilarities of a collection of objects. Clustering is used for pattern recognition, machine learning, etc. One of the approaches to clustering is optimization. The aim of the optimization is finding the best solution in the search space of a problem as much as possible. Many optimization methods were modified to solve clustering problems in literature. Gray Wolf Optimizer (GWO) is one of the nature-inspired meta-heuristic algorithms simulating the hunting of gray wolves. GWO has applied to solve several optimization issues in different fields. In this study, GWO was examined in the case of data clustering. GWO was modified to get better clustering results and applied to well-known benchmark data sets. The performance of GWO was compared to the other algorithms used as clustering. The results show that GWO can be used for data clustering successfully.
SMART Technologies as the Innovative Way of Development and the Answer to Challenges of Modern Time

One of the promising tasks in education lies in reforming it into the knowledge economy, integrating and creating a market oriented towards results of intellectual activity. On the other hand, globalization process requires transition of the educational environment to the format of information, communication and digital space. Primarily these areas are set as the nodal tasks, which directs authors of this article to the comparative analysis of educational system making it possible to identify general and particular, positive or negative consequences and characteristics of digitalization in the higher education system. In accordance with current trends and processes of globalization and informatization, the authors are considering the prospects for interaction and mutual influence of Smart technologies used in building a future educational model in the higher education area. Technological innovations today are called upon not only to qualitatively change methods, forms and technologies in the education content, but rather to train personnel capable of operating in the new information and telecommunication community. Therefore, studying the influence and the capabilities of modern digital technologies that meet needs of society, on the one hand, and, on the other hand, contribute to formation of professional competencies in students, which requires major alterations in the learning process, changes in its state towards flexibility, adaptation, personalization, continuity, multidimensionality and systematicity, becomes of specific relevance for authors of this article.
Refinement Orders for Quantitative Information Flow and Differential Privacy

Quantitative Information Flow (QIF) and Differential Privacy (DP) are both concerned with the protection of sensitive information, but they are rather different approaches. In particular, QIF considers the expected probability of a successful attack, while DP (in both its standard and local versions) is a max-case measure, in the sense that it is compromised by the existence of a possible attack, regardless of its probability. Comparing systems is a fundamental task in these areas: one wishes to guarantee that replacing a system A by a system B is a safe operation that is the privacy of B is no worse than that of A. In QIF, a refinement order provides strong such guarantees, while, in DP, mechanisms are typically compared w.r.t. the privacy parameter ε in their definition. In this paper, we explore a variety of refinement orders, inspired by the one of QIF, providing precise guarantees for max-case leakage. We study simple structural ways of characterising them, the relation between them, efficient methods for verifying them and their lattice properties. Moreover, we apply these orders in the task of comparing DP mechanisms, raising the question of whether the order based on ε provides strong privacy guarantees. We show that, while it is often the case for mechanisms of the same “family” (geometric, randomised response, etc.), it rarely holds across different families.
Reconsidering the Cybersecurity Framework in the Road Transportation Domain

The Automotive Industry has advanced significantly and this process is constantly continuing.Cars are controlled by hundreds of electrical units, which affects directly numberless safety and security related risk factors, especially considering incidents realized in the cyberspace.Due to the increasing number of connected cars, new testing facilities and methods need to be established, to support the solutions, for these novel challenges.For this reason, the Hungarian government has founded the Zalaegerszeg test track, and its special purpose is to provide space for the validation of future vehicles.The overview focused on the presently applied validation methods and processes has highlighted, that cybersecurity related testing methodsfrom a scientific point of vieware only slightly detailed in the sector of Automobile Industry.In a sector specific term, the lack of a holistic approach, may result a relevant barrier, in the future, for providing a socially tolerable level of cybersecurity.Accordingly, this paper aims to introduce a comprehensive cybersecurity reference model, to provide a solid basis for describing attack patterns and characterizing malicious intervention profiles.
Media literacy in the system of the secondary education in Russia

This article is devoted to the issue of media education in Russian school. The main attention is paid to the concepts of media education and media literacy. The authors have studied the issue of media literacy in the education system of Russia. They have discovered that today an insufficient attention is paid to the problem of media literacy and media education at the school level. The authors argue that the educational standards and the school textbooks do not take into account the need to teach methods of protection against manipulation in the media.
Innovative approaches to the formation of future specialists’ professional competencies in summer educational practices course

The article examines the issues of pedagogical science through a modern approach, the search for new forms and practices of teaching. The current issue of forming future specialists’ professional competencies, namely, in the field of art and additional art education, is shown in the article through the organization of the educational environment during summer practices and schools. The task is to trace how much the effectiveness of future specialists depends on the construction of educational practice programs using interactive and interdisciplinary forms of training. As successful factors in this process of training future specialists in the field of art and additional art education, such forms as a master class and a web quest are considered, as well as the principle of a harmonious set of theoretical and practical materials used, including the process of independent work of a student. The article deals with the issue of interactive communication through a master class, which also develops the ability of professional communication of students. The use of the teacher-student dialogue in an extracurricular format, the use of interactive, multimedia and visual technologies, as well as setting tasks for independent work contributes to more effective development of professional skills and their application in the real conditions of future career guidance.
Analisis Unsur Intrinsik Puisi Pada Buku Siswa Bahasa Indonesia Dikelas 4 Sekolah Dasar

This study aims to describe the intrinsic element of the fourth grade Indonesian language poetry learning material in theme 6 used by elementary schools. This type of research is a qualitative research with a qualitative descriptive approach. This research was conducted in the even semester of the academic year 2018/2019. The place of the research was SDN Cisarua 1, located at JL.Tipar Gg. Meralaya II No. 142 City of Sukabumi. This research uses quota sampling technique with the object book theme 6 My dreams are grade IV elementary school. Data collection techniques used were interview and documentation techniques which the implementers used interview and documentation guidelines to analyze the intrinsic elements of poetry material in the theme book. The results of this study are poetry learning in class IV Indonesian subjects contained in Theme 6 regarding my goals. Poetry material is explained about the meaning of poetry, the types of poetry, and the intrinsic elements of poetry. There are 7 intrinsic elements in poetry, namely theme, sound, rhyme, diction, imagination, cooperation, and rhythm.
Digital Image Steganography Scheme Based on DWT and SVD

In this work, a grayscale image steganography scheme is proposed using a discrete wavelet transform (DWT) and singular value decomposition (SVD). In this scheme, 2-level DWT is applied to a cover image to obtain the high frequency band HL2 which is utilized to embed a secret grayscale image based on the SVD technique. The robustness and the imperceptibility of the proposed steganography algorithm are controlled by a scaling factor for obtaining an acceptable trade-off between them. Peak signal to noise ratio (PSNR) and Structural Similarity Index Measure (SSIM) are used for assessing the efficiency of the proposed approach. Experimental results demonstrate that the proposed scheme still holds its validity under different known attacks such as noise addition, filtering, cropping and JPEG compression
The search for science and technology verses in Qur’an and hadith

Currently, the Vector Space Model algorithm has been widely implemented for the document search feature because of its reliability in retrieving information. One of them in the search for verses of the Qur'an based on the translation. However, if the phrase or word used is different (even though it has one meaning) with the word in the document in the database, the system will not display the verse. As we know that the Qur'an has a very deep meaning, so an interpretation of the verse is needed. Therefore, this research focuses on implementing the Vector Space Model (VSM) algorithm for searching verses and hadiths in science and technology by using the discussion parameters of these verses or hadiths. The test results obtained with 20 keyword samples using metric recall were 81% with an average time of 2.24 seconds.
The Analysis of Mathematical Skills Used in the Software Industry

Mathematical skills can provide a scientific basis for software engineering (SE) activities by helping practitioners comprehend the unambiguous logic, which is critical for both abstract thinking and problem solving besides development of any applications in the software industry. Many practitioners in different roles take different mathematics courses at the university education; however there are various perceptions about the necessity and the weight of these courses. \textit{"What are their usage in the software industry?}" or \textit{"do we need such a (heavy) mathematical curriculum at the university?}" are frequently asked questions for software teams. To better understand different opinions, we utilized the results of our survey, which investigated the skills gaps in the software industry. 628 Turkey-educated practitioners, whose undergraduate degree was completed in Turkey, working in 13 countries responded the survey. In this paper, we examined different perceptions (1) about the usage of the mathematics courses in the software industry; and (2) about current weight of these courses in the curriculum. The findings shed light on the perceptions about mathematical skills in the industry by presenting various cross-factor analysis.
NLP based Deep Learning Approach for Plagiarism Detection

Plagiarism detection represents an application domain for the NLP research area, which has not been investigated too much by researchers in the context of lately developed attention mechanism and sentence transformers. In this paper, we present a plagiarism detection approach which uses state-of-the-art deep learning techniques in order to provide more accurate results than classical plagiarism detection techniques. This approach goes beyond classical word searching and matching, which is time-consuming and can be easily cheated because it uses attention mechanisms and aims for text encoding and contextualization. In order to get proper insight regarding the system, we investigate three approaches in order to be sure that the results are relevant and well-validated. The experimental results show that the systems that use BERT pre-trained model offers the best results and outperforms GloVe and RoBERTa
Voice as a Mouse Click: Usability and Effectiveness of Simplified Hands-Free Gaze-Voice Selection

Voice- and gaze-based hands-free input are increasingly used in human-machine interaction. Attempts to combine them into a hybrid technology typically employ the voice channel as an information-rich channel. Voice seems to be “overqualified” to serve simply as a substitute of a computer mouse click, to confirm selections made by gaze. It could be expected that the user would feel discomfort if they had to frequently make “clicks” using their voice, or easily get bored, which also could lead to low performance. To test this, we asked 23 healthy participants to select moving objects with smooth pursuit eye movements. Manual confirmation of selection was faster and rated as more convenient than voice-based confirmation. However, the difference was not high, especially when voice was used to pronounce objects’ numbers (speech recognition was not applied): Score of convenience (M ± SD) was 9.2 ± 1.1 for manual and 8.0 ± 2.1 for voice confirmation, and time spent per object was 1269 ± 265 ms and 1626 ± 331 ms, respectively. We conclude that “voice-as-click” can be used to confirm selection in gaze-based interaction with computers as a substitute for the computer mouse click when manual confirmation cannot be used.