Reviewerly/exHarmony huggingface dataset:
ENHANCEMENT OF ENGINEERING SOFT SKILLS DEVELOPMENT: EU COUNTRIES EXPERIENCE

Recently, the quality of graduates and their ability to meet the needs of employers became widely discussed worldwide. Despite the enormous importance of academic knowledge and cognitive abilities, today employment opportunities are more determined on the basis of the concept of soft skills. In Russia, this problem is still unsolved: the training programs of most universities are based on traditional methods with little or no time devoted to the soft skills development. In Europe, this situation has been relevant for several decades due to the global changes in the economy and, accordingly, an acute shortage of jobs. Therefore, the issues of employment and the quality of competencies and skills of graduates have already been studied a lot. The vast experience of Europe can be borrowed by Russian universities, which are just beginning to move towards the development of soft skills and the awareness of the need for their development within the framework of university disciplines. The article describes terminological variations, analyzes different methods and approaches to training and assessing soft skills, as well as presents the best practices of EU countries on teaching soft skills.
Machine Learning Approaches for Convergence of IoT and Blockchain

Abstract Blockchain plays a significant role in providing security during data handling operations and defines how trusted transactions can be carried out addressing the solution for internet facing vulnerability problems. Blockchain solves the security fault line among AI and IoT, where most of the IoT devices are connected to each other through public networks. This special issue aims to publish a collection of high quality research articles in the area of blockchain, IoT and Machine Learning.
Network Traffic Classification Using Deep Learning

The large amount of network traffic generated by Internet applications brings great challenges to Internet security. In order to facilitate network management and realize automatic classification of network traffic, this paper proposes a network traffic classification model NTCNET based on CNNs. Use open data set to do simulation verification experiment, then compare the test results with a variety of traditional classification methods. The experimental results shows that the constructed traffic classification model NTCNET has better precision, robustness and accuracy, with an accuracy of 99.66%.
Students Impression About Using Mobile Phones in Classroom

Mobile phones are widely used by university students and there is a controversial topic whether students should be able to use them freely during lectures. A survey was distributed seeking student opinions on using mobile phones in classroom. We used two-phase sampling method to reduce the nonresponse bias. There were 392 valid responses. To test the effect of different factors on students’ opinions a number of statistical techniques were utilized. The results showed Qatar University students marginally favored using mobile in classroom but using mobile phone distracted some students who are less in favor of using phone during lectures. The proportion of students who believe that student should decide to use or not use mobile in the classes is 0.635. Apparently, students expected implementation of a policy that controls the use of mobile phones in class. Recommendations include a blanket policy for the University on the use of mobile phones during class, mobile phone breaks given by lecturers during class, utilizing mobile phone as an education tool, with social media distractions blocked through the university system.
Psychological aspects of digitalization of education

The article substantiates the relevance of digitalization of education. The economic, technological and psychological reasons for this process in modern society are given in the article. The main types of training using digital technologies are considered. One of the ways to digitalize education, which is gamification, is presented in the article in detail.
APPLICATION OF THE MOODLE APPLICATION AS ONE OF THE DISTANCE LEARNING MODELS IN THE PANDEMIC PERIOD

Education is a factor that determines human welfare so that it can be used as an indicator of the progress of a nation and state. The higher the quality of education of a nation and country, it is certain that the more developed and prosperous the nation and country will be. Educators are expected to be able to change the old paradigm in teaching, namely to convey as many lessons as possible with a new paradigm that emphasizes efforts to help students to be better able to understand, comprehend, or master concepts to solve a problem. The model uses a moodle application that offers a pleasant learning atmosphere, so students are invited to practice growing curiosity, responsibility, honesty and activeness of students. Moodle is a software package produced for internet-based learning activities and websites that use social constructionist pedagogy principles. Moodle is one of the applications of the concepts and mechanisms of teaching and learning that utilize information technology, which is known as the concept of electronic learning or e-learning. Moodle application is very good and interactive in online learning, thus making students not bored in distance learning during the pandemic.
Near-optimal spontaneous photon sources on a silicon quantum photonic chip

We develop integrated spontaneous photon sources in silicon photonics simultaneously satisfying all requirements for large-scale photonic quantum computing: 0:9904 ± 0:0006 spectral purity, 0:985 ± 0:002 indistinguishability, &gt; 90% heralding efficiency, and 0:96 ± 0:02 on-chip heralded quantum interference visibility.
Robust Visual Tracking Based on Adaptive Multi-Feature Fusion Using the Tracking Reliability Criterion

Multi-resolution feature fusion DCF (Discriminative Correlation Filter) methods have significantly advanced the object tracking performance. However, careless choice and fusion of sample features make the algorithm susceptible to interference, leading to tracking failure. Some trackers embed the re-detection module to remedy tracking failures, yet distinguishing ability and stability of the sample features are scarcely considered when training the detector, resulting in low effectiveness detection. Firstly, this paper proposes a criterion of feature tracking reliability and conduct a novel feature adaptive fusion framework. The feature tracking reliability criterion is proposed to evaluate the robustness and distinguishing ability of the sample features. Secondly, a re-detection module is proposed to further avoid tracking failures and increase the accuracy of target re-detection. The re-detection module consists of multiple SVM detectors trained by different sample features. When the tracking fails, the SVM detector trained by the most reliable sample feature will be activated to recover the target and adjust the target position. Finally, comparison experiments on OTB2015 and UAV123 databases demonstrate the accuracy and robustness of the proposed method.
Model selection and local geometry

We consider problems in model selection caused by the geometry of models close to their points of intersection.In some cases-including common classes of causal or graphical models, as well as time series models-distinct models may nevertheless have identical tangent spaces.This has two immediate consequences: first, in order to obtain constant power to reject one model in favour of another we need local alternative hypotheses that decrease to the null at a slower rate than the usual parametric n -1/2 (typically we will require n -1/4 or slower); in other words, to distinguish between the models we need large effect sizes or very large sample sizes.Second, we show that under even weaker conditions on their tangent cones, models in these classes cannot be made simultaneously convex by a reparameterization.This shows that Bayesian network models, amongst others, cannot be learned directly with a convex method similar to the graphical lasso.However, we are able to use our results to suggest methods for model selection that learn the tangent space directly, rather than the model itself.In particular, we give a generic algorithm for learning Bayesian network models.
Street-view Panoramic Video Synthesis from a Single Satellite Image.

We present a novel method for synthesizing both temporally and geometrically consistent street-view panoramic video from a given single satellite image and camera trajectory. Existing cross-view synthesis approaches focus more on images, while video synthesis in such a case has not yet received enough attention. Single image synthesis approaches are not well suited for video synthesis since they lack temporal consistency which is a crucial property of videos. To this end, our approach explicitly creates a 3D point cloud representation of the scene and maintains dense 3D-2D correspondences across frames that reflect the geometric scene configuration inferred from the satellite view. We implement a cascaded network architecture with two hourglass modules for successive coarse and fine generation for colorizing the point cloud from the semantics and per-class latent vectors. By leveraging computed correspondences, the produced street-view video frames adhere to the 3D geometric scene structure and maintain temporal consistency. Qualitative and quantitative experiments demonstrate superior results compared to other state-of-the-art cross-view synthesis approaches that either lack temporal or geometric consistency. To the best of our knowledge, our work is the first work to synthesize cross-view images to video.
The correlations among English language exposure, learning motivation, and reading comprehension achievement of high school students in Palembang

Language exposure and learning motivation are two of the most essential aspects in learning any language. In English language acquisition, the development of these two components facilitates the development of other language skills, such as reading comprehension. In turn, reading comprehension is one of the significant indicators of fluency in second or foreign language acquisition. If a correlation can be determined between either language exposure or learning motivation and the development of reading comprehension, educators and specialists can focus their efforts on increasing these predictor variables in curriculum development to further reading comprehension and, ultimately, fluency. This study focused on the reading comprehension of senior high school students in Palembang, Indonesia and sought to find out whether or not there was any significant correlation between students’ English language exposure and reading comprehension, students’ learning motivation and reading comprehension, and among the predictor variables (English language exposure, learning motivation) and the criterion variable (reading comprehension). The samples of this study were 210 eleventh graders in SMA Unggulan, a high-performing senior high school in Palembang. The data were analyzed by using correlational and regression analyses. The findings showed significant positive correlations among English language exposure, learning motivation, and reading comprehension. The contribution of English language exposure to reading comprehension was 41.8%, while the contribution of learning motivation to reading comprehension was 51.7%. Moreover, the contribution of predictor variables to the criterion variable was 58.9%.
Repercusión psicológica en niños, adolescentes y la familia relacionada con el aislamiento social por la COVID-19

Background : research on the impact that preventive social isolation due to COVID-19 will have on children and adolescents is still insufficient. Objective : to characterize aspects of the psychological impact of social isolation due to COVID-19 on children, adolescents and family, from March through June 2020, at the “Jimmy Hirzel” Polyclinic of Bayamo, Granma. Methods : a cross-sectional descriptive and observational study was carried out with a sample of 47 children and adolescents, as well as their parents, all belonging to the aforementioned health district. They were interviewed and the family was given a coping interview and the FF-SIL test. Results : the highest psychological repercussion was found in children from 5 to 9 years old (48,8 %), without significant differences regarding sex; with a history of psychiatric disorders and follow-up by the specialty, with a predominance of attention deficit disorder and neurotic disorders. Anxiety (12,7 %) was the most frequent psychological manifestation at one month, while at three months were motor and verbal hyperactivity (48,8 %), fear of death (46,8 %) and somatization anxiety (44,6 %). Regarding the family, the result was related to alterations in its dynamics, with a predominance of manifestations of anxiety and fear (both with 57,4 %) and depression (38,2 %), especially in mothers. Conclusions : aspects of the psychological impact of social isolation due to COVID-19 on children, adolescents and the family were characterized.
AN OVERVİEW OF SOUTH KOREA’S K-12 EDUCATİON DURİNG COVID-19 PANDEMİC

Although the human history witnessed many previous epidemic diseases, the world was still unprepared for this COVID-19 pandemic. During this pandemic, in parallel to the suggestions of Korea Center for Disease Control unit, South Korean government initially delayed schools’ start date and then began the new semester online. The government has decided starting offline education gradually by mid-May. While the government has been fighting against COVID-19, it has also been supporting teachers, students, school managers and families for online lectures. As being a new mode of instruction, sudden move to online lectures have been offering its own challenges. Therefore, the researchers implemented qualitative content analysis method in cooperation with archival research technique to portray K-12 education in South Korea during COVID-19 times from initial online education practices to reopening schools phases. The analysis results were grouped under five categories; (i) emergency policies employed, (ii) government guidelines including instructional strategies and student evaluation methods for teachers to implement online learning, (iii) hardware and software preparation for online learning, (iv) teachers’ competence enhancement for online learning, and (v) efforts to return to offline education.
Cohesion and coherence in the final project abstracts written by undergraduate students

Writing an abstract was not different from writing other kinds of text. As a part of the final project, an abstract should be written clearly, cohesively and coherently. This study aimed to examine cohesion and coherence of the students’ final project abstracts. It also focused to reveal the factors which influenced the quality of the students’ final project abstracts. This study concerned with text analysis. The investigation approach of this study used written discourse analysis. The data of this study were thirty abstracts which were taken from final project abstracts of English Department undergraduate students of Universitas Negeri Semarang in the academic year of 2017-2018. The findings showed that the students did not utilize the five types of cohesive devices to build cohesion in their abstracts. However, it was sufficient to utilize at least three types of cohesive devices to make the abstracts cohesive. Based on Theme and Rheme theory by Halliday, the abstracts were written coherently because they applied the use of cohesive devices. This research also found that there were two factors which influenced the quality of the abstracts. They were the usage of cohesive devices to build cohesion in the abstracts and the usage of Theme and Rheme and thematic progression to build coherence in the abstracts. In conclusion, it was clear that the usage of cohesive devices and Theme and Rheme helped the students write the abstracts cohesively and coherently.The abstract should be written in one paragraph of 150-250 words. Calisto MT font size 9 single spacing. It should containgeneral statement about the primacy of the topic under investigation, research gap, the objectives, method, main findings, and the conclusions.
DRHNet: A Deep Residual Network Based on Heterogeneous Kernel for Steganalysis

Convolutional neural networks as steganalysis have problems such as poor versatility, long training time, and limited image size. For these problems, we present a heterogeneous kernel residual learning framework called DRHNet—Dual Residual Heterogeneous Network—to save time on the networks during the training phase. Instead of using the image as an input of the network, we extract and merge the images into a feature matrix using the rich model and use the generated feature matrix as the real input of the network. The architecture we proposed has good versatility and can reduce the computation and the number of parameters while still getting higher accuracy. On BOSSbase 1.01, we evaluate the performance of DRHNet in the setting of the spatial domain and frequency domain. The preliminary experimental results show that DRHNet shows excellent steganalysis performance against the state-of-the-art steganographic algorithms.
(In)Security of Ring-LWE Under Partial Key Exposure

Abstract We initiate the study of partial key exposure in Ring-LWE (RLWE)-based cryptosystems. Specifically, we (1) Introduce the search and decision Leaky R-LWE assumptions (Leaky R-SLWE, Leaky R-DLWE), to formalize the hardness of search/decision RLWE under leakage of some fraction of coordinates of the NTT transform of the RLWE secret. (2) Present and implement an efficient key exposure attack that, given certain 1/4-fraction of the coordinates of the NTT transform of the RLWE secret, along with samples from the RLWE distribution, recovers the full RLWE secret for standard parameter settings. (3) Present a search-to-decision reduction for Leaky R-LWE for certain types of key exposure. (4) Propose applications to the security analysis of RLWE-based cryptosystems under partial key exposure.
An Encryption Transmission Scheme for Industrial Control System

With the integration of information technology such as industrial Internet of Things (IoT), cloud computing and Industrial Control System (ICS), the security of industrial data is at enormous risk. In order to protect the confidentiality and integrity of data in such a complex distributed environment, a communication scheme is proposed based on Attribute-Based Encryption (ABE) algorithm, which integrates data encryption, access control, decryption outsourcing and data verification. In addition, it has the characteristics of constant ciphertext length. Finally, the scheme is analyzed in detail from three aspectsie correctness, security and performance overhead. The simulation results show that the algorithm has the advantage of low decryption overhead.
PSO Control under Partial Shading Conditions

The change observed in new habits and traditions of human beings has significantly influenced the way of their current life especially in their needs for electrical energy.This has consequently increased the demand for energy, which has forced the researchers and the industrials to focus their efforts in the development of electricity production systems based on renewable energies, such as photovoltaic (PV), thermal, hydraulic, etc. In the case of the photovoltaic installations, the random change in climatic conditions is considered as a challenge for the engineers in the field. Moreover, the phenomenon of partial shading prompts scientists to suggest practical solutions to overcome it while ensuring the desired performance of the installation. The Maximum Power Point Tracking technique is considered to improve the dynamic performance of the photovoltaic system. Our contribution deals the research of a point of maximum power for a photovoltaic system, under conditions of partial shade using particle swarm optimization algorithm (PSO). The simulation of this structure under the Matlab/ Simulink environment shows that the system ensures better performance and good efficiency.
Professional Development of English Teachers at a State Islamic Senior High School in Palembang

The objectives of this study were to investigate teachers’ professional development, the obstacles faced by the teachers, teachers’ efforts to resolve the obstacles, and school support for the teachers in continuing professional development. To reveal the phenomenon, qualitative case study design was used. This study combined observation, questionnaires, and interviews in data collection. English teachers, vice-principal of the curriculum, teachers who taught other subjects, and students who had been taught by the English teachers were the participants of this study. Findings revealed that English teachers’ professional development was good. The results also revealed that teachers’ scientific publications were in the poor category. Innovative works for both English teachers were in the average category. Lack of time and information were the obstacles faced by the English teachers in continuing professional development. Teachers attempted to resolve the obstacles by looking for information needed through the internet. They also had discussions with other teachers to gain information for their knowledge. The school frequently conducted some programs related to teachers’ professional development and allowed them to attend professional development activities held by other institutions.
Considering a new platform for academic campus interviews: Entering the virtual world

As a May 2020 Social Work PhD Graduate, I spent the spring semester interviewing for academic tenure track positions. When COVID-19 reached the United States, the interview process quickly changed course. My in-person campus visits became 8-hour long virtual interview days, which were quite exhausting. Since I completed five in-person campus visits and two virtual interviews, I compiled my lived experiences. As a “now” normal emerges and institutions must be more financially aware and uphold social distancing guidelines, in the reflexive essay I provide suggestions for improving virtual campus interviews—for both the candidate and the search committee. Some suggestions for the search committee include not using an 8-hour interview day, offering a delivered lunch for the candidate, and assembling the virtual visit to incorporate the school and University environment. For the candidate, I propose to dress as if one is on-site, integrate self-care throughout the day, and request a meeting with only students. I hope my experiences will motivate others who faced similar situations to consider their virtual interview experiences and further produce suggestions for their institutions.