Reviewerly/exHarmony huggingface dataset:
Holistic Authentication Framework for Virtual Agents; UK Banking Industry

The exponential growth and advancements of information technology and artificial intelligence significantly enhance the capabilities of virtual agents. This creates opportunities for businesses such as banks to adapt virtual agents for banking operations. Security and privacy are the ultimate concern for any e-banking system or application. Therefore, virtual agents inherit this concern. The previous literature lacks the knowledge of a suitable authentication framework for the virtual agents use in the UK banking industry. This forms a vulnerability for the banks while providing an opportunity for cyber attackers to attack the endpoints of the virtual agents. Moreover, it produces the risk of exposing sensitive customer data. Therefore, this research was conducted to fill the knowledge gap by proposing an authentication framework for the banking virtual agents. The proposed framework considers several security standards and controls to ensure that the proposed framework fits for the purpose. A simulation was used to validate accessibility, scalability, security and vulnerability of the proposed authentication framework. Finally, the analysis of results demonstrated that the proposed authentication framework carries the ability to fulfil the requirements of securing the virtual agents in the UK banks from external and internal cyber threats.
To Detect and Classify Breast Cancer Using Convolution Layer Depth Filter for Prediction of Disease with Feature Validation

Globally, the threat of Bio-war cannot be overlooked. Today, there are many popularly known biological diseases and treatments, of which few are dangerous and few are not. And breast cancer is one of them. The study proposes a model to analyze the severity of this problem and find a credible solution to it. Object detection and classification plays a major role for an in-depth analysis into neural network methods. The convolution layer helps in prediction and subsequently the features extracted from this layer are forwarded to feed forward networks. The data set is divided into two ways for the trained set and test set. Filter depth is considered as a measure to feature extraction with utmost quality.  Breast histopathology helps in confirmation of the presence of cancerous cells but it is time consuming.
SegDiff: Image Segmentation with Diffusion Probabilistic Models

Diffusion Probabilistic Methods are employed for state-of-the-art image generation. In this work, we present a method for extending such models for performing image segmentation. The method learns end-to-end, without relying on a pre-trained backbone. The information in the input image and in the current estimation of the segmentation map is merged by summing the output of two encoders. Additional encoding layers and a decoder are then used to iteratively refine the segmentation map using a diffusion model. Since the diffusion model is probabilistic, it is applied multiple times and the results are merged into a final segmentation map. The new method obtains state-of-the-art results on the Cityscapes validation set, the Vaihingen building segmentation benchmark, and the MoNuSeg dataset.
The Intellectual Property Policy of Universities and Research Institutions in Conditions of Digitization and Development of Innovations

The article describes the process of restructuring of education in Russia, and in particular the writing of policy on intellectual property in the Russian universities. The article provides an overview of the activities of the leading Russian and foreign educational institutions in this area.
Les Effets du Rythme sur l'Expérience Utilisateur lors de l'utilisation de Gestes Mid-Air en Réalité Virtuelle

In this work, we examine the effect of mid-air gesture rhythm on user experience in Virtual Reality. In particular, we investigate gesture regularity, speed and highlighting with a sound guide. We measure the effect of these components on the perceived fatigue, presence, difficulty, success and helpfulness. Our findings indicate that an irregular and slow rhythm leads to a lower arm fatigue. We also find that such an irregular rhythm could increase the user perceived difficulty of the task and the absence of a sound guide could decrease the sense of presence.
IMPROVING WRITING SKILL THROUGH THINK-PAIR-SHARE ON DESCRIPTIVE TEXT IN THE SECOND GRADERS OF MTs. NURUL ISLAM SLATRI , BREBES

Writing is assumed as the most difficult skill among other skills. Meanwhile, explaining all the materials until the end of the class is still used in the teaching process of descriptive text. This research is generally to answer whether or not think-pair-share can improve students’ writing skill on descriptive text and specifically to know: (1) the teacher’s activities, (2) the students’ activities, and (3) the students’ responses during the implementation of think-pair-share on writing class of descriptive text. The subjects are 16 students of the second graders of MTs Nurul Islam Slatri in the academic year of 2013/2014. The data were obtained from the observations and writing test. The students’ writing skill improved after the implementation of Think-Pair-Share that could be seen in the improvement of mean score, from 68.4 and 72.5 in cycle one to 73.4 and 77.2 in cycle two. The percentage of students who passed the minimum mastery criteria also improved, from 62.5 % and 69 % in cycle one to 75 % and 100 % in cycle two. In conclusion, think-pair-share can improve students’ writing skill on descriptive text.
Co-authorship to indicate maturity of regional academic community: On the problem statement

The author substantiates the approach of evaluating academic maturity with the system of indicators including academic co-authorship analysis. The term «regional academic community» is defined. The vectors of bibliometric analysis by the criteria of external and internal co-authorship, good merit of scientific communication within regional community and citation of coauthored works, are examined.
Quantitative Appraisal for Mineral Resources with Integrated Information

The theory of Quantitative Appraisal for Mineral Resources with Integrated Information (QAMRII) includes three parts. The first is the theory of metallogenic prognosis. The second is the theory of statistical prognosis of resource occurrences. The third is the theory of quantitative prediction of mineral resources. QAMRII regards the geological hypothesis as geological prerequisite, the geological bodies and the possible mineral resources bodies as the basic prognosis units in prediction. Different kinds of geo – data are generally not unanimous. QAMRII studies the interrelation of geological, geochemical, geophysical, and remote sensing information for the prognosis of mineral resources, including larger and superlarge deposits and blind deposits, of known and unknown types. A wide range of methods to transform, associate, and synthesize the different kinds of information, to delineate the geological and mineral resource bodies, to select the model units, to choose the mathematical models, to determine the necessary variables are studied in QAMRII.
Exploring Low-Cost Transformer Model Compression for Large-Scale Commercial Reply Suggestions.

Fine-tuning pre-trained language models improves the quality of commercial reply suggestion systems, but at the cost of unsustainable training times. Popular training time reduction approaches are resource intensive, thus we explore low-cost model compression techniques like Layer Dropping and Layer Freezing. We demonstrate the efficacy of these techniques in large-data scenarios, enabling the training time reduction for a commercial email reply suggestion system by 42%, without affecting the model relevance or user engagement. We further study the robustness of these techniques to pre-trained model and dataset size ablation, and share several insights and recommendations for commercial applications.
Aula virtual NEO LMS como estrategia para la enseñanza de la Matemática

The objective of the research is to develop and design a thematic block of mathematics in the NEO LMS virtual platform in a technical and pedagogical way that supports the teacher for the virtual educational process that Ecuador requires, in which the ADDIE methodology will be used. The research is descriptive, not experimental. 70 mathematics teachers surveyed 87.1% stated that they have not used the NEO LMS virtual platform and 11% indicated that they have done so sporadically than sporadically and finally only 1.4% use it daily, in this way they shows the lack of knowledge that teachers have on this platform. An instructional design focused on learning and developing the ability to design databases is proposed, it can be applied in any subject whose objective is aimed at training the Computer Science Engineer's own skills.
Assessment of information security threats to information systems in Federal University Libraries, Nigeria

The study assessed information security threat to information syatems in Federal University Libraries in Nigeria with the aim of uncovering the various threats militating against the smooth operations of the system. Sixty four (64) Librarians were randomly selected and surveyed across Federal University Libraries in Nigeria. Questionnaire was used as the instrument for the data collection and bench mark for analysis is determined as positive when the mean is 3.00 and above while a mean of less than 3.00 was regarded as negative. The study found that hardware equipment failure, unauthorized change of software settings, transmission error, data loss due to wrong procedure, power supply failure and employee’s misconducts as the major information security threat in the libraries. It was concluded that the libraries will be at risk operating a dysfunctional system which might tamper with its reputation and integrity if the necessary steps are not taken. The study recommended that: the library management should ensure regular maintenance of hardware to eliminate hardware equipment failure and system threats. System administrators should monitor the activities of patrons to avoid unauthorized change of software settings, password attack and installation of unauthorized programs.
The effect of social presence, loyalty, and group polarization on online learning motivation in Moodle-based online learning

AbstractIn this study, as an independent variable that can affect online learning motivation, it is to analyze theeffect of social presence and group polarization induced on online learning motivation in Moodle-basedonline learning according to the recent change in the educational environment based on onlinelearning. The subjects of this study were 83 students enrolled in 4-year university E in Gyeonggi-do. Thesurvey was conducted for about one month from January 3, 2021 to January 30, 2021. The PASW Statistics18.0 program was used to analyze the data collected to test this research question, and it was confirmedwhich variable among the sub-factors of social presence, loyalty, and group polarization inducement isthe variable that has the greatest influence on online learning motivation. Multiple regression analysis wasperformed to find out which model is most suitable to explain online learning motivation. As a result of theanalysis, it was found that loyalty, perceptual bias, and mutual support concentration had a positiveeffect on online learning motivation. In-group alignment was found to have a negative effect on onlinelearning motivation. In conclusion, it was found that the perception bias of loyalty, mutual support ofsocial presence, concentration, and group polarization induction in Moodle-based online learning hada positive influence on the learning motivation of college students. In addition, it was found that the ingroup conformity of the group polarization inducer had a negative effect.
ANALISIS KETERAMPILAN MENULIS NASKAH DRAMA DENGANMETODE KOOPERATIF TIPE STAD DAN MEDIA AUDIO VISUAL

AbstractThis study aims to determine the form of drama scripts written by students of class VIII DSMP NEGERI 2 Padangan, Bojonegoro on STAD type cooperative learning and audiovisual media. This research is a qualitative study using a case study approach with a totalof 31 student subjects. From 31 students then researchers took 5 students to beinterviewed on the grounds that the drama script was made according to indicators. Datacollection techniques such as observation, test methods, interviews and documentation.Data validated using source triangulation, method triangulation and time triangulation.Data analysis techniques use (1) data reduction (2) data presentation (3) conclusion orverification. The results showed that the structure of the drama script written by studentsof class VIII D SMP NEGERI 2 Padangan, Bojonegoro already referred to the structureof the drama script according to Kosasih theory well. The conclusion of this research islearning using STAD type cooperative methods and audio visual media is veryappropriate to be applied in the learning of drama script writing skills. STADcooperative learning can make students more active in skills, more confident, and moreable to think creatively in pouring ideas into drama scripts.Keywords: Drama script, STAD type cooperative learning and audio visual media
THE TRADITIONAL EVALUATION AND INNOVATIVE ASSESSMENT METHODS IN THE TEACHING OF SCIENCES AND MATHEMATICS

The mathematics and science teaching cardinal question is also the measuring of teaching effectiveness. In our research we focuse on the application of non-traditional teaching methods especially group work in mathematics classes. The importance of the evaluation process has been emphasized by the teachers in Complex Instruction Program methodology and in traditional teaching and learning processes as well, so it became apparent a need to measure the effectiveness of learning. In addition to traditional evaluation methods, innovative evaluation methods have also proved to be justified.Traditionally used assessment methods measure the learning effectiveness of our students up to a par-ticular point in time. These summative assessments are realised after a unit or certain time period and determine how much learning has taken place. In addition to traditional assessment methods, innovative assessment methods also come to the fore in the teaching of science and mathematics. These types of assessments - formative assessments - are more sensitive to learning during the learning process. They provide important information about what students understand and what they do not, what student know at the given moment, and help teachers to make decisions how to plan and manage further teaching to students' needs and abilities. In this article we present various specific examples of formative evaluation methods in the teaching natural science objectives and mathematics.
Generating and printing bar codes in IRBIS64

Several technological aspects of generating and printing bar codes are examined. Options of using bar codes in libraries, bar code generation models and appropriate IRBIS applications are discussed in brief. SB RAS SPSTL has developed the new system for generating bar code batches for further literature processing. The system is generated on IRBIS64 platform. Optional use of the system is discussed; possible improvements for bar code generation system are offered.
High School Students’ Perception on The Implementation of Full Day School

The purpose of this study was to determine students' perceptions related to the implementation of FDS (full day school) and Want to know also how to implement FDS in SMA 2 Kolaka. The population of this study were all students of grade 2 and 3, SMA Negeri 2 Kolaka consisting of 100 students. The sample of this study was 100 students, namely grade 2 and 3 students. There are two types of instruments in this study, Questioner and interview. Questioners divided into grade 2 and 3 students in the questionnaire. There are two first parts, positive perception 8 questions, two negative perceptions, 8 questions, so the total is 16 questions, after that the interview is done after all the questionnaires are given. All data obtained in this study were analyzed using the formula, the value for the answer yes is 1, and while the value for no is 0. Yes = 1x100 = 100%, No = 0x100% (no need to be counted), then the data from the results is added Interview. Based on the findings from this study it can be concluded that most students agree with the implementation of FDS in their school. This is supported by the results of the questionnaire (see 4.1) which shows that 62% of the 100 students agreed with the full day of school implementation and 38% disagreed and were also supported by records of interviews with researchers named Andi Nur Fadillah rahma yatte class XII IPA 1, he said to myself I really agree with the implementation of full day school, in my school because in addition to extra extracurricular hours for a full day my character really formed because of extra curricular hours, and I felt my fashion was more orderly and good
Improving Deep Learning Networks for Profiled Side-Channel Analysis Using Performance Improvement Techniques

The use of deep learning techniques to perform side-channel analysis attracted the attention of many researchers as they obtained good performances with them. Unfortunately, the understanding of the neural networks used to perform side-channel attacks is not very advanced yet. In this article, we propose to contribute to this direction by studying the impact of some particular deep learning techniques for tackling side-channel attack problems. More precisely, we propose to focus on three existing techniques: batch normalization, dropout, and weight decay, not yet used in side-channel context. By combining adequately these techniques for our problem, we show that it is possible to improve the attack performance, i.e., the number of traces needed to recover the secret, by more than 55%. Additionally, they allow us to have a gain of more than 34% in terms of training time. We also show that an architecture trained with such techniques is able to perform attacks efficiently even in the context of desynchronized traces.
1st International Conference on Physical Problems of Engineering (1st ICPPE)

The reciprocal contribution that physics has had in engineering and engineering in physics is undeniable. The First International Conference on Physical Problems of Engineering (1st ICPPE) tries to highlight this symbiotic relationship between both sciences through different presentations that reflected their results in separate articles that will be published in the Journal of Physics: Conference Series. List of Organizing Committee, National Scientific Committee, International Scientific Committee, Sponsor, Partners, Images are available in this pdf.
COVID-19 Mandatory Self-Quarantine Monitoring System

In response to rising COVID-19 cases, the Malaysia government imposed multiple measures to prevent virus transmission. Among the efforts taken by MoH were the enforcement of a-14 day self-quarantine for PUI, PUS and positive Covid-19 patients without symptoms to curb local transmission. However, the violations of self-quarantine are still being reported due to the location data of the users that cannot be tracked by the available system. Thus, this project proposes a system to monitor and track the self-quarantine PUI and PUS in real time by implementing geolocation tracking and face recognition. The geolocation tracking is developed to monitor users to stay in their quarantine place within 30 meters radius, and prevent unauthorized movement by scanning the users’ faces every two hours. User also requires updating their health status every day in the proposed system. If the user is classified as a very high risk dependent, admin and user will receive an alert notification for further action. The test result shows the developed map API can track multiple people with accuracy of 99% and the location of users can be displayed in real time in the map.  In addition, the system can detect the user’s face accurately in various conditions within 1 second. As a result, this system could be used by the authority in order to reduce the self-quarantine violation rate in which subsequently help to prevent the spread of COVID-19.
The Contribution of Entrepreneurial Skills to the Social Stratification of Highly Qualified R&amp;amp;D Personnel: The Case of Russia

The focus of the analysis in the paper is how entrepreneurial skills contribute to the social stratification of researchers working in research and development (R&D) organizations. One of the main trends and challenges for the contemporary labour market is increased demand for complex skills that cannot be transferred to automata: entrepreneurial thinking, initiative, creativity, and the ability to solve complex analytical problems. The paper examines the prevalence and level of ownership of entrepreneurial skills among those personnel engaged in R&D by science and higher education institutions, industry, and services in Russia. A composite index of entrepreneurial skills including 7 singular skills was used for it. In addition, the relationship between entrepreneurial skills and the professional achievements of researchers is estimated.To investigate the social differences between crucial researcher’s characteristics and assess the premium for entrepreneurial skills, we used mixed methods. The main tool is perception mapping and the corresponding analysis, which results in the combination of statistical techniques and is a graphical way to represent relationships and differences in data. The results made it possible to build a social space of scientific personnel engaged in R&D organizations and to identify the main differentiating factors. Among them, the application of entrepreneurial skills, scientific productivity, as well as the job sector (educational, research, government, commercial sectors). The “premium” from the application of entrepreneurial skills mostly expressed in a higher wage. Respondents who regularly apply entrepreneurial skills have the highest incomes of all interrogated researchers.
FERCaps: A Capsule-Based Method for Face Expression Recognition from Frontal Face Images

A novel method which is named FERCaps (capsule-based method for facial expression recognition) is presented in this paper. We process these images containing the faces, and segment these gray images only containing the front faces for training the model. The model framework consists three layers of convolutional neural networks (CNN), two layers of capsules, and a decoder with a fully connected layer and four deconvolution. The ReLU activation function is used to speed up the experimental model training speed in the convolution layers and constructed a decoder suitable for facial expressions using deconvolution. The original capsule is the bottom layer of the multidimensional entity. Through an iterative routing process, each raw capsule selects a corresponding high-level capsule in the upper level. Instantiated parameters can characterize facial expressions in high-level capsules. We verified the effectiveness of the model through experiments on the public benchmarking datasets JAFFE and extended Cohn-Kanade (CK+), and achieved an accuracy of 98.18% in ck+ and 88.33% in JAFFE