Reviewerly/exHarmony huggingface dataset:
FORMATION OF FUTURE TEACHERS’ PROFESSIONAL SUCCESS BY MEANS OF ACTIVE SOCIAL PSYCHOLOGY TRAINING

The article deals with the problem of future teacher’s formation of professional success, his self-realization, creative abilities development, educational self-efficiency as the indicators of social and professional maturity. The most active social psychology training of communication, training work, in particular, promotes the formation of goal-setting skills and the prospect design of professional success.Project activities, especially, team building, aimed at uniting the team, is fulfilled with active methods of the participants’ interaction training.Game techniques promote the development of communicative, interactive and perceptual components of communication, where the experience of interpersonal communication, the formation of emotional self-regulation skills, the ability to interact in conflict situations and the use of non-verbal means for effective interaction are enriched.Active social and psychological training in communication is one of the means for optimizing the professional success and effectiveness of the future teacher.The most important area of work in training sessions is the identification and awareness of the students’ features of communication activities, which is a prerequisite for improving the ability of pedagogical communication.To conclude, modern social relations require the ability of people to show understanding, mutual sympathy, positive emotional relationships, creating a comfortable psychological climate, which greatly optimizes the effectiveness of the teacher.Only a professionally successful teacher can raise a professionally successful student.
Fuzzy Rule-Based Classification with Hypersphere Information Granules.

Fuzzy rule-based classification has been studied by a number of classification architectures. In this study, hypersphere information granules are used to form initial fuzzy classification model in an intuitive and interpretative way. The principle of justifiable granularity offers a certain way to optimizing information granules while facing the coverage and specificity criteria. By engaging a synergy of the principle of justifiable granularity and migrating prototypes, the refined classification model is constructed for better classification performance. A series of experiments concerning synthetic datasets and comparative studies are also implemented to exhibit the feasibility and effectiveness of the proposed classification method.
대학생 인성교육을 위한 코칭역량프로그램의 효과성 분석

Personality in the future is more important in the higher vocational education stage. Thus, personality enhancement education is inevitable in universities. The purpose of this study is to supplement the diversity of personality education in universities by verifying that it is effective to increase coaching capacity for college students who have remained primarily as a coaching object, and to expand the use of coaching as a tool for personality enhancement education for college students.To this end, a coaching competency program was implemented and a pre- and post-program questionnaire survey was conducted. As a result, students who carried out the coaching competency program showed a high improvement in all 10 items. These results suggest that the development of coaching competencies of college students does not only develop mind and skill as a coach, but also positively affects the personality of students. Based on the results of these study, suggestions were made to supplement the diversity of personality education in universities and to expand the applicability of coaching as a tool for personality enhancement education.
STIMULATION OF STUDENTS’ COGNITIVE ACTIVITIES IN THE PROCESS OF FOREIGN LANGUAGE LEARNING

&#x0D; &#x0D; &#x0D; Currently the stimulation of students’ cognitive activity in learning a foreign language is one of the most pressing problems of pedagogy. In organizing the educational process of a foreign language, the motivation of students plays a significant role. Successful student motivation helps to set priorities, activate and concentrate thinking, increase interest in learning a foreign language as a whole and perform specific tasks in particular. The article considers the most relevant and actual methods of stimulating students’ cognitive activity in the modern technical non- linguistic university, aimed at modern youth, their interests and hobbies. The most expedient methods of activating students' educational activities have been mentioned.&#x0D; &#x0D; &#x0D;
Counterfactual Off-Policy Training for Neural Response Generation

Open-domain dialogue generation suffers from the data insufficiency problem due to the vast size of potential responses. In this paper, we propose to explore potential responses by counterfactual reasoning. Given an observed response, the counterfactual reasoning model automatically infers the outcome of an alternative policy that could have been taken. The resulting counterfactual response synthesized in hindsight is of higher quality than the response synthesized from scratch. Training on the counterfactual responses under the adversarial learning framework helps to explore the high-reward area of the potential response space. An empirical study on the DailyDialog dataset shows that our approach significantly outperforms the HRED model as well as the conventional adversarial learning approaches.
UPAYA MENINGKATKAN KETERAMPILAN MENULIS PANTUN MELALUI METODE PEMBERIAN TUGAS PADA SISWA SD

Lately research on improving skills has become the focus of experts, one of the skills that must be supported by the ability to write poetry. This research supports to see the improvement of poetry writing skills through the method of giving assignments to elementary students. This type of research is classroom action research. The sample of this research is 6th grade students. The technique of collecting data uses observation sheets, interviews, tests and non-tests. Data analysis uses data reduction, data presentation and conclusions, then quantitative Likert scale analysis. The results obtained from this study about increasing students in poetry writing skills using the help method in accordance with the cycle that is applied then students increase a significant increase
GraCIAS: Grassmannian of Corrupted Images for Adversarial Security

Input transformation based defense strategies fall short in defending against strong adversarial attacks. Some successful defenses adopt approaches that either increase the randomness within the applied transformations, or make the defense computationally intensive, making it substantially more challenging for the attacker. However, it limits the applicability of such defenses as a pre-processing step, similar to computationally heavy approaches that use retraining and network modifications to achieve robustness to perturbations. In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation. Due to its simplicity, the proposed defense is computationally efficient as compared to the state-of-the-art, and yet can withstand huge perturbations. Further, we develop proximity relationships between the projection operator of a clean image and of its adversarially perturbed version, via bounds relating geodesic distance on the Grassmannian to matrix Frobenius norms. We empirically show that our strategy is complementary to other weak defenses like JPEG compression and can be seamlessly integrated with them to create a stronger defense. We present extensive experiments on the ImageNet dataset across four different models namely InceptionV3, ResNet50, VGG16 and MobileNet models with perturbation magnitude set to ε = 16. Unlike state-of-the-art approaches, even without any retraining, the proposed strategy achieves an absolute improvement of ~ 4.5% in defense accuracy on ImageNet.
How Does Selective Mechanism Improve Self-Attention Networks?

Self-attention networks (SANs) with selective mechanism has produced substantial improvements in various NLP tasks by concentrating on a subset of input words. However, the underlying reasons for their strong performance have not been well explained. In this paper, we bridge the gap by assessing the strengths of selective SANs (SSANs), which are implemented with a flexible and universal Gumbel-Softmax. Experimental results on several representative NLP tasks, including natural language inference, semantic role labelling, and machine translation, show that SSANs consistently outperform the standard SANs. Through well-designed probing experiments, we empirically validate that the improvement of SSANs can be attributed in part to mitigating two commonly-cited weaknesses of SANs: word order encoding and structure modeling. Specifically, the selective mechanism improves SANs by paying more attention to content words that contribute to the meaning of the sentence. The code and data are released at https://github.com/xwgeng/SSAN.
Architecture Design of Resource Support System for the Rail-Water Intermodal Transportation Cloud Platform

Abstract Due to the unbalanced development of intermodal information development in different regions of China, the rigid structure of resource system and the low degree of information sharing, the development of intermodal transportation informationization is restricted to a large extent. According to the architecture design of the rail-water intermodal transportation cloud platform, this paper reconstructs the traditional IaaS and PaaS architectures, and establishes a multi-area redundant architecture, so that these virtual resources can form an organic whole in the cloud and interact with the intermodal organizations. At the same time, the direct coupling between the hardware infrastructure and software of the “chimneys” system architecture are eliminated by using the layered structure based on the virtualization technology. The resource support system can reduce the use cost of information, strengthen the management efficiency of software and hardware resources of cloud platform and a large number of heterogeneous business applications.
Adaptive Temporal Segmentation for Action Recognition

Learning deep representations have been widely used in action recognition task. However, the features extracted by deep convolutional neural networks (CNNs) have many redundant information. This paper aims to discover the relevance between temporal features extracted by CNNs. Different fromTemporal Segment Networks (TSN) to randomly select video clips. Based on the matrix-based Rényi's α-entropy, we estimate the mutual information between temporal domain features. Through our experiments, we propose an adaptive temporal segmentation scheme to represent the entire videos. We also combine the features of RGB and optical flow frames extracted by 3D ConvNets to verify the complementary information between them. We show that the proposed approach achieves 94.4 and 72.8 percent accuracy, in the UCF- 101 and HMDB-51 datasets.
Australia’s ‘COVIDSafe App’: An Experiment in Surveillance, Trust and Law

The joint Australian governments’ coronavirus contact tracing app, marketed as ‘COVIDSafe’, was released on 26 April 2020 for public download by the federal government, together with an emergency Determination under the Biosecurity Act to govern its operation, a Privacy Impact Assessment (PIA) with the Health Department’s response to that PIA, and (not least) the App itself and its privacy policy. It is a package intended to create sufficient public confidence to result in downloads of the app by a sufficient percentage of the Australian mobile-phone-owning population, for it to have a significant effect on the tracing of persons infected with the COVID19 virus. In the first few days since its launch nearly 3 million Australian’s have downloaded the app.When Parliament resumes, probably on May 12, it is expected that the government will introduce legislation to replace the non-disallowable Determination. This article analyses the steps that Australian governments need to take if public trust is to be justified, and aims to make a constructive contribution to the development of better legislation and greater transparency.We conclude that the conditions necessary to justify sufficient public trust in government for the Australian public to opt in voluntarily to the installation and use of the COVIDSafe app, and to not opt out, are lacking. Many of the main deficiencies we identify in this article are remediable: five deficiencies in transparency; and nine categories of improvements to the current Determination by the proposed COVIDSafe Act. However, the question of whether an individual Australian would be well advised to install and run the app remains a decision which depends on individual circumstances. The Act referred to above, the Privacy Amendment (Public Health Contact Information) Act 2020 (Cth) (‘the COVIDSafe Act’) was enacted on 15 May 2020. The authors' analysis of that Act is G. Greenleaf & K. Kemp 'Australia’s COVIDSafe experiment, Phase III: Legislation for trust in contact tracing' at https://ssrn.com/abstract=3601730.
Finding the maximum-a-posteriori behaviour of agents in an agent-based model

In this paper we consider the problem of finding the most probable set of events that could have led to a set of partial, noisy observations of some dynamical system. In particular, we consider the case of a dynamical system that is a (possibly stochastic) time-stepping agent-based model with a discrete state space, the (possibly noisy) observations are the number of agents that have some given property and the events we're interested in are the decisions made by the agents (their ``expressed behaviours'') as the model evolves. We show that this problem can be reduced to an integer linear programming problem which can subsequently be solved numerically using a standard branch-and-cut algorithm. We describe two implementations, an ``offline'' algorithm that finds the maximum-a-posteriori expressed behaviours given a set of observations over a finite time window, and an ``online'' algorithm that incrementally builds a feasible set of behaviours from a stream of observations that may have no natural beginning or end. We demonstrate both algorithms on a spatial predator-prey model on a 32x32 grid with an initial population of 100 agents.
Multi-finger Hydraulic Tough Hand and its Evaluation Test Results Attached to the Dual Arm Construction Robot

We developed a prototype of hydraulically driven tough robot hand having 4 fingers. This hand is designed as an attachment of the lower arm of the ImPACT dual arm Construction Robot. The feature of this hand is a simple structure and a simple control system which has only one pressure sensor, and 6 valves. The hand can operates not only as a hand but also as a bucket. We conducted evaluation tests in the Robot Test Filed in Fukushima Prefecture. In this paper is presented the outline of this hand and its test results.
Aplikasi Pendataan Aset pada UPT. Pengusahaan Aset Aceh PT. Kereta Api Indonesia (PERSERO)

The use of computer technology has been proven to help humans in various fields and aspects of life, including its use in the field of data management. The compilation of a computer technology-based asset data collection system application program design is expected to be able to help in the accuracy and speed of recording the utilization of data collection assets to see the wide coverage area under the supervision of the UPT. The exploitation of Aceh's Assets PT. Indonesian Railways (PERSERO). The general objective of this research is to design and build a more accurate asset database using the asset application system using Visual BASIC.NET and be able to know the usage of the asset application system that has been created and can present the asset application system that has been built at the UPT. The exploitation of Aceh's Assets so that the data collection system is more effective and efficient. The test results of this application are the systems that are built already meet the needs, the input data process has met the needs and the resulting report has met the needs.Keywords:Application Systems, Data Collection, Assets, UPT. Exploitation of Aceh's Assets PT. Kereta Api Indonesia (PERSERO), Visual Basic.NET.
Visual approach to clustering large-scale meteorological datasets

&amp;lt;p&amp;gt;Identifying the atmospheric processes which lead to extreme events requires careful generalisation of the meteorological conditions surrounding such events such as sea-level pressure and air temperature. Through the case study of clustering the processes behind extreme rainfall events (annual maximum 1-day rainfall totals) in Great Britain, this presentation shows how visualising the iterative processes used by clustering algorithms can aid in algorithm selection and optimisation. Here two big data datasets, namely the CEH-GEAR (gridded observed rainfall) and NCEP/NCAR Reanalysis datasets are synthesised and clustered using different methods such as k-means, linkage methods and self-organising maps. The performances of these methods are compared and contrasted through analysis of the clusters created at each iteration, highlighting the importance of algorithm selection and understanding. The key findings of this clustering process result in three large-scale meteorological condition types which lead to extreme rainfall in Great Britain as well as a novel approach to comparing clustering mechanisms when using meteorological data.&amp;lt;/p&amp;gt;
“Jamás deja de ser entendido el orador que pronuncia criollamente”. La disputa por la lengua: la pronunciación como marca de identidad nacional

The objective of this article is to analyze the long polemic that surrounds pronunciation as a national identity mark, as some “hommes des lettres” maintained in the pages of the Diario de México. Jacobo de Villaurrutia, editor and collaborator of the newspaper, was the most important voice that led the controversy towards a more open and less normative position.
Inovasi Pelayanan Rapor Berbasis Online Di SMA Ipiems Surabaya

Online report card is a web-based value management system created by utilizing technology in its process to overcome the fundamental problems of processing and reporting grades in the 2013 Curriculum. Online report cards have been applied in various schools in the Surabaya area and currently the application has spread to various regions of Indonesia. In the application of online report cards there are various positive and negative impacts felt by the community. The research aims to analyze and see how the innovation of online report card services at IPIEMS Surabaya High School. The method used in this research is descriptive qualitative approach with data collection techniques using riview literature. The focus in this research is based on Everett M Rogers' theory of innovation attributes, namely relative advantage, suitability, complexity, probability, observability. The results of the analysis in the study using the attribute theory of online report card service innovation at IPIEMS Surabaya High School are in accordance and meet the 5 elements of innovation attributes by Everett M Rogers. Online report card service innovation implemented at SMA IPIEMS Surabaya can be carried out effectively and efficiently, besides that there is still a lack of relative benefits that do not cover the whole of online report card innovation
Egg Incubator Design Using Artificial Intelligent

In order for success rate of egg hatching to be high, the egg incubator system must be able to maintain the temperature and level it in the chamber. The ideal temperature for hatching chicken eggs is around 36°C - 39°C and 50% - 60% humidity. They must stable and even. The hatching chamber is equipped with 4 temperature sensors installed in all corners of the chamber and with 2 air heaters and 3 blowers. The control system used is fuzzy logic with MIMO (Multiple Input Multiple Output) which has been optimized using the GA (Genetic Algorithm). GA is one type of AI (Artificial Intelligent). Optimization is used so that the system is able to adapt to environmental conditions. The temperature obtained is an average of 37.68℃ with a deviation of 0.47℃. The success rate of hatching reaches 83.33%.
Would You Like Sashimi Even If It's Sliced Too Thin? Selective Neural Attention for Aspect Targeted Sentiment Analysis (SNAT).

Sentiments in opinionated text are often determined by both aspects and target words (or targets). We observe that targets and aspects interrelate in subtle ways, often yielding conflicting sentiments. Thus, a naive aggregation of sentiments from aspects and targets treated separately, as in existing sentiment analysis models, impairs performance. We propose SNAT, an approach that jointly considers aspects and targets when inferring sentiments. To capture and quantify relationships between targets and context words, SNAT uses a selective self-attention mechanism that handles implicit or missing targets. Specifically, SNAT involves two layers of attention mechanisms, respectively, for selective attention between targets and context words and attention over words based on aspects. On benchmark datasets, SNAT outperforms leading models by a large margin, yielding (absolute) gains in accuracy of 1.8% to 5.2%.
DETECTION OF WEB APPLICATION VULNERABILITIES USING VATSCAN SCANNER

Information security is the heart of the computer's current renaissance our project deals with detecting Web Application attacks under the standards of OWASP (Open Web Application Security Project) which is an NGO (Non-Governmental Organization) for universal Web Application Security.The scanner focuses on security threats like XSS (Cross Site Scripting) and (LFI) Local File Inclusion Vulnerability which occurs in Interactive Web Applications.The scanner uses crawling Operation to detect the web application end points, vulnerable input parameters and evaluating whether the input parameter is Vulnerable to exploit or not by executing harmless malicious scripts.If the parameter is vulnerable to Exploit then the parameter is considered as a threat vector and the vulnerability name is displayed.The vulnerabilities are classified according to its severity as low, medium, high by OWASP standards.The scanner is built using Python to collaborate with the web modules and documentation is published for future references.By using this VATSCAN testing strategy the vulnerability scanner detects the vulnerabilities and categorizes them based on its severity, and the documentation is used to prevent from future Threat vectors and Attack vectors.