Reviewerly/exHarmony huggingface dataset:
Platt-Bin: Efficient Posterior Calibrated Training for NLP Classifiers

Modern NLP classifiers are known to return uncalibrated estimations of class posteriors.Existing methods for posterior calibration rescale the predicted probabilities but often have an adverse impact on final classification accuracy, thus leading to poorer generalization.We propose an end-to-end trained calibrator, Platt-Binning, that directly optimizes the objective while minimizing the difference between the predicted and empirical posterior probabilities.Our method leverages the sample efficiency of Platt scaling and the verification guarantees of histogram binning, thus not only reducing the calibration error but also improving task performance.In contrast to existing calibrators, we perform this efficient calibration during training.Empirical evaluation of benchmark NLP classification tasks echoes the efficacy of our proposal.
Repositories of scientific data in ecology and environmental sciences

The studies of global problems, e. g. climate change, geological perils or biodiversity losses result in skyrocketing data volume. The repositories enable long-term archiving and data preservation. The modern academic libraries provide recommendations and technological infrastructure to support legal and ethical exchange of quality science data, including recommendations for planning data management. The most established re3data register enables to load and use research data. As for the late September 2021, re3data comprised 217 repositories retrieved by “ecology” term. The authors review individual large foreign repositories of research data in ecology and environmental sciences registered in re3data, as well as the number of Russian repositories specializing in this area. The authors conclude on the efficiency of reference user services like navigators of open access repositories and research data repositories.
A User-Interaction Parallel Networks Structure for Cold-Start Recommendation

Abstract The goal of the recommendation system is to recommend products to users who may like it. The collaborative filtering recommendation algorithm commonly used in recommendation systems needs to collect explicit/implicit feedback data, and new users do not leave behavioral data on the product, which leads to cold-start problem. This paper proposes a parallel network structure based on user interaction, which extracts features from user interaction information, social media information, and comment information and forms a matrix. The graph neural network is introduced to extract high-level embedded correlation features and the role of parallelism is to reduce computing cost further. Experiments based on standard data sets prove that this method has a certain degree of improvement in NDCG and HR indicators compared to the baseline.
Challenges of English Language Teaching in Post-Pandemic Era at Iain Salatiga

This article explains the challenges of English language teaching in the postpandemic era at IAIN Salatiga.Several challenges become the setback in maintaining the continuity of English language teaching in the post-pandemic era.Technological infrastructure and curriculum development are during the Covid-19 pandemic and in every discussion about national education development.Another challenge is the learning system, which transitioned from traditional (face-to-face) to online learning.Learning administration must also be updated to be totally online, for example, learning materials employing audio, video, YouTube, and Google forms.When Lecturers' roles moved to more of a facilitator, the curriculum became their reference.It is critical to remember that lecturers still require aid, training, and technical direction in order to prepare for change.
Super-metauniverse system engineering for ecosphere in internet of everything sky-earth computing (II) beyond cloud computing

With the support of IT and network technology, we can develop and build a new system centered on cyber-tech users, integrating cyber-physical system (CPS-1) and its processes with cyber-physiological system (CPS-2) and its processes, cyber-psychological system (CPS-3) and its processes, as well as cyber-eventlogic system (CES) and its processes. It is proposed in this series of research to develop and produce a global service dispatcher (GSD) as the main component of a Sky-Earth computing console (SECC) for every user (individual, group, whole), and provide a customized world-wise brain. According to the analysis and design of this series of articles, through big data platform, Internet of things and artificial intelligence technology, we build intelligent integrated system, carry out data reconstruction system engineering, so as to establish the computer-like system (CLS) for big data processing, It combines all kinds of resource elements involved in computing in the information ecosphere with those involved in computing in the real ecosphere.
About the approach to use headless CMS in developing corporate internet sites

Today, content management systems (CMS) are often used to manage the content of an organization's corporate website. There are two large groups of CMS: traditional and headless. Both groups have their pros and cons. Different types of CMS should be used depending on the complexity of the site. Most traditional CMS support their own templates for graphic design of the site and are often difficult for ordinary users to use. In view of this, the article will propose an approach that, although it somewhat complicates the process of developing a site, but provides ease and flexibility in its further support. Aim. Consider the varieties of content management systems. Describe the advantages and disadvantages of the two types of CMS. Consider the cases in which it is worth using this or that type of system when developing corporate sites for an organization. Propose a component-based approach for use in a headless CMS in order to facilitate the development, maintenance and updating of a corporate website. Materials and me-thods. Two types of CMS systems are considered. The advantages and disadvantages of different types of content management systems are described. A comparative analysis of headless CMS is carried out. Requirements for the content management system are formulated. The most suitable headless CMS is selected with the possibility of using the component approach when developing a corporate website. Results. This article describes the pros and cons of different types of CMS. A description of the cases in which one or another type of CMS should be used is given. Describes the requirements for a CMS to apply the component approach. Conclusion. As a result of the study, a comparative analysis of headless CMS was carried out and a brief example of using the component approach for designing the pages of a company's corporate website was developed.
Particle System Parametrization with Bimanual Hand Gestures

General approaches in computer graphics to compose visual effects (VFX) usually involve editing textual modules and parameters or constructing procedural node networks. These techniques are used in many game engine editors and digital contents creation (DCC) tools. However, contemporary interfaces arc not intuitive, especially for inexperienced users. Therefore, the main aim of this research is to develop an intuitive interface for visual effects design which is easy to use for beginners of design and creating DCC but still useful for advanced and expert VFX artists. In this project, we developed a particle system parametrization (to define a particle system) and instantiation interface with a hand gesture recognition system in a VR (virtual reality) environment. This project uses an HMD (head-mounted display) device that supports 6 degrees-of-frcedom (DoFs) and hand gesture recognition via its front cameras. In an immersive environment, the user composes a visual effect by answering questions shown on GUI with hand gestures. As a result of this project, we succeeded in developing an application, deployablc to Android-based HMDs, to compose visual effects in VR scenes. The application features an archiving system, so that an exported visual efTect can be imported into Unity Editor to use in other projects or game compositions. Therefore, our application can be easily integrated into a DCC production workflow (“pipeline”).
An Empirical study to understand the Compositional Prowess of Neural Dialog Models

In this work, we examine the problems associated with neural dialog models under the common theme of compositionality.Specifically, we investigate three manifestations of compositionality: (1) Productivity, (2) Substitutivity, and (3) Systematicity.These manifestations shed light on the generalization, syntactic robustness, and semantic capabilities of neural dialog models.We design probing experiments by perturbing the training data to study the above phenomenon.We make informative observations based on automated metrics and hope that this work increases research interest in understanding the capacity of these models.
FINISHED SOFTWARE PRODUCTS IN TRAINING SPECIALISTS OF IT INDUSTRY

The article is devoted to the problem of training specialists in the IT industry, the experience of learning the basics of programming students. The purpose and tasks of studying programming courses are determined. The purpose of this article is to identify and select fragments of developed software products and applied technologies that contribute to the formation and development of professional competencies of IT professionals. Modern application interfaces use objects and events with them. Everything about the frontend has a nice interface design. That is why modern training programs for computer science teachers and software engineers are focused on the study of object-oriented programming. Thanks to modern ICT tools, all subjects of the educational process have the opportunity to join modern scientific research, see the results and determine the directions of their own research to improve educational programs for engineers and educators. The article reviews software applications and fragments of their software code that contribute to the formation and development of professional competencies of IT professionals. Among the software applications selected Optics (for building models of optical phenomena), GRAPH (for calculating electrical circuits and graphs), PhysHelper (demonstration of models of physical phenomena: conservation law, kinematics, dynamics and others). The Optics example demonstrates how to describe an object, create an array, process it, and one way to implement dynamic constructions in Pascal in Delphi. GRAPH uses the Dijkstri algorithm. The essence of this algorithm is to find the shortest paths from the initial vertex to all the others. The program forms an array of distances to each of the vertices. The distance to vertices that are in another component of connectivity is defined as infinity. PhysHelper was created using basic tools such as JavaScript (Electron framework and p5 and KaTeX libraries), HTML and CSS. The selection of selected technologies and approaches is substantiated.
Personalized Music Playlist Generation Method Based on Personal Impression and Pleasure

This study proposes a method for the automatic design of personalized playlists that places a listener in a positive mood (uplifting or relaxing feelings) based on individual’s impressions and pleasurable feelings of audio tracks. This study investigated the changes in a listener’s mood before and after listening to personal impression playlists, and personal impression and personal or common pleasure playlists using a psychological scale. Consequently, the playlists based on personal or common pleasure can shift a person into a positive mood while increasing a person’s friendly emotion.
Leveraging Emotion-Specific features to improve Transformer performance for Emotion Classification

This paper describes team PVG's AI Club's approach to the Emotion Classification shared task held at WASSA 2022.This Track 2 subtask focuses on building models which can predict a multi-class emotion label based on essays from news articles where a person, group or another entity is affected.Baseline transformer models have been demonstrating good results on sequence classification tasks, and we aim to improve this performance with the help of ensembling techniques, and by leveraging two variations of emotion-specific representations.We observe better results than our baseline models and achieve an accuracy of 0.619 and a macro F1 score of 0.520 on the emotion classification task.
PEECH RECOGNITION ALGORITHM FOR WORKING ON MOBILE DEVICES

Бұл жұмыста дамушы елдер үшін мобильді құрылғыларда сөйлеу қосымшалары бойынша тәжірибе бен анализдер сипатталады. Біз қазіргі кезде қолданылатын мобильді құрылғылардағы автоматты түрде сөйлеуді тану (ASR) жүйелерін пайдаланудың үш моделін қарастырамыз - кірістірілген сөйлеуді тану, бұлттағы сөйлеуді тану және сөйлеуді үлестіру; олардың артықшылықтарын бағалау және олардың кемшіліктері. Бұл жұмыста біз дамушы елдер үшін мобильді құрылғыларда сөйлеу қосымшаларын құру бойынша тәжірибеміз бен ойларымызды сипаттаймыз. Бұл ұсынылған модель барлық үш модельдегі артықшылықтарды пайдаланады, сонымен қатар қазіргі модельдердің кез-келгенін мүмкін емес ететін қиындықтарды азайтады, мысалы, ұялы байланыстың сенімсіздігі немесе мобильді құрылғылардағы төмен қуаты, дамушы аймақтардағы сөйлеуді қолданудың типтік қажеттілігі.
Natural Language Processing Algorithms for Understanding the Semantics of Text

Vector representation of words is used for various tasks of automatic processing of natural language. Many methods exist for the vector representation of words, including methods of neural networks Word2Vec and GloVe, as well as the classical method of latent semantic analysis LSA. The purpose of this paper is to investigate the effectiveness of using network vector methods LSTM for non-classical pitch classification in Russian and English texts. The characteristics of vector methods of word classification (LSA, Word2Vec, GloVe) are described, the architecture of neural network classifier based on LSTM is described and vector methods of word classification are weighted, the results of experiments, computational tools and their discussion are presented. The best model for vector word representation is Word2Vec model given the training speed, smaller word corpus size for training, greater accuracy and training speed of neural network classifier.
QR Code Card Media on Science Learning to Overcome Misconception of Elementary School Student

The education development has entered the Literacy Acceleration era in The Knowledge Age.Learning media innovation plays an important role for students in the development of technology as a learning medium, particularly in the usage of technology-based learning media.During the COVID-19 epidemic, many problems have arisen connected to online learning, one of which is student misconceptions, especially in science material, in the high class of Keboromo elementary school.This study aims to describe the need for developing QR Code-based learning media used by educators and students today, which can be used at home to support students' understanding and overcome misconceptions.According to Borg & Gall, research and development involves various processes.This study is limited to the first stage, which is the research and information collecting stage.The research subjects for this need analysis were 3 educators and 8 students representing one elementary school, namely Elementary School of Keboromo.Data collection techniques in this study using interviews.An interactive model is used in the data analysis technique, including processes such as data reduction, data presentation (data display), and conclusions drawing or verification.The findings of this study show that in order to overcome misconceptions in science subject learning, educators and students require technology-based media designs that are simple to use and accessible via smartphones or other devices.As a result, students will be more efficient with their time, more practical with their knowledge, and the material students receive will be more focused.
EFFECTIVENESS OF LEARNING A FOREIGN LANGUAGE ONLINE IN HIGHER EDUCATION INSTITUTIONS OF UKRAINE: CURRENT CHALLENGES

The article considers the application of distance learning technologies within foreign language learning in higher education institutions of Ukraine.The regulations for the development of distance education, examples of its application, services used for online learning, as well as the problems that arose in the organization of study process during the COVID-19 pandemic were analyzed.In particular, the possibilities of using the Internet in the study process were studied; the available resources and tools for learning foreign languages were presented.It is noted that with the advent of quarantine restrictions related to the COVID-19 pandemic, there was a significant change in the education strategy.At the same time, the emergency transition to online work, which took place in 2020, should be distinguished from properly planned online learning, requiring much longer time and careful preparation.The shortcomings and problems having arisen as a result of the rapid transition to online learning were identified.On the other hand, the experience of planned and prepared use of distance technologies in higher education is presented, which provides for long-term training, careful development of educational support and appropriate qualification of scientific and pedagogical staff.It is emphasized that learning a foreign language remotely should be distinguished from self-training.The role of the teacher is important, especially due to the need to provide feedback and choose effective learning tools.In this context, it is noted that the effective distance learning of foreign languages depends primarily on the quality of training of educators in technical and methodological terms.Both synchronous and asynchronous learning tools were analyzed.The advantages and disadvantages of their use were identified.On the basis of the conducted research the ways for optimization of distance technologies use in modern conditions were outlined.
THE EFFECTIVENESS OF THE LEARNING MODULE FOR PROCEDURE TEXT WRITING SKILLS TO IMPROVE WRITING PROCEDURE TEXT SKILLS FOR CLASS XI STUDENTS AT 2 SMK CENTER FOR EXCELLENCE IN HOSPITALITY SECTORS IN KULON PROGO ACADEMIC YEAR 2021/2022

A module is a book written with the aim that students can study independently without or with the guidance of a teacher (Abdul Majid). Meanwhile, in another view, the module is interpreted as a set of teaching materials that are presented systematically so that their use can be learned with or without a facilitator or teacher. Thus, a module must be able to be used as a teaching material as a substitute for the teacher's function. Teaching modules are arranged according to the phase or stage of student development, considering what will be learned with learning objectives, and based on long-term development. So, teachers must understand the concept of teaching modules so that the learning process is more interesting and meaningful (Kemendikbud, 2020). This study aims to determine the effectiveness of the learning module to improve the ability to write procedural texts for class XI students at 2 SMK Centers of Excellence in the Hospitality sector. Quantitative research will be used to research and determine the effectiveness of module development. Respondents in this study were Class XI Hospitality students and teachers. The data collection technique used is a questionnaire method, pretest and posttest. Instrument reliability test was measured using SPSS. The effectiveness of the procedural text writing skill learning module to improve the procedural text writing skills of class XI students at 2 SMK Hospitalilty Sector Excellence Centers. The result data of students writing procedural texts showed that the average score before and after using the learning module increased from 78.16 to 85.09.
3D Object Detection in LIDAR Point Cloud Based on Background Subtraction

Autonomous vehicles have a key role in transportation systems of the future, but there are still many difficulties to overcome.Nowadays one of the most critical problems in autonomous driving is the precise and robust detection of traffic participants.This paper presents a LIDAR-based 3D object detection method.The algorithm uses HD Map to subtract the static background points from the LIDAR point cloud.The remaining points are grouped by clustering, then 3D boxes are fitted to the clusters.The object detection method presented in this paper was tested on real sensor data collected by a solid-state LIDAR on the highway module of the ZalaZONE proving ground.The results showed that the developed algorithm performs as intended in a highway scenario, detecting vehicles even more than 100 meters away from the sensor by a framerate of ~20FPS.
Expensive Multiobjective Optimization Algorithm Based on Equivariate Component Analysis

In order to reduce the cost of candidate solution evaluation in the process of solving expensive optimization problems, an expensive multi-objective optimization algorithm based on equivalence component analysis was proposed to study the influence of decision space equivalence components on the prediction accuracy of agent models.Based on the analysis of the equivalence of decision space attributes, a limit learning network based on the equivalence components was constructed for Pareto dominance prediction among candidate solutions.A multi-objective test problem with equivalent components was selected in Pareto dominance prediction experiments, the results of which showed that the algorithm can effectively improve the accuracy of Pareto dominance prediction among candidate solutions.Successively the candidate solutions were scored with multiple ELM (Extreme Learning Machine) models, selected for evaluation and updated, and integrated into the Pareto-based multi-objective evolutionary algorithm.Through comparative experiments on the test problem, the method could achieve a better Pareto approximation solution under the limitation of a limited number of evaluations, and the goal of reducing the cost of expensive multi-objective optimization calculations.
Property Lists

Abstract Property List files (*.plist) are a widely used data storage format used by Apple software. Most of the system properties are stored in plists, but also, many apps store their configuration in plist-files. The data held within Property is regularly of high evidential value for forensic analysts, so understanding the format is essential for the forensic investigation of Apple mobile devices and computers. Not all of today’s digital forensics tools recover plists properly. Especially for carved or damaged plists, the support is insufficient. So the forensic examiner must understand the principles of this file format. This chapter gives an overview of the plist structure to give the examiner the knowledge to get the most information out of the evidence possible.
Kernel Computations

In Chapter 1, we learned that the kernel $$k(x,y)\in {\mathbb R}$$ represents the similarity between two elements x, y in a set E. Chapter 3 described the relationships between a kernel k, its feature map $$E\ni x\mapsto k(x,\cdot )\in H$$ , and its reproducing kernel Hilbert space H.
Gamification On Verb Form In Teaching Verbs, A Fun And Challenging Way On Learning English

Learning verbs and tenses at once is often quite heavy and boring for students in learning English. Most students will know the verbs and tenses at one time at the moment and will forget at another. To learn it would take time as well. To memorize the kinds of verbs would be provoking and uninterest