Reviewerly/exHarmony huggingface dataset:
DIGITAL BULGARIA IN PROLOG PROJECT

2022 marks the 50th anniversary of the logic programming language Prolog. In this regard, the authorsreceived an invitation from the creator of the language – Robert Kowalski, to participate in a multi-year international initiative known as „The virtual Prolog ‘School Bus’ activity“. The initiative aims to acquaint students mainly with logical programming and Artificial Intelligence through the Prolog language. The article presents an idea for a project called „Digital Bulgaria in Prolog“, which was confirmed as part of the above international initiative. Furthermore, the structure of the project and the possibilities for involving students from Bulgarian schools are discussed. The main topics of the project, such as Bulgarian cultural-historical heritage, folklore, history, geography, language, literature, etc., are also presented. Some examples demonstrating the application of logic programming to implement knowledge repositories and suitable inference engines, regardless of the subject, are presented as well.
Human-Computer Interaction Problem in Learning: Could the Key Be Hidden Somewhere Between Social Interaction and Development of Tools?

Homo sapiens is not just a tool-using species, they also can invent and develop tools. This is the feature that distinguishes humans from other species. It is necessary to get rid of the perceptual dominance of the present state of the material to invent and develop a tool. It is based on a mental process: designing. So how was that possible? In this article, I propose an evolutionary hypothesis in response to this question: the referential triangle. Accordingly, the relationship that people establish with things is mentally indirect, but the relationship they establish with each other is mentally direct. The hypothesis claims that the mental solutions of people have naturally established with each other are also used to invent and develop tools. The latest and most interesting product of this mechanism is artificial intelligence. Because artificial intelligence also acts as an inorganic system. What distinguishes it from other machines in this context is its social behavior. Artificial intelligence can generate social signals. So can artificial intelligence be both a tool and a partner at the same time in the referential triangle established with the tools by the human? In other words, can children, for instance, socially interact with artificial intelligence, just as they do naturally with people around? The article draws attention to that this problem should be included in the cultural psychological research agenda.
Mobile Application for Teaching and Learning

When Covid-19 was first reported in Kenya on March 13, 2019, education at every level – from elementary school to professional training – was disrupted and had to shift online. Many learning institutions including universities were unprepared for online or distance learning modes of learning. Those that were unable to shift online were forced to close down indefinitely. Some took over a year to figure out how to resume learning. Internet connectivity presented another challenge. The staff and students had to be trained on how to use education portals, Moodle, Zoom or Google Meet. Going online required teachers and students to have internet bundles which according to the Communication Commission of Kenya (CCK) costs Sh240 (USD2.25) for one GB of the Internet. This is too expensive for many to afford. My innovation seeks to help learners access learning content through their mobile phones. It also seeks to address the issue of climate change by reducing the over-reliance on papers in teaching at the university.
Sentiment Analysis on Movie Review Data Using Ensemble Machine Learning Approaches

Today’s machine learning application has a huge impact on the current scenario that is big changes is afoot in the marketing world and shifts are largely down to the power of machine learning. It is about finding pieces of predictive knowledge, it has capable in the field of NLP to understand, analyze, manipulate and potentially generate human language, Truly listening to a customer's voice requires deep understanding of what they have expressed in natural language. NLP is the best way to understand this and uncover the sentiment behind it. Here we have proposed a sentiment analysis for movie review, from the movie review dataset. We have implemented the data set by using of Naive Bayes, Logistic Regression, Random Forest algorithm, Support Vector Machine, Multinomial Naïve Bayes, Stochastic Gradient descent classifier along with combining all those as an ensemble with voting majority techniques with features parameters such as positive, negative, neutral, partially positive, partially negative. We get the result of the proposed ensemble technique is better than individual algorithms with 94% of accuracy.
Realization of Presentation layer information of Legacy Java Enterprise Applications Through Design Pattern’s Recovery

The presentation layer is the outermost layer of an application that provides user interface and communication services. This layer is responsible for session management, controlling client access, and validations within data from the client .In legacy enterprise applications like Java Enterprise Edition Platform (Java EE),the design considerations of the presentation layer are spread over different design patterns and cross-language constructs. Resultantly, the analysis of such applications becomes quite challenging due to their heterogeneity, essentially required for the extraction of design-level information and further modernization. In this research ,a flexible technique is presented to extract presentation tier information based on customizable feature types by recovering instances of presentation tier patterns of the Java Enterprise Edition Platform. The proposed approach is evaluated on well-operative open-source Enterprise Applications. The validation results demonstrate the extraction of presentation tier information through Design Pattern’s recovery.This prototype is validated on the repository of source code of Java applications as well on open source java applications.
Connecting the Unconnected in the Global South: Bridging the Digital Divide from a Critical Perspective

Extant literature on information and communication technology for development (ICT4D) has increased the understanding of specific aspects of digital inclusion and development. However, theoretical questions regarding local social issues discussed by locally based researchers remain underrepresented in the ICT4D realm. Through a narrative review of the digital inclusion studies in IS literature and applying as the theoretical lenses the capability approach, network society and critical consciousness, this study proposes a Digital Inclusion for Emancipation (DI4E) conceptual model. This model recognizes the digital inclusion dimensions, as well as the underlying assumptions and limitations in the context of low- and middle-income countries (LMICs). Grounded on a critical interpretive approach, the main objective of the study is to shed light on the different aspects and viewpoints of digital inclusion from a local perspective to unveil potential challenges and opportunities to promote emancipation for the most marginalized groups.
Orthogonal SVD Covariance Conditioning and Latent Disentanglement

Inserting an SVD meta-layer into neural networks is prone to make the covariance ill-conditioned, which could harm the model in the training stability and generalization abilities. In this article, we systematically study how to improve the covariance conditioning by enforcing orthogonality to the Pre-SVD layer. Existing orthogonal treatments on the weights are first investigated. However, these techniques can improve the conditioning but would hurt the performance. To avoid such a side effect, we propose the Nearest Orthogonal Gradient (NOG) and Optimal Learning Rate (OLR). The effectiveness of our methods is validated in two applications: decorrelated Batch Normalization (BN) and Global Covariance Pooling (GCP). Extensive experiments on visual recognition demonstrate that our methods can simultaneously improve covariance conditioning and generalization. The combinations with orthogonal weight can further boost the performance. Moreover, we show that our orthogonality techniques can benefit generative models for better latent disentanglement through a series of experiments on various benchmarks. Code is available at: https://github.com/KingJamesSong/OrthoImproveCond.
Optimal Decoding of Hidden Markov Models with Consistency Constraints

Hidden Markov Models (HMM) are interpretable statistical models that specify distributions over sequences of symbols by assuming these symbols are generated from hidden states. Once learned, these models can be used to determine the most likely sequence of hidden states for unseen observable sequences. This is done in practice by solving the shortest path problem in a layered directed acyclic graph using dynamic programming. In some applications, although the hidden states are unknown, we argue that it is known that some observable elements must be generated from the same hidden state. Finding the most likely hidden state in this contrained setting is however a hard problem. We propose a number of alternative approaches for this problem: an Integer Programming (IP), Dynamic Programming (DP), a Branch and Bound (B &B) and a Cost Function Network (CFN) approach. Our experiments show that the DP approach does not scale well; B &B scales better for a small number of constraints imposed on many elements and CFNs are the most robust approach when many smaller constraints are imposed. Finally, we show that the addition of consistency constraints indeed allows to better recover the correct hidden states.
OptimizingMARL: Developing Cooperative Game Environments Based on Multi-agent Reinforcement Learning

Intelligent agents are critical components of the current game development state of the art. With advances in hardware, many games can simulate cities and ecosystems full of agents. These environments are known as multi-agent environments. In this domain, reinforcement learning has been explored to develop artificial agents in games. In reinforcement learning, the agent must discover which actions lead to greater rewards by experimenting with these actions and defining a search by trial and error. Specifying when to reward agents is not a simple task and requires knowledge about the environment and the problem to be solved. Furthermore, defining the elements of multi-agent reinforcement learning required for the learning environment can be challenging for developers who are not domain experts. This paper proposes a framework for developing multi-agent cooperative game environments to facilitate the process and improve agent performance during reinforcement learning. The framework consists of steps for modeling the learning environment and designing rewards and knowledge distribution, trying to achieve the best environment configuration for training. The framework was applied to the development of three multi-agent environments, and tests were conducted to analyze the techniques used in reward design. The results show that the use of frequent rewards favors the emergence of essential behaviors (necessary for the resolution of tasks), improving the learning of agents. Although the knowledge distribution can reduce task complexity, dependency between groups is a decisive factor in its implementation.
Contributing Factors and Problems Affecting the English Listening Comprehension Skills of EIC Undergraduate Students at RMUTL Tak

This research aimed to investigate the significant contributing factors and to discover the existing problems affecting the English listening comprehension skills of EIC undergraduate students at RMUTL Tak. The participants were 46 3rd and 4th year students. The research instrument for this quantitative study was an online questionnaire by which data was gathered and analyzed by calculating the mean (x ̅) and the standard deviation (SD) for each factor. Open coding was used to color code the data and then frequency distribution (f) was used to calculate percentage (%) for the existing problems. The results revealed that an overview of the 4 categories of factors affecting English listening comprehension was at a high level (x ̅ = 3.80, SD = 0.763), and the significant contributing factor which most influenced English listening comprehension skills was the factor relating to a speaker’s influence on listening comprehension skills: the speaker spoke too quickly (x ̅ = 4.10, SD = 0.706). Speakers’ accents were the most outstanding problems affecting English listening comprehension (f = 21, 31%). Furthermore, both the factors and the problems highlighted were crucial information required to remedy and improve students' English listening comprehension skills.
ASSESSMENT OF THE PROFESSIONAL COMPETENCE OF A RURAL SCHOOL TEACHER

The purpose of the work is to summarize the results of a pilot study of the assessment of the structural components of the professional competence of a rural school teacher, to determine the principles and directions of research on the design of a diagnostic map for assessing the professional deficits of a teacher working in a rural school.&#x0D; Research methods. In the course of the research, a theoretical analysis of pedagogical literature was carried out, which allowed to clarify the degree of relevance and depth of study of the problem under study. The conducted pilot study of each teacher’s assessment of their level of pedagogical competence made it possible to determine the direction of designing practice-oriented tasks in order to diagnose professional deficits of a rural school teacher.&#x0D; The results of the study. The theoretical analysis of the structural components of the professional competence of the teacher in the aspect of the implementation of the requirements of the updated Federal State Educational Standards and the developed draft Professional standard of the teacher. The results of a pilot study of the assessment of the structural components of the professional competence of a rural teacher, including a small school, are shown. The blocks of teachers’ competencies to be evaluated are defined: normative, subject, psychological and pedagogical, methodological and analytical. Various models for assessing the level of formation of professional competencies of teachers are presented and analyzed. The principles and directions of applied research on the design of a diagnostic card for assessing professional deficits of a teacher working in a rural school are determined.&#x0D; The scope of the results. The article will be useful for teachers of pedagogical universities, organizations of additional professional education, students studying in the areas of “Pedagogical education”.
Analysis of Reasons for Financial Fraud of TY Shares

China's capital market lags behind that of western countries.The Shanghai Stock Exchange, the first stock exchange after China's reform and opening up, was formally established on November 26, 1990, and the Shenzhen Stock Exchange began its trial operation on December 1, 1990.The overall market has great potential for development.So far, there has been a fairly good market scale.No matter from the number of listed companies, the amount of financing and investment, or the number of shareholders involved, we can see the vigorous development of securities in China.However, in recent years, financial frauds of listed companies in China have frequently occurred, such as the inflated huge amount of money of Kangmei Pharmaceutical, the illegal disclosure of information by Wanfu Shengke, and the fraudulent issuance of shares by Green Land, such as the falsification of documents and fabricated statements by Yinguangxia, etc. From a macro point of view, financial fraud disturbs the order of China's capital market, reduces China's ability to allocate resources in the market and hinders the sound development of the capital market.Due to the various financial fraud methods of TY shares, the inflated operating profit amount is huge, the personnel involved are numerous, and the fraud case is serious, which was given the top punishment by the CSRC and 2019.Therefore, it is necessary to analyze the financial fraud cases of TY shares, which can be used as a reference for subsequent financial fraud of other companies and also as a warning for other companies.
The Construction of Knowledge Graphs in the Field of Education under the Perspective of Artificial Intelligence

Knowledge graphs in education are a hot topic for research and application in the era of artificial intelligence and big data. Based on the systematic review of the existing research on the construction of knowledge graphs in education, the article proposes a knowledge graph framework for education domain - construction of a corpus of knowledge graphs in education domain - automatic recognition of named entities - parallel mining of entity relations - fusion of disciplinary knowledge graphs in education domain under the view of artificial intelligence using deep learning algorithms. This paper proposes a method for automatic construction of knowledge graphs in the education domain, with a view to promoting the development of knowledge graph research in the education domain in China.
Pengembangan E-Book Interaktif sebagai Media Tambahan Pembelajaran Bahasa Inggris di Kelas X SMA

This study aims to develop interactive electronic book learning media (E-book) in English subject and determine the feasibility of electronic book learning media (E-book). This study uses a Research and Development approach. The setting of this research is in SMA Darul Karamah Singosari. The object of this research is an interactive electronic book of English subject. The data collection technique is a questionnaire method, which is used to analyze the needs of students in learning English online, then the second questionnaire is used to collect data about the feasibility of English learning media, including: material expert and media expert. The method of data analysis is expressed in the distribution of scores of five scales to a predetermined rating scale category. From the research results, among others: the material expert test obtained a score of 76% in good category, the media expert test obtained a score of 78% in good category, so that the interactive e-book learning media in English subjects is appropriate in the teaching and learning activities for the students of Tenth Grade Senior High School
Computer Vision-based Waste Detection and Classification for Garbage Management and Recycling

Waste management systems and their inherent problems are still a matter of great concern amid this cutting edge of science and technology. Piles of untreated waste in open locations contribute to a variety of issues, including air and water pollution, messy and unpleasant workplaces, waste of recyclable products, potential health risks for waste management workers, and so on. The root cause of this problem points to a single fact—too much manual labour involved in the garbage treatment process (collection, separation and recycling)—that can’t keep up to the pace with which garbage generation happens. An efficient recycling method is imperative to solve this problem, which can be achieved by a fast, real-time garbage detection and classification system. In this research, we will propose a novel Deep Learning-based approach for automatic detection and classification of five kinds of waste materials, namely Kitchen Waste, Glass Waste, Metal Waste, Paper Waste and Plastic Waste, from the garbage dump for an efficient recycling process, which not only improves the efficiency of the current manual approach but also provides a scalable solution to the problem. The contributions of this paper include a fully human labelled dataset consisting of 2,200 images of a garbage dump with 135,541 annotated objects from the aforementioned categories and a real-time garbage object localisation and classification framework based on a lightning-fast, fairly accurate and end-to-end deep learning algorithm. For the baseline, we have articulated a single-stage object detection and classification framework and initialised the detector training process with pre-trained weights (trained on MS COCO Dataset) of the feature extractor. Then, with some fine-tuning and employing a few transfer learning tricks, we have proposed a waste object detection framework that yields a mAP of 66.08% at an IoU threshold of 0.35 with an inference speed of roughly 55–58 milliseconds in a single GPU environment on both images and videos. It surpasses the performances of all the contemporary frameworks which deal with waste separation tasks.
Task-based approach in the training of future teachers-musicians to creative work with learners

The article reveals the peculiarities of the implementation of the problem-based approach in the professional training of future teachers of Art disciplines. The methods of systematization of scientific research, analysis of psychological and pedagogical literature, classification of pedagogical tasks are used for research. The author proves the leading role of pedagogical tasks in the innovative and creative activity of the teacher, analyses their essential importance for the implementation of pedagogical innovations to provide for the productive education of students. A pedagogical task is defined as a meaningful pedagogical situation aimed at transforming reality, as a means of student activity to achieve certain educational goals. The scientific position is substantiated: the solution to the pedagogical problem is ensured by the dialectical processes of pedagogical thinking being in a complex interaction of its theoretical and practical types. Basing on generalisation of scientific material, the article formulates the requirements for the construction of musical and creative tasks for elementary school students: taking into account the psycho-physiological features of the development of learners; optimality of the ratio between complexity and accessibility of musical material; originality of the form used for material representation; informativeness of the artistic content of the task. The author formulates the principles of organizing the heuristic activity of students-musicians based on the problem-based approach: integrity, informativeness, technological capability, problematicity, integrability, variability. Their importance is determined in the context of professional training of students in practical musical and creative work with a schoolchildren team. Thus, the author proved the responsibility of the higher school for the personality formation of a professional teacher, capable of creativity, able to find new creative means, original and highly productive forms and methods of musical and creative interaction in the process of artistic activity while working with children.