Reviewerly/exHarmony huggingface dataset:
Improved two-stage hate speech classification for twitter based on Deep Neural Networks

Hate speech is a form of online harassment that involves the use of abusive language, and it is commonly seen in social media posts. This sort of harassment mainly focuses on specific group characteristics such as religion, gender, ethnicity, etc and it has both societal and economic consequences nowadays. The automatic detection of abusive language in text postings has always been a difficult task, but it is lately receiving much interest from the scientific community. This paper addresses the important problem of discerning hateful content in social media. The model we propose in this work is an extension of an existing approach based on LSTM neural network architectures, which we appropriately enhanced and fine-tuned to detect certain forms of hatred language, such as racism or sexism, in a short text. The most significant enhancement is the conversion to a two-stage scheme consisting of Recurrent Neural Network (RNN) classifiers. The output of all One-vs-Rest (OvR) classifiers from the first stage are combined and used to train the second stage classifier, which finally determines the type of harassment. Our study includes a performance comparison of several proposed alternative methods for the second stage evaluated on a public corpus of 16k tweets, followed by a generalization study on another dataset. The reported results show the superior classification quality of the proposed scheme in the task of hate speech detection as compared to the current state-of-the-art.
WeaSuL: Weakly Supervised Dialogue Policy Learning: Reward Estimation for Multi-turn Dialogue

An intelligent dialogue system in a multi-turn setting should not only generate the responses which are of good quality, but it should also generate the responses which can lead to long-term success of the dialogue. Although, the current approaches improved the response quality, but they over-look the training signals present in the dialogue data. We can leverage these signals to generate the weakly supervised training data for learning dialog policy and reward estimator, and make the policy take actions (generates responses) which can foresee the future direction for a successful (rewarding) conversation. We simulate the dialogue between an agent and a user (modelled similar to an agent with supervised learning objective) to interact with each other. The agent uses dynamic blocking to generate ranked diverse responses and exploration-exploitation to select among the Top-K responses. Each simulated state-action pair is evaluated (works as a weak annotation) with three quality modules: Semantic Relevant, Semantic Coherence and Consistent Flow. Empirical studies with two benchmarks indicate that our model can significantly out-perform the response quality and lead to a successful conversation on both automatic evaluation and human judgement.
Pengaruh Kesesuaian Kompensasi Dan Efektivitas Pengendalian Internal Terhadap Kecenderungan Kecurangan Akuntansi

The fact that accounting fraud is caused by several factors including weak internal control and inadequate compensation. The purpose of this study was to determine the effect of compensation and the effectiveness of internal control on accounting fraud. This research was conducted in village administrations in West Denpasar District. The number of samples taken was 31 respondents. The sampling technique used is saturated sampling, while the data analysis technique used is the descriptive test, classical assumption test consisting of normality test, multicollinearity, and heteroscedasticity test, instrument test consisting of validity and reliability test, test the right model that consists of coefficient of determination (adjusted R2), F test, and statistical test (t-test), and multiple linear regression analysis. The test results show that there is a positive and significant effect of Compensation Suitability on Accounting Fraud. Judging from the results of the t-test, the parameter coefficient value is 0.442 with a significance level of 0.001. There is a positive and significant effect on the Effectiveness of Internal Control on Accounting Fraud. Judging from the results of the t-test, the parameter coefficient value is 0.713 with a significance level of 0.000.
Preface to the First Issue

Welcome to the first issue of Artificial Intelligence and Applications (AIA) Journal. Recently, we are seeing the rapid development of Artificial Intelligence (AI) field, evidenced by the fast-growing published papers and increased practical applications. Artificial intelligence is a discipline of science and technology for making intelligent machines to simulate human abilities in learning, perception, thinking, decision, behaviour and interaction. The technology is widely used in various areas in the society, including sensing data analysis and understanding, big data analytics, security surveillance, management and planning, manufacturing, education, medical care, robotics, unmanned driving, and so on. The artificial intelligence field was found in 1956, when the term “artificial intelligence” was coined and came into the open. Since then, many theories, methods and algorithms have been developed under the main paradigms of symbolism, connectionism and behaviourism. The large boom came in the latest decade. The milestones of successful applications of deep neural networks (DNNs) in large scale image classification in 2012 and computer Go game in 2016 drew the eye of AI researchers to the deep learning approach and made AI widely known to the society. The research is extended to all aspects of intelligence science and technology, and the application of AI is extended into all scenarios of our life. The published papers are quickly increasing in existing journals and conferences, and new journals and conferences are being established. In preparing this journal, we selected the name Artificial Intelligence and Applications (AIA) because both research and application are important to the field and both are fast developing. Real-life applications of AI are enabled in recent years because the developed methodologies have led to improved performance satisfying users’ needs. On the other hand, the extensive applications also pose scientific and technical challenges which attract researchers’ attention. Such interaction between research and application will continually drive the evolution of the field. The Artificial Intelligence and Applications (AIA) is a peer-reviewed journal publishing original contributions to the theory, methods and applications of artificial intelligence. Qualified papers are the most important factor to establish the fame and impact of the journal. We solicit papers reporting novel ideas, innovative technology or applications in the AI field. We thank the authors of all submitted papers for their great efforts in supporting our journal. Let’s grow up together with the AIA Journal!
CIP- Efficient Method for Mining Frequent Itemsets from Data streams using Landmark Window Model

Continuous stream transactions like network monitoring, retail market data analysis and stock market prediction need the “frequent patterns” to be detected recurrently. Literature suggests that several pattern mining solutions are being developed over years. Still lot of challenges need to be addressed due to rapidness in generation of continuous, unbounded and ordered data real time. Hence extraction of frequent patterns from recent data will improve the analysis of stream data. In this article, a new landmark window model CIP (candidate indexing and pruning) is considered for mining the datasets. CIP allows us to mine over entire history of data streams, which improves the accuracy. This article also proposes the candidate indexed sub (CIS)-tree scheme to extract the essential information from each incoming transactions of data streams. Our proposal is compared with the existing “improved data stream mining” (ISDM) for maximal frequent itemsets algorithm. Extensive experimental analyses prove the superiority of the proposed CIP over the popular ISDM in terms of accuracy and time complexity for high-speed data stream. This article also covers up a case study where the proposed approach is applied for an application called “web prefetching”.
Didactic Strategies to Improve the Reading Comprehension Skill in the English Language Teaching-Learning Process

To analyze the didactic strategies to improve reading comprehension skills in the teaching-learning process of the English language, a descriptive qualitative and quantitative research was carried out at the "Nuevo Ecuador" Senior High School, in Esmeraldas, in 2019.The population was made up of 1.089 students and 5 teachers.The sample of this research consisted of 102 students and 5 teachers, the data being intentionally selected, as it was the author's workplace and was immersed in the problems of the research addressed.The methods used were analysis and synthesis and hermeneutics, with survey and test techniques.The results showed that most of students read when the teacher ordered them, reflecting that there was no adequate motivation to read information in English.In addition, there was little evidence of the students' likes to read aloud.Some students showed understanding of reading texts using dictionaries, with scanning as the most frequently used reading strategy.Despite their low motivation, the greater percentage of them felt more comfortable looking at pictures and reading magazines.It was concluded that the students needed to reinforce more strategies to understand texts efficiently.
Research on current situation of physical education in universities of central north cluster in Vietnam

On the basis of theoretical and practical research on improving physical education in comprehensive educational reform and surveying the current situation of physical education quality assessment of students in Central North cluster university according to the current training program. A survey of 1586 students and 40 experts in Physical Education from 03 universities in the Central North cluster: Hanoi Pedagogical University 2, Tan Trao University and Hung Vuong University showed that learning Physical education is affected by many subjective and objective factors. This work investigates and researches the current situation of physical education in the universities of the Central North cluster, thereby emphasizing that in order to improve the quality of physical education, learning outcomes and movement activities, schools need to focus on three innovative training solutions in the direction of competency-based approach for students, including: Solution 1: Solution on extracurricular program; Solution 2: Solution on teaching method innovation; Solution 3: Solution on socialization of facilities and equipment. The solutions received high consensus of the research subjects.
Implementation Prototype Method on Queue System Development on Android Application

The rapid development of information and communication technologies has brought many changes and solutions to facilitate human work.One of the problems that can be solved through the use of technology is by providing a solution using a queuing system.For example, at the Bank BRI Tanjungpinang Branch Office, the queuing system is still carried out manually by humans.For faster and better management, an individual can use the android application for a digital queuing system where users do not need to come to the bank and wait until the queue arrives, users can take a number and wait an hour.Therefore, this research aimed to design and build an Android-based digital queuing system using the prototype method, which consists of three stages, namely listening to the user, building a prototype, and testing by the user.Additionally, an Android-based digital queuing system was built and testing was also carried out using BlackBox testing.
The Use of Blended and Hybrid Learning in Contemporary Russian Pedagogical Practice

In the conditions of high dynamics of digitalization of all spheres of public life, the active use of blended and hybrid learning in educational practice is of particular importance. These phenomena have been introduced into the Russian education system relatively recently, which requires their careful consideration and detailed analysis. In the course of the study, the author of the article found that in the conditions of Russian pedagogical practice today there is no common understanding of the term “blended learning”, as there is no specifics in the differentiation of blended and hybrid learning, which gives rise to frequent substitution of concepts and the lack of detailing the difference in these phenomena. At the same time, the analysis showed that blended and hybrid learning are, although similar, but still not identical processes. In particular, hybrid learning involves the priority use of online formats, as well as the parallel, rather than sequential, implementation of innovative digital and traditional educational technologies, so that the difference between online participants and face-to-face participants is practically erased. The study also revealed the advantages, disadvantages and main problems in the implementation of blended and hybrid learning in Russia; emphasis is placed on the transformation of the teacher’s functions in the context of the transition to mixed forms. In particular, it has been established that it is not teaching that becomes the dominant function, but the organization and management of the pedagogical process, its methodological support using the synthesis of various educational technologies, advising students at all stages of mastering educational programs.
Rationale for the Need to Introduce Interactive Cases in the Practice of Training Managers

The main purpose of the article is to substantiate the need to change the concept of training managers. On the basis of practical experience, the difficulties that arise when mastering the skills necessary for future professional activity are formulated. The limitations of the applied teaching methods are revealed: cases, business games, discussions and internships. The essence of the new teaching methodology "interactive case" is determined, its technology is described. A demonstration of an example of using an interactive case in the practical work of a teacher is given. The project is implemented by the winner of Master's Program Faculty Grant Competition of the Vladimir Potanin Fellowship Program.
Implementation of Fuzzy Logic Algorithm on Robot Arm Sorting Goods Based on Weight and Color Using the Mamdani Method

The development of goods sorting technology has used automatic control systems and robotic that work more effectively and save energy.Therefore, this research aimed to design a robot arm for sorting goods based on weight and color by implementing a fuzzy logic algorithm with the Mamdani method to simplify human work.The robot arm uses the MG995 servo, while weight and color use the Load Cell and TCS3200 sensors.The results showed that the Load Cell sensor calibration has the lowest and highest accuracy of 97.64% and 100%, respectively.The TCS3200 had the smallest and largest standard error of 0.163 and 2.468, which indicated that both sensors had good accuracy.Decision-making using the fuzzy logic algorithm Mamdani method was successfully implemented with 100% accuracy.Therefore, the robot arm can move goods in the box based on the fuzzy decision quality output.
Syntax-Aware On-the-Fly Code Completion

Code completion aims to help improve developers' productivity by suggesting the next code tokens from a given context. Various approaches have been proposed to incorporate abstract syntax tree (AST) information for model training, ensuring that code completion is aware of the syntax of the programming languages. However, existing syntax-aware code completion approaches are not on-the-fly, as we found that for every two-thirds of characters that developers type, AST fails to be extracted because it requires the syntactically correct source code, limiting its practicality in real-world scenarios. On the other hand, existing on-the-fly code completion does not consider syntactic information yet. In this paper, we propose PyCoder to leverage token types, a kind of lightweight syntactic information, which is readily available and aligns with the natural order of source code. Our PyCoder is trained in a multi-task training manner so that by learning the supporting task of predicting token types during the training phase, the models achieve better performance on predicting tokens and lines of code without the need for token types in the inference phase. Comprehensive experiments show that PyCoder achieves the first rank on the CodeXGLUE leaderboard with an accuracy of 77.12% for the token-level predictions, which is 0.43%-24.25% more accurate than baselines. In addition, PyCoder achieves an exact match of 43.37% for the line-level predictions, which is 3.63%-84.73% more accurate than baselines. These results lead us to conclude that token type information (an alternative to syntactic information) that is rarely used in the past can greatly improve the performance of code completion approaches, without requiring the syntactically correct source code like AST-based approaches do. Our PyCoder is publicly available on HuggingFace and GitHub.
Combination of multiple neural networks using transfer learning and extensive geometric data augmentation for assessing cellularity scores in histopathology images

Classification of cancer cellularity within tissue samples is currently a manual process performed by pathologists. This process of correctly determining cancer cellularity can be time intensive. Deep Learning (DL) techniques in particular have become increasingly more popular for this purpose, due to the accuracy and performance they exhibit, which can be comparable to the pathologists. This work investigates the capabilities of two DL approaches to assess cancer cellularity in whole slide images (WSI) in the SPIE-AAPM-NCI BreastPathQ challenge dataset. The effects of training on augmented data via rotations, and combinations of multiple architectures into a single network were analyzed using a modified Kendall Tau-b prediction probability metric known as the average prediction probability PK. A deep, transfer learned, Convolutional Neural Network (CNN) InceptionV3 was used as a baseline, achieving an average PK value of 0.884, showing improvement from the average PK value of 0.83 achieved by pathologists. The network was then trained on additional training datasets which were rotated between 1 and 360 degrees, which saw a peak increase of PK up to 4.2%. An additional architecture consisting of the InceptionV3 network and VGG16, a shallow, transfer learned CNN, was combined in a parallel architecture. This parallel architecture achieved a baseline average PK value of 0.907, a statistically significantly improvement over either of the architectures' performances separately (p<0.0001 by unpaired t-test).
Developing E-Learning Courses in a Gaming Environment with an Integrated Assistant Bot for Secondary School Students

Currently, information technologies are mainly used in higher education. At the same time, there are problems in the process of teaching secondary school students. Therefore, it is necessary to offer technologies that would increase the effectiveness of the learning process. Given the popularity of the Minecraft game and the development of the use of the chatbot, it is proposed to integrate these technologies. The paper investigates the use of various information technologies by secondary school students. As a result, conclusions are drawn about the need for the development of information technologies. The development of electronic training courses with an integrated assistant bot for secondary school students of courses on the basics of digital circuitry and programming in Python is described. The behavior of the assistant bot during training and completing course tasks is described. It is assumed that such an approach will increase the involvement of students in the learning process and homework.
Flexibility in Learning Management and Technology Preparedness Among Faculty Members in Higher Education Institutions

Faculty members were challenged to continue teaching beyond the traditional mode during the COVID- 19 pandemic. Therefore, the research focused on the level of management functions and technological preparedness of the faculty members with permanent employment status under the business programs of higher education institutions (HEIs) in Nueva Vizcaya. The idea behind the research was drawn from the theories of planning, organizing, leading, and controlling and technology preparedness in terms of technology access, technology skills and literacy, and attitude toward technology. The study’s findings revealed that faculty members have a high level of management and technology preparedness. Moreover, the study revealed that the profile of the faculty members had no relationship to their level of implementation of management strategies. On the other hand, younger faculty members have greater technology access and literacy skills. Furthermore, profiles were not a significant factor in their level of attitude toward technology. More results showed that when the level of implementation of management strategies is high, the result is also high in terms of technological preparedness. Based on the study’s findings, the researcher recommended an enhanced program for better implementation of flexible learning in HEIs.
Federated Knowledge Distillation

Distributed learning frameworks often rely on exchanging model parameters across workers, instead of revealing their raw data. A prime example is federated learning that exchanges the gradients or weights of each neural network model. Under limited communication resources, however, such a method becomes extremely costly particularly for modern deep neural networks having a huge number of model parameters. In this regard, federated distillation (FD) is a compelling distributed learning solution that only exchanges the model outputs whose dimensions are commonly much smaller than the model sizes (e.g., 10 labels in the MNIST dataset). The goal of this chapter is to provide a deep understanding of FD while demonstrating its communication efficiency and applicability to a variety of tasks. To this end, towards demystifying the operational principle of FD, the first part of this chapter provides a novel asymptotic analysis for two foundational algorithms of FD, namely knowledge distillation (KD) and co-distillation (CD), by exploiting the theory of neural tangent kernel (NTK). Next, the second part elaborates on a baseline implementation of FD for a classification task, and illustrates its performance in terms of accuracy and communication efficiency compared to FL. Lastly, to demonstrate the applicability of FD to various distributed learning tasks and environments, the third part presents two selected applications, namely FD over asymmetric uplink-and-downlink wireless channels and FD for reinforcement learning.
PENGEMBANGAN PERANGKAT PEMBELAJARAN MENULIS CERPEN BERBASIS PROYEK DAN BERPIKIR TINGKAT TINGGI PESERTA DIDIK KELAS IX SMP

The learning process cannot be seperated from learning tools, namely tools that assist learning that contain lesson plans and describe in detail the skills students will acquire. The research aims to provide appropriate and effective learning tools for project-based short story writing and higher order thinking. The work employes the development research, which refers to the ADDIE model. The subjects of this study were Indonesian language teachers and students of SMPN Satu Atap 4 Tungkal Ulu. The results showed that the development of learning materials for project-based short story writing and higher-level thinking was declared valid, practical with an average score of 84.58%. The use of project-based short story writing learning tools and higher-order thinking is declared effective for improving student learning outcomes.