Reviewerly/exHarmony huggingface dataset:
EXPLORING TEACHERS’ TECHNIQUE IN TEACHING ENGLISH OF BILINGUAL CLASS AT SMP NEGERI 1 KOTA TERNATE

This study aims to determine the teacher's technique in teaching English in bilingual class written by students of the English language education study program at Khairun University. This study uses a qualitative descriptive method in data collection and analysis. The instruments used were observation and interviews. The results of this study involved 1 subject, namely an English teacher, especially those who teach in bilingual class. From the results of this study, there are several teaching techniques that are used and some that are not used by the teacher to teach in bilingual class. Of the 8 techniques proposed by Amidinah (2015), there are only 3 techniques used during the learning process in the classroom. Those are panel discussion techniques, assignment techniques, and also questionanswer techniques. While the techniques that are not meant are lecture methods, discovery and simulation techniques, Inquiry techniques, travel technique and also experimental techniques. So the reason behind those techniques that do not occur in this teaching process is because they are adapted to the conditions and learning materials taught by the teacher in the classroom. where for travel techniques require students to study outside the classroom, experimental techniques are present if the material taught by the teacher is related to observing an object, lecture techniques only focus more on the teacher, while for discovery and simulation techniques only focus on understanding concepts, meanings and relationships, through an intuitive process to draw a conclusion from the learning material.
STUDENTS’ TRANSLATION OF ENGLISH TEXT INTO INDONESIAN TEXT AT THE FIFTH SEMESTER OF KHAIRUN UNIVERSITY

This study aims to find out: (1) students' difficulties in translating English texts into Indonesian, and (2) the causes of difficulties in translating texts at the fifth Semester students of English Language Education Study Program of Khairun University. This study applies a qualitative descriptive method and in analyzing data using content analysis. This study involved 12 participants. The research instrument used a test in the form of a literary text entitled "An Immortal Soul". The results showed that students had difficulty understanding the meaning of the text, because lack of vocabulary mastery, grammatical issues, and difficulty translating literary texts. The causes of these difficulties include a lack of background knowledge about literary texts, most of the students are still translating word for word and also cultural differences. This shows that fifth semester students still have difficulty translating English texts into Indonesian, especially in literary texts. As a result, students need more practice to get a good translation.
Non-linearities in Driven Spin-Wave Active Ring Oscillator

We have studied the interaction between external microwave signal and the magnetostatic surface spin-wave modes generated in the active ring oscillator. By tuning the strength of the injected signal, the spin-wave ring oscillator can be moved from a non-linear operation region to a linear one and vice-versa.
Physical reservoir computing utilizing voltage controlled magnetic anisotropy effect

Physical reservoir computing using spin-torque oscillator succeeded human-voice recognition with high accuracy. It accompanies, however, large energy dissipation because the magnetization dynamics in the oscillator is driven by electric current and an external magnetic field is also necessary. Here, we show that the magnetization dynamics driven by voltage controlled magnetic anisotropy (VCMA) effect can be applied to physical reservoir computing. It does not accompany Joule heating because of the absence of current. An external magnetic field is also unnecessary because physical reservoir computing does not requires magnetization switching. Instead, a second-order magnetic anisotropy is required for a deterministic computing. Solving the Landau-Lifshitz-Gilbert equation with the second-order magnetic anisotropy field, we find that the computational capability of a magnetic tunnel junction can be tuned by the VCMA effect.
Simple Additive Weighting (SAW) Method to Provide Recommendations for Selection of Graphic Processing Unit (GPU)

CV. Venes Jaya is a company that operates in the field of technological equipment such as computer hardware, monitors, recording equipment, communications equipment, networks, and many more. With the complete range of products sold and reliable teams, this company still has problems in providing recommendations for GPU selection to customers. This process is still carried out manually, namely by providing recommendations according to the funds owned by the customer. If there is more than one alternative that suits the funds owned by the customer, employees will have difficulty choosing them. Such a manual process is likely to be wrong or mistaken. Therefore, there is a need for innovation that can help and make it easier for employees to provide recommendations for GPU selection to customers, namely by implementing a Decision Support System (SPK) that uses the Simple Additive Weighting (SAW) method built with the VB programming language, and a MySQL database. in determining GPU recommendations that will be given to customers. This system is expected to help CV. Venes Jaya in providing GPU recommendations quickly, precisely and accurately. The results obtained from this research are that Alternative 5, namely the Gigabyte RTX 3080 Ti Gaming OC, was selected as the best alternative with a total value of 1. These results are the results obtained from the Decision Support System created by the author, where these results will be used as recommendations to customers in choosing Graphics . Processing Unit (GPU). This system can help CV. Venes Jaya in providing GPU recommendations to customers quickly, precisely and accurately
An Empirical Study on Punctuation Restoration for English, Mandarin, and Code-Switching Speech

Punctuation restoration is a crucial task in enriching automated transcripts produced by Automatic Speech Recognition (ASR) systems. This paper presents an empirical study on the impact of employing different data acquisition and training strategies on the performance of punctuation restoration models for multilingual and codeswitching speech. The study focuses on two of the most popular Singaporean spoken languages, namely English and Mandarin in both monolingual and codeswitching forms. Specifically, we experimented with in-domain and out-of-domain evaluation for multilingual and codeswitching speech. Subsequently, we enlarge the training data by sampling the codeswitching corpus by reordering the conversational transcripts. We also proposed to ensemble the predicting models by averaging saved model checkpoints instead of using the last checkpoint to improve the model performance. The model employs a slot-filling approach to predict the punctuation at each word boundary. Through utilizing and enlarging the available datasets as well as ensemble different model checkpoints, the result reaches an F1 score of 76.5% and 79.5% respectively for monolingual and codeswitch test sets, which exceeds the state-of-art performance. This investigation contributes to the existing literature on punctuation restoration for multilingual and code-switch speech. It offers insights into the importance of averaging model checkpoints in improving the final model’s performance. Source codes and trained models are published on our Github’s repo for future replications and usage.( https://github.com/charlieliu331/Punctuation_Restoration )
Tutor’s support of students in the age of digitalization: integration of digital and social systems (T-University DSTU Case)

This article is devoted to the personalization of education in the case of the strategic project “T-university”, where the educational process is built on the principle of individual educational trajectories. The implementation of the new educational model requires the support the choice of the student arises. The aim of the study is to describe the organization of psychosocial work with students in the conditions of digitalization and its impact on the educational agenda. The choice of the research topic is caused by the rapidly changing trends in the labor market. The chosen methods are individual consultations, creating portfolios and personal resource-maps to help students become future professionals. There is a struggle for specialists with knowledge in several scientific fields simultaneously and with core and professional competencies. There is a shortage of such unique personnel in the market and it creates a demand for their emergence, which is possible only in the course of changing the educational model. The results tutorial sessions have shown that a significant attention should be paid to psychological self-regulation and digitized stress to create a modern professional and created a list of necessary tutor duties.
Organization of elective courses for secondary school students through a specialized web application

The development of information and telecommunication technologies in the field of education is happening rapidly, which leads to the emergence of many different types of web applications. The largest number of web applications in education is aimed at teaching the material directly in the classroom. This article is devoted to that part of the educational process, which web application developers are practically not interested in - optimizing the organization and conduct of elective courses. The authors presented a web application for the time-cafe company "Your time-cafe", focused on booking and organizing elective courses for students in schools. The novelty of the development lies in the use of a PWA approach focused on using the Node.js server platform and the React.js client platform. To work with the platform, the integrated development environment Visual studio code and the JavaScript programming language were chosen. A web application for a time cafe will make the company popular and successful among young people.
Classification of Sentiment Using Optimized Hybrid Deep Learning Model

Sentiment classification plays a pivotal role in natural language processing (NLP), and prior research has established the efficacy of utilizing convolutional neural networks (CNNs) and long short-term memory (LSTM) in this task. However, these approaches suffer from individual performance limitations: CNNs are limited to extracting local information and fail to express context information adequately, while LSTM networks excel at extracting context dependencies but exhibit long training times. To address this issue, we propose a novel text classification algorithm based on a hybrid CNN-LSTM model that leverages the strengths of both approaches and overcomes their limitations by combining them. Our approach is evaluated on the IMDB dataset, and we present a hyperparameter optimization framework utilizing Random Search to increase the likelihood of producing an optimally performing model.
Large Scale Fine-Tuned Transformers Models Application for Business Names Generation

Natural language processing (NLP) involves the computer analysis and processing of human languages using a variety of techniques aimed at adapting various tasks or computer programs to linguistically process natural language. Currently, NLP is increasingly applied to a wide range of real-world problems. These tasks can vary from extracting meaningful information from unstructured data, analyzing sentiment, translating text between languages, to generating human-level text autonomously. The goal of this study is to employ transformer-based natural language models to generate high-quality business names. Specifically, this work investigates whether larger models, which require more training time, yield better results for generating relatively short texts, such as business names. To achieve this, we utilize different transformer architectures, including both freely available and proprietary models, and compare their performance. Our dataset comprises 250 928 observations of business names. Based on the perplexity metric, the top-performing model in our study is the GPT2-Medium model. However, our findings reveal a discrepancy between human evaluation and perplexity-based assessment. According to human evaluation, the best results are obtained using the GPT-Neo-1.3B model. Interestingly, the larger GPT-Neo-2.7B model yields poorer results, with its performance not being statistically different from that of the GPT-Neo-125M model, which is 20 times smaller.
Steganography Approach to Image Authentication Using Pulse Coupled Neural Network

This paper introduces a model for the authentication of large-scale images. The crucial element of the proposed model is the optimized Pulse Coupled Neural Network. This neural network generates position matrices based on which the embedding of authentication data into cover images is applied. Emphasis is placed on the minimalization of the stego image entropy change. Stego image entropy is consequently compared with the reference entropy of the cover image. The security of the suggested solution is granted by the neural network weights initialized with a steganographic key and by the encryption of accompanying steganographic data using the AES-256 algorithm. The integrity of the images is verified through the SHA-256 hash function. The integration of the accompanying and authentication data directly into the stego image and the authentication of the large images are the main contributions of the work.
THE PROGRAM FOR THE DEVELOPMENT OF EMOTIONAL INTELLIGENCE OF ENTREPRENEURS IN STRESS-RELATED CONDITIONS AND EVALUATION OF ITS EFFECTIVENESS

The article is devoted to the scientific substantiation, development and evaluation of the effectiveness of the «Program for the Development of Emotional Intellect of Businessmen in Stress-Associated Conditions». Based on the theoretical and practical results of previous studies, the author outlines the causal and pathogenetic circle of conflict development by entrepreneurs in stressful conditions. It has been found that professional activity is characterized by entrepreneurs as «rather unsatisfactory and unsatisfactory» in all phases of emotional burnout. In order to develop measures for the development of emotional intellect, the concepts of «Emotional potential of businessman» and «Emotional flexibility in stress-associated conditions» are proposed. The consequences of psychosocial stress and coping strategy are classified in three «areas» of the emotional flexibility resource: sufficient, impaired, insufficient. The Program for the Development of Emotional Intellect of Businessmen under Stressful Conditions was developed and implemented, which consists of 4 modules: information and analytical, emotional awareness of businessmen in business activities, management of own emotions by businessmen in business activities, recognition of emotions of others and empathy. Evaluation of the effectiveness of the developed program was conducted on 90 businessmen (study group, n=50; control group, n=40) and revealed the following: 38.0% of the study group had a low level of stress, while only 17.5% of the control group had a low level of stress (p&lt;0.05). The distribution by the integral indicator of emotional intelligence was as follows: GD – 46.0%, HC – 30.0%, which indicated an increase in GD’s ability to be aware of their emotions and feelings, to feel responsible for their own emotional state (p&lt;0.05). In contrast to the HC businessmen, after completing the program activities, in general, in stressful conditions of activity they resumed the use of adaptive and relatively adaptive coping behavior.
Knowledge-Associated Embedding for Memory-Aware Knowledge Tracing

Knowledge tracing (KT) refers to predicting learners’ performance in the future according to their historical learning interactions, which has become an essential task for the computer-aided education (CAE) system. Recent studies alleviate the data sparsity problem by mining higher-order information between questions and skills. However, the effect of multiple skills in the question is not distinguished, and various learning behaviors need to be better modeled. In this article, we propose a knowledge-associated embedding for the memory-aware KT (KMKT) framework. Specifically, we first construct a question-skill bipartite graph with attribute features. A knowledge-associated embedding (KAE) module is proposed to capture the distinctiveness of multiskills via the process of knowledge propagation and knowledge aggregation based on predefined knowledge-paths. Then, to simulate the memory recall phenomenon of the learners in KT, we design a memory-aware module for long short-term memory (MA-LSTM) networks. A temporal attention layer in MA-LSTM is proposed to learn the forgetting mechanism of the human brain. Finally, we introduce a learning-gain (LG) layer to obtain learners’ benefits after each exercise. Extensive experiments on four real-world datasets illustrate that our KMKT model performs better than the other baseline models, which verifies the effectiveness of our work.
Time-Restricted, Verifiable, and Efficient Query Processing over Encrypted Data on Cloud

Outsourcing data users' location data to a cloud server (CS) enables them to obtain k nearest points of interest. However, data users' privacy concerns hinder the wide-scale use. Several studies have achieved Secure k Nearest Neighbor (SkNN) query, but do not address time-restricted access or result privacy, and randomly partition data items which degrades efficiency. In this paper, we propose Time-restricted, verifiable, and efficient Query Processing (TiveQP). TiveQP has three distinguishing features. (1) Expand SkNN: data users can query k nearest locations open at a specific time. (2) Adopt a stronger threat model: we assume the CS is malicious and propose complementary set (i.e., transform proving “in” a set to proving “in” its complementary set) to allow data users to verify results without leaking unqueried data items' information. (3) Improve efficiency: we design a space encoding technique and a pruning strategy to improve efficiency in query processing and result verification. We formally proved the security of TiveQP in the random oracle model. We conducted extensive evaluations over a Yelp dataset to show that TiveQP significantly improves over existing work, e.g., top-10NN query over 100 thousand data items only needs 10 ms to get queried results and 1.4 ms for verification.
Incentive Design for Heterogeneous Client Selection: A Robust Federated Learning Approach

Federated learning (FL) allows the collaborative training of machine learning (ML) models between an aggregation server and different clients without sharing their private data. However, the FL archetype is mostly vulnerable to malicious model updates from various clients because of the privacy feature that makes the server see clients as a black-box. When selecting clients, the server has no control on their contributions during training. This opacity of the server towards clients’ data associated with the huge amount of heterogeneous data brings a security risk and poses a deterioration of the model performance in FL. The impact of client selection and data heterogeneity on FL robustness has been overlooked. In this paper, we develop an Incentive Design for Heterogeneous Client Selection (IHCS) to improve the performance while reducing the security risks in FL. The IHCS approach applies a smarter client selection method using cooperative game theory and dynamic clustering of clients based on their heterogeneity level to overcome the challenges of lacking access to clients’ data, unbalanced data, and the lack of applicable data contribution from clients in FL. To do so, we attribute a recognition value to each client using Shapley Value. This recognition index is then used to aggregate the probability of participation level. We also implement, within the IHCS, a heterogeneity-based clustering (HIC) method that helps inhibit the negative influence of data heterogeneity and increase client contributions. Through extensive experiments with empirical results, the proposed approach outperforms the representative works on robustness of FL.
Mobile Service Robot Path Planning using Deep Reinforcement Learning

A mobile service robot operates in a constantly changing environment with other robots and humans. The service environment is usually vast and unknown, and the robot is expected to operate continuously for a long period. The environment can be dynamic, leading to the generation of new routes or the permanent blocking of old routes. The traditional path planner that relies on static maps will not suffice for a dynamic environment. This work is focused on developing a reinforcement learning-based path planner for a dynamic environment. The proposed system uses the deep Q-Learning algorithm to learn the initial paths using a topological map of the environment. In an environmental change, the proposed β-decay transfer learning algorithm trains the agent in the new environment. This algorithm uses experience vs. exploration vs. exploitation-based training depending on the similarity of the old and new environments. The system is implemented on the Robotic Operating System framework and tested using Turtlebot3 mobile robot in the Gazebo simulator. The experimental results show that the reinforcement learning system learns all the routes based on the initial topological map of different service environments with an accuracy of over 98%. A comparative analysis of the β-decay transfer learning and non-transfer learning agents is performed based on various evaluation metrics. The transfer learning agent converges twice faster than the non-transfer learning agent.
A Hitting Time Analysis for Stochastic Time-Varying Functions with Applications to Adversarial Attacks on Computation of Markov Decision Processes

Stochastic time-varying optimization is an integral part of learning in which the shape of the function changes over time in a non-deterministic manner. This paper considers multiple models of stochastic time variation and analyzes the corresponding notion of hitting time for each model, i.e., the period after which optimizing the stochastic time-varying function reveals informative statistics on the optimization of the target function. The studied models of time variation are motivated by adversarial attacks on the computation of value iteration in Markov decision processes. In this application, the hitting time quantifies the extent that the computation is robust to adversarial disturbances. We develop upper bounds on the hitting time by analyzing the contraction-expansion transformation appearing in the time-variation models. We prove that the hitting time of the value function in the value iteration with a probabilistic contraction-expansion transformation is logarithmic in terms of the inverse of a desired precision. In addition, the hitting time is analyzed for optimization of unknown continuous or discrete time-varying functions whose noisy evaluations are revealed over time. The upper bound for a continuous function is super-quadratic (but sub-cubic) in terms of the inverse of a desired precision and the upper bound for a discrete function is logarithmic in terms of the cardinality of the function domain. Improved bounds for convex functions are obtained and we show that such functions are learned faster than non-convex functions. Finally, we study a time-varying linear model with additive noise, where hitting time is bounded with the notion of shape dominance.