Reviewerly/exHarmony huggingface dataset:
Weighted Ensemble LSTM Model with Word Embedding Attention for E-Commerce Product Recommendation

Nowadays, the proliferation of social media and e-commerce platforms is largely due to the development of internet technology. Additionally, consumers are used to the idea of using these platforms to share their thoughts and feelings with others through text or multimedia data. However, it is difficult to identify the best categorization methods for this type of data. Furthermore, users are seen to have difficulty understanding aspect-based feelings conveyed by other users, and the currently existing modelsâ accuracies are not up to par. Deep learning models used for sentiment analysis (SA) provide improved performance by finding out the actual emotions in the presented data. The aim of this research is to develop a weighted ensemble with Long Short-Term Memory (LSTM), and a specialised deep learning model using unique word embedding approaches to enhance its performance in sentiment analysis. The words with a strong connection to a particular class are given more weight by the Word Embedding Attention (WEA) technique. The weighted ensemble with LSTM yields superior outcomes because of its excellent generalization capabilities. By integrating the advantages of several models and mitigating the effects of each modelâs shortcomings, ensemble voting raises the prediction accuracy. By lessening the influence of outliers or errors in individual model categorization, ensemble voting increases the robustness of categorization. This LSTM weighted ensemble achieves 99.82 % accuracy, 99.4% precision, 99.02% f-score, and 99.7% recall in sentiment analysis which is much higher when compared to the outcomes of conventional methods.
USING SELF-ASSESSMENT CHECK LISTS FOR STUDENTS’ PRODUCTIVE SKILLS DEVELOPMENT AT THE LESSONS OF LEGAL ENGLISH

The article analyzes the main forms of assessment of students’ skills and abilities. The study focuses on the role of self-assessment as a type of formative assessment which encourages active involvement of students in the learning process, the development of metacognitive skills, which consist in reflecting on the results of their own learning, as well as finding ways to overcome difficulties. It has been established that application of self-assessment for the development of oral and written communication at the lessons of Legal English for law students fosters effective formation of students’ productive skills. Two samples of selfassessment check lists for the tasks of preparing and conducting a lawyer-client interview and writing a letter of advice to a client have been analyzed. The author has introduced an algorithm for using self-assessment check lists, recommended approaches to creating criteria for self-assessment check lists, and described the main functions criteria-based self-assessment check lists can perform during various stages of dealing with communicative tasks.
ПІЗНАННЯ ТА КОМУНІКАЦІЯ В ОСВІТНЬОМУ СЕРЕДОВИЩІ

The article examines the relationship between cognition and communication in the educational environment and educational process as a factor in the formation of an individual educational trajectory and the formation of the student’s professional identity in higher educational institutions. A transition is being made from considering the relationship between knowledge and communication in the epistemological dimension, when communication is taken beyond the limits of cognition, to considering the relationship between knowledge and communication at the institutional and personal levels. At the institutional level, we are talking about the role of communications in the development of scientific research. The role of pedagogical intentionality in the formation of an orientation toward cognition as a motivator and determinant of the development of professional qualities of a student is considered. The role of communications in obtaining new knowledge and information, as well as in forming connections with the scientific and professional community is revealed. The importance of the value, worldview and ethical dimension of the formation of a professional personality is highlighted to strengthen its connection with the professional environment of one’s own country. This is especially important in the context of the formation of a new communicative structure of needs, which is focused on obtaining knowledge and information that corresponds to the current level of problem areas in modern science.
Research algorithm and dynamic characteristics of three-phase electromagnetic current transducers in filter-compensation devices

The article presents a three-phase current transducer for filter-compensation devices, representing the value of the stator current of an asynchronous motor. An algorithm for determining the values of the output electric quantities in them was developed based on the methods of interconnecting the loops of the sensitive element of the three-phase electromagnetic current transducer arranged in accordance with the stator slots of the asynchronous motor. In addition, based on this algorithm, the time-dependent dynamic characteristics of the output signal of the sensitive element loops are determined according to the connection method. Based on the dynamic description, it is possible to see the time for the output voltage signal of the three-phase current transducer to reach stability and the effect of high harmonic currents affecting it. Theoretical studies were carried out in Matlab program, and practical studies were carried out using CassyLab device. The theoretical and practical results determined because of research are compared with each other.
Digital competencies of higher education’s scientific and pedagogical staff in Russia: The practice of empirical research

The article presents the results of theoretical generalization and empirical verification of the developed model of digital competencies of scientific and pedagogical staff of the Russian universities. A brief review of publications confirmed the relevance of the research and showed the main vectors of scientific search. The analysis of digital competencies of scientific and pedagogical staff of higher education was carried out by the method of an expert survey using a Google Form on the basis of the universities of Stavropol Krai (Russia). A total of 297 people took part in the survey. In the questionnaire of the expert survey, information modules were presented. They characterize the digital competencies of scientific and pedagogical staff of the university: information literacy, communication and cooperation, digital content design, security and responsible use of the online environment, organization of training in a digital environment. The results of the study allow us to update the model of digital competencies of scientific and pedagogical staff of higher education in Russia. It helps to describe their structural components: possession of the technological component of digital processes in educational activities; accessible and understandable positioning of the teacher and the subject area in the digital space, ensuring effective interaction in the “mentor – student” system; comprehensive digital security of educational activities. The empirical results obtained make it possible to develop additional educational programs for scientific and pedagogical staff, taking into account the actual need for the development of knowledge, skills and abilities in the subject area.
Development of Language Literacy Based on Local Culture of Pakpak as Support of Implementation of Problem-Based Learning Model in SMA Negeri 1 Sidikalang

This research was motivated by the lack of reading material books owned by students so they could not have enough information. Reading materials in schools do not support literacy activities, especially at the habituation stage. Then the availability of reading materials containing local cultural va
A Case-Study on Applying Mixed Reality Technology in an Architecture Design Course at a Taiwan University

This paper explores an important aspect of today's architecture student training, namely the development of high technology, such as the mixed-use of virtual reality and augmented reality skills, which is essential for integration into the labor market. After an overview of how the topic is currently applied, practical education in higher education is also investigated. The study is expected to yield a number of key findings through in-depth interviews with the lecturers and a needs analysis and questionnaire-based feedback with students aimed to make recommendations to support the role of building designers in emphasizing immersive visual skills and competencies. The research findings will provide recommendations for the effective use of MR in teaching, including the best practices for designing MR-based curriculums and how to integrate them with other teaching methods.
Language Politeness of Asahan University Students Semester V Language Education Study Program

There is poor communication between students and lecturers, so lecturers feel students are not polite in delivering messages. Therefore, this study aims to examine the form of language politeness of Asahan University students in Semester V of the Indonesian Education Study Program for the 2022/20
Implementation of Interactive Electronic Books Based on Project Based Learning for Class XI High School Even Semester on Student Motivation and Learning Outcomes

The use of interactive electronic books based on project based learning is expected to be able to increase motivation and student learning outcomes in learning chemistry. This study aims to look at student learning outcomes and student motivation by using interactive electronic books based on pro
Development of Mobile Application-Based Learning Media Material for the Nine Compulsory Malay Dance Using a Blended Learning Model to Improve Students' Dance Skills

The purpose of this research is to determine (1) the feasibility of mobile application-based learning media on the Nine Mandatory Malay Dances using the Blended Learning model. (2) determine the effectiveness of mobile application-based learning media on the Nine Compulsory Malay Dances using the
Continuous Sign Language Recognition for Hearing-Impaired Consumer Communication via Self-Guidance Network

Consumer communication is one of the most important factors impacting consumption, especially for hearing-impaired people. Continuous sign language recognition (CSLR) is a technology to help people understand hearing-impaired people’s expressions, which focuses on predicting sign language sentences from sign language videos. The current paradigm of CSLR suffers from the overfitting problem, making the model provide insufficient information in backpropagation. To overcome these limitations, this work proposes the Self-Guidance Network (SGN), to fully enhance model power toward self-guidance. The SGN involves the self-spatial guidance constraint (SSGC), self-temporal guidance constraint (STGC), self-category guidance constraint (SCGC), and self-compounding spatial module (SCSM). The SSGC can guide the SCSM in paying comprehensive attention to spatial positions where sign sub-actions occur via combining attentions of the last and current model. Besides, the STGC is able to achieve self-temporal consistency between temporal modules. Moreover, the SCGC can produce informative soft labels as frame-wise supervision by combining category information of the last and current model. Furthermore, the SCSM can enrich model feature diversity by conducting a compounding architecture to achieve data augmentation. Experimenting on challenging CSLR benchmarks such as RWTH-2014, RWTH-2014T, and CSL-Daily, our SGN achieves excellent scores on the WER metrics of 19.5%/20.2%, 19.1%/20.4%, and 30.4%/30.3%, respectively.
Online Discriminative Cross-modal Hashing

Online cross-modal hashing has received increasing research attention due to its capability of encoding streaming data and updating hash functions simultaneously. Despite significant progress, there is still room for further improving accuracy from two aspects, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e</i> ., 1) enhancing discrimination of hash codes with an efficient training process; 2) elevating generalization performance by harmonizing the training and retrieval process. Inspired by this, we propose an Online Discriminative Cross-modal Hashing method, called ODCH. To enlarge the inter-class margin and magnify the intra-class similarity, ODCH skillfully constructs a discriminative semantic space and seamlessly integrates bit balance and uncorrelation constraints, discrete optimization, and asymmetric strategy for embedding the discriminative semantic information into hamming space. Furthermore, ODCH attempts to boost the generalization process by bridging the gap between learning and generalization. It develops adaptive bit-wise weights to reflect different learning conditions among bits and transmits them into the generalization process. Besides, the proposed discriminative embedding and adaptive weighting can be adopted by existing supervised cross-modal hashing methods, achieving more precise performance than the original versions. Extensive experiments on three benchmarked datasets show that ODCH achieves up to an average of 4.17% mAP score gains compared to state-of-the-art online cross-modal hashing methods, indicating its superiority.
Hyperspectral Image Denoising via $L_{0}$ Regularized Low-Rank Tucker Decomposition

This paper studies the mixed noise removal problem for hyperspectral images (HSIs), which often suffer from Gaussian noise and sparse noise. Conventional denoising models mainly employ the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{1}$</tex-math></inline-formula> -norm-based regularizers to remove sparse noise and ensure piecewise smoothness. However, the denoising performance is poor for highly structured images with severe noise since the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{1}$</tex-math></inline-formula> -norm over-penalizes large entries. To tackle this limitation, we propose a denoising model that combines tensor decomposition with two kinds of <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{0}$</tex-math></inline-formula> -norm-based regularizers. Firstly, we use low-rank Tucker decomposition with the Stiefel manifold to characterize the global correlation of HSIs. Then, we utilize the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{0}$</tex-math></inline-formula> -norm to leverage the intrinsic sparsity information of the corruption domain, thereby enhancing the effectiveness of sparse noise removal. Simultaneously, we introduce a weighted <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$L_{0}$</tex-math></inline-formula> -norm regularizer on the gradient of each pixel to promote the local spectral-spatial smoothness. To solve the proposed model, we design a hard-thresholding-based alternating direction method of multipliers (ADMM) algorithm. Instead of spending time to find a proper rank in advance, we adopt a rank-increasing scheme to dynamically adjust the tensor rank during the optimization procedure. In this way, our algorithm avoids the rank selection burden and improves computational efficiency. Finally, we test the proposed method on both synthetic and real datasets. Numerical results demonstrate its superiority, especially, the improvements of our method over the best-compared results up to 2.07dB for mixed noise removal.
Enhancing Arabic Aspect-Based Sentiment Analysis Using End-to-End Model

The majority of research on the Aspect-Based Sentiment Analysis (ABSA) tends to split this task into two subtasks: one for extracting aspects, Aspect Term Extraction (ATE), and another for identifying sentiments toward particular aspects, Aspect Sentiment Classification (ASC). Although these subtasks are closely related, they are performed independently; while performing the Aspect Sentiment Classification task, it is assumed that the aspect terms are pre-identified, which ignores the practical interaction required to properly perform the ABSA. This study addresses these limitations using a unified End-to-End (E2E) approach, which combines the two subtasks into a single sequence labeling task using a unified tagging schema. The proposed model was evaluated by fine-tuning the Arabic version of the Bidirectional Encoder Representations from Transformers (AraBERT) model with a Conditional Random Fields (CRF) classifier for enhanced target-polarity identification. The experimental results demonstrated the efficiency of the proposed fine-tuned AraBERT-CRF model, which achieved an overall F1 score of 95.11% on the SemEval-2016 Arabic Hotel Reviews dataset. The model’s predictions are then subjected to additional processing, and the results indicate the superiority of the proposed model, achieving an F1 score of 97.78% for the ATE task and an accuracy of 98.34% for the ASC task, outperforming previous studies.
Embedded Firmware Rehosting System through Automatic Peripheral Modeling

Embedded devices are becoming increasingly common and, as a result, more susceptible to security threats. Consequently, analyzing the firmware of these devices is essential for detecting and mitigating vulnerabilities. Hardware dependencies pose a major challenge for firmware analysis, as they require either running the firmware on the original hardware or emulating various hardware behaviors in a virtualized environment. Firmware rehosting, which allows firmware to run in a virtualized environment (i.e., emulation), is a recent research approach to overcome the hardware dependency problem. However, this approach faces several challenges, such as: limited applicability, path elimination, and lack of support for dynamic direct memory access (DMA). To address these challenges, we propose VDEmu, a novel firmware rehosting system that integrates hybrid fuzzing-based memory-mapped I/O (MMIO) modeling and dynamic DMA support. VDEmu can handle MMIO accesses without requiring precise implementation of peripherals and can access overlooked DMA logic by creating and removing DMA streams through a virtual DMA controller. Therefore, VDEmu can mitigate limited applicability and path elimination through fuzzing and explore more firmware logic through DMA support. We evaluated our approach on real-world targets comprising a total of eight hardware platforms and 14 firmware images. Compared with state-of-the-art works, VDEmu was the only work that could model all interactions between firmware and hardware (i.e., MMIO, DMA, and interrupts), and VDEmu achieved a code coverage that was up to 9.15 times higher. VDEmu discovered two previously unknown bugs, including ones previously analyzed in other works.
Attention-Based Policy Distillation for UAV Simultaneous Target Tracking and Obstacle Avoidance

Nowadays, deep reinforcement learning (DRL) has made remarkable achievements in the research of unmanned aerial vehicle (UAV) applications. However, much of the current research on UAVs focuses on a single task, which limits its applicability in many scenarios. Therefore, we have conducted research on UAV target tracking and obstacle avoidance tasks. The DRL-based solutions integrate the objectives of multi-tasks into a common reward function making the training unstable and difficult to converge, resulting in the loss of objectives or collisions. To that end, we propose a novel target following and obstacle avoidance solution based on policy distillation of the task attention mechanism. First, we train the two tasks of UAV target following and UAV obstacle avoidance respectively. Both networks are trained using Dueling Double Deep Q Network to learn the corresponding policy in an end-to-end manner. Then we extract the two policies that have been trained separately into a memory buffer. Meanwhile, we can perceive the collision risk through the state of the current environment of the UAV to assign the weights of the two tasks in the attention mechanism. Therefore, our method can adaptively focus on the corresponding tasks according to the current state. We conducted simulation experiments using the Virtual Robot Experimentation Platform. Our study presents compelling experimental findings: (1) Our novel approach outperforms state-of-the-art methods by achieving superior tracking accuracy and extended tracking durations across diverse environments, all while mitigating collision incidents. (2) The distilled policy we have developed exhibits robust generalization capabilities when applied to previously unencountered environments.
Penggunaan Media Pembelajaran NHT Untuk Meningkatkan Hasil Pembelajaran Matematika Siswa Kelas 2 Di Tanjungsari Probolinggo

The Teaching Campus Program created by the Directorate General of Higher Education is an effort to improve the education system in Indonesia after the Covid-19 pandemic. In its implementation, students are assigned to target elementary schools with a focus on increasing understanding of literacy and numeracy which is applied to all subjects in elementary schools. One of the programs held by the researchers was to increase understanding of mathematics with material on multiplication arithmetic operations for students in class 2 of Tanjungsari Probolinggo Public Elementary School. The reason for choosing this class was because the average grade 2 math score was very low. To deal with these problems, the researcher used the Number Head Together cooperative learning model. In carrying out the research using the Classroom Action Research method with quantitative data analysis. The results of the research implementation showed that the NHT learning model was very effective in improving the learning outcomes of class 2 students at Tanjungsari Probolinggo Public Elementary School.
Examining enablers and constraints of e-learning/teaching: Lecturers and students’ perspectives at the University of Namibia

This study, conducted at the University of Namibia (UNAM) in the Faculty of Humanities and Social Sciences, explores the perspectives of students and lecturers on the experience of learning/teaching foreign languages during the COVID 19 pandemic. The study sought to establish and delimit the enablers and constraints of Foreign Language online teaching/learning, to improve the quality of UNAM service. A quantitative method using online questionnaires was proposed to collect data from all foreign language lecturers of the Department of Languages and Literature Studies at UNAM and their students enrolled in the first and third years in the Applied and Business Language stream. The main results revealed that both lecturers and students had no prejudices about Foreign Language e-learning/teaching but students had mixed feelings about their overall experience. Although the student reported some key advantages in e-learning (independence, flexibility and possibility to freely consult learning material), their lack of equipment and difficult access to platforms and electronic documents presented significant constraints in their digital learning experience. For most of them, their motivation and self-organizational skills were hardly tested and not always successful. In addition, lecturers were not sufficiently trained or experienced to adapt their usual pedagogical practice to virtual teaching while some of them were unequally equipped with ICT skills.