Reviewerly/exHarmony huggingface dataset:
Overcoming Posterior Collapse in Variational Autoencoders Via EM-Type Training

Variational autoencoders (VAE) are one of the most prominent deep generative models for learning the underlying statistical distribution of high-dimensional data. However, training VAEs suffers from a severe issue called posterior collapse; that is, the learned posterior distribution collapses to the assumed/pre-selected prior distribution. This issue limits the capacity of the learned posterior distribution to convey data information. Previous work has proposed a heuristic training scheme to mitigate this issue, in which the core idea is to train the encoder and the decoder in an alternating fashion. However, there is still no theoretical interpretation of this scheme, and this paper, for the first time, fills in this gap by inspecting the previous scheme under the lens of the expectation maximization (EM) framework. Under this framework, we propose a novel EM-type training algorithm that gives a controllable optimization process and it allows for further extensions, e.g., employing implicit distribution models. Experimental results have corroborated the superior performance of the proposed EM-type VAE training algorithm in terms of various metrics.
Sandformer: CNN and Transformer under Gated Fusion for Sand Dust Image Restoration

Although Convolutional Neural Networks (CNN) have made good progress in image restoration, the intrinsic equivalence and locality of convolutions still constrain further improvements in image quality. Recent vision transformer and selfattention have achieved promising results on various computer vision tasks. However, directly utilizing Transformer for image restoration is a challenging task. In this paper, we introduce an effective hybrid architecture for sand image restoration tasks, which leverages local features from CNN and long-range dependencies captured by transformer to improve the results further. We propose an efficient hybrid structure for sand dust image restoration to solve the feature inconsistency issue between Transformer and CNN. The framework complements each representation by modulating features from the CNN-based and Transformer-based branches rather than simply adding or concatenating features. Experiments demonstrate that SandFormer achieves significant performance improvements in synthetic and real dust scenes compared to previous sand image restoration methods.
Alternative assessment as a means of business English skills formation in non-linguistic students

Introduction. According to the requirements of the labor market, a modern specialist with a higher education must speak a foreign language in his or her professional field, i.e. be able to communicate in a business foreign language. Within the competence-based approach, teaching a professional business foreign language involves the use of not only practice-oriented teaching methods and technologies, but also alternative techniques of evaluating students' learning activities. In this regard, the purpose of the study is to identify how the use of alternative assessment affects the formation of Business English skills among non–linguistic students. Materials and Methods. The study involved 96 undergraduate students of Vyatka State University studying in the fields of "Biology", “Biotechnology”, “Chemistry” and "Chemical Technology". The formation of Business English skills is based on the “assessment as learning” approach. This assessment includes practice-oriented methods: Case Study, electronic portfolio (iPortfolio), pair work (We Create Activity), imitation of an interview (Mock job interview), reports (Reports) in the form of formal letters. Pearson’s χ2 test was used for the result statistical processing. Results. The results of the experiment showed that the number of students in the experimental group with a low level of Business English skills decreased from 37.5% to 4.2%, with an average level decreased from 50% to 41.7%, with a high level increased from 12. 5% to 54.1%; χ2 = 18.006, p ≤ 0.05. Conclusion. The significance of the conducted research lies in the "assessment as learning" approach used to solve the problem of the Business English skills and abilities formation among undergraduate students. This approach provided not only conditions for the development of Business English skills, but also for the development of critical thinking, creativity, and cooperation. Keywords: Business English, Business English skills, alternative assessment techniques, Case Study
Fuzzy Use Case Points as a Basis for Effort Estimation

Many software development projects encounter problems related to over- or under-estimation of effort. Accurate effort estimation is crucial for successful project management, but it can be challenging when resources are limited, and little is known about the project. The commonly used method for effort estimation is Use Case Points (UCP), which is mainly used for application-based objects and takes use cases as input. However, UCP has weaknesses, particularly in the high variation of weight factor values for Unadjusted Use Case Weight (UUCW). To address this problem, Fuzzy Use Case Points (FUCP), which is a combination of fuzzy logic and use case points, can be used. By applying fuzzy logic to the UUCW category, FUCP derives new weight factor values for UUCW. The implementation of FUCP to calculate effort estimation in ten government-based projects in this research has shown that FUCP yields the closest value to the actual effort required. It has also been demonstrated that FUCP outperforms UCP in terms of accuracy, with an improvement of 6.51%.
Analisis Proses Identifikasi Kebutuhan Pembelajaran Siswa Kelas 3 SD Negeri Cikokol Kota Tangerang

Needs analysis is an important stage that is carried out at the beginning of the learning process. This study aims to analyze the learning needs of grade 3 students at SD Negeri Cikokol. The design of this research is descriptive qualitative. The information needed in this study was obtained from observation and interviews. The respondent in answering is the third grade teacher. The stages in the needs analysis are observing the teacher's learning and teaching process in the classroom, and conducting interviews with teachers. The results of this study are in the form of a report on the learning needs of grade 3 students at SD Negeri Cikokol which shows that students need more digital teaching materials in the form of modules so that they can support learning activities. The results of this study are expected to be a reference for teachers and schools as material for consideration in carrying out teaching and learning processes that suit the needs of students.
Retraction

Retraction: Cheng Y. Application of VR computer image technology in 3D virtualization of sports human science. J Concurrency Computat: Pract Exper . 2018; 30–24. The above article, published online on 14 Sep 2018 Wiley Online Library ( https://onlinelibrary.wiley.com/doi/10.1002/cpe.4934 ) has been retracted by agreement between the journal Editor, Professor David W. Walker, School of Computer Science and Informatics, Cardiff University, Cardiff CF24 3AA, UK and Wiley Periodicals, LLC. The retraction has been agreed following an investigation based on allegations raised by a third party. Several inconsistencies including general logical flaws and irrelevant citations were found, making the motivation and conclusions of the article untrustworthy. The editorial office did not receive a response from the authors, and so requested underlying experimental data was not available for evaluation. Accordingly, the editors consider the conclusions of this manuscript invalid.
Iterative Training Attack: A Black-Box Adversarial Attack via Perturbation Generative Network

Deep neural networks are vulnerable to adversarial examples. While there are many methods for generating adversarial examples using neural networks, creating such examples with high perceptual quality and improved training remains an area of active research. In this paper, we propose the Iterative Training Attack (ITA), a black-box attack based on a perturbation generative network for generating adversarial examples. ITA generates such examples by randomly initializing the perturbation generative network multiple times, iteratively training and optimizing a refined loss function. Compared to other neural network-based attacks, our proposed method generates adversarial examples with higher attack rates and within a small perturbation range even when the advanced defense is employed. Despite being a black-box attack, ITA outperforms gradient-based white-box attacks even under basic standards. The authors evaluated their method on a TRADES robust model trained with the MNIST dataset and achieved a robust accuracy of 92.46%, the highest among the evaluated methods.
EM-Gaze: eye context correlation and metric learning for gaze estimation

Abstract In recent years, deep learning techniques have been used to estimate gaze—a significant task in computer vision and human-computer interaction. Previous studies have made significant achievements in predicting 2D or 3D gazes from monocular face images. This study presents a deep neural network for 2D gaze estimation on mobile devices. It achieves state-of-the-art 2D gaze point regression error, while significantly improving gaze classification error on quadrant divisions of the display. To this end, an efficient attention-based module that correlates and fuses the left and right eye contextual features is first proposed to improve gaze point regression performance. Subsequently, through a unified perspective for gaze estimation, metric learning for gaze classification on quadrant divisions is incorporated as additional supervision. Consequently, both gaze point regression and quadrant classification performances are improved. The experiments demonstrate that the proposed method outperforms existing gaze-estimation methods on the GazeCapture and MPIIFaceGaze datasets.
Preempting Criteria Affecting Future Software Development Methodologies to Construct A Better SDLC Framework

- Anthropological studies and system sciences show humans thrive under locus, where structural integrity is intact. It is only natural, that the software developed by humans, order the same. The process and methodology involved in building these applications are complex and chaotic, yet elegant at the same time. Dividing tasks into phases was considered the optimal method, to take advantage of the specializations of various individuals and teams. But, in the ever-rampant world, where AI and machine learning enable, concurrent implementation, it's just to question the basis of the same. The software development industry constantly evolves, with emerging new methodologies and technologies. To stay competitive and continuously evolve, better frameworks for future development and micromanaging need to be created. To do the same, this research paper aims to preempt the criteria that are likely to affect software development models and methodologies, by deep diving into the SLDC's origins, central idea, and relevance throughout our modern history. Key Words: Management, Software Development Life Cycle (SDLC), Software Engineering, Agile, Waterfall Models, Incremental Models, Generative
Student perceptions of Grammarly, teacher’s indirect and direct feedback: Possibility of machine feedback

Empirical data on automated writing evaluation (AWE) has been accumulating over the past several years, but previous research has concentrated on the impact of using machine feedback alone, and only a little is known about how learners view the usage of AI-assisted tools when combined with teacher feedback. More empirical evidence from instances in which machine and teacher feedback is used in combination is necessary to determine optimal approaches to incorporating AWE tools in classroom instruction. A study was conducted to assess Japanese college students’ perceptions of three types of feedback when they experienced them in a certain order. Participants in two groups received Grammarly feedback on their first drafts, the teacher’s indirect feedback on their second drafts, and the teacher’s direct feedback on their third drafts. The questionnaire administered to the first group examined participant perceptions of the respective type of feedback, whereas the questionnaire given to the second group verified the strengths and weaknesses of each type of feedback and investigated the type of feedback participants believed was most effective in improving their writing in English. The content analysis approach was used for textual analysis of the responses to open-ended questions, and a chi-square test was performed for scale responses. The quantitative and qualitative analysis of questionnaire data revealed that, though machine feedback was favorably received, participants appreciated human direct and indirect feedback more highly for its appropriateness and reliability. Moreover, the largest number of participants rated the teacher’s direct feedback as the most beneficial to their writing progress. The findings imply that machine feedback should not be used alone in writing instruction but as a supplement to teacher feedback. In addition, the participants’ response suggests that using Grammarly feedback in a “form then content” sequence might be one feasible pedagogical approach in the classroom.
Learning Sparse auto-Encoders for Green AI image coding

Recently, convolutional auto-encoders (CAE) were introduced for image coding. They achieved performance improvements over the state-of-the-art JPEG2000 method. However, these performances were obtained using massive CAEs featuring a large number of parameters and whose training required heavy computational power.In this paper, we address the problem of lossy image compression using a CAE with a small memory footprint and low computational power usage.In this work, we propose a constrained approach and a new structured sparse learning method. We design an algorithm and test it on three constraints: the classical ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1</inf> constraint, the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,∞</inf> and the new ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</inf> constraint. Experimental results show that the ℓ <inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1,1</inf> constraint provides the best structured sparsity, resulting in a high reduction of memory ( 82 %) and computational cost reduction (25 %), with similar rate-distortion performance as with dense networks.
Design &amp; Model Making of Emergency Medicine Supply Drone

Abstract: Our study involves the implementation of a drone-based medicine delivery system utilizing the KK2.1.5 Flight Controller Board for Multirotor Drone. Our optimized drone is specifically designed to deliver medicine in rural areas, where transportation infrastructure may be lacking or geographical terrain may be difficult to traverse. Also, it can be used for contactless delivery of medicine in pandemic situation This innovative technology has the potential to be of great benefit in areas where traditional transportation services are inadequate or unavailable.
The Potential of Augmented Reality in Education: A Scoping Review

The augmented reality experience is becoming increasingly popular, especially among today's digital-born generation who have technology permeating almost every aspect of their lives.Because of its capacity to augment computer-generated graphics into the natural environment on screen, several research has been conducted to determine the benefits and challenges of this advanced technology in educational settings.This study intended to provide the current state of augmented reality literature in education.Specifically, this study attempts to identify research gaps and suggest future research directions.A total of forty-nine articles that were published in scientific journals between 2017 and 2022 were obtained using Scopus and categorized as Q1 to Q4 journals were analyzed for this study.The findings provide the current trends in the application of augmented reality in education, the impact of this technology on learning processes, and opportunities and challenges for educators and students.Based on the data, augmented reality is maturing in some countries and has successfully taken root in education today.
EFFICIENT SUMMERIZATION OF TEXT THROUGH EXTRACTIVE METHODS

In natural language processing, text summarization is an essential process that wants to distil long texts into simpler representations while maintaining important details.The extraction-based method is a simple strategy that entails picking out important lines or words from the initial writing and organising them logically by employing algorithms that detect their significance to the subject.In today's data-rich environment, this approach may help people quickly recover important data, which is vital.In conclusion, it minimises waiting time and improves content comprehension.
Evaluation of Information Technology Governance Order to Cash Based on Cobit 5.0 with Domain Monitor, Evaluate and Assess (Mea 01)

The application of technology that has become the needs and demands of the company of PT Semen Tonasa, to overcome the problem of technological implementation requires the evaluation of IT governance. The purpose of this research, in general, is to know the condition of the governance of IT order to cash specifically to know the current capability level, knowing the gap between current capability and target capability is expected, and provide the appropriate recommendation case study of PT Semen Tonasa by using the COBIT Framework 5.0 domain MEA. The data analysis method used is survey research with a quantitative descriptive approach that later results from the distribution of questionnaires will be managed to generate information in the form of figures and diagram results from the evaluation of the level of information technology governance capabilities based on the COBIT framework 5. In this study, also conducted a statistical test of T paired test, double linear regression test, and factor analysis to get a more accurate recommendation. Respondents to this research are outlined in the RACI chart according to the SAP application user in order to cash. Based on the results of the research and analysis of data conducted stated that the capability level in the MEA 01.01 sub Process (training and communication), MEA 01.02 (target responsibility and brainware quality targets), and MEA 01.03 (Measurement of responsibility and brainware quality measurement) is at level 3 or established process with a rating scale located on largely achieved. Besides, there is a comply gap in each sub-process to reach target level 4. Related to the analysis of MEA 01.04 the company needs to plan training and knowledge sharing structured, involving top management in motivating employees concerning the responsibility of SAP application use. As for the expected condition that is at level 4 (predictable process) recommended by the company is expected to make a clear design of HUMAN development measurement and make control design.
The Effectiveness of Health Education Using Media Booklets and WhatsApp on Tuberculosis Patients at the Linggang Bigung Health Center, West Kutai Regency

The main obstacle to controlling tuberculosis is knowledge and behavior to prevent transmission among at-risk contacts. Health education is needed to increase public knowledge. The purpose of this study was to analyze the effectiveness of health education using Booklet and WhatsApp media among tuberculosis sufferers. This type of research was quasi-experimental with a control group pre- and post-test approach, samples were taken from 41 tuberculosis patients who were currently being treated. Knowledge measurements were carried out before and after health education was carried out using Booklet and WhatsApp media. The analysis to test the effectiveness of the booklet and WhatsApp media used the Wilcoxon test. The Wilcoxon test results obtained a p-value of 0.046 (booklet) and a p-value of 0.014 (WhatsApp) lower than α = 0.05, meaning that both are effective in increasing knowledge, but Whatsapp media is more effective than booklet media.
Analisis Upaya Guru Mengatasi Kesulitan Membaca pada Siswa Kelas 1 SDN 195 Palembang

This study aims to find out how the teacher’s efforts to overcome reading difficulties in grade 1students at SDN 195 Palembang. This study uses a qualitative approach with a descriptive method.The subjects of this study were grade 1 teachers, parents of grade 1 stundents and grade 1 stundents. Data collection in this study used interview techniques, questionnaires, anddocumentation. Data analysis techniques used are data collection, data reduction, data presentation, and drawing conclusions. Based on the result of the study, it can be concluded that the teacher’s efforts to overcome reading difficulties for grade 1 stundents are the teacher providing advice, additional tutoring, and using learning media such as letter picture books using the lecturemethod so that students are motivated.
New Approach of Generating Ground-Truth for Local Surveillance Dataset Tested with Benchmark Background Subtraction Models

Background subtraction is the dominant approach in the domain of moving object detection. Lots of research has been done to design or improve background subtraction models. However, there are a few well-known and state-of-the-art models that can be applied as a benchmark. Generally, these models are applied to different dataset benchmarks. Most of the time, choosing an appropriate dataset is challenging due to the lack of dataset availability and the tedious process of creating ground-truth frames for the sake of quantitative evaluation. Therefore, in this article, we collected local video scenes of a street and river taken by a stationary camera, focusing on dynamic background challenges. We presented a new technique for creating ground-truth frames using modeling, composing, tracking, and rendering each frame. Eventually, we applied three promising algorithms used in this domain: GMM, KNN, and ViBe, to our local dataset. Results obtained by quantitative evaluations revealed the effectiveness of our new technique for generating the ground-truth scenes to be benchmarked with the original scenes using a number of statistical metrics.
Morphological analysis strategy used to enhance the student’s vocabulary acquisition and reading comprehension

This study aims to investigate the use of morphological analysis strategy in enhancing the student’s vocabulary acquisition and reading comprehension. The research was experimented in the English Department at Universitas Muhammadiyah Parepare, with a total sample of 28 students. This study was pre-experiment research with a pretest-posttest design. The result of this study indicated that: (1) in teaching vocabulary through vocabulary analysis strategy, there are significant differences between the mean score of the pre-test (58.57) and the mean score of the post-test (84.28). Besides that, the result of the t-test calculation shows that the t-test value (7.75) is higher than the t-table value (2.052) at a significance level 0.05 from df = 27. (2) In teaching reading comprehension through vocabulary analysis strategy, there are also significant differences between the mean score of the pre-test (54.64) and the mean score of the post-test (80.51). In addition, the result of the t-test calculation shows that the t-test value (9.75) is higher than the t-table value (2.052) at the significance level 0.05 from df = 27. Thus, the student’s vocabulary acquisition and reading comprehension significantly improve after they are taught using the morphological analysis strategy.