Reviewerly/exHarmony huggingface dataset:
Joint Pixel and Frequency Feature Learning and Fusion via Channel-wise Transformer for High-Efficiency Learned In-Loop Filter in VVC

Block-based video codecs such as Versatile Video Coding (VVC)/H.266, High Efficiency Video Coding (HEVC)/H.265, Advanced Video Coding (AVC)/H.264 etc. inherently introduces compression artifacts. Although these codecs have in-loop filters to correct these distortions, they are not always effective due to the complexity of the noise. Recently, deep-learning approaches emerged as a promising solution for in-loop filtering. However, most of the previous approaches were designed solely for learning from images and neglected the high-frequency signals present in the reconstructed video frames. Furthermore, some previous methods employed a multi-level feature-extraction and feature-fusion strategy to enhance performance. However, they utilized complex feature-extractors while relying on naive feature-fusion methods. In this article, we propose a novel framework called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSF-Net</i> , which jointly learns from both the pixel (spatial) and frequency-decomposed information and through powerful capability of a channel-wise transformer, it fuses both these information to improve performance. Our approach deviates from previous approaches by employing a simple feature-extractor coupled with an advanced transformer-based feature-fusion module. Simultaneously, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSF-Net</i> introduces a few fundamental modifications in the multi-head self-attention module of the channel-wise transformer to make it computationally efficient. Our experimental results show that the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSF-Net</i> achieves a Bjøntegaard Delta (BD) - bitrate saving of up to 10.258% for the luma (Y) component under all-intra (AI) profile outperforming the VVC baseline and other state-of-the-art methods. Moreover, the proposed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSF-Net</i> with an efficient channel-wise transformer is twice as efficient as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TSF-Net</i> with a vanilla channel-wise transformer.
KILO - Fitness application with posture estimation

The lockdown measures taken by countries across the globe in light of the COVID-19 pandemic has made people realize the importance of a healthy lifestyle. As more people try to change their habits to adopt fitness routines, they tend to perform exercises without knowing which ones are for beginners and how long they should be working out for. In proposed work, we focus on implementing a fully functional workout app built using flutter, a cross-platform development framework to develop applications for iOS and android, supported by a backend service provided by firebase. This paper surveys different technologies used in pose estimation /classification as well as the different architectures used for the app development in Flutter. It also focuses on the importance of yoga and a technology aided approach for performing yoga poses. The grave nature of challenges faced while doing yoga such as performing it with incorrect form or postures as well as the cost of classes. With novel computer vision techniques, human pose estimation sees a great future. The work delves into the means of deploying yoga pose estimation on an android application, the modeling of the app and the working of each component.
Gaussian Mixture Reduction with Composite Transportation Divergence

Gaussian mixtures are widely used for approximating density functions in various applications such as density estimation, belief propagation, and Bayesian filtering. These applications often utilize Gaussian mixtures as initial approximations that are updated recursively. A key challenge in these recursive processes stems from the exponential increase in the mixture’s order, resulting in intractable inference. To overcome the difficulty, the Gaussian mixture reduction (GMR), which approximates a high order Gaussian mixture by one with a lower order, can be used. Although existing clustering-based methods are known for their satisfactory performance and computational efficiency, their convergence properties and optimal targets remain unknown. In this paper, we propose a novel optimization-based GMR method based on composite transportation divergence (CTD). We develop a majorization-minimization algorithm for computing the reduced mixture and establish its theoretical convergence under general conditions. Furthermore, we demonstrate that many existing clustering-based methods are special cases of ours, effectively bridging the gap between optimization-based and clustering-based techniques. Our unified framework empowers users to select the most appropriate cost function in CTD to achieve superior performance in their specific applications. Through extensive empirical experiments, we demonstrate the efficiency and effectiveness of our proposed method, showcasing its potential in various domains.
Developing Basic English Module for Junior High School to Promote Students’ Cognitive Development

A module is a supplementary tool for existing teaching materials, allowing students to engage in independent learning by exploring the concepts presented within the module. This approach can enhance students' competence and foster their enthusiasm for the subject. The research aims to ascertain the English material requirements of seventh-grade students at SMPN 4 Bandar Lampung and create a comprehensive English curriculum for this grade level at the abovementioned school. The ADDIE model was utilized to achieve the stated objectives. The need analysis was carried out before the creation of the material. Based on the expert judgment of the material, it has been determined that the average score (X) is 3.6. The score is classified as good due to its placement within the 3.5 &lt; X &lt; 3.75 interval. However, there are two aspects that experts believe require further improvement. These aspects include enhancing the accessibility of the module's materials for students to comprehend and ensuring that the illustrations align with the content presented in the module. To enhance the comprehensibility of the module's content for students, the expert recommended providing more explicit instructions for each step of the activities students must undertake.Regarding the illustrations provided per the module's content, experts have recommended incorporating additional illustrations to enhance the module's level of engagement. Based on the expert judgment and evaluation of the presentation, it has been determined that the average score (X) is 3.75. Based on the obtained score, the categorization can be deemed satisfactory, as the value of X falls within the interval of 3.5 &lt; X &lt; 3.75. The expert has recommended an enhancement of the references or supporting materials. The expert recommended incorporating additional references to enhance the comprehensiveness of the module. Based on the expert judgment of the material, it has been determined that the average score (X) is 3.75. Based on the obtained score, it can be classified as satisfactory, as the X value falls within the 3.5 &lt; X &lt; 3.75 range. The expert recommended one aspect for improvement, specifically regarding the alignment of language usage with the student's cognitive development stage. The expert posited that certain languages may present a level of complexity that surpasses students' cognitive abilities. Consequently, it is recommended that these languages be modified or adapted to better align with the student's learning capacities.
Proposal for providing secure Zoom meeting through Corona pandemic

The Zoom program is widely used to manage meetings around the world, but it is easy to leak the associated Personal Meeting ID. The present study aims at improving the security for Zoom meetings during the transmission process. A new encryption algorithm is proposed to achieve this goal. The system includes three steps namely; separating images, scrambling one-half of the image, encrypting, and substituting the other scrambled half. Implementation of these three steps is achieved by using: cutting the image vertically, Arnold Cut Map, and tRNA respectively. The Bioinformatics technique (tRNA) used in encrypting (red and green) colors, and the Arnold cut map used in the scrambling and shifting process in the encryption method. The performance of the proposed system is measured by using a series of tests such as key sensitivity, histogram analysis, information entropy, correlation analysis, NCPR, similarity, and PSNR test. The results reveal a high sensitivity for initial values, resistant to statistical attack, and have the best performance in the NCPR test. It also passes the evaluation criteria for the both encryption method and S-Box. The complexity of the suggested system is represented by the attacker's need to know many chaos parameters, the logistic method (Arnold cut map), the utilized visual cryptographic techniques, and the methods used to generate the S-Box and keys. The consumed time is fewer than AES algorithm because AES takes huge time in the encryption process especially for big data. The proposed key needs to refresh only one time to make big changed and produces high security.
Sparse and semi-attention guided faults diagnosis approach for distributed online services

Despite the rapid advance of unsupervised reconstruction models in online service fault diagnosis, existing methods still lead to frequent false positive or false negative alarms. Tracing to the cause, popular reconstruction frameworks may fall into an “identical shortcut”, where both normal and anomalous cases can be well-recovered and hence faults are escaped from monitoring. Moreover, regularly “concept drift” deployed by online services always leads to sequences distribution dramatically change, which is easily wrongly-detected as anomalies. In this work, we present SS-Attention to avoid two contradictory false alarms simultaneously in a unified framework. Specifically, we first revisit the frameworks of MLP, CNN, and transformer, and confirm the important role of self-attention in preventing the model from learning shortcut, which we therefore select as our backbone. Second, we come up with a sparse attention layer to help model capture more long-term dependencies and distinguish local “concept drift” from anomalies. Third, we propose a semi-attention layer that urges model only learn from normal regions and further avoid well-recovering the faults. We evaluate our model on three public online service datasets and surpass the state-of-the-art alternatives by a sufficiently large margin, achieving an average 95.91% F1-Score with an approx. 34.48% economy of training time.
THE HIGHER EDUCATIONAL INFORMATION SYSTEM: MANAGEMENT OF THE TIMETABLE SCHEDULING AND LOGISTICS OF THE EDUCATIONAL PROCESS

In modern conditions, one of the important areas of development of a higher educational institution is the automation of educational activities through the implementation of a complex information system. In order to increase the effectiveness of simultaneous management of various aspects of educational activity, the information system should combine a wide range of functionality, including the timetable scheduling module. This paper is dealing with the one of the important issues of the most significant university administrative processes - timetable scheduling. This module should support working with big data and have the appropriate software and technical infrastructure, analytical tools, reporting units and error detection units. The article discusses some aspects of the timetable module. A description of the software and technical infrastructure of the business process of forming the university schedule is given.
Pengaruh Literasi Digital Terhadap Prestasi Belajar Pada Siswa SMP Pusaka Bangsa Karawang

Advances in information technology have ushered in a new era of digital world. With digital developments, students have new ways of finding information through digital media. This certainly helps students meet their information needs more practically, effectively, and efficiently. Students at SMP (Middle School) Pusaka Bangsa in Karawang have also experienced the impact of this development. The school is located in an urban area where technology is widely accessible. In addition, the students of this school have an average grade of 88 and have won various competitions in the academic field. This study aims to determine the effect of digital literacy on student achievement at SMP Pusaka Bangsa Karawang. Aspects of digital literacy include: technology, cognitive, and ethics. While aspects of learning achievement include: affective, cognitive, and psychomotor. The research method used is quantitative by collecting data through questionnaires. This study examined a sample of 95 respondents who were students of SMP Pusaka Bangsa Karawang. The data collection method in this study used population research techniques by distributing questionnaires online in the form of Google Forms. The data obtained were then processed using SPSS version 26 software with instrument tests (validity and reliability tests), classical assumption tests (in the form of normality tests, linearity tests, and heteroscedasticity tests), and hypothesis testing (simple linear regression tests, R2 tests, and Q). The results of the study show that digital literacy has an influence of 27% on student achievement at Pusaka Bangsa Karawang Middle School; the remaining 73% is caused by other variables outside the scope of this study. Suggestions were given to the school to improve students' digital literacy in the cognitive aspect and student achievement in the psychomotor aspect.
A New Software Approach to Automated Translation (On the Example of the Logistics Sublanguage)

Digital translation plays an increasingly significant role in the modern world. The machine translation deals with simple texts on common topics; however, when translating specialized texts in various fields, certain difficulties arise that require a special approach and comprehensive solution. The main problem arises when translating specialized texts, including terms, abbreviations, and other linguistic elements related to a specific sublanguage. The problem of post-editing can be resolved manually, but the volume of texts and limited timeframes caused a pressing need for the automation of the process by developing and introducing professional tools, which is capable of performing a semi-automatic translation of sublanguages. The area of logistics develops dynamically and acquires more and more semantic units, thus requiring the speed and accuracy of providing information. Based on the key advantages and better experience of modern automated translation programs, a universal Web tool was developed that makes it possible to quickly receive a high-quality translation of keyword terms related to the logistics sublanguage. This program is unique in a wide range of functions aimed at performing practical tasks and without any exaggeration can change the idea of a computer translation due to a new approach to interlinguistic perception.
Data Mining Technique in Detecting and Predicting Cyber In Marketplace Sector

Marketplace is one business solution that can be profitable, because it is not bound by time and place. However, marketplace can be misused by irresponsible parties, and can harm others. Then a pattern is needed to predict cybercrime in order to prevent it. To get a pattern, we can use data mining. This paper presents a general idea about the model of Data Mining techniques and diverse cybercrimes in market place applications. This paper implements data mining techniques like K-Means, Influenced Association Classifier and J48 Prediction tree for investigating the cybercrime data sets. K-means selects the initial centroids so that the classifier can mine the record and also formulate predictions of cybercrimes with J48 algorithm. The knowledge of K-Means, Influenced Association Classifier and also J48 Prediction tree tends certainly to afford a enhanced, incorporated, and precise result over the cybercrime prediction in the marketplace sectors and prevent the cybercrime.
Proposal Writing Boot Camp

Nonprofit Business AdvisorVolume 2023, Issue 410 p. 4-4 Conferences & Events Proposal Writing Boot Camp First published: 09 October 2023 https://doi.org/10.1002/nba.31573Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2023, Issue410November 2023Pages 4-4 RelatedInformation
Antecedents predicting digital contact tracing acceptance: a systematic review and meta-analysis

Abstract An awareness of antecedents of acceptance of digital contact tracing (DCT) can enable healthcare authorities to design appropriate strategies for fighting COVID-19 or other infectious diseases that may emerge in the future. However, mixed results about these antecedents are frequently reported. Most prior DCT acceptance review studies lack statistical synthesis of their results. This study aims to undertake a systematic review and meta-analysis of antecedents of DCT acceptance and investigate potential moderators of these antecedents. By searching multiple databases and filtering studies by using both inclusion and exclusion criteria, 76 and 25 studies were included for systematic review and meta-analysis, respectively. Random-effects models were chosen to estimate meta-analysis results since Q , I 2 , and H index signified some degree of heterogeneity. Fail-safe N was used to assess publication bias. Most DCT acceptance studies have focused on DCT related factors. Included antecedents are all significant predictors of DCT acceptance except for privacy concerns and fear of COVID-19. Subgroup analysis showed that individualism/collectivism moderate the relationships between norms/privacy concerns and intention to use DCT. Based on the results, the mean effect size of antecedents of DCT acceptance and the potential moderators may be more clearly identified. Appropriate strategies for boosting the DCT acceptance rate can be proposed accordingly.
Academic Engagement: A Bibliometric Analysis in Scientific Production

Purpose: To conduct a bibliometric analysis of the existing literature on academic engagement in order to quantify academic output, identify influential authors and journals, and assess the temporal evolution of research, providing a comprehensive bibliometric perspective with guidance for future studies in the academic field. Theoretical framework: Through a systematic search in academic databases, relevant studies about student commitment and motivation in educational contexts were identified. Design/methodology/approach: The methodology has a descriptive and exploratory approach, 816 articles were reviewed. By analyzing bibliometric indicators, it was revealed a constant growth in research in this field over the last decades, proving a growing interest in understanding and enhancing student participation in their learning process. Findings: The study identified an evolution on research themes, going from individual factors such as motivation to broader contextual issues, such as the impact of technology on the engagement and educational quality. The influence of key authors and institutions in shaping the field was highlighted, as well as a multidisciplinary approach encompassing psychology, education, medicine, and educational technology. Research, Practical &amp; Social implications: This bibliometric study offers a panoramic and updated vision of academic engagement research. Originality/value: Its findings offer a solid foundation to future investigations and enrich the comprehension of how student participation and motivation contributes to a more meaningful learning and the improvement of educational systems.
FlexCon-CE: A Semi-supervised Method with an Ensemble-Based Adaptive Confidence

Semi-supervised learning is characterized by a low number of labeled instances and a high number of unlabeled instances. FlexCon-C (Flexible Confidence Classifier) is a well-known semi-supervised method that uses the self-training learning algorithm as basis to generate prediction models. The main difference between self-training and FlexCon-C is that the former uses a fixed threshold to select the unlabeled instances, while the latter has a dynamically adjusted confidence. FlexCon-C applies a confidence adjustment equation based on the classifier performance. In this sense, the classifier performance is used to select and to label unlabeled instances. In Machine Learning, it is well-known that the classifier performance can be further improved through the use of classifier ensembles. Therefore, this study proposes the use classifier ensembles in the FlexCon-C confidence adjustment equation, aiming to provide a more efficient measure to select and to label unlabeled instances. In order to assess the viability of the proposed method (FlexCon-CE), an empirical analysis will be conducted, using 20 datasets, three different classification algorithms and five different configurations of initially unlabeled data. The results indicate that the proposed method outperformed the traditional method, therewith proving itself promising for the task of automatic data selection and labeling in the semi-supervised context.
Ecc‐RCNN: An efficient and high‐accuracy object detection framework for transmission line defect identification

Abstract In order to improve the accuracy of image‐based transmission line defect detection, while reducing the computational complexity and the high demand on chip performance, an object detection framework is proposed, which aims to improve model performance without increasing the scale of the model and the amount of calculation. An efficient feature fusion module to combine different‐level semantic features in non‐linear transformations is introduced. It includes channel‐level hierarchy features, linear projection and residual mappings to gather task‐oriented features across different spatial locations and scales. Then a context information modelling module is proposed to extract features around the target objects, which further increases the detection accuracy. Finally, an Intersection‐over‐Union‐based training examples sampling strategy is adopted to alleviate the class imbalance problem. Experiments on dataset show that the proposed method, with a similar number of model parameters, has an accuracy improved by 8.1% compared to the baseline, and outperforms all the competitors in the area of transmission line defect detection.