Reviewerly/exHarmony huggingface dataset:
DWT-HOG-Based Facial Expression Recognition System

A new facial expression recognition system is presented in this work that utilizes a combination of discrete wavelet transform and Histogram of Oriented Gradients (DWT and HOG) techniques to extract the useful features. The research demonstrates that the previously developed DWT-HOG-based tool for face recognition can also be used for extracting representative features related to facial expressions. The proposed system performance is measured using the CK+ database, which is a standard reference for facial expression images. The experimental results prove that the proposed DWT-HOG-based system outperforms many contemporary methods for facial expression recognition. This paper provides a comprehensive discussion on the design steps and the performance of the proposed system, which highlights the potential of using DWT-HOG-based techniques for developing robust facial expression systems.
EFEKTIVITAS PENGEMBANGAN MODUL BIMBINGAN KLASIKAL UNTUK KESADARAN DIRI PESERTA DIDIK KELAS VIII SMP FK BINA MUDA

Development of modules as a medium of innovation in guidance and counseling activities, especially in classical guidance services. This module is used as teaching material for service materials that are focused on the context of learners' self-awareness. This research uses an R&amp;D method developed by Borg and Gall which was adapted to stage seven. The purpose of this study is to produce a product that has been tested by several experts such as material experts, media experts, and practitioner experts. The research subjects consisted of 39 students at SMP FK Bina Muda who were specially selected to measure product feasibility through modules. Based on the product testing results that have been obtained from several experts and the response of students, this module has an average percentage of 9 1.63% according to the criteria that this product is very feasible to use for the Junior High School level. Meanwhile, the results of the t-test analysis on the pretest and posttest result data were obtained by sig. (2-tailed) by 0.000. Thus, the results of the research on the effectiveness of this analysis showed that there was a positive change between the use of modules and the self-awareness of class VIII students at SMP FK Bina Muda.
Special Issue on: Application of Technologies for Sustainable Business and Service Management

Technologies, especially emerging technologies, are playing a big role in making business and management practices sustainable. COVID-19 has also taught the world that businesses which are using technologies in an effective manner can survive and grow in a sustainable manner. We are witnessing a different phase of technology. The world is moving from technology 2.0 towards technologies 4.0 and 5.0 and their applications can be seen in achieving business excellence predominantly in the service sector. The organizations have pressure to improve employee engagement, e-commerce, business outreach, supply chain, operations, customer perception, customer engagement and overall workplace integrity.
Analysis of Flood-Prone Areas on Bekasi River Watershed

Das Kali Bekasi is an area that has physical conditions that support the occurrence of local and natural flood disasters during the rainy season. This research aims to: (1) Determine the distribution of floodprone areas in the Bekasi River Watershed, (2) Find out the most dominant factors of flood vulnerability that occur in the Bekasi River Watershed. The method used in the research is quantitative descriptive analysis. Analyze the data to determine the distribution area prone to flooding by weighting scoring and overlaying rainfall, land use, slope, soil type, and river density. To find out the dominant factors that cause flood vulnerability, a correlation test is used. The results of the research are (1) Flood vulnerability in the Bekasi river basin, based on the results of this research, is divided into 3 classes, namely the not vulnerable class with an area of 82.61 Km² (5.93%), the moderately vulnerable class with an area of 323.85 Km² (23, 26%), very vulnerable class with an area of 985.96 Km² (70.81%). (2) Correlation test to determine the dominant factor causing flooding in the Bekasi River basin and it is known that the dominant factor is the slope parameter, followed by the river density parameter, soil type parameter, land use parameter, and rainfall parameter with the lowest correlation value.
PENGARUH PENGGUNAAN MEDIA SCRAPBOOK DALAM BIMBINGAN KELOMPOK TERHADAP SELF-CONTROL PESERTA DIDIK KELAS VIII SMP NEGERI 1 JALANCAGAK

This study aims to determine the effect of using scrapbook media on self-control in class VIII SMP N 1 Jalancagak. The method used is descriptive method. The population in this study were 30 students. Data analysis using normality test and t test (paired sample test) using SPSS 26.0 program with a significance value (2-tailed) less than 0.005. The results of data analysis showed that the scrapbook media was in the very decent / good category with an average value of 86.86% of the questionnaire answers and the results of increasing self-control abilities obtained a percentage of 4.4%. While the results of the t-test analysis on the pre-test and post-test data obtained a significant value of 0.000. Thus, the results of this study indicate that: there is a positive influence between the use of scrapbook media on student self-control in class VIII SMP N 1 Jalancagak which can be categorized as quite high
Building a Lightweight Trusted Execution Environment for Arm GPUs

A wide range of Arm endpoints leverage integrated and discrete GPUs to accelerate computation. However, Arm GPU security has not been explored by the community. Existing work has used Trusted Execution Environments (TEEs) to address GPU security concerns on Intel-based platforms, but there are numerous architectural differences that lead to novel technical challenges in deploying TEEs for Arm GPUs. There is a need for generalizable and efficient Arm-based GPU security mechanisms. To address these problems, we present <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StrongBox</small> , the first GPU TEE for secured general computation on Arm endpoints. <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StrongBox</small> provides an isolated execution environment by ensuring exclusive access to GPU. Our approach is based in part on a dynamic, fine-grained memory protection policy as Arm-based GPUs typically share a unified memory with the CPU. Furthermore, <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StrongBox</small> reduces runtime overhead from the redundant security introspection operations. We also design an effective defense mechanism within <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">secure world</i> to protect the confidential GPU computation. Our design leverages the widely-deployed Arm TrustZone and generic Arm features, without hardware modification or architectural changes. We prototype <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StrongBox</small> using an off-the-shelf Arm Mali GPU and perform an extensive evaluation. Results show that <sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">StrongBox</small> successfully ensures GPU computation security with a low (4.70% – 15.26%) overhead.
Development of a Robust Vision-Based Interstory Deformation Sensing Method Using a Kernelized Correlation Filter

In this study, we developed a vision-based interstory deformation sensing system. In vision-based vibration sensing, the uncertainty of measurement accuracy is a problem caused by the lack of robustness of image processing algorithms. We developed an image processing algorithm to improve robustness using a kernelized correlation filter (KCF). Evaluation of our KCF-based algorithm shows that the KCF-based algorithm is more robust than conventional image-processing methods. For example, when the extent of motion blur was large, the proposed algorithm was over 2.5 times more accurate than other algorithms in the reported literature. Our algorithm demonstrates advantages over other methods in allowing measurements in low-light environments and not requiring the use of a high-sensitivity camera.
Investigating the Extent and Usability of Webtext Available in South Africa’s Official Languages

Large collections of text data are a central part of many aspects of natural language processing (NLP) development, and the availability of such data is a prerequisite for advances in the field. With the proliferation of very large, multi-lingual, web-based data sets over the last decade, it is often overlooked that there are still many languages that are not well represented in these data sets, and for which collecting even moderately sized data sets remains a challenge. Furthermore, a lack of data for a language in one or more of the core data sources, such as Wikipedia, often leads to such languages being further excluded in other collections of web-based data or having a detrimental effect on the quality of the data collected. The systematic review and investigation of the quality of these data sets are relatively limited, and the quality of the data is primarily extrinsically evaluated by measuring the improvements on downstream tasks, rather than implicitly evaluating the data. This paper reports on some of the text data that is currently available for South Africa’s official languages (except for English and South African Sign Language) in various widely available web-derived corpora. The aim of the study was to harvest text from the web that could be used as prompts on the Mozilla Common Voice Platform. Towards this aim the extent of the resources available in each language as well as the degree of overlap between different sources were quantified. Results show that there remain several South African languages for which web-based corpora are still severely limited and that, for languages with at least some web presence, the majority of the text is of disputable quality.
Binary Arithmetic Optimization Algorithm for Feature Selection in Pattern Recognition

Feature selection provides a technical way for pattern recognition to identify the important features from a dataset. However, Arithmetic Optimization Algorithm (AOA), which shows competence in solving continuous optimization problems, still cannot be employed in feature selection directly. In this paper, we propose a binary version of Arithmetic Optimization Algorithm (BAOA) that introduces a novel V-shaped time-varying transfer function to implement the transition from continuous search space to binary space. The proposed BAOA is proved to have superior performance in terms of convergence speed and ability to escape local optima. A set of evaluation indicators are employed to evaluate and compared the different algorithm over 15 datasets from the UCI repository. The simulation results prove the capability of the proposed binary version of the Arithmetic Optimization Algorithm to search for the optimal subset of features.
11.2 Gbps 100-meter Free-space Visible Light Laser Communication Utilizing Bidirectional Reservoir Computing Equalizer

In this paper, we introduce an innovative post-equalization technique leveraging bidirectional reservoir computing (BiRC), and apply it to waveform-to-symbol level equalization for visible light laser communication for the first time. This strategy is more resistant to nonlinearities compared to traditional equalizers like least mean square (LMS) equalizer, while requiring less training time and fewer parameters than neural network (NN) -based equalizers. Through this approach, we successfully conduct a 100-meter transmission of a 32-amplitude phase shift keying (32APSK) signal using a green laser operating at a wavelength of 520 nm. Remarkably, our system achieves a high data rate of 11.2 Gbps, all while maintaining a satisfying bit error rate (BER) below the 7% hard decision forward error correction (HD-FEC) threshold of 3.8E-3.
Semantic Segmentation Algorithm Fusing Infrared and Natural Light Images for Automatic Navigation in Transmission Line Inspection

Unmanned aerial vehicles (UAVs) are widely used in power transmission line inspection nowadays and they need to navigate automatically by recognizing the category and accurate position of transmission pylon equipment in line inspection. Semantic segmentation is an effective method for recognizing transmission pylon equipment. In this paper, a semantic segmentation algorithm that fuses infrared and natural light images is proposed. A cross-modal attention interaction activation mechanism is adopted to fully exploit the complementation between natural light and infrared images. Firstly, a global information block with a feature pyramid structure is used to deeply mine and fuse multi-scale global contextual information of fused features, and then the block is used to conduct feature aggregation in the decoding processing, and enough aggregation with multi-scale features of infrared and natural light images is used to enhance the expression ability of the model and improve the accuracy of semantic segmentation of transmission pylon equipment in complex scenes. Our method guides the process of low-level up-sampling and restoration by denser global and high-level features. Experimental results on a dataset of transmission pylon equipment collected by us show that the proposed method achieved better semantic segmentation results than the state-of-the-art methods.
Dynamic Priority Coflow Scheduling in Optical Circuit Switched Networks

OCS (Optical Circuit Switch) is increasingly popular for accelerating data transmission of coflows due to its higher bandwidth and lower power consumption compared with EPS (Electronic Packet Switch), where a coflow is a collection of related parallel flows between two computation stages in data-intensive applications. However, the extra port constraints and reconfiguration delay of OCS obstruct the efficiency of OCS operations. This paper studies the problem of coflow scheduling in the OCS of datacenter networks to minimize the total Coflow Completion Time (CCT). We propose a Dynamic Priority Coflow Scheduling Algorithm that schedules coflows preemptively by considering coflow transmission time and OCS reconfiguration delay jointly to dynamically update each coflow's priority, which can significantly reduce the waiting time of small coflows and reduce head-of-line blocking. Extensive simulations based on Facebook data traces show that our approach outperforms the state-of-the-art scheme OMCO [19] significantly, and transmits multiple coflows 1.30× faster than OMCO.
Remote Sensing Image Ship Matching Utilising Line Features for Resource-Limited Satellites

The existing image matching methods for remote sensing scenes are usually based on local features. The most common local features like SIFT can be used to extract point features. However, this kind of methods may extract too many keypoints on the background, resulting in low attention to the main object in a single image, increasing resource consumption and limiting their performance. To address this issue, we propose a method that could be implemented well on resource-limited satellites for remote sensing images ship matching by leveraging line features. A keypoint extraction strategy called line feature based keypoint detection (LFKD) is designed using line features to choose and filter keypoints. It can strengthen the features at corners and edges of objects and also can significantly reduce the number of keypoints that cause false matches. We also present an end-to-end matching process dependent on a new crop patching function, which helps to reduce complexity. The matching accuracy achieved by the proposed method reaches 0.972 with only 313 M memory and 138 ms testing time. Compared to the state-of-the-art methods in remote sensing scenes in extensive experiments, our keypoint extraction method can be combined with all existing CNN models that can obtain descriptors, and also improve the matching accuracy. The results show that our method can achieve ∼50% test speed boost and ∼30% memory saving in our created dataset and public datasets.
Multi-head attention based candidate segment selection in QA over hybrid data

Question Answering based on Tabular and Textual data is a novel task proposed in recent years in the field of QA. At present, most QA systems return answers from a single data form, such as knowledge graphs, tables, texts. However, hybrid data including structured and unstructured data is quite pervasive in real life instead of a single form. Recent research on TAT-QA mainly suffers from the higher error of extracting supporting evidences from both tabular and textual content. This paper aimed to address the problem of failure evidence extraction from more complex and realistic hybrid data. We first proposed two types of metrics to evaluate the performance of evidence extraction on hybrid data, i.e. wrong evidence ratio (WER) and missing evidence ratio (MER). Then we utilize a candidate extractor to obtain supporting evidence related to the question. Third, an origin selector is designed to determine from where the question’s answer comes. Finally, the loss of origin selector is fused to the final loss function, which can improve the evidence extraction performance. Experimental results on the TAT-QA dataset showed that our proposed model outperforms the best baseline in terms of F1, WER and MER, which proves the effectiveness of our model.
Evolution of Web Development Frameworks

Abstract: Webapps have now become an important part of our industry. Be it small or big business house,they all depend upon webapps to attract more audience The field of web development has very rich history and it is ever evolving. The internet’s everchanging nature and users’ and developers’ evolving needshave led to a dramatic shift in the ever-changing and dynamic world of web development in the last few decades. This review paper presents an in-depth analysis of theevolutionary journey of web development frameworks, fromthe early days of static web pages to the complex, feature-rich web applications of today. An eye–catching and aproper design can only attract the users which can only bedone using a good design framework. Libraries and classesthat offer a broad range of features called frameworks —frameworks layout a foundation of great architecture which is further used to build an application. Frameworks are the building blocks of a web application. They are the foundation upon which web applications are constructed. There are many frameworks for web development that support various programming languages. In this review paper, we will discuss some of the most important frameworks for web development. We will discuss the front-end framework, the back-end framework, and the databaseenvironment. Based on the information provided in thisreview, you can predict which framework will be mosteffective for increasing user engagement.
MySSOF: Gamification Reward System for Enhancing Employee Participation and Activeness in Organizational Activities

This research introduces MySSOF, a gamification-based reward system that aims to increase employee participation and activeness in organizational activities. This system combines elements such as points, experience levels, activities, and leaderboards to encourage employee involvement. The evaluation was carried out using quantitative research by distributing questionnaires to 60 respondents according to the Slovin formula. It was concluded that in End-User Computing Satisfaction (EUCS), respondents perceived the content of the instrument with an average score of 4.18, the accuracy of the instrument with an average score of 4.28, the format of the instrument with an average score of 4.46, the ease of use of the instrument with an average score of 4.46, and punctuality. Instrument with an average score of 4.72, so the overall average score is above 4, which indicates a high level of respondent satisfaction with the MySSOF system in the very satisfied category.
Development Features of Informal Educational Campus Spaces

The research aims to analyze the role of the university campus and its informal educational spaces as one of the resources in ensuring the main university functions. The article discusses the features of informal educational spaces and approaches to their management. The empirical part of the study is based on interviews with university administrators and experts. A typology of informal educational spaces and examples of management practices in development are proposed.
Comic Strip on Students’ Narrative Text Writing Ability

The aim of this research is to prove the effectiveness of comic strips on students' narrative text writing ability. Comic strips are sequential art associated with text with short sequences of images telling short stories. This research applied a pre-experimental method with one group pre-test and post-test design chosen as the research methodology. The eight-grade students in Islamic Middle Schools are taken as the population of this research. Thirty-six students were selected as the samples using the random sampling with the lottery system. The tests were given to measure their narrative writing ability, and the data were analyzed using the t-test formula. From the calculation, the researcher finds the result of the t-test value is 8,49 with the degree of freedom (df) of 35, and the t-table value at the significant level of 0.05 is 2.042. The result shows that the value of the t-test is higher than t-table. It means that the alternative hypothesis (ha) is accepted. Therefore, the researcher concludes that using comic strips positively affects students' ability in writing narrative texts.
Influence of Neural Network Receptive Field on Monocular Depth and Ego-Motion Estimation

Abstract We present an analysis of a self-supervised learning approach for monocular depth and ego-motion estimation. This is an important problem for computer vision systems of robots, autonomous vehicles and other intelligent agents, equipped only with monocular camera sensor. We have explored a number of neural network architectures that perform single-frame depth and multi-frame camera pose predictions to minimize photometric error between consecutive frames on a sequence of camera images. Unlike other existing works, our proposed approach called ERF-SfMLearner examines the influence of the deep neural network receptive field on the performance of depth and ego-motion estimation. To do this, we study the modification of network layers with two convolution operators with extended receptive field: dilated and deformable convolutions. We demonstrate on the KITTI dataset that increasing the receptive field leads to better metrics and lower errors both in terms of depth and ego-motion estimation. Code is publicly available at github.com/linukc/ERF-SfMLearner.
The effectiveness of environment-oriented e-books based on problem-based learning for problem-solving skills

This study aims to test the effectiveness of using environment-oriented e-books based on Problem-Based Learning (PBL) to empower problem-solving skills. The research design used is a Nonrandomized Control Group, Pretest-Posttest Design. The study used a quasi-experimental method with a 2 x 2 factorial design. The research sample was 70, grade X students of Colomadu State High School. The sample was divided into two groups: the control group using powerpoint media and conventional learning models and the treatment group using PBL-based environment-oriented e-books. The material used in the research is ecosystems in biology subjects. The instrument used to obtain data on problem-solving skills is an essay test. Data analysis using ANCOVA (p = 0.05%) with pretest value as a covariate. The results showed that PBL-based environment-oriented e-books had the most significant increase in problem-solving skills scores compared to the control class.