Reviewerly/exHarmony huggingface dataset:
Decoupled 3-D object detector

This paper proposes an efficient cascaded 3-D object detection architecture. Such an architecture decouples the 3-D object detection pipeline to maximise the utilisation of the inherent advantages of RGB images and LiDAR point clouds in order to perform 3-D object detection while maintaining low computational complexity. Our proposed architecture relies on a cascade of two networks, the first leverages the texture density in images and the maturity of state-of-the-art 2-D object detectors to classify and obtain initial region proposals for objects in the scene. These proposals are fed to a light-weight secondary network that leverages the compactness of bird-eye view point cloud representations to perform orientation and size estimation. The 3-D bounding box proposal is constructed by fusing predictions inferred from both networks, as predictions lie on orthogonal planes. Evaluated on the KITTI benchmark data set, we show that the proposed method obtains results on-par with more complex end-to-end 3-D detection methods while greatly reducing computational and memory requirements. This work also presents results from the deployment within a perception pipeline, and analyses challenges faced in deployment within a frontal perception pipeline.
Sentiment Analysis and Topic Modeling of Citizen Satisfaction with the Indonesian Government in Handling a Pandemic

When the COVID hit the world, many countries issued policies to stop the spread of the virus. In Indonesia, various opinions and surveys have emerged regarding citizen satisfaction with the government's performance in handling the pandemic. But the survey method still has many weaknesses, for example: bias from the researcher, lack of confidentiality, hello effect, and so on. The purpose of this study is to classify this phenomenon into positive and negative sentiments taken from Twitter data. Every record is pre-processed to clean the data. Data labelling using a lexicon consisting of positive or negative polarities. Sentiment classification using Support Vector Machine (SVM). Each positive and negative sentiment will be processed using Latent Dirichlet Allocation (LDA) method to find out the interpretation of the main topics that are often discussed, then made into a visualization using a word cloud. The best model obtained was the model with TF-IDF feature extraction with a precision value of 0.87, a recall of 0.95, an accuracy of 0.89, and an F1-measure of 0.93. Our findings indicate that people are more likely to be satisfied with the performance of the government's fight against COVID than with the policies they introduce. People are also satisfied because they can feel mudik (go back to hometown) again after two years of the pandemic. Dissatisfaction comes from people who think that there is a business game in vaccine policy as well as the government's lack of transparency regarding the number of COVID cases.
Electroencephalographic (EEG) Correlates of Visually Induced Motion Sickness (VIMS) in the Virtual Reality (VR) Based Simulations

In the presented study, the possibility of detecting Visually Induced Motion Sickness (VIMS) in Virtual Reality (VR) simulations using electroencephalographic (EEG) recordings was investigated. 31 adult participants were tested with VR and EEG in both VIMS and non-VIMS conditions. A correlation between EEG signals and the Simulation Sickness Questionnaire (SSQ) scores was shown, indicating that VIMS can be detected through EEG recordings.
PERAN GURU DALAM MEMOTIVASI BELAJAR PESERTA DIDIK DALAM PEMBELAJARAN MATEMATIKA KELAS V SD NEGERI KALIDERES 05 PAGI JAKARTA BARAT

This research is entitled The Role of Teachers in Motivating Learners in Learning Mathematics Class V SD Negeri Kalideres 05 Pagi West Jakarta, the purpose of this research is to find out the role of teachers in motivating student learning in learning mathematics class V SD Negeri Kalideres 05 Pagi West Jakarta. This research uses descriptive qualitative research. The subjects of this study were 1 teacher, 2 students, and 1 parent of grade V students. This research was conducted at SD Negeri Kalideres 05 Pagi West Jakarta. The data collection techniques used in this study were observation of the role of teachers and students' learning motivation, then interviewing 1 teacher, 2 students, and 1 parent, and supported by documentation. The results of the research conducted show that the role of the teacher in motivating students' learning has basically tried to carry out its role to arouse students' learning motivation by providing encouragement in the form of praise and encouraging words to students. However, the way teachers provide motivation is not enough to generate motivation to learn mathematics.
Non-Rayleigh signal of interacting quantum particles

The dynamics of two interacting quantum particles on a weakly disordered chain is investigated. Spatial quantum interference between them is characterized through the statistics of two-particle transition amplitudes, related to Hanbury Brown-Twiss correlations in optics. The fluctuation profile of the signal can discern whether the interacting parties are behaving like identical bosons, fermions, or distinguishable particles. An analog fully developed speckle regime displaying Rayleigh statistics is achieved for interacting bosons. Deviations toward long-tailed distributions echo quantum correlations akin to noninteracting identical particles. In the limit of strong interaction, two-particle bound states obey compound Rician distributions.
Prediksi Curah hujan di Kota Pekanbaru Menggunakan lSTM (Long Short Term Memory)

Based on data obtained from BMKG Pekanbaru City in 2010-2020 there was an increase and decrease in the intensity of rainfall that occurred in Pekanbaru city. The increase in rainfall in the city of Pekanbaru will cause problems such as the occurrence of flooding of several roads and several areas in the city of Pekanbaru and the occurrence of other unexpected disasters that will cause problems and experience difficulties. In overcoming this problem, research was conducted in the form of Rainfall Prediction in Pekanbaru City Using LSTM (Long Short Term Memory) using 2 methods, namely in finding the accuracy of the error rate using RMSE (Root Mean Square Error) and MSE (Mean Square Error). The results showed that the predictions made were quite good. With the lowest error rate of 21,328 in the train and 454,901 in the test, the composition of the train data and the test data half gave the best results.
FAIRsharing Guestpost: What Criteria Matters with Data Repository Selection?

&lt;em&gt;As part of the FAIRsharing Community Network (see previous blog) and joint Force11 and Research Data Alliance (RDA) FAIRsharing Working Group we have been involved in efforts to develop a shared, cross-publisher list of recommended data deposition repositories. The first fruits of these are a preprint from the working group and DataCite (of which we are also members) summarising what we feel should be the key criteria for selection.&lt;/em&gt;
Study of Visual Technqiues for Determing Clustering Tendnecy Over the Unlabelled Datasets

Finding similarity features between a set of data objects is a primary step in assessment of clusters. Currently, visual techniques such as visual access tendency (VAT), spectral VAT (SpecVAT) and other variants of VAT are widely used for determining the number of clusters. Determining the number of clusters for given data is known as cluster tendency. Popular clustering techniques, such as k-means and other graph-based techniques produces the clusters without knowing the knowledge of cluster tendency. Thus, this paper surveys the visual approaches for addressing the problem of cluster tendency that can be useful for improving the quality of clusters in k-means and graph-based clustering approaches. VAT and SpecVAT are major visual approached and that can be tested on synthetic datasets and presented cluster assessment results in observation study.
A Self-adaptive and Secure Approach to Share Network Trace Data

A large volume of network trace data are collected by the government and public and private organizations and can be analyzed for various purposes such as resolving network problems, improving network performance, and understanding user behavior. However, most organizations are reluctant to share their data with any external experts for analysis, because they contain sensitive information deemed proprietary to the organization, thus raising privacy concerns. Even if the payload of network packets is not shared, header data may disclose sensitive information that adversaries can exploit to perform unauthorized actions. So network trace data need to be anonymized before being shared. Most of the existing anonymization tools have two major shortcomings: (1) they cannot provide provable protection, and (2) their performance relies on setting the right parameter values such as the degree of privacy protection and the features that should be anonymized, but there is little assistance for a user to optimally set these parameters. This article proposes a self-adaptive and secure approach to anonymize network trace data and provides provable protection and automatic optimal settings of parameters. A comparison of the proposed approach with existing anonymization tools via experimentation demonstrated that the proposed method outperforms the existing anonymization techniques.
Blockchain Technology And Its Impact On European Bank’s Cyber Security And Data Integrity

Cybersecurity Ventures anticipates that the worldwide expenses of cybercrime would increase by 15 percent yearly over the course of the next five years, reaching USD 10.5 trillion annually by 2025, which is an increase from USD $3 trillion in 2015. It’s imperative to investigate the implementation of new technologies and their outcome in raising the threat to the banking industry. Using CFA and EFA statistical analysis and gathering the data from N=291 respondents from the managerial level, blockchain consultants, and cybersecurity experts. The findings confirm that the implementation of blockchain technologies has efficiently reduced customer cyber security risks and enhanced the European banking sector's data integrity.
Assessing the Effectiveness of 360-Degree Performance Appraisal System to Enhance Employees’ Productivity in Jordanian Listed Banks

The performance appraisal is not limited to evaluate the performance of banks’ employees, but it also facilitates highlighting the particular objectives of organisations. Performance appraisal is considered as a very effective tool for evaluating and improving employees’ knowledge and training with an intension to establish a long term learning guide, and strategies for nourishing employees’ productivity for enhanced working hours. Therefore, the study investigated the effect of “360-degree performance appraisal system” in the productivity of Jordanian listed banks’ employees. Through examining data that was collected from a sample of 120 employees, those are working in Jordanian Islamic and commercial banks; the results indicated that the current performance appraisal system is significantly and positive affecting the level of employees’ productivity. Moreover, the study suggested that Jordanian banking sector must perform performance appraisals more frequently to have positive outcomes, as well as it is necessary to evaluate the strengths and weaknesses of employees to allocate responsibilities accordingly.
Digital Experience Theatre: Ideation Tool for Developing Socially Significant Digital Products

The problem identified in the Digital Experience Theater (DXT) research project is related to the development of interactive digital products, particularly mobile devices, with relevance for empowering a place and its community. Digital products, increasingly ubiquitous in contemporary societies, are a powerful medium for the sociocultural mapping of a community in a simultaneously local and
Emergency Remote Teaching (ERT) in the COVID-19 Pandemic: Impact on the Education of Higher Education Students in the Health Area

Emergency Remote Teaching (ERT) emerges as a temporary resource for teaching students of courses in the health area, in higher education institutions, during the global pandemic COVID-19. The remote teaching modality brought with it positive aspects, as well as important challenges regarding the learning process in the area of higher education under analysis. This study
Advancing Autonomous Navigation: Deep Learning Techniques for Self-Driving Cars

Abstract: The fast development of self-driving car technology has brought forth radical breakthroughs in the world of transportation and movement. This study paper looks into the paradigm shift allowed by deep learning methods in getting stable and reliable autonomous steering for self-driving cars. Leveraging the power of artificial intelligence, particularly deep learning models, this study presents a comprehensive exploration of the multifaceted aspects involved in developing a self-driving car system that can adeptly perceive its environment, make intelligent decisions, and ensure passenger and pedestrian safety. The paper first reviews the basic concepts of self-driving cars, getting into the challenges associated with real-time awareness, scene understanding, and decision-making in complex and dynamic settings. It then shows the important role that deep learning plays in handling these challenges by studying various neural network designs designed for different tasks, including picture recognition, object detection, semantic segmentation, and path planning. The study encompasses convolutional neural networks (CNNs) for picture analysis.
The Impact of IT Controls on Reducing Tax Evasion in Jordan “From the Point of View of the Auditors”

The study tackled the impact of information technology controls (general controls and applied controls) in reducing tax evasion rate in Jordan. This study used current and historical data to identify trends and relationships. Component of the study consisted of (250) ISTD auditors, (170) auditors representing 68% of the study component were involved as a sample study. To measure the study variables, a questionnaire was texted and distributed over the study sample. The number of retrieved questionnaires that were valid for analysis was (154). Descriptive statistics methods were used in analyzing the primary data. The study realized several endings, the most important is that information technology controls are available in their dimensions in a moderate degree in the ISTD, and there is an impact for both general controls and applied controls in reducing tax evasion. The study recommended that the ISTD should revise and reevaluate the information technology controls, (the general and the applied ones) to improve the obtainability of such controls paralleled with the best-updated technologies. The study also recommended revision of the accounting systems in one hand and the revision of regulatory controls related to information technology on the other hand to ensure consistency in the implementation of tasks and to help in reduction of tax evasion cases.
RESEARCH ON SUPPORT METHODS FOR HIGH SCHOOL TEACHERS EDUCATING SDGS WHILE UTILIZING DESIGN METHODS

In order to respond to rapid changes in society and the economy, it is expected to develop human resources who understand the social issues of the SDGs, can proactively propose solutions and possess skills based on conventional knowledge-oriented education. In Japan, the SDGs are being addressed in the education sector: many high schools have started to offer classes on the SDGs. However, promoting education on the SDGs is a huge challenge for teachers in the existing high school education system, as there are no clear teaching methods or educational materials. In particular, there are no good methods for educating students on how to discover social issues from familiar places and more in-depth. Teachers urgently need to know how to support students in this relatively new education process. This study aims to focus on the part of social issue discovery in the process of SDG education for high school students using design thinking and to propose a support method that can help teachers solve the problems they are facing so that high school students can discover better and deeper social issues. Using the SDGs Challenge Project of Japan Fukusho High School as a case study, 27 high school teachers from various domains such as language, society, and arts were educated on the SDGs for third-year high school students in 2021. A questionnaire survey of teachers was conducted on the difficulties in identifying social issues in this context, and a questionnaire survey of 314 students was conducted after the class. In response to the results, support proposals were made according to the educational process to be carried out in 2022. A questionnaire of 22 teachers in charge in 2022 who received this support and a questionnaire of 301 students were conducted, the results of which were compared with those of the previous year to analyze the effectiveness of the support methods and to make support proposals for teachers so that students can better identify social issues. The 2022 teacher questionnaire showed that more teachers are confident about education on social issues and that many teachers think that the educational tools are effective. Total support for the education process is needed. Specifically, it was found that preliminary support such as workshops on understanding design thinking and understanding SDGs, a support system to facilitate teaching, as well as a reflection after the project and improvement of instructional materials based on the content of the questionnaire, are needed.
“DIGITAL PHILOSOPHY”: THE PROBLEM OF USING TERM IN SOCIO-PHILOSOPHICAL STUDY

The author is analyzing the modern socio-philosophical discourse of Internet transformation of the modern world and raises the issue of correct use of term “digital philosophy” in scientifi c publications. Using hermeneutic and system methods, the author concludes that modern social philosophy departs from traditional historical context of using the term to new global meaning for the description of modern digital being.
TRANSFORMATION OF THE MODERN EDUCATIONAL PROCESS

The process of digitalization of social reality, including education, is investigated. The active development of electronic and digital technologies, aff ecting all spheres of social existence, has a signifi cant impact on human educational activity, which, in turn, corresponds to the existing model of the world. The diff erence between the two models of the world in which knowledge is formed is shown. The metaphysical model of the world determines the individualistic type of society and highlights the ability of the individual as a cognizing subject to arbitrarily construct social reality, revealing only the consumer properties of the surrounding world. In the metaphysical model of the world, education acts as a tool for constructing social reality. The dialectical model of the world determines the collectivist type of society, revealing the completeness and perfection of the world. The statement of the collectivist type of social relations is based on the universal connection of historical and social experience, revealing the degrees of cognition and self-knowledge. In the dialectical model of the world, education refl ects the viability of a collectivist society, the level of development of the cognizing subject, and the perfection of social relations. It is established that in modern society, education, thanks to the development and active introduction of digital technologies, develops according to the metaphysical model of the world, revealing the consumer properties of objective reality.
CLASSICAL PHILOSOPHY AND THE PROBLEM OF REASONABLE REFORM OF THE SYSTEM OF PHILOSOPHICAL SCIENCE AND EDUCATION IN MODERN RUSSIA

The authors present an original vision of reforming the system of philosophical education in higher education, based on many years of pedagogical experience. A reasonable reform of philosophical education involves an increase in the workload as a whole to 8 s. e., classroom classes — up to 4 s. e. The approximate content and structure of the general education discipline «Philosophy» is proposed.
Analyzing student perception on using webtoon as a learning medium for reading comprehension

As is commonly understood, educational requirements, especially in Indonesia, necessitate students' mastery of English reading skills due to the consistent inclusion of reading assessments in examinations. However, the hurdles students often face in reading comprehension remain numerous, including challenges such as limited understanding of grammar usage and unfamiliarity with vocabulary meanings. To surmount these challenges, the provision of suitable learning tools becomes imperative. One effective tool for addressing these obstacles is Webtoon, an innovative comic platform originating from South Korea. Comics, through their blend of sentences, vocabulary, and illustrative depictions of character actions, offer an avenue for enhanced learning. Hence, Webtoon holds the potential as a medium to bolster students' reading comprehension abilities. The current study sought to delve into students' perceptions concerning the use of Webtoon as a learning aid for reading comprehension. Employing a qualitative approach, the research utilized questionnaires and interviews as data collection tools. The findings unveiled a prevailing positive inclination among students towards integrating Webtoon into their reading practices. This perception was gauged across various indicators such as interest, experience, emotional response, object color, and the nature of perceptions. Overall, these indicators showcased that a majority of students held a favorable view of using Webtoon for reading exercises. Their feedback indicated that engaging with Webtoon during reading activities was conducive to heightening their comprehension and fostering familiarity with the vocabulary employed.
SciDG: Benchmarking Scientific Dynamic Graph Queries

Dynamic graphs are increasingly being utilized in domain knowledge modeling and large-scale scientific data management. Managing dynamic graph data requires a graph database system that can handle constantly changing volumes and data versions, while maintaining an acceptable query latency related to versioning. To understand how the design of storage structures affects database performance and assist scientific application developers in finding the optimal storage structure for their dynamic graph application scenarios, we have designed an easy-to-use benchmark framework called SciDG. We also conducted a study on the latencies of five fundamental version-related queries for various scientific application scenarios using SciDG. We evaluated the performance of databases based on three distinct storage principles: Sp-DB, Dp-DB, and Tp-DB. The experimental results indicate that SciDG is a valuable tool for assessing the strengths and weaknesses of different storage structures for dynamic graphs in various scenarios. Additionally, it assists scientists in selecting the most suitable dynamic graph database system for their work.
InfoMoD: Information-theoretic Model Diagnostics

Validating and debugging machine learning models is done by testing them on unseen data. Analyzing model performance on various subsets of the data is critical for fairness, trust, bias detection and explainablility. In this paper, we describe a new way to do this. Our solution, called InfoMoD, applies recent work in information-theoretic data summarization to the problem of model diagnostics. Using real-life datasets, we show how InfoMod concisely describes how a model performs across different subsets of the data and produces expected performance indicators for individual test instances.