Reviewerly/exHarmony huggingface dataset:
FRAUD MECHANISMS - ACCOUNTING AND FISCAL EVALUATION - SEEN AS POSSIBILITIES FOR ESTIMATING THE UNDERGROUND ECONOMY

The interrelationship between activities specific to the shadow economy and those specific to corruption is obvious and long-noted between academia, numerous authors, and international institutions. A deeper understanding of this economy can help to provide a basis for the behavioral analysis of those who are prone to fraud, so that opportunities for manifestation are limited by the creation of effective control mechanisms. The research issue, therefore, proves to be useful and front-runner to new directions for further development, taking into account its importance, timeliness, significance, and necessity, for financial management, both public and private, for accounting and tax professionals, and for any person interested in the harmonious good-boarder development of the society we live in. Statistical estimates are drawing strong alarm signals in global economies, where we are dealing with huge proportions of the â€œundergroundâ€ economy, with estimates of up to half of the economic activity carried out. It is noted that the global trend toward macroeconomic modeling in the area of measuring the â€œshadowâ€ economy is present. One example, which comes to support this idea, is our own country. According to the information provided by the National Statistics Institute, 20% share of the â€œundergroundâ€ economy in GDP, while some specialists, including the Romanian Intelligence Service, estimate a 45% share of the â€œundergroundâ€ economy. The differences are colossal and come to broaden the line of arguments that are not suitable for measuring the economy of this kind and its relevance.
Analysis and Modeling of Downlink Traffic in Cloud- Rendering Architectures for Augmented Reality

One way to enable augmented reality (AR) on light-weight glasses, which are limited in battery capacity and computational power, is to shift intense operations from the device to the cloud. Instead of rendering on the device, the cloud could take over this task and stream the results to the device. However, this comes with challenges, such as low latency and high data rates, on the wireless network, especially when multiple users have to be served simultaneously. In order to derive the network requirements and to assess network performance, appropriate traffic models are necessary. Therefore, we study the video traffic in the downlink of such a cloud-rendering architecture, as it accounts for the major part. Furthermore, we expect the cloud-rendered AR video traffic to differ significantly from other video traffic due to latency-critical compression, the sparsity of content per video frame, the dependency between content and human movements, as well as the massive resolution requirements. For this paper we create and analyze a video data set, based on which we propose a realistic traffic model.
A Low Power Binary Square Rooter using Reversible Logic and Modified Non-Restoring Algorithm

The calculation of the square root is a fundamental mathematical operation that has a broad variety of applications. The design of a square rooter must accomplish low power consumption, small footprint, and fast speed. On many occasions, there might be a trade-off between the three measures. Because modern technology strives for low power consumption, designs must undergo significant architectural alteration. An ultra-low-power binary square rooter based on reversible circuitry and a modified non-restoring algorithm is presented in this study. It achieves low power by the use of reversible logic. The binary square rooter is created and implemented using the RCSM programming language (Reversible Controlled Subtract Multiplexer). SRG is used to create a binary square rooter, which allows for additional improvement in areas such as number of quantum costs, garbage outputs, and constant inputs (Samiur Rahman Gate). The binary square rooter is created utilising both the SRG and the traditional approaches, and it uses a non-restoring method. Power is determined by utilising the X-Power Analyzer in conjunction with ModelSim software, which is used to carry out the simulations. The power gained using the SRG approach and the standard technique are both compared.
Test Sample Accuracy Scales with Training Sample Density in Neural Networks

Intuitively, one would expect accuracy of a trained neural network's prediction on test samples to correlate with how densely the samples are surrounded by seen training samples in representation space. We find that a bound on empirical training error smoothed across linear activation regions scales inversely with training sample density in representation space. Empirically, we verify this bound is a strong predictor of the inaccuracy of the network's prediction on test samples. For unseen test sets, including those with out-of-distribution samples, ranking test samples by their local region's error bound and discarding samples with the highest bounds raises prediction accuracy by up to 20% in absolute terms for image classification datasets, on average over thresholds.
MegBA: A High-Performance and Distributed Library for Large-Scale Bundle Adjustment.

Large-scale Bundle Adjustment (BA) is the key for many 3D vision applications (e.g., Structure-from-Motion and SLAM). Though important, large-scale BA is still poorly supported by existing BA libraries (e.g., Ceres and g2o). These libraries under-utilise accelerators (i.e., GPUs), and they lack algorithms to distribute BA computation constrained by the memory on a single device. In this paper, we propose MegBA, a high-performance and distributed library for large-scale BA. MegBA has a novel end-to-end vectorised BA algorithm that can fully exploit the massive parallel cores on GPUs, thus speeding up the entire BA computation. It also has a novel distributed BA algorithm that can automatically partition BA problems, and solve BA sub-problems using distributed GPUs. The GPUs synchronise intermediate solving state using network-efficient collective communication, and the synchronisation is designed to minimise communication cost. MegBA has a memory-efficient GPU runtime and exposes g2o-compatible APIs. Experiments show that MegBA can out-perform state-of-the-art BA libraries (i.e., Ceres and DeepLM) by up to 33x and 3.3x respectively, in public large-scale BA benchmarks. The code of MegBA is available at: \url{https://github.com/MegviiRobot/MegBA}.
일본 취업을 위한 물류 분야 어휘 분석 ― 『日本語能力試験出題基準』의 어휘와의 비교를 중심으로 ―

In this study, it is categorized the 『Basics of Logistics』 in KH-Coder, and is made a comparative analysis on the major parts of speech (noun, verb, and adjectives) with the vocabulary from Japanese Language Proficiency Test Guidebook. The results are as below.First, nouns need to learn a variety of logistics󰠏related vocabulary in addition to vocabulary of level 1 or higher.Second, verbs need to learn vocabulary of level 1 or higher and compound verbs related to logistics.Third, Na adjectives need to learn vocabulary of level 1 or higher and Na adjectives of foreign languages.Fourth, I adjectives and adverbs need to learn vocabulary of level 2.
AirObject: A Temporally Evolving Graph Embedding for Object Identification

Object encoding and identification are vital for robotic tasks such as autonomous exploration, semantic scene understanding, and re-localization. Previous approaches have attempted to either track objects or generate descriptors for object identification. However, such systems are limited to a fixed partial object representation from a single viewpoint. In a robot exploration setup, there is a requirement for a temporally evolving global object representation built as the robot observes the object from multiple viewpoints. Furthermore, given the vast distribution of unknown novel objects in the real world, the object identification process must be class-agnostic. In this context, we propose a novel temporal 3D object encoding approach, dubbed AirObject, to obtain global keypoint graph-based embeddings of objects. Specifically, the global 3D object embeddings are generated using a temporal convolutional network across structural information of multiple frames obtained from a graph attention-based encoding method. We demonstrate that AirObject achieves the state-of-the-art performance for video object identification and is robust to severe occlusion, perceptual aliasing, viewpoint shift, deformation, and scale transform, outperforming the state-of-the-art single-frame and sequential descriptors. To the best of our knowledge, AirObject is one of the first temporal object encoding methods.
의약품 설계 기반 품질 고도화를 위한 QbD-6시그마 적용에 관한 연구

국내 제약사가 글로벌 신약개발회사로 발전하기 위해서는 국제의약품 사용 기술 요구사항 조정위원회(ICH; The International Council for Harmonisation of Technical Requirements for Pharmaceuticals for Human use)의 Quality Guideline에 도입된 의약품 설계 기반 품질고도화(Quality by Design)의 올바른 적용이 시급하다. 이에 본 연구는 ICH에서 가이드 하는 QbD의 체계적인 실행을 제약사에 조기에 정착하고 활성화하기 위한 방법으로 6시그마 경영혁신 기법에 QbD의 철학과 사상을 교육하는 QbD 6시그마의 교육과정 개발과 실행을 제안하였다.
Basic requirements for television communications with laser illumination when creating integrated underwater vehicle search systems.

The intensive development of autonomous underwater robotic systems is of great importance for the development of the world’s oceans. One of the main problems of using underwater robotic systems is to increase the search performance of underwater vehicles based on the integration of the capabilities of onboard radio-electronic systems for obtaining information in various physical fields. Analyzing the physical basis of the search, including consideration of the environment of its conduct, objects, as well as means of detection, it can be noted that the result of conducting search operations of the same objects in the same water areas for different underwater robotic complexes depends on what means of detection they are equipped with. The presence of a certain composition of technical means will dictate the development of an optimal sequence of actions that make up the search algorithm. The creation of such algorithms in relation to the task of increasing the search potential of underwater robotics will determine the direction of scientific research that ensures the formation of an optimal underwater search system on it. The effective use of search resources justifies the conclusion that the radio-electronic means of underwater vehicles that form the underwater search system, in total, are a set of devices that are functionally combined with each other for its effective implementation. This association is aimed at forming the so-called integrated search system, which provides for the exchange of information between different search tools about all the objects found by them. The article presents the requirements for the creation of integrated search systems, taking into account practical experiments and experience in the design of underwater vehicles capable of implementing an optimal underwater search system based on laser technologies.
Concurrent Composition of Differential Privacy.

We initiate a study of the composition properties of interactive differentially private mechanisms. An interactive differentially private mechanism is an algorithm that allows an analyst to adaptively ask queries about a sensitive dataset, with the property that an adversarial analyst's view of the interaction is approximately the same regardless of whether or not any individual's data is in the dataset. Previous studies of composition of differential privacy have focused on non-interactive algorithms, but interactive mechanisms are needed to capture many of the intended applications of differential privacy and a number of the important differentially private primitives. We focus on concurrent composition, where an adversary can arbitrarily interleave its queries to several differentially private mechanisms, which may be feasible when differentially private query systems are deployed in practice. We prove that when the interactive mechanisms being composed are pure differentially private, their concurrent composition achieves privacy parameters (with respect to pure or approximate differential privacy) that match the (optimal) composition theorem for noninteractive differential privacy. We also prove a composition theorem for interactive mechanisms that satisfy approximate differential privacy. That bound is weaker than even the basic (suboptimal) composition theorem for noninteractive differential privacy, and we leave closing the gap as a direction for future research, along with understanding concurrent composition for other variants of differential privacy.
Interesting Object, Curious Agent: Learning Task-Agnostic Exploration.

Common approaches for task-agnostic exploration learn tabula-rasa --the agent assumes isolated environments and no prior knowledge or experience. However, in the real world, agents learn in many environments and always come with prior experiences as they explore new ones. Exploration is a lifelong process. In this paper, we propose a paradigm change in the formulation and evaluation of task-agnostic exploration. In this setup, the agent first learns to explore across many environments without any extrinsic goal in a task-agnostic manner. Later on, the agent effectively transfers the learned exploration policy to better explore new environments when solving tasks. In this context, we evaluate several baseline exploration strategies and present a simple yet effective approach to learning task-agnostic exploration policies. Our key idea is that there are two components of exploration: (1) an agent-centric component encouraging exploration of unseen parts of the environment based on an agent's belief; (2) an environment-centric component encouraging exploration of inherently interesting objects. We show that our formulation is effective and provides the most consistent exploration across several training-testing environment pairs. We also introduce benchmarks and metrics for evaluating task-agnostic exploration strategies. The source code is available at https://github.com/sparisi/cbet/.
CONCEPTUALIZING DIGITAL LEADERSHIP FUNDAMENTALS FOR PRIVATE HIGHER EDUCATION INSTITUTIONS IN EMERGING COUNTRY

Globalization and the Industrial Revolution 4.0 have profound implications for many aspects of life, including leadership and education. The function of higher education institutions in fostering high-quality talent to ensure sustainable economic growth in the country is crucial. Private Higher Education (PHE) is one of the rapidly growing sectors in higher education of emerging countries. Sustaining the performance of PHE institutions is a critical agenda among leaders in these institutions as they manage stiff competition and ride the wave of digitalization. Empirical evidence indicated that leadership significantly impacted organizational performance. Recent research in leadership has evolved rapidly due to technology evolution, institutional and geopolitical changes. Adopting the methodology of Systematic Literature Review, this paper synthesized previous research related to leadership in education in the past decade. Results show several literature gaps and uncover elements of leadership being examined in previous studies. Subsequently, dimensions related to the context of PHE are identified and discussed. This paper then developed a holistic framework of digital leadership incorporating the interplay of three key elements found to significantly influence sustainable performance of PHE institutions. Specifically, the roles of global vision leadership, digital-age learning culture, and digital citizenship are justified as fundamentals of leading PHE institutions towards sustainable performance in today’s digital age. Leaders of higher education should take full responsibility and expertise from potential opportunities in Industrial Revolution 4.0 by providing their institutions with sustainable digital leadership. Strong and competent leadership is required to ensure the long-term viability of e-learning in higher education institutions. When the institution’s vision is clear, effective leadership will inspire and motivate the academic community to develop in accordance with that vision. When an individual receives leadership training, their ability to function in leadership roles within the organization will be enhanced. A leader's entrepreneurial mindset, creative use of technological devices and risk taking have a tremendous impact on organizational performance. Furthermore, education leaders must have the skills and information necessary to lead all stakeholders to implement the new millennium way of learning, as well as a learning method to empower students with 21st century skill, experience, and knowledge. In addition, today’s leaders need to drive the organization to understand the social, ethical, and legal concerns and responsibilities as it embarks into digital culture.In conclusion, the holistic digital leadership model discussed in this paper will guide players in the higher education industry to assess and strategically implement effective leadership. Consequently, this provides dynamic capabilities to the institutions towards sustainable performance in Education 5.0 Era.
Web Activity: Distribution of the P-values Under the Null Hypothesis of a One-Sample Z-test

The goal of the activity is for students to develop an intuition about the distribution of the P-value under the null hypothesis of a two-sided, one-sample z -test. The first part of the activity is a recap of hypothesis testing and z -tests in particular. The students then explore how P-values change when multiple settings of a z -test (such as the sample size) change. The third part of the activity prompts students to generate multiple P-values using an interactive resource and plot their histogram. During the final part of the activity, students use another interactive resource to generate thousands of P-values and relate their distribution with the significance level and the probability of a Type I error.
On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources.

Domain adaptation (DA) benefits from the rigorous theoretical works that study its insightful characteristics and various aspects, e.g., learning domain-invariant representations and its trade-off. However, it seems not the case for the multiple source DA and domain generalization (DG) settings which are remarkably more complicated and sophisticated due to the involvement of multiple source domains and potential unavailability of target domain during training. In this paper, we develop novel upper-bounds for the target general loss which appeal to us to define two kinds of domain-invariant representations. We further study the pros and cons as well as the trade-offs of enforcing learning each domain-invariant representation. Finally, we conduct experiments to inspect the trade-off of these representations for offering practical hints regarding how to use them in practice and explore other interesting properties of our developed theory.
Pengaruh Usability Dan Website Quality Terhadap Kepuasan Dan Loyalitas Pengunjung Website Perpustakaan Universitas Riau

The Library of Universitas Riau website on the domain https://lib.unri.ac.id isan online information medium that is used to provide information to users, bothacademicians and the general public. Website quality and usability are importantfactors used to measure a website's self. The higher the level of quality and usability,the level of user satisfaction will be higher as well as loyalty. The purpose of thisstudy was to analyze the influence of the usability and quality of the website on thesatisfaction and loyalty of visitors to the website of the Riau University Library.This study uses a quantitative approach. The analytical method used isStructural Equation Modeling (SEM), namely modeling to complete the path analysis.The statistical analysis tools used are PSPP and LISREL applications. The researchwas conducted at the Riau University Library with a website visitor population of911.156 people, while the sample used was 200 people. The final analysis usesConfirmatory Factor Analysis (CFA) which produces a Path Analysis Diagram(T-value), if the T-value is greater than 1.96 (critical value of significance) means asignificant effect.The results showed that reusability had a significant effect on satisfaction(T-values = 3.14) but not significant on Loyalty (T-values = 1.18), Website qualitywas significant on satisfaction (T-values = 2.576) but not significant to loyalty(T-value = 0.877). Satisfaction has a significant effect on loyalty (T-values = 3.28).Through service quality interventions, usability has a total effect of 2,216 (significant)on loyalty, while website quality has a total effect of 1,630 (insignificant).Key words : Usability, Website Quality, Satisfaction, Loyalty
Traditional Chinese Synthetic Datasets Verified with Labeled Data for Scene Text Recognition

Scene text recognition (STR) has been widely studied in academia and industry. Training a text recognition model often requires a large amount of labeled data, but data labeling can be difficult, expensive, or time-consuming, especially for Traditional Chinese text recognition. To the best of our knowledge, public datasets for Traditional Chinese text recognition are lacking. This paper presents a framework for a Traditional Chinese synthetic data engine which aims to improve text recognition model performance. We generated over 20 million synthetic data and collected over 7,000 manually labeled data TC-STR 7k-word as the benchmark. Experimental results show that a text recognition model can achieve much better accuracy either by training from scratch with our generated synthetic data or by further fine-tuning with TC-STR 7k-word.
Michigan coronavirus protest proceeds despite Facebook blocking event post

Hundreds gathered in Lansing, Michigan, even after Facebook deleted the organizer's event page.
Pengembangan Media Scrapbook Pada Pembelajaran Tematik Untuk Meningkatkan Minat Belajar Siswa Kelas I Sekolah Dasar

Teaching and learning activities need to support learning, namely learning media. Learning media is a tool to convey subject matter to students. The purpose of this study is (1) Knowing the feasibility of learning mediascrapbook in the thematic learning of first grade elementary school (2) Knowing the suitability of scrapbook media to increase interest in learning of first grade elementary school students. The type of research used is development research or research and development (R&D) with the Borg and Gall development model. The subjects of this study were the first grade students of Brongkal State Elementary School, which amounted to 22 students. The data collection technique used is a questionnaire. The data analysis technique for media feasibility uses quantitative descriptive and interest in learning uses the paired sample t test t test.