Reviewerly/exHarmony huggingface dataset:
Quantum random number generation using an on-chip nanowire plasmonic waveguide

Quantum random number generators employ the inherent randomness of quantum mechanics to generate truly unpredictable random numbers, which are essential in cryptographic applications. While a great variety of quantum random number generators have been realised using photonics, few exploit the high-field confinement offered by plasmonics, which enables device footprints an order of magnitude smaller in size. Here we integrate an on-chip nanowire plasmonic waveguide into an optical time-of-arrival based quantum random number generation setup. Despite loss, we achieve a random number generation rate of 14.4 Mbits/s using low light intensity, with the generated bits passing industry standard tests without post-processing. By increasing the light intensity, we were then able to increase the generation rate to 41.4 Mbits/s, with the resulting bits only requiring a shuffle to pass all tests. This is an order of magnitude increase in the generation rate and decrease in the device size compared to previous work. Our experiment demonstrates the successful integration of an on-chip nanoscale plasmonic component into a quantum random number generation setup. This may lead to new opportunities in compact and scalable quantum random number generation.
Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn’t require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat—wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI .
Differentially Private Synthetic Data Using KD-Trees

Creation of a synthetic dataset that faithfully represents the data distribution and simultaneously preserves privacy is a major research challenge. Many space partitioning based approaches have emerged in recent years for answering statistical queries in a differentially private manner. However, for synthetic data generation problem, recent research has been mainly focused on deep generative models. In contrast, we exploit space partitioning techniques together with noise perturbation and thus achieve intuitive and transparent algorithms. We propose both data independent and data dependent algorithms for $\epsilon$-differentially private synthetic data generation whose kernel density resembles that of the real dataset. Additionally, we provide theoretical results on the utility-privacy trade-offs and show how our data dependent approach overcomes the curse of dimensionality and leads to a scalable algorithm. We show empirical utility improvements over the prior work, and discuss performance of our algorithm on a downstream classification task on a real dataset.
Open-NAS: A customizable search space for Neural Architecture Search

As we advance in the fast-growing era of Machine Learning, various new and more complex neural architectures are arising to tackle problem more efficiently. On the one hand, efficiently deploy them requires advanced knowledge and expertise, which is most of the time difficult to find on the labor market. On the other hand, searching for an optimized neural architecture is a time-consuming task when it is performed manually using a trial-and-error approach. Hence, a method and a tool support are needed to assist users of neural architectures, leading to an eagerness in the field of Automatic Machine Learning (AutoML). When it comes to Deep Learning, an important part of AutoML is the Neural Architecture Search (NAS). In this paper, we propose a formalization for a cell-based search space. The objectives of the proposed approach are to optimize the search-time and to be general enough to handle most of state-of-the-art Convolutional Neural Networks (CNN) architectures, as well as being customizable.
The Effects of Sales-change Size on the Asymmetric Cost Behavior

ABJ focus on the current period sales-change direction and predicted asymmetric cost behavior. BBCM extended ABJ model by considering the prior period’s sales changes on asymmetric cost behavior. Recently, Ciftci and Zoubi(2019) investigated the impacts of current sales-change size on asymmetric cost behavior extending BBCM’s two-period model.&#x0D; The purpose of our study is to analyze the effects of current sales-change size on asymmetric cost behavior in Korea using Ciftci and Zoubi model.&#x0D; The results of the analysis are as follows. First, In the case of prior sales increase, current small(medium) sales change affects more significantly to the stickiness of cost than large current sales change. In addition, in the case of prior sales decrease, current small(medium) sales change lessens the stickiness of cost more significantly than large current sales change. These results are consistent with our hypotheses. However, medium-size sales change’ coefficient is large than that of small-size sales change, so the relationship between current sales change and cost stickiness is not monotonic, and the model including dummy variables’ main effect terms is somewhat unstable, and in the case of prior sales decrease, the model doesn’t show anti-stickiness. We need more additional research to find out these anomalies are due to difference of cost behavior between Korea and U.S. or defects of the model.
Cybersecurity Assurance Program by the U.S.Accounting Firms and its Implications: Focusing on the AICPA SOC Assurance Programs

This study introduces the cybersecurity assurance programs of the AICPA and other organizations outside Korea, and provides implications of implementing these cybersecurity assurance programs by accounting firms in Korea. Recently, cybersecurity incidents are increasing. The AICPA launched the SOC (System Organization Control) assurance programs in order to deal with the market demand. AICPA (2021a) reveals its survey result, saying that the demand of the SOC program has increased by 49% between 2018 and 2020. This paper provides the implementation cases of the AICPA SOC assurance programs. This study argues that implementation of cybersecurity assurance programs will play a pivotal role in enhancing the security level of the organizations. The cybersecurity assurance can be used when the insurance company decides the level of cyber－insurance fees. In addition, it can be used as the key legal ground by the management when the management is faced with the cyber－breach litigation in the court. This paper also suggests that the KICPA (the Korean Institute of Certified Public Accountants) should facilitate the implementation of the cybersecurity assurance program by accountants.
The Effect of Financial Statements Comparability on Idiosyncratic Risk

This study examines the effect of comparability in financial statements on idiosyncratic volatility. The analysis showed that the estimated coefficient of idiosyncratic volatility had a significant negative(－) in the analysis model, confirming that the higher the comparability of financial statements, the lower the idiosyncratic volatility. This could be determined by entities that are more comparable to financial statements, the more information asymmetry is mitigated and the greater the monitoring function of managers, resulting in lower idiosyncratic volatility. The contributions of this study are as follows. Comparability, a qualitative characteristic of accounting information, provides usefulness in accounting information users’ decision－making, which mitigates information collection costs and information cost symmetry, and provides implications to prepare accounting standards. In addition, if the usefulness of comparability is recognised in this study, comparability will be considered in revising accounting standards by establishing standards such as the Board of Accounting Standards. Furthermore, the research direction could be provided to accounting information writers, accounting standards－setting agencies, regulators, etc. by highlighting the need to find ways to increase comparability in financial statements.
The Relationship of the Impact of Production Process Integration on Logistics Stabilization and Inventory Management

This study would demonstrate the relationship of the impacts of the production process system integration of a manufacturing company on logistics stabilization and efficient inventory management. And applied the cluster sampling method. Thus, this study distributed 265 copies of a survey with car companies in selecting companies that could represent manufacturing companies for 20 days from March 12, 2020. Later, went through the data coding process for empirical analysis and interpreted the structural equation model based on 249 copies, excluding 16 copies of the questionnaire judged to have a systematic or unsystematic error for the survey. As a result of the study, the hypothesis on whether the production process system integration affected logistics stabilization and efficient inventory management was partly adopted. To examine the related results, the production process system integration had a statistically significant positive (＋) impact on logistics stabilization and process logistics. In addition, it was finally found that logistics stabilization sales logistics had a statistically significant positive (＋) impact on efficient inventory management. But process logistics was in a relationship of a statistically significant positive (＋) impact on inventory management.
Factors Influencing Learner Resistance in Online Learning Environment: Focused on the Computer Accounting Qualification Courses

This study analyzed the relationship between innovation characteristics(relative advantage, compatibility, complexity, perceived risk) and online learning quality(contents quality, interaction quality, system quality) factors on learner resistance and service continuance intention in an online learning environment. To this end, research model development and hypotheses were established through a comprehensive review of related prior studies, and data collection and hypothesis testing were performed on a total of 309 experienced persons who had learned the computerized accounting qualification course online. The main results of this study are as follows. First, it was found that all four innovation attributes, such as relative advantage, relevance, complexity, and perceived risk, had a significant effect on learner resistance. Second, it was found that three of the innovation attributes, such as relative advantage, complexity, and perceived risk, had a significant effect on continuance intention, while compatibility had no significant effect. Third, the resistance of online learning users was found to have a significant effect on the continuance intention. Finally, it was found that among online learning qualities, content quality and interaction quality had a positive (＋) effect on continuance intention, while system quality had no significant effect. Based on the results of this study, academic and practical implications and future research directions are presented.
Swin-RGC: Swin-Transformer With Recursive Gated Convolution for Substation Equipment Non-Rigid Defect Detection

Substation equipment defects are important factors affecting the safe operation of power grids. However, many non-rigid defects have low detection accuracy and poor robustness,due to boundary ambiguity, irregular shape and tiny size. To address these problems,we propose a swin-transformer with recursive gated convolution framework for substation equipment non-rigid defect. Firstly, in order to effectively detect non-rigid defect objects to improve the discriminability of image features, we design the Swin-Transformer with Recursive Gated Convolution(Swin-RGC) framework to extract the interaction features between spaces in the deep model. Secondly, to avoid the loss of object location information, the Task-aligned One-stage Object Detection(TOOD) head is improved by fusing Coordinate Attention modules. Finally, a substation equipment defect detection dataset is established to provide a baseline for detecting non-rigid defects in substation power equipment. Experiment results on our dataset demonstrate that our proposed method achieves the performance of 69.9% Mean Average Precision (mAP) in the substation equipment non-rigid defect detection, which outweighs the state-of-the-art approaches.
Secure TLBs

This paper introduces a new attack vector in modern processors: the timing-based channel attacks due to the Translation Look-aside Buffers (TLBs). This paper first presents a three-step modeling approach to exhaustively enumerate all possible TLB timing-based vulnerabilities and automatically-generated micro security benchmarks that test for the TLB vulnerabilities. After showing the insecurity of standard TLBs, two new secure TLB designs are implemented under RISC-V Rocket Core processor architecture: a Static-Partition (SP) TLB and a Random-Fill (RF) TLB. The proposed secure TLBs are shown to be able to defend against all attacks with a small performance and area overhead.
Exploratory Research on Establishing a Hub for Basic Area Cultural and Art Education Community

본 연구는 기초지역의 문화예술교육 거버넌스 구조를 살펴보고, 커뮤니티 핵심 요소인 매개자의 역할을 통한 지역 발전 방안을 탐색하였다. 이를 위해 질적연구방법인 사례분석(Case study)을 선택하여 포커스 그룹인터뷰진행하였다. 연구참여자는 목적적 표집방법을 적용하였으며, 충남 공주지역 문화재단의 소외지역 문화예술교육 거점 구축 사업에 참여한 5개 지역의 대표를 대상으로 진행하였다. 분석 결과, 지역자원을 활용하여 특화된 문화예술교육 커뮤니티 형성, 지역자산을 활용한 문화예술교육 브랜드화 전개, 소외지역의 문화예술교육 커뮤니티 형성의 취약점으로 나타났다. 공공과 민간 그리고 지역 내 구성원들의 거버넌스 환경을 구축하고 이를 문화예술교육으로 매개함으로써 지역의 정체성이 드러나는 콘텐츠로 경쟁력을 지속발전 가능하기 위해서는 문화예술 프로그램교육과 함께 환경 조성 마련의 필요성이 제기되었다. This study examined the governance structure of culture and arts education in the basic region and explored regional development strategies through the role of mediators which are core elements of the community. For this purpose, case study, a qualitative research method, was selected and focus group interviews were conducted. For research participants, a deliberate sampling method was applied to select representatives from five regions that participated in the project to establish a cultural and arts education base in an underprivileged area of the Gongju Regional Cultural Foundation in Chungcheongnam-do. As a result of the analysis, it was revealed that there were weaknesses in the formation of a specialized cultural arts education community using local resources, the development of a cultural arts education brand using local assets, and the formation of a cultural arts education community in an secluded area. The need to create an environment together with cultural and artistic program education was raised in order to sustainably develop competitiveness with content that reveals the identity of the region by establishing a governance environment for public, private and regional members and mediating this through cultural and artistic education.
Autonomous Exploration Using a Tree Structure For Goal Selection

Autonomous exploration of unknown environments using unmanned robots is a widely researched problem of our days. The focus of this paper is to provide a novel goal-selection method in response to the problems of the simple greedy goal selection algorithm. The presented method uses a tree structure built from the detected frontiers and selects the next goal of exploration using a depth-first search on the expanding tree. This prevents the exploring robot from leaving half-explored areas and backtracking to them later in the exploration. The method is tested in two simulated environments. The results are compared to those achieved using a simple greedy goal selection algorithm.
Learning Noise-Induced Reward Functions for Surpassing Demonstrations in Imitation Learning

Imitation learning (IL) has recently shown impressive performance in training a reinforcement learning agent with human demonstrations, eliminating the difficulty of designing elaborate reward functions in complex environments. However, most IL methods work under the assumption of the optimality of the demonstrations and thus cannot learn policies to surpass the demonstrators. Some methods have been investigated to obtain better-than-demonstration (BD) performance with inner human feedback or preference labels. In this paper, we propose a method to learn rewards from suboptimal demonstrations via a weighted preference learning technique (LERP). Specifically, we first formulate the suboptimality of demonstrations as the inaccurate estimation of rewards. The inaccuracy is modeled with a reward noise random variable following the Gumbel distribution. Moreover, we derive an upper bound of the expected return with different noise coefficients and propose a theorem to surpass the demonstrations. Unlike existing literature, our analysis does not depend on the linear reward constraint. Consequently, we develop a BD model with a weighted preference learning technique. Experimental results on continuous control and high-dimensional discrete control tasks show the superiority of our LERP method over other state-of-the-art BD methods.
Features and Problems of Developing a University Scientometric Policy

The subject of research in this article is the regularities of the process of developing the scientometric policy of the university and the methodology for assessing its effectiveness. In the course of the study, it was found that in order to increase the efficiency of organizing and conducting scientific research in universities, it is necessary to have a certain scientometric policy of the university, which is a set of actions and decisions aimed at determining certain quantitative scientometric indicators both for the organization as a whole and for individual employees of the organization: how many publications should be published on behalf of the organization per year, in which publications certain articles should be published, what should be the average impact factor of the journal, where certain articles and other scientific publications should be published, how many publications are needed, how many should to be cited or what should be the Hirsch index for occupying a particular position in a scientific organization or an educational organization of higher education, what are the necessary resources to achieve the goals, what factors will contribute to the achievement of the goals and objectives of the scientometric field and which ones will hinder this and how to neutralize the hindering factors. It seems that the presence of such a scientometric policy is one of the most important conditions for increasing the efficiency of scientific research in universities. The paper also analyzed the factors influencing the effectiveness of scientometric policy in educational institutions of higher education. Ways to improve the effectiveness of scientometric policy in universities are proposed, which include the following areas: a) the availability of a specialized course dedicated to the specifics of scientific research; b) the university has a unified scientometric policy; c) overcoming myths and stereotypes in the field of scientific research and publication of their results
Adaptive amplification of image texture boundaries

The texture boundaries normally have frequent co-occurrence in natural images, but existing image enhancement techniques for the most parts focus on sharpening the edges, i.e., intensity discontinuities. Moreover, these approaches often suffer from noise over-emphasis and extra artifact production. In this paper, we propose an adaptive texture boundary boosting algorithm. The proposal exploits the co-occurrence filter for dual-layer decomposition and pixel-wise amplification factor calculation for image synthesis. This leads to a various-scale enhancement framework, which allows our method to only highlight those co-occurrent features while avoiding unsalient edges and noise, gradient reversals, as well as halos. Both subjective and objective comparisons using different detail stretching schemes demonstrate the effectiveness of the presented approach.
A CABAC pre-coding based and lossless recompression method for JPEG images

As the number of application of images on the Internet increases, how to store and transmit these images becomes a big challenge. JPEG as the most widely used image compression format on the Internet is often applied to pictures compression. However, just using JPEG alone to compress images is not enough now. In hence, some methods use improved entropy coding to further recompress JPEG images losslessly or process the images on DCT domain for lossy recompression. These methods are useful and work for various images. But there is no special design for fixed surveillance applications. Depending on the feature of images generated by a same fixed surveillance camera, a JPEG image lossless recompression method based on CABAC pre-coding, residual coefficients between JPEG image group and simplified context prediction is proposed by us. With a little reduction of decoding time as well as little increase of encoding time, average 27% bits saving can be achieved in the experiment.
Research and Design of Electric Power Big Data Visualization

Based on the analysis and research of electric power big data and visualization technology, this paper analyzes the design and realization of the power big data visualization based on DataV, and describes the result display through the actual system design.The DataV large-screen display is a full-information simulation of the actual large-scale power grid in the virtual world.It can collect detailed data of power production and operation in any region at any time; clearly and intuitively display the comprehensive statistical data of the power grid, real-time operating data and equipment monitoring on the computer Data, count all the lightning damage area, bird damage area, and pollution flashover area of the power grid, and can intuitively display the power grid early warning situation, and obtain the latest data of the power grid operation at any time.