Reviewerly/exHarmony huggingface dataset:
The Family Learning(家學) and Political Activities of Soron(少論) Yoon Kwang-so(尹光紹) during the 18th Century

This study aims to investigate the influence of the academic tradition of the Nojong group of the Papyeong Yoon family(坡平尹氏 魯宗派) on the political activities of the Soron(少論) scholar Yoon Kwang-so(尹光紹). Regarding the political thought of Soron scholars and officials in the eighteenth century and especially their view on Tangpyeong(蕩平), previous studies paid more attention to the influence of their learning of the teachings of Wang Yangming(王陽明) rather than to their learning of the teachings of Zhu Xi(朱熹). For this reason, this study tried to explore the political thought of Soron scholars who focused their studies on the teachings of the Zhu Xi. Yoon Kwang-so is a scholar who inherited the tradition of learning of Yoon Jeung(尹拯), one of the most influential Neo-Confucianist in the academic circles of the Soron. It was the family learning(家學) of the Nojong group of the Papyeong Yoon family that he got immersed in. The characteristic of their Neo-confucianism was to avoid speaking of topics without properly understanding concepts of Neo-Confucianism. He emphasized a dignified and stern attitude as a prerequisite for Mushil(務實). This is also connected with the fact that he interpreted Gyong(敬), a method of cultivation of Neo-confucianism, as an unobtrusive and solemn attitude. These academic charateristics were also reflected in his political thoughts. The reason why Yoon Kwang-so criticized Soron Wanron(少論 緩論) was that they lost the identity of Soron, and their words and actions were similar to those of the Noron(老論). He thought that the Noron had a vain and profit-seeking nature based on Song Si-yeol(宋時烈)'s studies, and the ‘Tangpyeongdang(蕩平黨)’ resembles Noron in this regard. Yoon Kwang-so's negative perception of the ‘Tangpyeongdang’ was linked to his criticism of Park Se-chae(朴世采) who advocated ‘Tangpyeong(蕩平)’ during the reign of King Sukjong(肅宗). After a treason in 1755(Eulhae Oksa), which inflicted fatal political damage on the Soron Junron(少論 峻論), Yoon Kwang-so's political position weakened. After King Jeongjo(正祖) ascended to the throne, the offices of Yoon Seon-geo and Yoon Jeung were deprived. Yoon Kwang-so actively responded to the deprivation of the offices of Yoon Seon-geo and Yoon Jeung through appeals and book compilations. In those, he argued that King Sukjong called Yoon Jeung a ‘Sunjung(先正)’, which was based on the perception that Yoon Jeung's studies were ‘Silhak(實學)’. In contrast, he thought that Song Si-yeol and his descendants’ studies were ‘Heohak(虛學)’. In his view, both the death of Song Si-yeol’s descendants and the deprivation of the offices of Yoon Seon-geo and Yoon Jeung was the results of the fact that Yoon Jeung preached Silhak(實學) and Song Si-yeol earned a false reputation(虛名).
A New Perspective on Korean Studies Overseas; Regarding its Expansion of Research Areas and Themes

Korean studies overseas have grown based on their environment, social systems, and culture in each country, as a subject of Asian studies. Specifically, Korean studies overseas started from a part of regional studies. However, current Korean studies overseas tends to be seeking its own development, independent from regional Asian studies, closely interacting with domestic Korean studies. Recently, its broad expansion in research areas have diversified the research themes and interest topics in the field. Accordingly, Korean studies overseas need strategic approaches conducted in the basis of cultural differences and both academic and practical demands of each region. To this end, this paper classified Korean studies overseas into four regions applying its major characteristics; leading areas, developed areas, developing areas, and recently introduced areas. Furthermore, it is necessary to establish the strategic concept of ‘universal-comparative-intelligence’ of Korean studies according to the stage of development. In order to establish strategic approaches to Korean studies overseas in several countries or regions, Korean studies needs to Korean studies needs major shift from “unilateral spread and distribution of research results by domestic Korean studies centered on Korean language studies” to “presentation of academic Hallyu realizing the identity of Korean culture”. To this end, this paper makes specific proposals presented below. First, Korean studies overseas should develop in a continuous exchange with the achievements of neighboring studies such as humanities and East Asian studies for the new “academic Korean Wave.” Second, it is necessary to emphasize the importance of classical studies that have been neglected in Korean studies overseas. Second, this paper suggests that Korean studies should expand its research subjects to Korean classical studies written in Chinese letters even in the regions where they do not use Chinese characters. Through this convergence research with modern Korean studies and Korean classical studies, we can gain deeper understandings on Korean traditions, modern culture and even its future vision, extending our perspectives not only to humanities but also to social sciences. Lastly, The use of IT-based convergence research platforms will maximize the influence of traditional Korean studies and Korean studies overseas beyond their research methods. It is an important opportunity for its transition from ‘peripheral’ studies to ‘central’ studies in each overseas region. At the same time, it is hoped that Korean studies will become reflections and practical learning of current society.
Regulating Facial Recognition Technology: A Taxonomy of Regulatory Schemata and First Amendment Challenges

This article examines the patchwork of regulatory approaches policymakers have used to govern use of facial recognition technology (FRT). Without comprehensive federal legislation, state and local policymakers are left to fill the regulatory gap. The purpose of this article is twofold. First, it creates a taxonomy of the existing regulatory schemata governing uses of FRT. The authors’ systematic analysis of FRT regulatory approaches identified five main categories of policymaking options: (1) agents of use; (2) limitations on use; (3) accountability of use; (4) evaluation of use; and (5) enforcement of permitted use. Second, the authors analyze whether First Amendment protection of information as speech may serve as a barrier to any regulatory aspect of FRT. Building on the novel empirical framework of regulatory options, the authors examine which types of FRT regulations would likely survive a First Amendment challenge. Thus, this taxonomy of regulatory approaches, coupled with a First Amendment analysis, offers valuable insights for policymakers and scholars.
Progressive Attentional Manifold Alignment for Arbitrary Style Transfer

Arbitrary style transfer algorithms can generate stylization results with arbitrary content-style image pairs but will distort content structures and bring degraded style patterns. The content distortion problem has been well issued using high-frequency signals, salient maps, and low-level features. However, the style degradation problem is still unsolved. Since there is a considerable semantic discrepancy between content and style features, we assume they follow two different manifold distributions. The style degradation happens because existing methods cannot fully leverage the style statistics to render the content feature that lies on a different manifold. Therefore we designed the progressive attentional manifold alignment (PAMA) to align the content manifold to the style manifold. This module consists of a channel alignment module to emphasize related content and style semantics, an attention module to establish the correspondence between features, and a spatial interpolation module to adaptively align the manifolds. The proposed PAMA can alleviate the style degradation problem and produce state-of-the-art stylization results.
Software Defect Prediction Using a Combination of Oversampling and Undersampling Methods

Software quality can be improved by doing software testing, but the more features are developed the more resources are required, therefore software defect prediction (SDP) is introduced. Various kinds of machine learning methods are used to develop SDP. However, various kinds of problems arise in SDP activities, namely data redundancy, class imbalance and feature redundancy. In this study, a combination of oversampling and under-sampling (COU) model will be proposed to solve the problem of data redundancy and class imbalance. The oversampling method used is RSMOTE and the under-sampling method used is ENN. The application of the combination model will later provide a new set of datasets that are more balanced and cleaner from ambiguous, noisy and duplication of data. From the new data generated by the model, deep learning will then be applied as a prediction model. And the evaluation will be done by applying the f-measure measurement. The results of this study indicate that the COU model used gives good results in improving the quality of SDP. When compared with the average value generated by the RSMOTE model in making predictions, the COU model provides an increase in f-measure evaluation results by 11% where the average value obtained is 0.876. So, this combination method is able to improve the performance of software defect prediction activities
HOTD: A holistic cross-layer time-delay attack detection framework for unmanned aerial vehicle networks

Recently, unmanned aerial vehicle (UAV) networks have been widely used in military and civilian scenarios; however, they suffer various attacks. Time-delay attacks maliciously delay the transmission of packets without tampering with the contents or significantly affecting the transmission pattern, making detection difficult. In this paper, a holistic cross-layer time-delay attack detection framework (HOTD) is proposed for UAV networks. A holistic selection of the delay-related features available at all layers is performed, before adopting supervised learning to build a consistency model between these features and the corresponding forwarding delay to calculate the degree of consistency of each node. Finally, the clustering method is used to distinguish malicious from benign nodes according to their degree of consistency. Experimental results show that the performance of HOTD is superior to that of state-of-the-art detection methods, and it achieves a detection accuracy higher than 85% with less than 2.5% additional overhead.
Do Nfts Act as a Good Hedge and Safe Haven Against Cryptocurrency Fluctuations?

In this study, we test the hedge and safe haven properties of NFTs against Bitcoin market fluctuations. We employ daily returns of Bitcoin and ffour NFTS, namely Decentral Land, Cryptopunks, Cryptokitties and SuperRare, from 04-April-2018 to 7-July-2022. For analytical purposes, we estimate dynamic hedge effectiveness and wavelet quantile correlations. The dynamic hedge effectiveness results confirm the NFTs' hedge ability. The wavelet quantile correlation results show that NFTs offer short, medium and long-run hedge properties. Further, our results show that NFTs exhibit short-term safe haven properties against Bitcoin.
Blockchain Mania Without Bitcoins: Evidence from China Stock Market

Blockchain mania occurs in response to the quick rise of Bitcoin price in markets with cryptocurrencies circulation. However, Chinese government policies regarding the development of blockchain are inconsistent--block access to the offerings and exchanges of cryptocurrencies such as Bitcoins, but raise the blockchain technology to a strategic position. We empirically investigate whether the government’s inconsistent policies will lead to blockchain mania and how it affects the blockchain-related firms’ activities and performance. Our results are threefold: First, the supportive policy can fully mitigate the negative effect due to the national boycott of cryptocurrencies. Second, Non-speculative firms experience a stronger and long-standing positive reaction, while the effect on Speculative firms is transient and vanishes after receiving a definitive warning ten days later. Third, the market reaction to government support appears more pronounced among firms having established blockchain technology alliances, or having blockchain-related products, or being endorsed officially.
Hand Detection and Grasp Analysis in the Wild: A Ego Vision Based Solution to Detect and Analyze Hand Grasps

The work presented in this paper introduces a two-phase ego-vision algorithm in an unconstrained environment for hand detection and grasp classification. We aim to build an automated system for detecting hands from images and recognizing hand grasp types from those detected hand regions using wearable cameras. We proposed RetinaNet to detect hands and extract hand feature representations from detected regions. A new bilinear attention-based low-rank sparse feature representation is proposed to improve grasp classification. Then a linear classifier is trained to classify the different classes of grasps available in the predefined grasp taxonomy. In experiments, we evaluated both hand detection and grasp classification algorithms in a laboratory as well as in real-world scenarios. We achieved improved performance on widely utilized state-of-the-art datasets for hand detection as well as grasp classification algorithms. The best average precision (AP) our proposed hand detection system achieved is 83.86 on the Oxford hand, 94.70 on the VIVA hand, 70.26 on the TV Hand, and 90.20 on the 100DOH dataset. The highest grasp recognition accuracy our method achieved is 87.25% on the YGD dataset, 94.07% on the single-user UTG dataset, 42.8% on the GUN-71 dataset, and 63.07% on the static grasp dataset. The system can generalize to the different cross datasets in varying tasks as well as users for grasp classification.
Sign Language Hand Gesture Identification Using Yolov3

Abstract— the community of hearing and speech- impaired people has been dependent on sign language as a medium to communicate with people. It has always been difficult to communicate between a verbally impaired person and a normal person. The Sign Language Identifier will be very essential in communicating with deaf people. It will help people to communicate with verbally impaired people by detecting the signs and providing the output in real time. The model is based on You Only Look Once (YOLO). We would be building the model with Yolov3 version. The existing recognition systems are less reliable as they detect letters and take time to make a single word or they detect very few words. Our model will solve this problem as it will recognize words directly. The dataset will be built using Label Image software and detect sign language through a live feed. The system is a Graphical User Interface (GUI) that is built by drags and drop functionality through PAGE tool. The identification of signs will take place by two methods, one by uploading an image and the other through a live feed. These images and live inputs will be taken by using OpenCV.
Intelligent Learning System to Help People with Color Impairment Using Image Processing Algorithms

This study presents a novel framework to help people with color impairment in identifying colors. The proposed framework consists of three stages. These stages are electronically performing the Ishihara test, performing the color blindness type recognition test, and guiding the person to color by voice. The first stage, the person is subjected to an electronic color blindness test, by displaying different plates containing several points of different sizes and colors. The person is required to correctly identify the number or shape in the plate and at the end, the system determines the extent to which a person is color blind. The second stage is a color recognition test to determine the type of color blindness. If there is difficulty in determining red, this is called protanopia. But the difficulty in identifying the green color is called deuteranopia. While the inability to recognize the blue color is called tritanopia. And finally, the difficulty in identifying the colored style is called achromatopsia. The third stage is assistance phase and is divided into three subsectors are: smart educational system, identifying colors and extracting the content. The proposed system differs from other systems in that it is an integrated system. It includes identifying color blindness, determining its type, and finally aiding color blindness person. Also, it is the first system that deals with the rare type of color blindness called achromatopsia in addition to its other three types. The results obtained confirmed that the proposed system as well as the smart educational system are characterized by high accuracy and effectiveness.
From Antlr Grammars to Peg Parsers

ANTLR is a popular parsing tool. As there are ANTLR grammars available for around 270 languages, new applications related to such languages often use these grammars as a guide. However, the task of getting an equivalent non-ANTLR parser for these applications may not be trivial and it may involve a much grammar rewriting.Parsing Expression Grammars (PEGs) is a formalism used to define deterministic top-down parses, which is the basis of dozens of parser generators.To facilitate the development of new applications based on PEGs parsers, we investigate the automatic translation from ANTLR grammars to PEGs. We discuss the main issues involved in this translation and we implement tools that build a PEG-based parser from an ANTLR grammar.We evaluate our automatic translation on grammars from a public repository of ANTLR grammars. To assess the equivalence between the ANTLR and the PEG parsers generated from a grammar, we use a grammar-based test generation approach, which uses the original ANTLR grammar, as also example files used to validate the ANTLR parser.Results indicate that our approach can reduce significantly the manual intervention necessary to get from an ANTLR grammar a PEG parser whose behavior looks equivalent to the corresponding ANTLR one.
Design and Implementation of Low-Cost Data Acquisition System for Small and Medium Enterprises (SMEs) of Pakistan

This paper presents the development of low-cost and robust industrial IoT based data acquisition system primarily focused on domestic manufacturing industries striving to achieve goals and benefits of “Industrial 4.0”. This proposes aims to promote DAQ System integration in traditional manufacturing process of the small and mid-sized industries of Pakistan with limited capacity of investment. Proposed method comprises of Arduino and it’s IoT features for Data Collection, along with a self-developed PC based Centralized Software for Collection of Data, Graphical User Display and Storing collected Data in Local SQL Database. PC based Software replaces requirement of multiple software in case of traditional low-cost DAQ systems, like OPC Software for collecting data from industrial hardware, Java or PHP based any GUI and SQL Data storage. The analysis of work is done with the help of the Message Queue Telemetry Transport (MQTT) protocol. This project will be in further stages evaluated to add features of Supervisory Control, along with Data Acquisition hardware with minimum increase in cost and further upgrading PC Software to add more features of Industry 4.0, as compared to costly commercial solutions available in the market. A machine learning algorithm, k-nearest neighbors algorithm has been used to classify sensitive and non-sensitive data for improvising cloud security. K-Nearest Neighbors is also called KNN algorithm which is supervised machine learning classifier.
Clone consistent-defect prediction based on deep learning method

Many consistent changes take place across code clones in software, and this has emerged as a severe issue for software security. If the developers forget to modify the relevant clones consistently, such modifications will introduce consistent-defect. Researchers leverage machine learning techniques to predict clone consistent-defect by representing these changes with the designed attributes. Meanwhile, deep learning technology has demonstrated tremendous potential in characterizing source code. As such, we explore whether deep learning technology can enhance the effectiveness of clone consistent-defect prediction. In this study, we investigate various neural networks for modeling clone consistent change. Specifically, our approach models code clones and their evolution to capture the semantic properties automatically from the perspectives of clone fragment, clone group, and clone evolution, as opposed to manually generated attributes. To evaluate the effectiveness of our approach, we conduct an experiment on the dataset collected from 8 open-source projects. The results demonstrate that our neural network models are efficient both in cross-project and within-project scenarios, with F-measures of around 80% and recalls of around 90%. We conclude that deep learning technology may successfully assist developers in predicting clone consistent-defect, so helping to improve the security of code clones by alerting developers to confirm their consistency.
Two‐dimensional inversion of airborne radiometric data for source concentrations: Application to Cu–Au targeting at Mara Rosa Magmatic Arc, Brazil

Abstract Airborne radiometrics (radiometric surveying) has been used in many applications, such as geological mapping, environmental studies, mineral exploration and lithology mapping. The standard processing of gamma‐ray spectrometry data provides good results when the acquisition conditions such as the flight and sensor orientation are constant. In practice, abrupt changes in flight height when flying in mountainous terrain are common, and standard processing neglecting this factor can lead to erroneous interpretation. The primary cause is the fact that the successive corrections applied to radiometric data in standard processing do not consider the effective sampled area of a survey (i.e. the field of view), which can have significant and variable overlaps between adjacent samples due to changing observation height. For this reason, standard height and sensitivity corrections may lead to incorrect estimates of the concentrations of the radioelements on the ground. To ameliorate this problem, we have developed a two‐dimensional inversion‐based processing method that incorporates the aircraft height, replaces the sensitivity correction and accounts for a positivity condition using a logarithmic barrier. Some minerals associated with potassium, uranium and thorium allow the mapping of hydrothermal alteration, especially potassium which is an important constituent of hydrothermal fluids. We demonstrate the new methods by comparing the standard processing results with the inversion‐based processing of airborne gamma‐ray spectrometry data in the gold deposit‐rich area of Mara Rosa Magmatic Arc, Brazil. The inversion‐based processing results enhance the anomalies and suppress the interpolation artefacts as well as increase the signal‐to‐noise ratio. The application of airborne radiometric transformed maps also improved the footprint knowledge around Cu–Au mineralization and highlighted potential areas for further studies.
Novel Approach of Risk of Lack of Employee Commitment

Novel Approach of Risk of Lack of Employee Commitment
Scaffolding Real and Unreal: Strategies for the Development of Virtual Tour Guides

Mobile and wearable devices have become common communication media for various applications. Especially, in museums and galleries, audio and multimedia guides remain the most common solutions in which visitors can concentrate on appreciating the exhibits while gaining a deeper understanding of collections. Since the spread of Covid-19 reduced the accessibility of physical venues and limited