Reviewerly/exHarmony huggingface dataset:
A Study on the History of Korean Literature by Nason(羅孫) Kim Dong-Wook(金東旭)

The History of Korean Literature(國文學史), written by Nason(羅孫) Kim Dong-Wook(金東旭), focuses on the metadiscourse represented by the Motherland and the Nation. And the comparative literature method between Korea and Japan was applied. These two are the most prominent features in the history of Korean literature written by Nason. However, the metadiscourse and comparative literature contradict each other. The fact that the periodization in history, which is the core of literary history, was based on the royal family in the history of Korean literature, was the result of not being able to overcome the contradiction between the two.&#x0D; Nason(羅孫) made six basic positions to describe the history of Korean literature. It is ① The concept and category of literature. ② The tradition of literary history. ③ The descriptive attitude of literary history. ④ The method of writing of literary history. ⑤ The view of literary history. ⑥ The direction of literary history. In ①, included Chinese literature in the history of Korean literature according to the concept of literature in the East, and excluded oral literature enjoyed by words. However, Yadam(野談) and SamGukYuSa(三國遺事) were included in the history of Korean literature in that they were recorded in letters. In the tradition of literary history[②] and the direction of literary history[⑥], Nason noted the role of upper-class geniuses who emphasized our words and writings. He also emphasized the need to create a new Koreanology that combines upper-class geniuses and lower-class people who actively enjoyed our words and writings. However, his literary history did not provide such details. The descriptive attitude of literary history[③] and the method of writing of literary history[④] used a comparative literary method based on national pride. The direction of literary history[⑥] tried to mix the classical scholar scholarly spirit with the dynamic Hwarangdo(花郞道). However, Hwarangdo is connected with the Japanese samurai spirit, and the classical scholar scholarly spirit shows limitations in that it could not escape from the ideology that was passed down from Takahashi Doru(高橋亨) and Donam(趙潤濟).
Digital Grooming

Abstract This chapter brings together the book’s key findings and lays out how these may be applied in real-life scenarios to help combat digital sexual, commercial, and ideological grooming. Commonalities and nuances across the styling acts identified in the digital sexual, ideological, and commercial grooming datasets are examined. The identification of a common discourse of manipulation that deliberately mobilizes self and other styling to suit groomers’ goals, and of the specific nuances therein, is a significant step forward in academic understanding of digital grooming. To demonstrate the value of examining digital grooming—and more broadly manipulation—from an identity-focused approach, Chapter 9 also makes explicit the unique contribution that discourse-based disciplines can make to societal issues. This is done by reflecting on several academia–practitioner collaborations, which show readers how the analyses and approaches detailed in this book can, and are, impacting practitioner agendas, ranging from policymaking efforts at enhancing social media platform regulation so that it more efficiently clamps down on abusive, hateful, or otherwise manipulative communication to third-sector and industry development of targeted prevention and detection interventions to promote digital civility and safety.
Digital Commercial Grooming

Abstract This chapter acknowledges commonalities between crypto-drug markets and Clear Net shopping platforms: both are digital marketplaces reliant on vendor reputation systems and both use a number of mainstream marketing techniques. Nevertheless, critical review of extant literature evidences that the media-fueled image of crypto-drug markets as an e-Bay for drugs is misleading. The chapter explains how crypto-drug markets exploit the very economic theories of cooperation and freedom they claim to abide by. These markets’ “faux” libertarianism, moreover, often lures individuals into becoming not only (more frequent) consumers of illicit drugs but also to develop a “career” as drug dealers. Crypto-drug buyers progressively source stock intended for social dealing and wholesale offline distribution. Crypto-drug markets thus resemble traditional drug markets, with both operating according to economic principles of deception and coercion. The chapter also highlights that the sociotechnical affordances that underpin crypto-drug markets crucially reflect and shape the grooming practices undertaken in them. Technically, crypto-drug markets seek to minimize the risk of detection from law enforcement and scamming by users through third-party payment services such as escrow. Socially, they operate vendor reputation systems that, while largely fraudulent, purportedly demonstrate to potential and extant buyers the quality of products sold, the efficiency and stealth of transactional services provided, and the bona fide “identity” of those selling the drugs. These are crucial to vendors’ styling of themselves and their “brand” within the marketplace, which Chapter 8 discusses in detail.
Conditionals and Information Source Selection

Abstract This chapter applies the fragment of the erotetic theory developed in the previous chapter to reasoning with conditionals. The chapter considers Wason’s famous card selection task against the background of the more general problem of selecting information sources.
“Let Them Starve, You Idiots!!! Why Feed VERMIN?”

Abstract Digital ideological grooming relies primarily on argumentative polarization of social identities. In-group/out-group boundaries are clear-cut and diametrically opposed. Self-styling in digital ideological grooming supports this through e stances: broad expertise, toxic openness and impatient avidity. Broad expertise enables having a say about any issue, piggybacking on it as a means to present the in-group’s ideology. Toxic openness relies on mythopoesis, specifically first-person storytelling that highlights stereotypically—toxic—masculine emotions of anger. The groomers’ anger is rooted in perceived victimization and supports a logic of retaliation against the out-group. Impatient avidity entails convening a pressing call to action. In digital ideological grooming, groomer and target are regularly constructed as part of the same inner group, one to which the target may already belong or may join. This in-group is styled through stances that complement the groomers’. Overall, the virtues and strengths of in-group as target are profusely extolled. Occasionally, selective dissociation processes are also enacted whereby the target is presented as ideologically lacking. The chapter demonstrates styling of opponents is pervasive and varied. Both jihadi and radical right groups’ messaging construct multiple out-groups, which are othered, primarily via face-oriented and equity rights–oriented impoliteness. Insults, criticism/complaints, and threats are made to serve morality-based drivers such as distributive justice or an imperative to act urgently in the face of long-endured in-group victimization.
Digital Grooming

Abstract This chapter starts by providing a working definition for digital grooming, informed in part by its etymological evolution. It then maps the conceptual territory of digital grooming across three core features: digital mediation, manipulation, and identity construction. It argues that the “digitalness” of digital grooming is constitutive of it rather than merely contextual. A digital communication context marked by a celebration of sharing, a transformation of the value of trust, and the search for continued engagement/influence within fluid participation frameworks underpin the digital mediation of grooming as a criminal activity. The chapter then reviews key approaches to the broader concept of manipulation, as well as its constitutive features and the crucial part played by identity in digital grooming. This starts by considering the impact of networked publics, vis-à-vis pre-digital conceptualizations of participation frameworks, on how digital groomers position themselves in relation to those individuals and groups they seek to manipulate. Across these spaces, the chapter contends, digital groomers mobilize stances to style their personae and those of their target and perceived opponents in strategic ways that advance their manipulative goals.
SISTEM INFORMASI PENGOLAHAN DATA WARGA PADA RT 006 RW 003 KELURAHAN KALISARI

The growth of society in Indonesia has experienced a rapid increase as well as the development of information technology in today's increasingly sophisticated era, the use of computer technology can be utilized to assist especially in terms of data processing, compilation, storage, search, and manipulation of data in order to produce accurate information, quick and precise.The purpose of the research is to create an information system application for data processing so that it can be more effective, efficient and can also make reports accurately.This research was carried out using research and development (R&D), namely the method used to produce a product and the effectiveness of the product.From the results of the tests that have been carried out, the authors conclude that the Design of this Citizen Data Processing Information System can help simplify the processing of citizen data and can improve reports to make them look more presentable.In designing this information system using the Java programming language and MySQL as the database.
A lightweight API recommendation method for App development based on multi-objective evolutionary algorithm

Context: API is important in daily programming activities during app development, but finding appropriate APIs is time-consuming for developers. To simplify this process, many researchers pay attention to overcoming the task-API knowledge gap using semantic information mined from the large-scale dataset for API recommendation. However, only semantic information is not enough since API descriptions and developers' query may not share similar words, meanwhile, large-scale data mining brings high costs. These limit the efficiency of existing API recommendation methods. Objective: In this work, we aim at proposing a lightweight API recommendation method based on small-scale data with low costs, so that everyone can use it obtain appropriate API knowledge for supporting their development tasks. Method: We model API recommendation as a multi-objective optimization problem by considering both structural and semantic information of APIs, and use the genetic algorithm to gain optimal solutions for overcoming the limitations of the small-scale dataset. Specially, we extract the structural and semantic information of APIs from APK files and API descriptions with graph embedding and NLP techniques respectively. Then, we get the recommended APIs satisfying structural and semantic objectives according to the developers' target functionality with a genetic algorithm. Finally, we give the usage scenarios of our recommendation information and the guideline of our approach to help developers understand and use our method easily. Results and Conclusion: We conduct a series of experiments based on apps in Google Play, API descriptions in Android Tutorial and Q&As in Stack Overflow. The results show that Precision@N of our recommendation can be up to 0.89, MAP@N is up to 0.50, and MRR reaches 0.69, and our method achieves such good performance only utilizing less than 1/10 of the project number of our compared machine learning method with less time and lower device requirement. Besides, we conduct a survey and most of the participants confirm the understandability and usability of our recommended APIs in practice.
Converting a constituency treebank to dependency treebank for Vietnamese

Dependency parsing has become the norm for its advantages of representing syntactic information for numerous tasks of natural language processing (NLP). In Vietnamese, a challenging problem which arises in this domain is the insufficiency of the training resource. Our work presents a new method to automatically convert a Vietnamese constituency treebank into dependency trees. We designed new dependency labels for Vietnamese treebank. Furthermore, in this research, we proposed new head-percolation rules and dependency relations. The experimental results on two state-of-the-art parsers, MaltParser and MSTParser, indicated that our treebank were roughly 13% UAS and 21% LAS higher than previous works.
Improving Intent Detection and Slot Filling for Vietnamese

Intent detection and slot filling are critical tasks in spoken language understanding. In this paper, we propose a method to leverage word features for these tasks. We propose an extension of JointIDSF [1] with two additional contextual layers to incorporate bidirectional in-tent and slot information. We further investigate a variant of Co-Interactive Transformer (CoITrans) model [2] by employing BERT for sentence presentation as additional information for intent detection task. Using Vietnamese as a case study, the experimental results on PhoATIS dataset show that our proposals achieve the state-of-the-art performance.
A Novel Recommendation Approach for API Sequences

API is a common approach to utilizing third-party libraries in software development. Nonetheless, to correctly call APIs, developers need to carefully learn the API documentation and search for code samples to understand their usages. In order to efficiently and effectively support developers in properly calling APIs, this paper introduces MFLUTE, a new approach to exploit program analysis and statistical language model to recommend APIs to call. Specifically, for a given partially completed code (context), MFLUTE employs program analysis techniques to analyze the context and generate a list of potential API candidates. Then, we use a language model to rank and suggest the most promising ones. Our experimental results on a large dataset of real-world projects show that MFLUTE can correctly recommend API calls with the Top-1 accuracy of 71.34%. Furthermore, MFLUTE can improve state-of-the-art approaches by up to 12 % in recommending accuracy.
Denoising with Median and Bilateral on CT images for Liver segmentation

Liver segmentation from abdominal Com-puted Tomography (CT) images aims to separate the liver from other organs. It plays an important role in determining liver function and researching liver diseases. However, manual segmentation is time-consuming and labor-intensive for radiologists. The segmentation task is mitigated with emerging deep learning algorithms, and the liver region is effectively automatically extracted. In addition, images need to be appropriately processed before being used in learning algorithms. This study examines the effects of denoising techniques like 3D Median and 3D Bilateral filters on medical image segmentation tasks performed by U-Net and Dense-UNet algorithms. First, we apply one of two denoising techniques - 3D Median filter and 3D Bilateral filter - to the original CT images. Meanwhile, the manual liver masks with ten-level are converted to binary masks. Next, the dimension of each denoised CT slice and binary mask are normalized from 512 × 512 to 256 × 256. Finally, we conduct the model training based on U-Net and Dense-UNet architectures to perform the segmentation on denoised CT slices. The experimental results show that using a denoising technique such as Bilateral can benefit accuracy and inference time in liver segmentation tasks on CT images.
Proposing XLSR Multilingual Model for Vietnamese Language Recognition

The article presents issues related to the cross-lingual speech representations (XLS-R) model that can apply to training Vietnamese data. The goal of the article is to create an application for voice assistant software or virtual assistant in the home with Vietnamese data. The results show that the proposed model can improve the accuracy up to 61 % which has practical applicability.
Building a Recruitment System Based on Blockchain and Federated Learning

The problem of data privacy in Facial Recog-nition is one of the general public concerns. Facial recog-nition violates citizens' natural right to be constantly under government surveillance and to have their images stored without their permission. In addition, the issue of transparency in curriculum vitae is crucial. Some people are so desperate for a chance that they will do anything to increase their chances, even if it means lying on their resume about their experience. This study illustrates how Blockchain technology can be used to stop Cvs from being false and how federated learning can shield user images from facial recognition. The outcome demonstrates that users are no longer able to fabricate information in their CVs about their experience because that information is now stored in the Blockchain and the user's raw images are kept on their storage devices. Our system uses a Ethereum's Smart contract to enhance the transparent of candidates' CV. While federated learning provides security in facial recognition process.
Enriching Relation representation vectors using Entity types and Dependency parse For Entity and Relation Extraction Model

Joint entity and relation extraction is a branch of information extraction, from the input sentence, the model identifies named entities and the relation between them. In this paper, we propose two models for joint entity and relation extraction, including (1) SpERT.ET: uses entity types of two spans and update gate to enrich relation representation vector, (2) SpERT.PDP: integrates Dependency parse information of the words to enrich relation representation vector. Our models outperform baseline models for joint entity and relation extraction on SciERC dataset.
Methodology Interaction by Machine Learning Model to Detect Vulnerability in Smart Contract of Blockchain

Smart contracts have become increasingly popular in the development of trustworthy decentralized applications in recent years. These tools compare vulnerable contracts to a set of predefined rules. However, the emergence of new vulnerable types and programming skills to mitigate potential vulnerabilities results in many false positive and false negative tool reports. To address this, this data was analyzed using unsupervised machine learning to determine whether an algorithm can distinguish between shady/illegal owners and clean owners. Clustering algorithms are used in this paper. However, algorithms can cluster objects and detect fraud activity in Bitcoin transactions. Research on bitcoin network anomalies and suspicious transactions seeks to identify anomalous transactions, when all nodes on the bitcoin network are unlabeled. There is no evidence that any transaction is illegal. We are primarily interested in discovering irregularities in the bitcoin transaction network.
Signboards Detection From Street-view Image Using Convolutional Neural Network: A Case Study in Vietnam

In this paper, we refer to the problem of signboard detection at the instance level from street-view images. What makes this task challenging is the very heterogeneous appearance and dense distribution of signboards in urban areas. While many object detection datasets such as COCO, Pascal VOC, etc have been established, none of them pay great attention to signboard detection problems, especially in developing cities where buildings are not well planned. In this paper, a dataset of Streetview images for signboard detection is constructed covering the Cau Giay district, Hanoi, Vietnam. The dataset consists of 1217 images with signboards appearing in various shapes, sizes, and materials. Baselines are provided using a popular object detection model as Mask R-CNN with backbones Resnet-50 and Mobilenet-v2. Experimental results show that the Mask R-CNN model with Resnet-50 can achieve comparable results.
Building an Electronic Medical Records System using Blockchain 3.0 technology and new Data Mechanism

Nowadays, access to electronic medical records is an essential requirement. However, in the current healthcare system, patient records are created by the healthcare provider and stored locally in the provider's data system. This is particularly detrimental when a service user needs to query their profile from different providers or providers desire to query a service user profile from other providers as needed. Hence, blockchain is effectively background technology to resolve this problem because of its existing features. Currently, there are many models proposed based on blockchain technology 2.0. However, these systems still take a long time for a transaction (from 6 to 10 minutes). Another obvious problem is that healthcare providers participating in the system must use the SQL database because the integrity of data is required. Meantime, the NoSQL database is primary care on the decentralized environments. Therefore, the problem of converting big data from SQL to NoSQL is also important in the healthcare system. In this article, we propose to realize an electronic medical records system by using a third-generation version of blockchain technology with many advantages. Especially the time for a transaction is only about 6 seconds based on the new data mechanism. Besides, there is a solution to convert big data from SQL to NoSQL safely and effectively.
Mining Frequent Weighted Utility Patterns in Dynamic Quantitative Databases

Mining frequent weighted utility patterns (FWUPs) is an important activity in data mining to discover frequent patterns from quantitative databases with considering the weight (mean the importance) of each item. In practical, the weight of each item in a database may varies with time. For example, the weights of the products in a store may change by each month, each quarter, or each year. Therefore, this paper will concern with that issue. At first, we introduce a new problem of mining FWUPs with the dynamic weighted items in the quantitative databases (called dynamic quantitative databases - dQDB). Then, we propose an algorithm named dFWUT using Tidset data structure to solve this problem. Finally, some experiments were conducted to compare the proposed algorithm with a modification of WUN-Miner (denoted by WUN-Miner-Mod), a state-of-the-art algorithm for mining FWUPs in dQDB, in terms of time and usage memory. The experimental results indicate that the dFWUT algorithm outperforms the WUN-Miner-Mod algorithms for mining FWUPs in dQDB.
Analysis of Fog density on oriented object detection in aerial images

Object detection in aerial images having many practical applications in real life is becoming popular along with the surging development of deep learning and UAVs (Unmanned Aerial Vehicles). However, adverse weather conditions such as rain, night, and fog might reduce the quality of input images and significantly affect the performance of many perfectly trained detectors (detectors are trained in clear weather conditions). Moreover, object detection in aerial images often seeks high accuracy, while well-known object detection methods use horizontal bounding boxes to represent the object's location. These issues raise the inconsistency between classification and bounding box regression. Understanding the need for practical solutions, we create two experimental Fog datasets based on the original DOTA dataset to provide the in-depth analysis of fog density on multiple well-established oriented object detectors, namely Gliding Vertex, R3Det and ReDet. Furthermore, our training resources achieve promising results, flexibly ensemble to other methods to enhance models' performance and adapt to many adverse weather conditions.