Reviewerly/exHarmony huggingface dataset:
Acknowledgment of APP reviewers for 2022

Asian Politics & PolicyVolume 15, Issue 1 p. 153-154 LIST OF REVIEWERS Acknowledgment of APP reviewers for 2022 First published: 18 January 2023 https://doi.org/10.1111/aspp.12680Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume15, Issue1Special Issue: Comparative Perspectives on South Korea's Role in the Indo-PacificJanuary 2023Pages 153-154 RelatedInformation
GRU-GAT Model for Blockchain Bitcoin Abnormal Transaction Detection

With the rapid growth of Blockchain, Bitcoin, a key Blockchain application, has received a lot of attention. Bitcoin trading has made transactions more convenient. However, because of the anonymity, complexity, and lack of third parties, criminal activity against Blockchain Bitcoin applications is frequent. Individuals and society have suffered enormous losses as a result of money laundering, fraud, airdrop, ransom, and other peculiar abnormal transactions. We propose the GRU-GAT based model to detect abnormal transactions in Blockchain and reduce the loss and harm caused by abnormal transactions. Our proposed model employs a bidirectional recurrent neural network to extract the features of Blockchain Bitcoin transactions, graph attention networks for feature weighting, and the spatio-temporal aspects of transactions to integrate the features of Bitcoin transactions for anomaly identification. We conduct experiment through the publicly available elliptic dataset. The results reveal that the suggested method outperforms the comparison model in terms of accuracy and enhances the accuracy of detecting anomalous transactions in Blockchain digital currency.
The Language of Fatherhood 2.0

Abstract Chapter 8 examines the online construction of fatherhood. As such, this work dovetails with an emergent research agenda which is broadly concerned with the intersection of fatherhood and digital technologies, especially social media spaces. The chapter has three aims. The first is to sketch out some of the background literature on fatherhood and masculinities. The second is to discuss existing work in language and gender studies which focuses on parenting, tracing the main concerns of this research as they relate to motherhood and fatherhood. Building on the caring masculinities approach, the chapter then presents an analysis of data collected from a fatherhood forum. This analysis is one of the first attempts to unite a critical analysis of fatherhood and masculinities with close attention to the linguistic practice deployed by fathers in online spaces.
Side-Scrolling Platform Game Levels Reachability Repair Method and Its Applications to Super Mario Bros

Applying machine learning to game level design has become popular increasingly since it requires little human expertise. However, levels generated by machine learning may be unreachable. The traditional way to repair level unreachability is developing specific rule-based repairers to patch bugs. Due to the complexity of programming these constraints, we propose a new method to fix the reach ability of the level for the side-scrolling platform game in this paper. First, we fixed the logic errors in the level. Then we used the level break agent to locate the tiles that made the level unreachable. Finally, we used the deep learning model to replace the tiles to make the level reachable. The proposed method is to prove effective in the case study of fixing Super Mario Bros levels.
Research on Abnormal Data Monitoring of Intelligent Vehicle Communication Network

At present, to precisely ensure the routine use of intelligent vehicles' communication network, accurate data transmission is of great importance, which can also, to some extent, improve the network failure management and maintenance of smart cars, thus affecting the intellectual analysis ability of intelligent cars. Therefore, it is very necessary to perform real-time monitoring of abnormal data in the everyday use of smart cars and their regular operation. Based on the detection of anomalous data of intelligent vehicles, an instantaneous frequency estimation method of signals transmitted by a communication network is proposed in this paper. In terms of abnormal data detection, after the time-frequency decomposition of the output signal of the collected communication network, the collected anomalous data can be detected by extraction, and then the detected and captured abnormal data is filtered to finally complete the characteristic extraction of the strange frequency of the abnormal data.
Implementing “Superlary Game” To Improve Students’ Vocabulary Mastery

This research is aimed to overcome the problem in the classroom by improving students’ vocabulary mastery through game. The research is conducted in SMAN 1 Kademangan for grade tenth of MIPA 1. The game applied in this research was made by Wahyu Dwi Lestari (2020). It is a “Superlary game” game stand for Escapade game. To cater to a new way of overcoming this issue, classroom action research is focusing on intensifying the students’ vocabulary mastery through the game. The research instruments were pre-test and post-test which were analyzed using an independent sample t-test. Furthermore, the results obtained that the results of the post-test test of 84 are greater than the pre-test of 63. This means that the Superlary game application is effective for improving vocabulary mastery and learning achievement of students of class X MIPA SMAN 1 Kademangan Blitar. This finding shows that students show a very good increase in vocabulary enrichment in one cycle. Therefore, it can be concluded that the Superlary game application can be used effectively by teachers as an alternative application for teaching and learning vocabulary mastery, especially for the basic competencies of historical events. For the future researchers can use the results of this research as the first step to examine the effectiveness of students using “Superlary game” or even create similar games with different themes.
Categorization of Sources for Teachers' Practical Knowledge Understanding

본 연구는 실천적 지식과 관련된 연구를 통해 실천적 지식의 의미를 알아보고 그 원천을 범주화하여 살펴봄으로써 실천적 지식의 이해를 높이고 향후 발전 방향을 찾는 데 그 목적은 둔다. 교사는 자신이 가르치는 환경이 복잡하고 불확실하므로 이미 가지고 있는 지식과 행위를 통해 얻게 되는 경험 및 개인적 역사, 신념, 철학 등을 사용하여 상황에 맞게 교수 행위를 판단하고 결정하게 된다고 한다. 이렇게 사용되는 지식을 연구자들은 실천적 지식이라 하고 교사의 실천적 지식에 대한 이해가 우선되어야 실천적 지식 형성 및 발전을 할 수 있다고 본다. 하지만, 연구를 통해 알려진 그 의미와 내용이 종합적이고 통합적이라 이해하는 데 어려움이 있다. 이에 본 연구는 실천적 지식의 이해를 돕기 위해 본론에서는 이론적이며 실제적인 Elbaz의 연구를 바탕으로 실천적 지식의 성격, 내용, 정향, 수준을 소개하고, 연구자들의 실천적 지식 연구를 바탕으로 그 원천을 세부분(이론적 지식으로 보는 관점, 행위와 경험으로 보는 관점, 개인의 역사 · 신념 · 철학으로 보는 관점 등)으로 범주화하여 살펴본다. 결론 및 제언에서는 교사가 자신의 실천적 지식을 이해하고 인식한 후에 이를 개발시켜 나아가 하는 이유와 더 나은 교육 환경을 위해 교사 지식에 관한 연구가 필요함을 언급하였다.This study aimed is to enhance the understanding of practical knowledge and find a future development direction by examining the meaning of practical knowledge and categorizing its sources through research related to it. Because the teaching environment is complex and uncertain, it is said that teachers use the knowledge and experiences they already have and their personal history, beliefs, and philosophies to judge and decide upon teaching behavior according to the situation. The knowledge used in this way is called practical knowledge by researchers, and its formation and development is possible only when the teacher's understanding of practical knowledge is given priority. However, it is difficult to understand the meaning and content known through research because it is comprehensive and integrated. Therefore, to understand practical knowledge, this study introduced the nature, content, orientation, and level of practical knowledge based on the theoretical and practical Elbaz research; it identified the source based on the practical knowledge research of several researchers. We categorized it into parts (the viewpoint of theoretical knowledge; action and experience; personal history, beliefs, philosophy, etc.). The conclusions and suggestions mention the reasons why teachers develop practical knowledge after identifying and understanding it, and the need for further research on teacher knowledge to create a better educational environment.
Authorized and Rogue LTE Terminal Identification Using Wavelet Coefficient Graph with Auto-encoder

The wide popularity of 4G/5G mobile terminals increase the requirements of wireless security. Radio frequency fingerprint (RFF) technology can strengthen 4G/5G air interface accessing security at the physical layer. In this paper, a wavelet transform (WT) coefficient graphs RFF extraction with auto-encoder (AE) based rogue terminal detection scheme is proposed. At first, WT coefficients at 48 scales are extracted from the transient-power-off part of LTE physical random access channel (PRACH) preamble. Then, an AE network structure aimed for 2D WT coefficient graph is designed for rogue terminal detection. We successfully distinguish 7 mobile phones and 1 USRP under the proposed mechanism, where the authorized terminals from the same manufacturer can be identified with an accuracy of 90.08%. In addition, extensive experiments are carried out at LOS and NOLS scenarios, respectively, the proposed LTE identification scheme has demonstrated robustness in dynamic environments.
A loss-aware weighting strategy for improving object detection

Object detection is a prevalent task in the domain of computer vision. An object detection task is one where the goal is to localize one or many different classes of objects using bounding boxes. Object detection is usually formulated as a multi-task learning problem to simultaneously solve classification and regression tasks. State-of-the-art methods commonly address this problem by joint optimization. The overall loss function is a weighted sum of classification and regression losses. Previous works have shown that loss weighting is crucial in multitask learning and adaptive weighting can outperform optimal weights. This paper proposes a loss-aware weighting method to adaptively control loss weights in object detection. This method rebalances the contribution of localization and classification loss during training. All the experiments conducted on the challenging COCO dataset demonstrate the effectiveness of our solution. This method achieves 35.5% in terms of overall AP on the COCO dataset, which is 0.4% higher than the baseline with YOLOR-S4-DWT architecture. The code is available at: https://github.comlsun-asterisk-researchlloss-aware-weighting-strategy
A Context-Aware Exercise Facilitation System While Doing Other Tasks

We proposed a context-aware exercise-facilitation system that can be used in parallel with various tasks in daily life. The offline-evaluation results show that the proposed system can recommend appropriate exercises based on the user’s context.
Detecting Dialogue Breakdowns using Nonverbal Information in Text-based Dialogue System

In recent years, we have become increasingly familiar with dialogue systems. However, many “dialogue breakdowns”, in which the system responds out of context or abruptly changes the topic, have occurred. Therefore, research has been conducted of techniques for detecting dialogue breakdowns. This study specifically examines eye gaze information, which is one type of non-verbal information, with collection of a corpus of dialogues and eye gaze information during text chat conversations. We extracted 16 dimensional features from the corpus and eye gaze information, and conducted a t-test, which confirmed significant differences in three-dimensional features. We then used the extracted features to detect dialogue breakups, from which we confirmed detection accuracy of approximately 70%.
Generation of images based on emotions evoked by music using Generative Adversarial Networks

In recent years, video production is very popular among non-professional people. One of the key technologies supporting video production is image generation. There are many methods for generating images which are strongly associated by given textual descriptions or audio data. On the other hand, there has been very few researches on image generation from music. In this paper, we propose a method for image generation based on emotions evoked by music using Generative Adversarial Networks(GAN). We employ MidiBERT for feature extraction from music to estimate the emotions evoked by music and StackGAN for image generation based on the extracted features. By training an image generation network on our dataset, we show that it is possible to generate similar images for similar musical pieces.
Analysis of Relationships between Visual Cognitive Contents and Response of Each Brain Region via Visual Question Answering

This paper presents a brain activity analysis of the visual cognitive contents via Visual Question Answering (VQA). Our analysis enables generation of answers to various types of questions about viewed images such as objects, colors, numbers, and so on from only brain activity when a subject viewed the ones. In this paper, by comparing the answers about viewed images estimated from each brain region, we reveal differences in the brain response. Experimental results show that specific brain regions strongly respond to visual stimuli such as objects and colors.
Deep Semantic Coding for Wireless Image Retrieval

We address the image retrieval problem for a wireless system including an edge server and an edge device. The query image is first compressed by the edge device, and then transmitted into wireless channel, while the edge server retrieves the received image. Different from conventional schemes directly compressing features via unsupervised learning regardless of the database semantic distribution, we design a deep semantic coding (DSC) scheme by integrating the inverted semantic index structure of the database into the coding process, which can utilize the prior semantic information of the database to reduce the bandwidth. We extract the feature vectors from the images via a convolutional neural network and generate the semantic guided code head, which is followed by the product quantization. The experimental results verify the effectiveness of the DSC scheme in reducing the bandwidth as well as improving the performance of wireless image retrieval.
Disentangling Interest and Causality for Recommendation Effectiveness

Recommender systems have been widely deployed in various business platforms. Traditional models usually focus on recommending items of users’ interests, thus the accuracy is one major concern. However, many accurately recommended items would have been clicked by users even without recommendation. Given this, the causal effect was introduced to measure the difference of outcome (clicked or not) if an item is recommended or not. Previous works demonstrate that, most existing causality metrics are defined on all the items and suffer large variance. In this paper, we propose a new metric called R-CATE that measures the causal effect of the top-k recommended items, which can be regarded as a hybrid of accuracy and causality measures. Towards the R-CATE performance of recommendation lists, we design a learning-to-rank model that disentangles users’ interest embedding and causality embedding. The experiments on two semi-synthetic datasets show that, our proposed model is superior to other typical accuracy or causality oriented methods.
Hybrid model of alternating least squares and root polynomial technique for color correction

Color correction is an image-altering technique that modifies image color in such a way that it matches a reference image. Many approaches have already been proposed by various researchers; however, those models have been unable to reduce color errors between two images, which results in inefficiency and poor-quality images. This research paper presents an effective and improved color correction model wherein alternate least square (ALS) and root polynomial (RP) are used together. The main objective of the proposed model is to reduce the error between a reference image and a target image to enhance the image quality and make them look realistic. To achieve this objective, the proposed model used the Amsterdam library of object images which contains a picture of single objects captured under various illumination angles and colors. The main contribution of this paper is a hybrid ALS + RP color correction technique, implemented on the dataset image that fixes its color as per the reference image and enhances its quality. The target image is then converted into three color models, i.e., LAB, LUV, and RGB into XYZ format. Finally, the color difference between a reference image and a target image is observed by calculating values for parameters like Mean, median, 95% quantile, and maximum error. The effectiveness of the suggested hybrid color correction approach is assessed and validated in MATLAB software for each color model. Through extensive experiments, it is observed that the proposed hybrid model yields the least errors for the RGB color model. This is followed up by LUV and then LAB, to prove its supremacy over other models.
Development of an intelligent technology system for prefabrication and assembly of new ductile tunnel structures

In response to a series of problems existing in shield tunnels, an intelligent shield tunnel construction system based on ductility theory is constructed. By taking advantage of the ductility characteristics of materials and structures in conjunction with information technology like computers, a series of measures such as ductility design, intelligent perception, intelligent manufacturing, and intelligent assembly are adopted to make the materials in the tunnel structure intelligently perceivable, the structure accurately monitored, the data twinned in real time, the information efficiently managed, the manufacturing automatically manipulated, the process fully perceived, the model dynamically adjusted and the tube pieces intelligently assembled, so as to finally realize Intelligent construction of shield tunnels for ductile cities.
Towards a Robust and Scalable Information Retrieval Framework in Big Data Context

The proliferation of information in cyberspace is increasing exponentially, leading to challenges for information retrieval systems to satisfy demands for performance and accuracy. How-ever, most existing works concentrate more on designing natural language processing (NLP) models than building such systems, which require massive efforts. In this study, we propose a modular framework for an information retrieval system consisting of several large-scale components capable of processing massive data. In addition, the proposed framework provides a high level of customization by assisting end-users in quickly replacing the NLP models to suit different contexts. This shortens the deployment from research to production of novel NLP models. The evaluation results of our prototype integrated with Vietnamese retrieval models show that the proposed framework is highly robust and scalable in big data contexts.
Keynote Talk 3: Verifying Neural Networks Against Backdoor Attacks

Neural networks have achieved state-of-the-art performance in solving many problems, including many applications in safety/security-critical systems. Researchers also discovered multiple security issues associated with neural networks. One of them is backdoor attacks, i.e., a neural network may be embedded with a backdoor such that a target output is almost always generated in the presence of a trigger. Existing defense approaches mostly focus on detecting whether a neural network is ‘backdoored’ based on heuristics, e.g., activation patterns. To the best of our knowledge, the only line of work which certifies the absence of backdoor is based on randomized smoothing, which is known to significantly reduce neural network performance. In this work, we propose an approach to verify whether a given neural network is free of backdoor with a certain level of success rate. Our approach integrates statistical sampling as well as abstract interpretation. The experiment results show that our approach effectively verifies the absence of backdoor or generates backdoor triggers.
An SUI-based approach to explore visual search results cluster-graphs

Nowadays, exponential growth in online production and extensive perceptual power of visual contents (i.e., images) complicate the users' information needs. The research has shown that users are interested in satisfying their visual information needs by accessing the image objects. However, the exploration of images via existing search engines is challenging. Mainly, existing search engines employ linear lists or grid layouts, sorted in descending order of relevancy to the user's query to present the image results, which hinders image exploration via multiple information modalities associated with them. Furthermore, results at lower-ranking positions are cumbersome to reach. This research proposed a Search User Interface (SUI) approach to instantiate the non-linear reachability of the image results by enabling interactive exploration and visualization options. We represent the results in a cluster-graph data model, where the nodes represent images and the edges are multimodal similarity relationships. The results in clusters are reachable via multimodal similarity relationships. We instantiated the proposed approach over a real dataset of images and evaluated it via multiple types of usability tests and behavioral analysis techniques. The usability testing reveals good satisfaction (76.83%) and usability (83.73%) scores.
Super-resolution image reconstruction from sparsity regularization and deep residual-learned priors

BACKGROUND: Computed tomography (CT) plays an important role in the field of non-destructive testing. However, conventional CT images often have blurred edge and unclear texture, which is not conducive to the follow-up medical diagnosis and industrial testing work. OBJECTIVE: This study aims to generate high-resolution CT images using a new CT super-resolution reconstruction method combining with the sparsity regularization and deep learning prior. METHODS: The new method reconstructs CT images through a reconstruction model incorporating image gradient L0-norm minimization and deep image priors using a plug-and-play super-resolution framework. The deep learning priors are learned from a deep residual network and then plugged into the proposed new framework, and alternating direction method of multipliers is utilized to optimize the iterative solution of the model. RESULTS: The simulation data analysis results show that the new method improves the signal-to-noise ratio (PSNR) by 7% and the modulation transfer function (MTF) curves show that the value of MTF50 increases by 0.02 factors compared with the result of deep plug-and-play super-resolution. Additionally, the real CT image data analysis results show that the new method improves the PSNR by 5.1% and MTF50 by 0.11 factors. CONCLUSION: Both simulation and real data experiments prove that the proposed new CT super-resolution method using deep learning priors can reconstruct CT images with lower noise and better detail recovery. This method is flexible, effective and extensive for low-resolution CT image super-resolution.
Principles of open science in Antarctic research

New approaches to the reusability support of datasets are particularly important taking into account the wide variety of data types, documentation and file formats and the unique content. In this research, the authors tried to analyze the principle of open science based on official documents, websites and researches, available reports of international and Ukrainian organizations operating in Antarctic.