Reviewerly/exHarmony huggingface dataset:
The Study on the Violence Victimization Experience and Job-Related Stress among Probation Officers under EM

This study aims at investigating the relationship between victimized experience of probation officer charged in electronic monitoring and job related stress in South Korea. Although the use of the electronic monitoring continues to expand, discussions on the job stress of the officials in charge of it are relatively few.&#x0D; Negative job stress felt by probation officers in charge of community correction will inevitably result in enormous losses of nationwide security, eventually leading to great damage to the public. In particular, the experience of physical or mental violence victimization from probation officers under electronic monitoring will cause a high level of negative job stress. To empirically verify this, a hierarchical regression analysis was conducted in the current study. This study utilized secondary data surveyed by the Korean Institute of Criminology and Justice.&#x0D; As a result of this study, it was found that the victimization of mental violence was statistically significant factor that is associated with job related stress. More importantly, hierarchical regression analysis demonstrated that social support and mental victimization experience were significant factors that are related with stress of probation officers when other variables are controlled. Further policy implications will be discussed.
IMPLEMENTASI METODE DETEKSI TEPI CANNY UNTUK MENGHITUNG JUMLAH UANG KOIN DALAM GAMBAR MENGGUNAKAN OPENCV

Edge detection is often used in image processing to find the edge boundaries of image objects and this is important for selecting the edges of image parts. The image edge detection used in this study counts coins in an image, so that it can easily pass through the human eye but not by a computer. In a computer system, it is necessary to provide programs and test data so that the computer can read and count coins like a human. The purpose of this study is to count coins in an image using Canny edge detection and Gaussian filtering methods. The images used in this study are in the form of *jpg and tested using the Visual Studio Code application using OpenCV and Python. The results of this study indicate that the results of the number of coins read from the images inputted to the system can be read properly, with a data accuracy of 90%. Canny's edge detection method can be used to count the number of coins in an image.Keywords: Pengolahan Citra; Deteksi tepi Canny; Gaussian filtering; OpenCV;
COVID-19 and Sinophobia: Detecting Warning Signs of Radicalization on Twitter and Reddit

Hate crimes and hateful rhetoric targeting individuals of Asian descent have increased since the outbreak of COVID-19. These troubling trends have heightened concerns about the role of the Internet in facilitating radicalization. This article explores the existence of three warning signs of radicalization-fixation, group identification, and energy bursts-using data from Twitter and Reddit. Data were collected before and after the outbreak of COVID-19 to assess the role of the pandemic in affecting social media behavior. Using computational social science and Natural Language Processing techniques, we looked for signs of radicalization targeting China or Chinese individuals. Results show that fixation on the terms China and Chinese increased on Twitter and Reddit after the pandemic began. Moreover, tweets and posts containing either of these terms became more hateful, offensive, and negative after the outbreak. We also found evidence of individuals identifying more closely with a particular group, or adopting an "us vs. them" mentality, after the outbreak of COVID-19. These findings were especially prominent in subreddits catering to self-identified Republicans and Conservatives. Finally, we detected bursts of activity on Twitter and Reddit following the start of the pandemic. These warning signs suggest COVID-19 may have had a radicalizing effect on some social media users. This work is important because it not only shows the potential radicalizing effect of the pandemic, but also demonstrates the ability to detect warning signs of radicalization on social media. This is critical, as detecting warning signs of radicalization can potentially help curb hate-fueled violence.
Evaluation of Stratified K-Fold Cross Validation for Predicting Bug Severity in Game Review Classification

Steam review data provided a lot of information for the game development team, either positive or negative review. It contained an essential point that negative and positive reviews provide crucial information and 7% of positive reviews contained bug reports. These bug reports occurred after the game was released, and many reports of common incidents still exist. If players found an issue in the game, they could report it directly through the review feature provided by the online game platform. Nevertheless, the development team took a long time manually analyzing and categorizing the game review. This study proposed a new approach to automatically categorizing game reviews on Steam based on the bug severity level. Hence, to solve this problem, we suggested introducing a solution based on the research background indicated above. For this experiment, we analyzed reviews on two popular game titles namely, FIFA 23 and Apex Legends. We implemented three different classifiers namely, KNN, Decision Tree, and Naïve Bayes, which would be used to train a dataset to classify the bug severity level. Due to an imbalanced dataset, we performed cross-validation to reduce bias in the dataset. Performance in this model would be evaluated using accuracy rate, precision, recall, and F1 score. As a result, the experiment showed that game reviews of different game titles achieved different accuracy scores. The game review classification for FIFA 23 performed better than the game review classification for Apex Legends. The mean accuracy score of FIFA 23 was 72% with Decision Tree and Apex Legend was 64% with KNN.
An Empirical Comparison of Web Content Extraction Algorithms

Main content extraction from web pages-sometimes also called boilerplate removal-has been a research topic for over two decades. Yet despite web pages being delivered in a machine-readable markup format, extracting the actual content is still a challenge today. Even with the latest HTML5 standard, which defines many semantic elements to mark content areas, web page authors do not always use semantic markup correctly or to its full potential, making it hard for automated systems to extract the relevant information. A high-precision, high-recall content extraction is crucial for downstream applications such as search engines, AI language tools, distraction-free reader modes in users' browsers, and other general assistive technologies. For such a fundamental task, however, surprisingly few openly available extraction systems or training and benchmarking datasets exist. Even less research has gone into the rigorous evaluation and a true apples-to-apples comparison of the few extraction systems that do exist. To get a better grasp on the current state of the art in the field, we combine and clean eight existing human-labeled web content extraction datasets. On the combined dataset, we evaluate 14~competitive main content extraction systems and five baseline approaches. Finally, we build three ensembles as new state-of-the-art extraction baselines. We find that the performance of existing systems is quite genre-dependent and no single extractor performs best on all types of web pages.
Transformers are Universal Predictors

We find limits to the Transformer architecture for language modeling and show it has a universal prediction property in an information-theoretic sense. We further analyze performance in non-asymptotic data regimes to understand the role of various components of the Transformer architecture, especially in the context of data-efficient training. We validate our theoretical analysis with experiments on both synthetic and real datasets.
Development of Artificial Intelligence Convergence ProblemSolving Learning Instructional Model to Improve Data Literacy

4차 산업혁명의 핵심 기반 기술인 인공지능(AI, Artificail Intelligence)은 인지능력, 학습능력, 추론능력인 인간의 고차원적인 사고능력을 구현하게 한다. 본 연구의 목적은 이러한 4차 산업혁명의 시대에서 학습자가 본인 주변 생활 속에서 일어나는 사회적 이슈를 인공지능의 예측 기능을 통해 파악하고, 데이터를 근거로 문제의 해결책을 제안하는 일련의 과정을 통해 데이터 리터러시를 함양시킬 수 있는 수업모형 개발에 있다. 이를 위해 선행 문헌 검토를 통해 기존 문제해결학습 모형에 인공지능을 융합하여 데이터 리터러시 향상을 위한 수업 모형 및 설계원리와 상세지침을 개발하고, 이를 고등학생 대상의 수업에 적용하여 실행하였다. 연구 결과 학생들의 데이터 리터러시가 통계적으로 유의미(p<.001)하게 향상되었으므로 본 연구의 수업 모형이 학습자의 데이터 리터러시 향상에 긍정적인 영향을 주었음을 확인할 수 있었고, 앞으로 관련 연구로 이어지길 기대해 본다.Artificial intelligence (AI), a key base technology of the 4th Industrial Revolution, allows humans to realize high-dimensional thinking skills, which are cognitive, learning, and reasoning skills. The purpose of this study is to develop a teaching model that allows learners to develop data literacy through a series of processes that identify social issues in their lives through artificial intelligence prediction functions and propose solutions to problems based on data. To this end, the class model, design principles, and detailed guidelines were developed to improve data literacy by converging artificial intelligence with existing problem-solving learning models through prior literature review, and applied to classes for high school students. As a result of the study, students' data literacy improved statistically significantly (p<.001), so it was confirmed that the instructional model of this study had a positive effect on the improvement of learners' data literacy, and it is expected to lead to related studies in the future.
From Answering for Points to Commenting for Others

In this paper, we examine posting behavior on a large-scale online knowledge sharing platform, Stack Overflow, and investigate how that behavior changes as users gain experience and status. We use a trace ethnographic approach, analyzing both the sayings and doings of users and the numerical and categorical trace data produced by their activities on the platform. Through analysis of this data, as well as interviews with users at different levels of experience, we identify a pattern of behavior where users shift their focus over time from directly answering questions themselves to supporting others and maintaining the quality of knowledge on the platform thus playing a particular role as the knowledge community scales
Automatic Labeling of Natural Landmarks for Wheelchair Motion Planning

Labeling landmarks for the mobile plan of the automatic electric wheelchair is essential, because it can assist disabled people. In particular, labeled landmark images will help the wheelchairs to locate landmarks and move more accurately and safely. Here, we propose an automatic detection of natural landmarks in RGBD images for navigation of mobile platforms in an indoor environment. This method can reduce the time for manually collecting and creating a dataset of landmarks. The wheelchair, equipped with a camera system, is allowed to move along corridors to detect and label natural landmarks automatically. These landmarks contain the camera and wheelchair positions with the 3D coordinates when storing the labeled landmark. The feature density method is comprised of Oriented FAST and Rotated BRIEF (ORB) feature extractors. Moreover, the central coordinates of the marked points in the obtained RGB images will be mapped to the images with the depth axis for determining the position of the RGB-D camera system in the spatial domain. An encoder and kinematics equations are applied to determine the position during movement. As expected, the system shows good results, such as a high IoU value of over 0.8 at a distance of less than 2 m and a fast time of 41.66 ms for object detection. This means that our technique is very effective for the automatic movement of the wheelchair.
New Atlantis, Castalia, Thelema Abbey

The text gives a brief description of the history of the generation of intellectuals, which is commonly called the generation of the nineties. The author reflects on the path of this generation, choosing for analysis the fate of a small company of his classmates, those who at the moment have passed the fifty-year mark and have probably reached the acme of social maturity. It is emphasized that this generation has achieved great results in philosophical activity. The author notes the reason for his own alienation from the path of his generation and finds it in an internal protest against the tendency characteristic of his classmates to intellectually withdraw themselves from their time in order to be placed in a different context – temporal or spatial. It is noted that this very aspiration was borrowed by representatives of the generation of the nineties from the philosophical leaders of the sixties. The problem of the philosophical generation is considered in the context of the centuries-old search of the intellectual class for its place in society in obvious conflict with the social hierarchies existing in the traditional agrarian-class world.
Preparing for the New Era of Artificial Intelligence: My Experience of Teaching “Artificial Intelligence in Advertising”

With artificial intelligence (AI) technologies making significant evolutions over the past few years, there is a growing need for advertising students to get familiarized with the different types of AI technologies and explore and experiment with ways to adapt to such changes. This article illustrates the development and implementation of the course “AI in Advertising,” which is designed to introduce students to the basic concepts, underlying logics, and evolution of AI, as well as its various implementations in the field of advertising and marketing communications. Hands-on practices are offered to students with the aim of helping them work with AI and build AI-empowered campaigns for brands. The structure of the course is discussed in detail, and the author also offers reflections on the future of advertising education.
A technique to jointly estimate depth and depth uncertainty for unmanned aerial vehicles

When used by autonomous vehicles for trajectory planning or obstacle avoidance, depth estimation methods need to be reliable. Therefore, estimating the quality of the depth outputs is critical. In this paper, we show how M4Depth, a state-of-the-art depth estimation method designed for unmanned aerial vehicle (UAV) applications, can be enhanced to perform joint depth and uncertainty estimation. For that, we present a solution to convert the uncertainty estimates related to parallax generated by M4Depth into uncertainty estimates related to depth, and show that it outperforms the standard probabilistic approach. Our experiments on various public datasets demonstrate that our method performs consistently, even in zero-shot transfer. Besides, our method offers a compelling value when compared to existing multi-view depth estimation methods as it performs similarly on a multi-view depth estimation benchmark despite being 2.5 times faster and causal, as opposed to other methods. The code of our method is publicly available at the following URL: https: //github.com/michael-fonder/M4DepthU.
Quality Upshifting with Auxiliary I-Frame Splicing

This paper introduces the Auxiliary I-Frame Splicing method to reduce bandwidth waste in adaptive streaming. This method involves fetching a high-quality I-frame and splicing it into the already downloaded low-quality segment, resulting in a higher-quality rendering at a lower overhead than replacing the entire low-quality segment. In our experiments with three videos and four quantization parameters, the results show that the bandwidth can be saved up to 87% while still increasing the peak signal-to-noise ratio score by 20% and the video multi-method assessment fusion score by 73%. In the demo, we demonstrate the visual differences between the original and spliced videos.
Asymptotic Analysis of Federated Learning Under Event-Triggered Communication

Federated learning (FL) is a collaborative machine learning (ML) paradigm based on persistent communication between a central server and multiple edge devices. However, frequent communication of large ML models can incur considerable communication resources, especially costly for wireless network nodes. In this paper, we develop a communication-efficient protocol to reduce the number of communication instances in each round while maintaining convergence rate and asymptotic distribution properties. First, we propose a novel communication-efficient FL algorithm that utilizes an event-triggered communication mechanism, where each edge device updates the model by using stochastic gradient descent with local sampling data and the central server aggregates all local models from the devices by using model averaging. Communication can be reduced since each edge device and the central server transmits its updated model only when the difference between the current model and the last communicated model is larger than a threshold. Thresholds of the devices and server are not necessarily coordinated, and the thresholds and step sizes are not constrained to be of specific forms. Under mild conditions on loss functions, step sizes and thresholds, for the proposed algorithm, we establish asymptotic analysis results in three ways, respectively: convergence in expectation, almost-sure convergence, and asymptotic distribution of the estimation error. In addition, we show that by fine-tunning the parameters, the proposed event-triggered FL algorithm can reach the same convergence rate as state-of-the-art results from existing time-driven FL. We also establish asymptotic efficiency in the sense of Central Limit Theorem of the estimation error. Numerical simulations for linear regression and image classification problems in the literature are provided to show the effectiveness of the developed results.
Shoplifting Detection Using Hybrid Neural Network CNN-BiLSMT and Development of Benchmark Dataset

Shoplifting poses a significant challenge for shop owners as well as other stakeholders, including law enforcement agencies. In recent years, the task of shoplifting detection has gained the interest of researchers due to video surveillance generating vast quantities of data that cannot be processed in real-time by human staff. In previous studies, different datasets and methods have been developed for the task of shoplifting detection. However, there is a lack of a large benchmark dataset containing different behaviors of shoplifting and standard methods for the task of shoplifting detection. To overcome this limitation, in this study, a large benchmark dataset has been developed, having 900 instances with 450 cases of shoplifting and 450 of non-shoplifting with manual annotation based on five different ways of shoplifting. Moreover, a method for the detection of shoplifting is proposed for evaluating the developed dataset. The dataset is also evaluated with methods as baseline methods, including 2D CNN and 3D CNN. Our proposed method, which is a combination of Inception V3 and BILSTM, outperforms all baseline methods with 81 % accuracy. The developed dataset will be publicly available to foster in various areas related to human activity recognition. These areas encompass the development of systems for detecting behaviors such as robbery, identifying human movements, enhancing safety measures, and detecting instances of theft.
AIM Triad: A Prioritization Strategy for Public Institutions to Improve Information Security Maturity

In today’s world, private and government organizations are legally obligated to prioritize their information security. They need to provide proof that they are continually improving their cybersecurity compliance. One approach that can help organizations achieve this goal is implementing information security maturity models. These models provide a structured framework for measuring performance and implementing best practices. However, choosing a suitable model can be challenging, requiring cultural, process, and work practice changes. Implementing multiple models can be overwhelming, if possible. This article proposes a prioritization strategy for public institutions that want to improve their information security maturity. We thoroughly analyzed various sources through systematic mapping to identify critical similarities in information security maturity models. Our research led us to create the AIM (Awareness, Infrastructure, and Management) Triad. This triad is a practical guide for organizations to achieve maturity in information security practices.
Edukasi terkait langkah pengembangan formula dan registrasi kosmetik melalui webinar

Indonesia is a potential market for cosmetics. Students and lecturers at Departments of Pharmacy developed various cosmetic formulas, but limited to research or publications only. Lack of knowledge about the stages of developing good cosmetic formulas, good manufacturing cosmetics, and the steps in registration of cosmetic preparations is the main cause in developing cosmetic products. The method used in this community service is online conference. The participants were given a pretest and posttest to determine their level of understanding about cosmetics formula development and registration. About 190 participants attended the conference. Assessment by pretest and posttest obtained an average result of 50.22 ± 13.36 and 70.05 ± 18.97 respectively. The results show that there is a significant difference between pretest and posttest. The conference was considered good by the participants with an average score of 3.68 of 4. This conference provided increased knowledge to participants regarding the development of cosmetic formulas and the steps in cosmetic registration.
Technology overrule: pre-literate Akan orality and the musket

Abstract The history of the human–technology relation points to binary (positive and negative) evaluations of technology’s role. One reason for this binary is the limited view of technology in terms of physical and tangible devices. Another is an extreme global view of the relationship, which neglects global diversity. However, technology includes non-physical devices such as speech. Moreover, people hold different intellectual, historical and philological assumptions as the bases for their rule over technology. This article emphasizes the importance of language and global diversity as crucial dimensions of the human–technology relation. It is through language that humans are able to rule over technology, rather than being dominated by it. Taking language as a focal point, I expose the neglect of pre-literate orality as a way of engaging with technology and I espouse an orality perspective on our rule over technology. This perspective foregrounds human mindfulness as a basis for oral engagement with technology. It is developed based on analysis of historical data on oral language use by pre-literate Akan people of Ghana to rule over the musket. The article characterizes technology overrule according to a four-stage process: image recognition, technology reduction, technology reposition and image reproduction.