Reviewerly/exHarmony huggingface dataset:
Entanglement witnessing with untrusted detectors

Abstract We consider the problem of entanglement detection in the presence of faulty, potentially malicious detectors. A common—and, as of yet, the only—approach to this problem is to perform a Bell test in order to identify nonlocality of the measured entangled state. However, there are two significant drawbacks in this approach: the requirement to exceed a critical, and often high, detection efficiency, and much lower noise tolerance. In this paper, we propose an alternative approach to this problem, which is resilient to the detection loophole and is based on the standard tool of entanglement witness. We discuss how the two main techniques to detection losses, namely the discard and assignment strategies, apply to entanglement witnessing. We demonstrate using the example of a two-qubit Bell state that the critical detection efficiency can be significantly reduced compared to the Bell test approach.
Identification technique of cryptomining behavior based on traffic features

Recently, the growth of blockchain technology and the economic benefits of cryptocurrencies have led to a proliferation of malicious cryptomining activities on the internet, resulting in significant losses for companies and institutions. Therefore, accurately detecting and identifying these behaviors has become essential. To address low accuracy in detecting and identifying cryptomining behaviors in encrypted traffic, a technique for identifying cryptomining behavior traffic is proposed. This technique is based on the time series characteristics of network traffic and introduces the feature of long-range dependence, and the recognition effect is not easily affected by the encryption algorithm. First, 48-dimensional features are extracted from the network traffic using statistical methods and the rescaled range method, of which 47 dimensions are statistical features and 1 dimension is a long-range dependence feature. Second, because there is much less cryptomining traffic information than normal network traffic information in the dataset, the dataset is processed using oversampling to make the two types of traffic data balanced. Finally, a random forest model is used to identify the type of traffic based on its features. Experiments demonstrate that this approach achieves good detection performance and provides an effective solution for identifying encrypted network traffic with malicious cryptomining behavior. The long-range dependence features introduced therein together with the statistical features describe a more comprehensive flow characteristics, and the preprocessing of the dataset improves the performance of the identification model.
"GOOD DESIGN" IN THE METAVERSE: TOWARD A PROBLEM STATEMENT

The article actualizes design modernist principles of "good design" in the new operational, functional and semantic space of the meta-universe. The meta-universe is problematized and critically analyzed in the broad context of technological substitution, marketization, interoperability, and other socially significant phenomena of the real and digital environment. We have conducted a comparative analysis of the principles of good design by Dieter Rams and Adobe corp.; considered the current debate in science, experts and the public on the prospects of the meta-universe development, opportunities and risks associated with the design of competing with architecture; outlined urgent tasks to address problems and challenges in the selected problem area. Good design is considered as a key theoretical concept of contemporary (trendy) design, the required intellectual and humanitarian basis for conceptual management and the formation of aesthetic and ethical standards of the new socio-economic and cultural reality that the meta-universe opens
The Method User Experience Questionnaire Analysis of Identitas Kependudukan Digital Application

User Experience (UX) becomes important to explore and fulfill the needs for developing user-focused applications or products. The problems that exist to measure the level of user experience in the Digital Resident Identity (IKD) application and complaints submitted by users tend to lead to system and service quality problems in the IKD application. Then research purposes are assessed to evaluate the extent to which users can utilize and interact with the application. The method used by researchers is the User Experience Questionnaire (UEQ). UEQ is a widely used scoring methodology used to quantitatively measure user experience by administering a questionnaire to individuals that measures their subjective experiences and perceptions. Variables in UEQ, namely: efficiency, perspicuity, dependability, novelty, stimulation, and interestingness. The evaluation results of user experience in IKD applications using UEQ showed that there were 5 variables that received positive evaluation results, including attractiveness variables (mean 2.14), clarity variables (perspicuity) (mean 1.725), efficiency variables (efficiency) ( mean 1.725), accuracy variable (mean 1.525), and stimulation variable (mean 1.475). Whereas the novelty variable (mean 0.602) gets neutral evaluation results. From these results it can be used by IKD application developers to improve the user experience (user experience) of IKD applications on novelty variables by improving the appearance of applications that are more creative, inventive, and also cutting edge. The developer can pay attention to the recommendations that have been presented in this study. Parties who wish to conduct further research can use other evaluation methods such as Heuristic Evaluation.
Alternatif Solusi Mengontrol Cahaya Alami pada Desain Bangunan Komersial

Title: Alternative Solutions to Control Heat Radiation in Commercial Building Design; Case Study: Commercial Buildings In Bukit Keminting, Palangka Raya City A building envelope is a component that protects against environmental influences outside the building that are not expected to be a solution in controlling heat radiation and sunlight. However, the shops in Bukit Keminting, Palangka Raya, have challenges. In addition to its function as a commercial building with essential criteria like visibility aspect, the position of the storefront along the road also directly faces the sunset. This causes the store to receive heat and glare from 2 p.m. to 5 p.m. Using web-based software development from AndrewMarsh.com. This study focuses on effective shading devices for stores in Bukit Keminting Palangka Raya in controlling heat radiation without eliminating the vital aspect of visibility for commercial buildings. The software analysis shows that a highly flexible device is the most suitable solution. The type of shading device with these properties is the type of external Venetian blind, which can be adjusted according to the direction of the incoming light, which changes every month.
An Intelligent Model for Improved Breast Cancer Prognosis

This research suggests developing a deep learning model using customized CNN to categorize and predict breast cancer in a timely period. The model utilizes a large dataset of breast cancer images obtained from Kaggle, an online research repository. Pre-processing techniques were applied to the images to eliminate noise, such as shadows on the images, and resize the images to lessen the high computation cost. The dataset was separated into training set 80% (48, 852) and test set 20% (16, 284). CNN was employed to mine meaningful features from the images and to classify them based on predefined criteria, assessing the presence and severity of breast cancer. Additionally, the model could provide treatment recommendations depending on the patient's health account and other pertinent aspects. The model's performance was evaluated using a confusion matrix, revealing a 95% accuracy rate, 100% recall value, 90% precision value, and 95% F1 score. The classifier's AUC value was 88%, indicating high reliability for breast cancer prognosis. The proposed methodology may significantly increase diagnostic speed and accuracy, resulting in earlier detection and better patient outcomes.
Fuzzy-rule-decided small cell offloading for rate-adaptive SVC-DASH video streaming over the vehicle environment

This paper proposed an SVC-DASH video streaming method over the vehicular environment based on the Multi-access Edge Computing (MEC) architecture. For the streaming concern, a control scheme using the Segment-Set-based buffer-aware bitrate adaptation with the backward quality's increment control was proposed; for the mobility concern, a Small Cell Video Streaming Offloading (SC–VSO) control scheme adopting the multiple attribute fuzzy-based decision for deciding whether it can offload the video streaming traffic from a macro cell to a small cell or not was proposed. To have the suitable bitrate adaptation and stable quality variation, three factors that the proposed streaming control scheme considers are (i) estimated bandwidth, (ii) buffer occupancy and (iii) quality variation. Since the vehicle is moving across many types of Base Stations (BSs), the proposed mobility control scheme can decide whether to have SC-VSO or not before vehicle X entering into the ahead small cell's signal coverage based on (i) the network situations of the corresponding macro and small cell and (ii) the reported context from vehicle X using the proposed fuzzy logic mechanism. The performance evaluation results shown that the proposed method has the higher video quality and better quality stability for video streaming over the vehicle environment.
Teaching Method for Writing Self-Introductions Case Study Using Jigsaw Cooperative Learning

This study examines the efficacy of an educational plan centered on Jigsaw Cooperative Learning as an effective teaching method for writing self-introductions within a a university-level writing class K University. As writing self-introductions has became necessary for employment, the need for writing self-introduction education in university writing classes began to increase in the mid-2000s. Nevertheless compared to other writing learing the participation rates and achievements in writing self-introduction learing have been low. In reponse to these challenges, this study present Writing Self-Introduction teaching plan by applying Jigsaw Cooperative Learing. Jigsaw Cooperative Learing is a cooperative learning model and a teaching method that fosters knowledge exchange among group peers, thus enabling collaborative and independent learning among students emphasized by Fisher&amp;Frey(2021).&#x0D; The application of Jigsaw Cooperative Learning took place over two weeks in the first semester of 2023 at K University’s University Writing Class. The structured sequence encompassed ‘professor description→Jigsaw Cooperative Learning Step1→Jigsaw Cooperative Learning Step2→Jigsaw Cooperative Learning Step3→Individual Practice’. The end of semester survey revealed that 64.5% of participants wanted to learn how to write self-introduction: 69.7% stated that writing self-introductions was particulartly helpful. Additionally 27.8% of the classes were instructor lectures without applying Jigsaw Cooperative Learning. The findings demonstrate that writing self-introduction teaching using Jigsaw Cooperative Learning, which enables learners to learn efficiently on their own by collaborating with colleagues, is more effective than unilateral lecture-style methods. As writing self-introductions for employment is practical and strategic writing with a clear purpose, Jigsaw Cooperative Learning can be used to increase the learning effect through the active participation of learners.
Research Trends in the Korean Language Curriculum for Academic Purposes: Focused on Curriculum Research for Domestic University and Graduate Studies

This paper aims to evaluate the current status and performance of research on the Korean language curriculum for academic purposes by analyzing current trends and to suggest future research directions. The results of the study are as follows: 1) Academic-purpose Korean language curriculum research began in 2000; there was a constant number of papers until 2010, but they increased rapidly between 2011 and 2015, since when the number of studies has remained unchanged. 2) Research aimed at “curriculum design/development” accounted for 70% of the total. 3) 87% of all studies were conducted on the undergraduate curriculum, and half of them were Korean curriculum studies for general academic purposes. 4) About 76% of the studies did not subdivide the research area, while subdivided studies mainly focused on functional areas centered on writing and speaking. 5) In research methods, quantitative research, literature research, and mixed research were conducted in similar proportion, and only about 7% involved only qualitative research. Research on the Korean language curriculum for academic purposes is gradually becoming specialized and diversified in terms of research subjects, areas, and methods, but still shows a tendency to be biased. Accordingly, this paper proposes for future research: 1) improving research bias and non-connection, 2) developing a systematic curriculum passing through all stages of design, implementation, and evaluation, 3) implementing segmentation and a balanced approach by research area, and 4) developing a curriculum that reflects learners’ learning environment and academic performance.
Review of University Digital Literacy Course Cases

This study lays the groundwork for designing digital literacy classes for university students in South Korea by reviewing theoretical discussions and previous classroom practices. Based on an analysis of previous research and related materials, the study examines the syllabi of introductory courses at 10 institutions to explore their digital literacy curricula, determine the contents and values taught at each institution, and the intersection of these curricula with previous theoretical discussions on digital literacy. Evidently, there are inherent limitations due to the size of the dataset. However, it is possible to assess the approach of each institution regarding digital literacy education and explore the current status of education. The results are significant because the study identifies commonalities and differences among them. Moreover, the results can serve as a basis for designing digital literacy classes that fit the Korean context and provide directions for future research to identify other notable aspects.
Issues and Challenges for “Reading and Writing” in the 2022 Korean Language Curriculum: A comparison with “Speech and Writing” in the 2015 Korean Language Curriculum

The purpose of this study was to explore the various issues and challenges that have emerged in the newly created “Reading and Writing” course in the 2022 curriculum. To this end, we examined the changes in the “Reading and Writing” course in the 2022 curriculum, focusing on writing, through a comparison with the “Speech and Writing” course in the 2015 curriculum. The results show that the “Reading and Writing” course is characterized by a unified focus on written communication, with the goal of realizing a life of growth as a lifelong reader and writer. In addition, the content system was examined, and it was found that there were new core ideas, changes in content system categories and achievement standards, and teaching, learning, and assessment were stated similarly to the common Korean language course. Issues and challenges include, the need to ensure that the unique content elements of each discipline are reflected in the achievement standards for the integration of reading and writing, and that specific guidance and follow-up research are needed to implement them in the teaching and learning stages. In addition, specific statements of newly developed terminology, validity of core ideas, increasing complexity of achievement standards, and development of teaching, learning, and assessment measures appropriate to the course matter are issues that should be considered in future curriculum revisions.
Analysis of the Significance of Module Operation in University Writing Education: Focusing on the Operation of Writing Courses at S University

This study addresses the issue of creating persuasive individuals who possess critical thinking skills, analytical reasoning, and effective communication abilities.&#x0D; The focus is on cultivating talents with the ability to communicate persuasively through the development of an educational model for university writing. In order to achieve this, an examination of the general trends and changes in writing education courses at domestic universities over the past 20 years since the 2000s was conducted. By comparing these findings with the writing education curriculum at S University, the unique characteristics of S University’s writing education were explored.&#x0D; The writing education at S University has evolved into an educational model that effectively involves learners’ voluntary participation in class and maximizes their writing experiences. To verify this, the study proposed an educational module with a three-step structure, consisting of “Foundations of Writing - Process of Writing - Types of Writing.” It analyzed how S University’s writing education model positively reflects enhancement of learners’ motivation and efficacy in writing classes. Detailed lecture topics, learning contents, related learning activities, and assignments for each module are presented as examples. Additionally, to provide a detailed description of how the classes are conducted, the study examined evaluation cases utilizing achievement-based assessment criteria, presented the outcomes of a writing competition as a learning activity, and explored the effectiveness of the classes through self-efficacy diagnosis of writing skills. This study reaffirms the necessity of university writing education for cultivating communication skills and fostering creative reasoning in the foundational liberal arts curriculum of universities. Furthermore, it is expected to have significance as it proposes an educational module suitable for freshmen-level writing education.
A Study on Price Prediction Model of Used Cars Using Machine Learning: Focus on Domestic Brands

The domestic used car market continues to grow along with the used car online platform. The used car online platform discloses vehicle specifications, accident history, inspection history, and detailed options to consumers.BR Most of the studies for predicting used car prices were studies using vehicle specifications. In addition, the relationship between the mileage and period of use of the vehicle and the used car price tended to appear nonlinear. In order to solve this problem, recent studies have attempted to reduce Cost Function using Machine Learning models. Accordingly, this study sequentially executed Regression based and Decision Tree based Machine Learning models using vehicle specifications and vehicle options.BR The implications of this study were, first, to make the most of their advantages by continuously executing two types of Machine Learning models. Second, which of the vehicle
Efficient Low-Rank Matrix Completion Updating Algorithm for Recommender System

Recommender systems aim to provide personalized item recommendations to users based on users ratings. However, not all ratings are provided by users, so the rating matrix, which summarizes the user-item interaction data, has many missing values. To fill out these missing values, matrix completion problem is considered. One of the most popular approaches to matrix completion problem is based on low-rank approximation of the rating matrix. To do this, singular value decomposition is required in every iteration, however it is prohibitively expensive when large-scale rating matrices are used. Additionally, recommender systems are frequently updated when a new user or item is added, or existing information is updated. In this paper, we propose a new matrix completion algorithm by recycling the existing information to speed up the computation when a small part of the rating matrix is updated instead of computing the matrix completion from scratch. Experimental results demonstrate that our algorithm is very attractive when the rating matrix is updated frequently by showing that our algorithm has significantly faster execution speed while producing very similar or even better accuracy than the other algorithms.
Phishing Website Detection Model for User Decision Making Based on XAI

Phishing websites based on social engineering are significant cyber threats in the web environment. Recently, a number of studies have been implemented to detect phishing websites using AI (Artificial Intelligence), and they have demonstrated excellent detection performance. However, most of the proposed AI models are black-box. By the nature of black-box, it is difficult to explain how AI models determine if a website is phishing or not. Moreover, false negative is inevitable in the detection system using AI models. Therefore, it is unreliable to detect phishing websites based on the prediction result of an AI model. Because of these limitations, users need to interpret the output of an AI model and make the final decision. In this paper, we propose an interpretable phishing website detection model based on the XAI (eXplainable Artificial Intelligence) techniques so that users can make a reasonable decision with the interpretation of the outputs from the AI model.
PELATIHAN CALISTUNG SEBAGAI UPAYA PEMBERANTASAN BUTA AKSARA BAGI WARGA DESA PANTAI BAKTI

This community service discusses calistung as an effort to eradicate illiteracy for the residents of Pantai Bakti Village. Calistung is an activity that includes several abilities, namely the ability to read, write and count. The ability to read, write and count is very important for a person from an early age to old age. With the ability to read, write and count a person is able to learn knowledge and obtain information. Therefore, failure to master this skill would result in serious trouble. Pantai Bakti Village is a village where there are still residents who cannot read, write and count, this is due to dropping out of elementary school, family economic difficulties and parents who do not care about education. This activity is one of the KKN work programs, with this activity it will improve the quality of human resources for residents in Pantai Bakti Village. This activity was carried out at the Jamie Baitul Mu'minin mosque for three days. The method used was training through tutoring. This training can improve the ability of the residents of Pantai Bakti Village in reading, writing and arithmetic.