Reviewerly/exHarmony huggingface dataset:
An Analysis of the Effect of Economic Education on Economic Knowledge of Early Childhood Teachers

The purpose of this study was to examine how the experience of in-service teacher education related to economic education affects economic knowledge according to institutional type, teacher education type, and method for appropriate early childhood economic education. The subjects of this study were 256 early childhood teachers working in kindergartens and daycare centers located in G and T cities in Gyeongsangnam-do. To measure the economic understanding of early childhood teachers, the economic understanding test questions for schools from the Council on Economic Education (CEE) and the economic understanding test questions for elementary, middle, and high school students nationwide from the Korea Council for Economic Education (2013) are included in early childhood economic education. Economic concepts were extracted and constructed. The collected data were analyzed using the SPSS 21.0 program, and t-test and F-test were performed. As a result of the study, differences in understanding were found depending on the type of institution and training type, but no differences were found depending on the operation method. This study is significant in that it provides a teacher education plan to implement economic education for children in everyday life.
Quantum Neural Network Inspired Hardware Adaptable Ansatz for Efficient Quantum Simulation of Chemical Systems

The variational quantum eigensolver is a promising way to solve the Schrödinger equation on a noisy intermediate-scale quantum (NISQ) computer, while its success relies on a well-designed wave function ansatz. Inspired by the quantum neural network, we propose a new hardware heuristic ansatz where its expressibility can be improved by increasing either the depth or the width of the circuit. Such a character makes this ansatz adaptable to different hardware environments. More importantly, it provides a general framework to improve the efficiency of the quantum resource utilization. For example, on a superconducting quantum computer where circuit depth is usually the bottleneck and the qubits thus cannot be fully used, circuit depth can be significantly reduced by introducing ancilla qubits. Ancilla qubits also make the circuit less sensitive to noises in practical application. These results open a new avenue to develop practical applications of quantum computation in the NISQ era.
Application of secure semi-supervised fuzzy clustering in object detection from remote sensing images

In recent years, landslides are taking place very seriously, and tend to increase in both scope and scale, threatening people's lives and properties. Therefore, timely detection of landslide areas is extremely important to minimize damage. There are many ways to detect landslide areas, in which the use of satellite images is also an option worthy of attention. When performing satellite image data collection, there are many outliers, such as weather, clouds, etc. that can reduce image quality. With low quality images, when executing the clustering algorithm, the best clustering performance will not be obtained. In addition, the fuzzy parameter is also an important parameter affecting the results of the clustering process. In this paper will introduce an algorithm, which can improve the results of data partitioning with reliability and multiple fuzzifier. This algorithm is named TSSFC. The introduced method includes three steps namely as “labeled data with FCM”, “Data transformation”, and “Semi supervised fuzzy clustering with multiple point fuzzifiers”. The introduced TSSFC method will be used for landslide detection. The obtained results are quite satisfactory when compared with another clustering algorithm, CS3FCM (Confidence-weighted Safe Semi-Supervised Clustering).
Conceptualization and Development of a Scale for Brand Experience in Extended Reality

Purpose: This study aims to conceptualize the brand experience that users' avatars can experience on an XR platform and develop items to measure it.&#x0D; Design/methodology/approach: The author presents both a conceptual analysis of brand experience in XR (BXIXR) and a BXIXR scale by conducting a review of marketing research and immersive technology. Adhering to the relevant theory and psychometric principles, the BXIXR scale was developed and validated through a literature review and item generation process, followed by confirmatory factor analysis, reliability analysis, and predictive validity testing.&#x0D; Findings: This research demonstrates that BXIXR can be broken down into four dimensions that are differentially evoked by various brand spaces in XR: search, entertainment, relations, and assembly.&#x0D; Research limitations/implications: This research suggested that the brand experience that users have at the boundary between the physical and virtual worlds may differ from brand experience in the physical world. In this study, it is expected that meaningful implications can be proposed in that it attempts to derive a brand experience based on an immersive economy.&#x0D; Originality/value: The results of this study suggest that the BXIXR scale is a reliable and valid measure of BXIXR. The four-factor structure of the scale is consistent with the theoretical framework, and the results of the reliability and validity testing provide evidence for its robustnes.
Representasi Peran dan Relasi Gender dalam Kumpulan Bahan Bacaan Literasi Jenjang SMP

&lt;p&gt;&lt;strong&gt;In recent years, the&lt;/strong&gt;&lt;strong&gt; Indonesian&lt;/strong&gt;&lt;strong&gt; Ministry of Education and Culture has provided a series of reading materials for young readers with segmentation ranging from PAUD to SMA. This procurement is special because this reading material is disseminated on a national scale to improve the literacy skills of the younger generation within the framework of the National Literacy Movement (GLN). This research aims to describe gender roles and relations in this collection of reading materials. This research data is a collection of junior high school level literacy reading materials published in 2016. The data was analyzed using content analysis methods through the stages of categorization, tabulation, interpretation and inference using a feminist literary criticism approach. The results of this research show that the collection of literacy reading materials at junior high school level shows alignment with patriarchal ideology. There is a reproduction of traditional gender roles, which presents a framework of women's domestication, and a depiction of men's roles attached to the public sector. Apart from that, it seems that the depiction of agency polarization is dominant in male characters compared to female characters. The depiction of gender relations in this collection of reading materials generally shows a hierarchical form of relationship, which emphasizes the superiority of male characters. Thus, this collection of reading materials shows that there is no balance that leads to gender equality. This is important to do because this reading material becomes intake for teenagers in forming teenagers' attitudes and perceptions about gender behavior.&lt;/strong&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords&lt;/em&gt;&lt;/strong&gt; - &lt;em&gt;F&lt;/em&gt;&lt;em&gt;eminist &lt;/em&gt;&lt;em&gt;L&lt;/em&gt;&lt;em&gt;iterary &lt;/em&gt;&lt;em&gt;C&lt;/em&gt;&lt;em&gt;riticism&lt;/em&gt;&lt;em&gt;, &lt;/em&gt;&lt;em&gt;Gender role, &lt;/em&gt;&lt;em&gt;G&lt;/em&gt;&lt;em&gt;ender &lt;/em&gt;&lt;em&gt;R&lt;/em&gt;&lt;em&gt;elation, &lt;/em&gt;&lt;em&gt;L&lt;/em&gt;&lt;em&gt;iteracy&lt;/em&gt;&lt;/p&gt;
Melanoma Detection Based on SVM Using MATLAB

Skin cancer has become the fifth-most dangerous type of cancer. Melanoma, the most ferocious type of skin cancer, should be detected and treated to reduce the risk of spreading to the rest of the body’s organs. This study aims to provide fast and painless detection of skin cancer using image processing, including enhancement and extraction of interesting features for the characterization and classification of infected skin images into melanoma or nonmelanoma in MATLAB. The features used for texture analysis of inserted images are the Gray Level Co-occurrence Matrix (GLCM) and Local Binary Pattern (LBP). The classification of melanoma and non-melanoma is done by training a Support Vector Machine (SVM) using the radial basis function kernel. The accuracy of testing is 94.87%.
The Correlation Between Students’ Translation Ability and Their Reading Comprehension

The aim of this study was to examine the correlation between students' translation ability and their reading comprehension among eleventh-grade mathematics and science students at SMAN 1 Wonomulyo. This dtudy used a quantitative approach, employing a correlational research design. The sample consisted of 36 students selected through a simple random sampling technique from a total population of 212 students. The study employed translation and reading tests as its primary instruments. The data analysis using SPSS indicated a two-tailed significance value of 0.069, which was greater than the standard 0.05 threshold. Additionally, the result obtained using the multiple correlation formula showed a value of 0.329 with 34 degrees of freedom. The correlation coefficient (r-value) was -0.306, which fell below the calculated threshold of 0.329. A t-test was conducted, yielding a t-value of 1.8741724, which was less than the critical t-table value (2.03224) with 34 degrees of freedom at a 0.05 level of significance. Based on the data analysis, the study concluded that the research hypothesis (Ha) suggesting a significant correlation between students' translation ability and reading comprehension was rejected, while the null hypothesis (H0) suggesting no significant correlation was accepted.
Robust Email Spam Filtering Using a Hybrid of Grey Wolf Optimiser and Naive Bayes Classifier

Abstract Effective spam filtering plays a crucial role in enhancing user experience by sparing them from unwanted messages. This imperative underscores the importance of safeguarding email systems, prompting scholars across diverse fields to delve deeper into this subject. The primary objective of this research is to mitigate the disruptive effects of spam on email usage by introducing improved security measures compared to existing methods. This goal can be accomplished through the development of a novel spam filtering technique designed to prevent spam from infiltrating users’ inboxes. Consequently, a hybrid filtering approach that combines an information gain philter and a Wrapper Grey Wolf Optimizer feature selection algorithm with a Naive Bayes Classifier, is proposed, denoted as GWO-NBC. This research is rigorously tested using the WEKA software and the SPAMBASE dataset. Thorough performance evaluations demonstrated that the proposed approach surpasses existing solutions in terms of both security and accuracy.
[Use PICOS frame to keep facilitating scientific research via disease-specific databases].

临床研究是从临床问题出发，以人为研究对象，针对临床工作中实际问题开展的应用性研究。专科疾病数据库是临床研究的宝贵资源。本文以一项慢性肾脏病患病率调查所建立数据库并开展的多项临床研究为例，详述如何基于PICOS研究框架，在研究设计、研究人群、危险因素和终点事件方面规范和持续地开展临床研究。.
The Students' Motivation In Online English Learning In New Normal Era

The purpose of this study was (1) to find out the level of the students’ motivation in online English learning in New Normal Era. (2) To analysis the factors that influence the students’ motivation in online English learning in New Normal Era. (3) To find out whether or not the implementation of English online learning fulfills the principle of effective teaching and learning. The research was designed by Mixed Method. The students of Class XI TESHA Department of SMK Negeri 3 Makassar were chosen by the researcher as sample which consists of 30 students. In collecting data, the researcher used interview and questionnaire. From the result it can be show that most of the students has moderate degree motivation in joining English Online learning in New Normal Era. Second, there are three factors that influence the students’ motivation in English online learning, they are learning ability, learning tools and facility and students’ goals. Third, most of the students was interested to implementation of English online learning.
EFFECTIVENESS OF USING MONOPOLY GAME TO TEACH VOCABULARY AT SMP NAHDLATUTH THALABAH

This research was conducted to overcome the problems faced by students in learning English, especially in learning vocabulary mastery in class VIID of SMP Nahdlatuth Thalabah. Applying monopoly games as learning media is one solution to overcome these problems. Using monopoly games in learning can make the atmosphere in the classroom livelier, so that students are more interested in learning vocabulary mastery. The objectives of the study are to find out: 1) The studentsâ€™ vocabulary mastery before using monopoly game. 2) The studentsâ€™ vocabulary mastery after using monopoly game. 3) The difference of studentsâ€™ vocabulary mastery between before and after being given treatment using monopoly game. This study uses a quantitative approach with the type of research Pre-Experimental Design. The instrument used in this study was a test consisting of a pretest and posttest. The result of this research is that monopoly game is effectively used in learning vocabulary mastery. This effectiveness can be known through the results of student scores. In the pretest, the average value of class VIID students was 47.50. After being treated using a monopoly game, the average posttest score of class VIID students was 58.50. Based on the Paired Sample T Test, it is known that the value of Sig. (2 tailed) of 0.000 &lt;0.05, then H0 is rejected and Ha is accepted. So it can be concluded that there is a significant difference between vocabulary mastery in the pretest and posttest data of Class VIID students at SMP Nahdlatuth Thalabah.
Report on the 8th ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR 2023)

The CHIIR 2023 conference was conducted as a hybrid event in Austin, TX, USA, attracting over 110 attendees both physically and virtually. The main conference program spanned over three days and comprised of single-track presentations. Two workshops were organized on the day prior to the main conference, while the doctoral consortium and tutorial took place one day after the main event. This report has been prepared by the conference General Co-Chairs. Date: 19--23 March 2023. Website: https://sigir.org/chiir2023/.
Overview of Digital Finance Anti-fraud

The development of digital financial technology and its penetration into the traditional financial industry have become an irreversible trend. At present, the applications of digital financial technology have significantly reduced the information asymmetry in the financial field and made great contributions to improving the financial market. However, everything has two sides, especially new things. Digital financial technology is on the ascendant, but new fraud means based on it are growing and financial fraud risks are escalating. Faced with this situation, the importance of financial supervision and risk prevention has been raised to an unprecedented height under the macro background of holding the bottom line of no systematic financial risk. Due to the marriage between digitization and finance, financial fraud has taken on new features such as specialization, industrialization, concealment and cross region, which poses great challenges to traditional anti fraud methods. Therefore, the anti fraud technology should also be constantly innovated. It is not only necessary to accurately combat the existing risks, but also to take the lead to prevent problems before they occur. The behavior-based method is recognized as an effective paradigm for anti-fraud in digital finance. It can be used cooperatively as a second security line, rather than replaced with other types of existing anti-fraud methods. The behavior-based method is a highly-anticipated solution to pursue a non-intrusive and continuous authentication for online services. The efficacy of behavior-based methods significantly depends on the sufficiency and quality of behavior data. In this book, we propose anti-fraud engineering based on the behavioral modeling paradigm, which focuses on behavior associations to enhance behavior data.
Improving Graph Collaborative Filtering via Spike Signal Embedding Perturbation

Nowadays, graph collaborative filtering has proven to be a highly effective method in recommendation systems. It learns user preferences through interactions between users and items. During the training process of graph collaborative filtering, introducing suitable perturbations is crucial to model training. It is commonly used to prevent overfitting and enhance model robustness. Perturbation is widely adopted as a data augmentation technique in recommendation systems and extensively used in contrastive learning. Contrastive learning involves multi-task learning aimed at learning various views from diverse data augmentations. However, these tasks can sometimes interfere with each other, impacting their effectiveness. In contrast to methods that focus on learning various views in contrastive learning to achieve better embedding representations, we propose a straightforward yet highly effective approach to directly incorporate Spike Signal Embedding Perturbation (SEP) into the graph collaborative filtering process.Unlike many other approaches that introduce Gaussian-distributed noise, the spike signals generated by the Poisson encoder typically maintain a positive relationship with the original embeddings. Our experimental results demonstrate that this proposed method significantly enhances the performance of graph collaborative filtering when compared to LightGCN. It leads to substantial improvements in recommendation performance and training efficiency.
Teaching Virtual Forensic Anthropology Labs: Methods and Reflections

Development of virtual labs for Forensic Anthropology was complicated by the notion that the skeleton cannot be learned without physical manipulation. This was addressed by using free programs to teach using 3D models of bone. Successes and shortcomings are discussed based on student and educator feedback. Integration of 3D models in teaching is plausible as it reduces deterioration of specimens and increases accessibility of the lab, however, the ethics of digital archaeology, including curation of human skeletal models, is an unsolved challenge. Overall, although 3D modelling cannot replace hands-on learning, teaching virtually can indeed ensure high-quality instruction is delivered.
Anomaly Detection Algorithms: Comparative Analysis and Explainability Perspectives

In order to detect outliers and potential anomalies in datasets, anomaly detection plays a pivotal role in identifying infrequent and irregular occurrences. The purpose of this paper is to examine and compare the effectiveness of prominent anomaly detection algorithms, including Isolation Forest, Local Outlier Factor (LOF), and One-Class Support Vector Machines (SVM). A variety of datasets are used in our assessment to evaluate key metrics such as precision, recall, F1-score, and overall accuracy. We also introduce innovative techniques that enhance the interpretability of these algorithms, shedding light on the underlying factors that contribute to anomaly detection. By providing insights into the attributes and behaviors associated with anomalies, our research empowers decision-makers to cultivate a profound comprehension of the identified anomalies, subsequently facilitating well-informed decisions grounded in the outcomes of anomaly detection. Through our meticulous comparative analysis and our dedication to unraveling the elements of explainability, we provide invaluable perspectives and pragmatic suggestions to facilitate effective anomaly detection in real-world scenarios.
Website Fingerprinting on Encrypted Proxies: A Flow-Context-Aware Approach and Countermeasures

Website fingerprinting (WFP) could infer which websites a user is accessing via an encrypted proxy by passively inspecting the traffic characteristics of accessing different websites between the user and the proxy. Designing WFP attacks is crucial for understanding potential vulnerabilities of encrypted proxies, which guides the design of defensive measures against WFP. In this paper, we design a novel WFP attack against (popular) encrypted proxies that relay connections between the user and the proxy individually (e.g., Shadowsocks, V2Ray), and accordingly implement lightweight countermeasures to effectively defend against the attack. The attack features flow-context-aware and is both accurate and immediately deployable, because it fully considers the obstacle (dubbed training-testing asymmetry) that fundamentally limits the practicability of WFP and addresses the obstacle with built-in spatial-temporal flow correlation mechanism. We implement the countermeasure as middleboxes installed on both the client and server sides of encrypted proxies, without altering any existing infrastructures for compatibility. The middleboxes can obfuscate a website’s flow regularities across different visits. Large-scale experiments in real-world scenarios demonstrate that the WFP attack can generally achieve a detection rate above 98.8% with a false positive rate below 0.2%. The countermeasure forces the attack’s false positive rate to be above 0.2 and true positive rate to be below 0.9 with just five persistent TCP connections while introducing very limited bandwidth overhead (e.g., 0.49%) and almost-zero additional network latency.
ENSURING HIGHER LEARNING OUTCOMES THROUGH E-LEARNING: AN EXAMPLE OF KATHMANDU UNIVERSITY SCHOOL OF EDUCATION, NEPAL

In this reflective paper, the authors discuss their experiences of developing e-learning courses to teach and learn to create an engaged pedagogy in various semesters at Kathmandu University School of Education, Nepal, that involves their (PGD,
PANDEMIC IMPACT ON ARGENTINIAN HIGHER EDUCATION. THE CASE OF THE NATIONAL UNIVERSITY OF LA PLATA

This article analyzes the impact of the pandemic in Argentina, considering that the National Government and provincial governments issued isolation resolutions throughout the 2020 academic year and mandatory social distancing measures in 2021, which
A SURVEY WITH FUTURE PRIMARY SCHOOL TEACHERS ON USING GEOGEBRA FOR 2D AND 3D SHAPES

This article presents a quantitative and qualitative analysis conducted in Italy, regarding the planning of didactic units on the part of 23 university students. The degree course is in Primary Education Sciences, for future elementary school
EU SECURITY AND DEFENCE STUDENTS' PERCEPTION AND USE OF DIGITAL COMPETENCIES IN HIGHER EDUCATION

The use of digital tools in teaching and learning has become increasingly common in recent years. Even prior to 2020, their usage was steadily growing, but the outbreak of the COVID-19 pandemic in the early part of the current decade significantly
GAMIFICATION APPROACH FOR TEACHING PROGRAMMING

The aim of the article is to present a study of two different approaches for using a digital game for improving problem-solving skills of the students through introductory computer programming learning. The game itself it is not predefined, it is