Reviewerly/exHarmony huggingface dataset:
Development and Validation of “Hazard O’Clock”: A Home Hazard and Disaster Awareness Game

The Philippines is the fourth most disaster-prone country in the world due to its location in the Pacific Ring of Fire and Pacific Typhoon Belt. When it comes to these disasters, children below the age of 18 are considered to be among the most vulnerable. This study aimed to develop a mobile game about Disaster Risk Reduction and Management (DRRM) in the home setting that can be used as a teaching aid for children. The information integrated into the game was from different resources made by various government agencies. The Analysis, Design, Development, Implementation, and Evaluation (ADDIE) model was used in the development of the game, and game development educators and STEM educators evaluated it. Using a 5-point Likert scale survey, the game’s quality and appropriateness were evaluated for the following categories: Instructional Content, Functional Suitability, Performance Efficiency, and Usability. For each category, the mean score ratings were 4.43, 4.43, 4.80, and 4.60 respectively. Overall, the game received a rating of 4.52 indicating that it is Very Appropriate for its purpose. The research findings have shown that the game, Hazard O’Clock, could be used as a teaching aid for DRRM.
THE MODEL OF BIPA LISTENING EVALUATION AND ITS IMPLICATIONS FOR THE DESIGN OF LISTENING EVALUATION

In this paper, an analysis of the results of the evaluation of Indonesian language proficiency for foreign speakers is presented. The development of a standardized Indonesian language for foreign speakers (Bahasa Indonesia bagi Penutur Asing - BIPA) evaluation tool is currently considered very important. Currently, the existing evaluation tools are still made partially depending on the needs of institutions that provide the Indonesian language program for foreign speakers (BIPA). In addition, this need is also driven by the need for foreign speakers to measure their proficiency in Indonesian. Although there is an Indonesian Language Proficiency Test (UKBI) as an evaluation tool in Indonesia, UKBI is considered not in accordance with the needs of measuring the Indonesian language proficiency of foreign speakers because UKBI is still used to measure the Indonesian language skills of native speakers. This has become one of the problems in the field of evaluation of the Indonesian language, especially BIPA. The language evaluation tool used to measure a person’s foreign language proficiency always begins with a listening proficiency test. Listening is considered the gateway to other language proficiency. The solution to this problem is to analyze listening evaluation tools in foreign languages that are used continuously and continuously, such as the Test of English for International Communication (TOIEC), the Japanese-Language Proficiency Test (JLPT), and Diplôme d'études en langue française (DELF), which represents three continents; American, Asian, and European spoken by Indonesian speakers. This study used a descriptive method to solve this research problem. The results of this study describe the profile of the listening proficiency evaluation tool in foreign and Indonesian languages in three ways: 1) speakers (speaker stimulus presenter), 2) content, and 3) audio. Furthermore, the results of this analysis can also be used as the basis for developing the BIPA listening competency evaluation model.
The effectiveness of adding BTS teaching materials to improve the reading ability of Korean language students

In the current era, knowledge and technology are rapidly advancing. The rapid development of technology makes it easier for people to access the world. This study aims to determine the effectiveness of Learn Korean with BTS teaching materials in improving Korean reading skills with a sample of 46 Korean Language Education students year of 2020. The data collection technique used test instruments and questionnaires totalling 20 valid and reliable items each. This study used a quasi-experimental research method and the non-equivalent control group design. The results showed that the experimental class got an average pre-test result of 60.87 and an average post-test result of 85.43, while the control class got an average pre-test result of 62.83 and an average post-test result of 68.04. The paired sample t-test was used to determine differences in learning outcomes and the test results showed the significance value (2-tailed) was 0.00 (0.05), which means that there is a significant difference in the results of students’ learning before and after using the Learn Korean with BTS teaching materials. The results of the independent sample t-test show a significance value (2-tailed) was 0.00 (0.05), which means that there is a significant difference in learning outcomes between students who used Learn Korean with BTS teaching materials and those who used traditional learning materials. The results of the questionnaire show that students' opinions about learning using the Learn Korean with BTS teaching materials are positive.
MULTISPECTRAL IMAGE RESTORATION USING A VECTOR-VALUED REACTION-DIFFUSION BASED MIXED NOISE REMOVAL TECHNIQUE

Abstract. A novel multispectral image filtering technique is proposed in this article. Since the multispectral images are often corrupted by mixed Poisson-Gaussian noise during the sensing and acquisition process, a nonlinear anisotropic diffusion-based restoration approach that deals efficiently with this type of noise mixture is considered here. A second-order vector-valued reaction-diffusion model that leads to a system of well-posed single-valued anisotropic diffusion equations coupled by correlation terms is introduced for this purpose. A finite difference method-based fast-converging approximation algorithm that solves numerically this nonlinear diffusion-based system is then proposed. This iterative numerical approximation scheme is successfully used for removing both the additive Gaussian and quantum noises while preserving the essential features of the multi-valued image. The effectiveness of the described mixed denoising technique is illustrated by the results of the restoration experiments and method comparisons that are also presented here. The proposed restoration approach enhances considerably the spectral image quality, making it well-prepared for the further MSI analysis and computer vision processes, such as the geospatial and remote sensing image analysis.
FUNCTIONALITY OF MOODLE E-AVIATION METEOROLOGY IN AVIATION POLYTECHNIC OF SURABAYA

In learning Aviation Meteorology e-modules that specifically study it have not been facilitated in the Learning Management System of Surabaya Aviation Polytechnic. This research aims to develop a learning module for Aviation Meteorology. This Final Project uses a research method, namely Research and Development (R&amp;D) by conducting data collection techniques in the form of interviews and questionnaires. From this study, it was found that the results of the Material Expert Validation Test obtained the category "Valid" and "Understandable". Then the Media Validation Test obtained the "Valid" and "Understandable" categories, and the Product Trial to the cadets obtained a correlation coefficient score of 0.85 in the "Very Strong" category because the results produced did not produce a minus score, meaning that the relationship was positive and the Aviation Meteorology E-Module was effective in helping cadets understand the Aviation Meteorology course
Digital Storytelling with a Motion Graphic into Stopping Children’s in Verbal Bullying

Character education in young children is an important aspect in forming individual integrity in society. Digital storytelling is a modern information medium that can be used to disseminate useful information through interactive videos in the form of moving graphics without degrading or belittling other people as children. The aim of this research is to reduce verbal harassment behavior in young children and as a socialization medium that increases children's awareness to stay away from harassment behavior, both in everyday life and in the school environment. This research used moving graphics as a socialization tool to avoid harassment, both verbal and non-verbal. The results of this research show that the use of this media can provide effective learning to all individuals who watch moving graphic videos as a socialization tool through the pre-production, production and post-production stages. The results of this research indicate that information media can change individuals' perceptions about the dangers of abuse that they may experience as children. By this media, researchers emphasize the role of education in preventing harassment.
Research on Data Asset Value Evaluation Model Based on AHP

The management of data assets helps to improve the level of data application in electric power enterprises and accelerate the transformation of enterprises to intelligence and digitalization. The paper constructs a data asset evaluation model, in which the evaluated elements include two elements of data quality value and data asset application value, and then introduces a hierarchical analysis method to construct an index evaluation system and conduct an arithmetic analysis. The research results show that the data asset value assessment model constructed in the thesis can well reflect the value of data assets and is an important supplement to the study of data asset application.
Unsupervised Analysis of Daily Routine Evolution for Elderly People Using Room-Level Localisation

This work proposes the use of room-level or symbolic localisation for the analysis of the daily life of elderly people. The elderly people tend to be quite routine, so variations in behaviour patterns can be an indication of physical or cognitive impairment. Then, it is essential to develop solutions for the early detection of changes or anomalies that can be the onset of physical or cognitive impairment. In this work, the behaviour of the monitored person is modelled by the probability of staying in each room of their place of residence. The proposed system is based on the generation of a baseline behaviour, defined by groups of similar days during an initial training period. From this model, each new day is compared with the baseline behaviour and the evolution is analysed. Two databases are used for the evaluation of the proposed system: a real database and a synthetic one. The initial results are very promising, being possible to classify location-based daily activities as main and secondary routines, detecting when a day is considered unusual.
Quantum signatures of the topological phase in bosonic quadratic systems

Quantum entanglement and classical topology are two distinct phenomena that are difficult to be connected together. Here we discover that an open bosonic quadratic chain exhibits topology-induced entanglement effect. When the system is in the topological phase, the edge modes can be entangled in the steady state, while no entanglement appears in the trivial phase. This finding is verified through the covariance approach based on the quantum master equations, which provide exact numerical results without truncation process. We also obtain concise approximate analytical results through the quantum Langevin equations, which perfectly agree with the exact numerical results. We show the stationary entanglement originates from the matching between the near-zero eigenenergies of the topological edge states and the system-environment coupling (denoted by the dissipation rate). Our work reveals that the stationary entanglement can be a quantum signature of the topological phase in bosonic systems and, inversely, the topological quadratic systems can be powerful platforms to generate robust entanglement.
Tiered Access to Research Data for Secondary Analysis

Research data have expanded in their gradation of the risks associated with both re-identification and harm, which has created a need for multiple levels of access controls beyond public and restricted access. Public-access data have typically been available for download from websiteswhile restricted-access data usually require an application and formal authorization process. The old paradigm of classifying data as public-access or restricted-access is no longer sufficient. Access to research data requires more nuance to ensure the protection of human subjects.In this paper, we describe seven tiers of access to research data. Each tier adds requirements that are necessary to mitigate disclosure risk and confirm appropriate management of the data. Improper handling of the data includes attempting to find a specific individual or household or failing to follow disclosure protection rules for data and output included in papers and presentations. By establishing a ladder of access conditions, each higher tier meets and exceeds the requirements of the lower tiers. While the highest tier meets all requirements, this tier will impede legitimate research for most data. The challenge for repositories is to provide access in a manner that promotes research while specifying security that provides appropriate protections against the risks of re-identification and harm.&#x0D; The tiers operationalize risk management options. The requirements of the research data determine the appropriate tier.Several articles and reports develop frameworks for providing access to research data. We review these frameworks as well as the current practices at data archives and describe how our approach differs from these paradigms. While tiered access to research data is not a new idea, more than two or three levels are needed to meet the diverse needs of the research community. The tiered approach enables repositories to require sufficient security controls without creating unnecessary impediments to research
Protecting Sensitive Data Early in the Research Data Lifecycle

How do researchers in fieldwork-intensive disciplines protect sensitive data in the field, how do they assess their own practices, and how do they arrive at them? This article reports the results of a qualitative study with 36 semi-structured interviews with qualitative and multi-method researchers in political science and humanitarian aid/migration studies. We find that researchers frequently feel ill-prepared to handle the management of sensitive data in the field and find that formal institutions provide little support. Instead, they use a patchwork of sources to devise strategies for protecting their informants and their data. We argue that this carries substantial risks for the security of the data as well as their potential for later sharing and re-use. We conclude with some suggestions for effectively supporting data management in fieldwork-intensive research without unduly adding to the burden on researchers conducting it.
<i>AWWA Water Science</i> Reviewer Recognition

Journal AWWAVolume 115, Issue 10 p. 19-19 Departments AWWA Water Science Reviewer Recognition First published: 06 December 2023 https://doi.org/10.1002/awwa.2194Read 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. Volume115, Issue10December 2023Pages 19-19 RelatedInformation
TOWARDS EXPLAINABLE MACHINE LEARNING OPERATIONS (MLOPS)

In the dynamically expanding AI applications, there is a need for an effective operational framework for managing ever increasing number of predictive models. A new field of Machine Learning Operations (MLOps) has recently emerged to address the
CYBERAGGRESSION AMONG YOUNG STUDENTS IN THE DEMOCRATIC REPUBLIC OF THE CONGO (DRC): PREVALENCE AND RISK FACTORS

Intentionally aggressive acts perpetrated on the Internet are steadily increasing. In the digital lexicon, these acts are known as cyberaggression. Contrary to what some may think, these behaviors are also very present in Africa, particularly in the
Parallelization of Recurrent Neural Network-Based Equalizer for Coherent Optical Systems via Knowledge Distillation

The recurrent neural network (RNN)-based equalizers, especially the bidirectional long-short-term memory (biLSTM) structure, have already been proven to outperform the feed-forward NNs in nonlinear mitigation in coherent optical systems. However, the recurrent connections still prevent the computation from being fully parallelizable. To circumvent the non-parallelizability of recurrent-based equalizers, we propose, for the first time, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">knowledge distillation</i> (KD) to recast the biLSTM into a parallelizable feed-forward 1D-convolutional NN structure. In this work, we applied KD to the cross-architecture regression problem, which is still in its infancy. We highlight how the KD helps the student's learning from the teacher in the regression problem. Additionally, we provide a comparative study of the performance of the NN-based equalizers for both the teacher and the students with different NN architectures. The performance comparison was carried out in terms of the Q-factor, inference speed, and computational complexity. The equalization performance was evaluated using both simulated and experimental data. The 1D-CNN outperformed other NN types as a student model with respect to the Q-factor. The proposed 1D-CNN showed a significant reduction in the inference time compared to the biLSTM while maintaining comparable performance in the experimental data and experiencing only a slight degradation in the Q-factor in the simulated data.
One Model to Find Them All Deep Learning for Multivariate Time-Series Anomaly Detection in Mobile Network Data

Network monitoring data generally consists of hundreds of counters periodically collected in the form of time-series, resulting in a complex-to-analyze multivariate time-series (MTS) process. Traditional time-series anomaly detection methods target univariate time-series analysis, which makes the MTS analysis cumbersome and prohibitively complex. We present DC-VAE (Dilated Convolutional -Variational Auto Encoder), a novel approach to anomaly detection in MTS data, leveraging convolutional neural networks (CNNs) and variational autoencoders (VAEs). DC-VAE detects anomalies in MTS data through a single model, exploiting temporal information without sacrificing computational and memory resources. In particular, instead of using recursive neural networks, large causal filters, or many layers, DC-VAE relies on Dilated Convolutions (DC) to capture long and short-term phenomena in the data. We evaluate DC-VAE on the detection of anomalies in the TELCO TELeCOmmunication-networks dataset, a large-scale, multi-dimensional network monitoring dataset collected at an operational mobile Internet Service Provider (ISP), where anomalous events were manually labeled by experts during seven months, at a five-minutes granularity. We benchmark DC-VAE against a broad set of traditional time-series anomaly detectors from the signal processing and machine learning domains. We also evaluate DC-VAE in open, publicly available datasets, comparing its performance against other multivariate anomaly detectors based on deep learning generative models. Results confirm the advantages of DC-VAE, both in terms of MTS data modeling, as well as for anomaly detection. For the sake of reproducibility and as an additional contribution, we make the TELCO dataset publicly available to the community and openly release the code implementing DC-VAE.
Anomaly detection in trade declarations using deep learning techniques: A risk-assessment approach to identify misclassification and incorrect valuation

In Hong Kong, merchandise trade statistics are compiled based on the commodity information given on the trade declarations submitted by traders. Due to the complexity of the standardised commodity classification system (i.e. Hong Kong Harmonized System, or HKHS in short), there are often reporting errors, especially in the commodity codes and quantities. With around 20 million declarations received annually, the availability of this big data source motivates us to adopt deep learning techniques to detect the reporting errors. This paper proposes a mechanism consisting of three deep learning models for checking the commodity code, quantity and value, which offers an end-to-end solution to data quality assurance for declarations. The results show that the proposed mechanism could enhance the accuracy of error detection, which is conducive to improving the quality of trade statistics. With the use of text analytics techniques, the mechanism could fully utilise free-text commodity descriptions declared by traders to check the accuracy of the declared information comprehensively. It also overcomes some limitations of the traditional rule-based models. The whole study demonstrates the potential of using deep learning approach in quality assurance of existing statistical systems for official statistics.
Open Access Literature in LibrariesBrunsting, K., Harrington, C., &amp; Scott, R. E. (2023). <i>Open Access Literature in Libraries</i> . ALA Editions. 144 pp., $59.99, ISBN 9780838939543.

"Open Access Literature in Libraries." Journal of Web Librarianship, ahead-of-print(ahead-of-print), pp. 1–2
PEDAGOGICAL CONDITIONSOF FORMATION OFCREATIVE POTENTIAL OF ARTS FACULTY STUDENTS

Based on the analysis of scientific literature, the article summarizes views of scientists on pedagogical conditions for effective training of specialists in the field of art education. The specifics of the professional training of teachers of musical art and choreography, which is conditioned by pedagogical orientation and different types of performing activities, have been determined. The author's definition of the creative potential of students of art faculties is offered. Based on the definition of the concept of "potential", the role of adequate pedagogical conditions for the formation of the creative potential of students of the faculties of arts, which is the result of the formation and self-realization of the individual through the disclosure of his potential opportunities for creative activity, is emphasized. The application of the methods of theoretical analysis, systematization and generalization made it possible to identify and substantiate the pedagogical conditions for the formation of the creative potential of students of the faculties of arts, which ensure its effectiveness.Key words: potential, students, creativity, art education, pedagogical conditions.
Emerging Cyber Crimes in Pakistan: A Case Study of Online Fraud through Digital Microloan Apps

This study aims to investigate the newly emerging phenomenon of cyber-crimes in Pakistan with the lens of a case study focusing on online fraud through digital microloan apps. The study employed qualitative methodology with an inductive approach of thematic analysis to examine the responses of interviewees. This thematic approach allowed the emergence of themes from the data. A sample size of 15 male and female students was chosen from 5 Public and private sector Universities of Lahore. The study found that online fraud through microloan apps has become a common phenomenon in Pakistan with weak implementation of cyber-laws, thus putting most social media users especially those who are using online digital apps to acquire microloans under the threat of cyber-fraud. The study also found that female students were comparatively easy prey to these loan apps than male students. The study recommended that public and private media houses launch an awareness campaign among digital media users to avoid such kind of fraud. The study further recommended that law enforcement agencies should ensure strict implementation of cyber-security laws to ensure a safe and secure environment.