Reviewerly/exHarmony huggingface dataset:
Keterampilan Statistika dan Data Science: Manfaatnya di Berbagai Bidang pada Era Digital

Data Scientist is a trending career in recent years. The survey results show that the need for a data scientist is high, but the availability is very low with limited capabilities. A data scientist needs statistics and programming skills. But, from a student's point of view, statistics is considered the same as mathematics, so it is less desirable because it is mathematical and is considered difficult. Students as prospective workers need to have insight into how to work and skills in the era of the industrial revolution 4.0. Failure to adapt to this new era will lead to increased unemployment of working age in the future. It is important to make the younger generation understand about statistics and data science skills, including the opportunities and job that exist, so efforts are needed to disseminate them. It is hoped that after understanding it, the younger generation will be interested in choosing a profession related to digital. The target audience is students of SMAN 3 Jember. The method used is presentation, then evaluation using a questionnaire. More than 79% of students rated the material presented as important, interesting and up to date. Students can also understand most of the material given, and the level of student interest in both fields is very high. This activity was generally successful.
Data Literacy: A catalyst for improving research publication productivity of Kyambogo University Academic Staff

Objective: The aim of this study is to explore how data literacy can influence the research and publications productivity of Kyambogo University academic staff.Methods: The study employed a literature review to collect detailed information. It observed lessons, and studied patterns of the phenomenon to explore data literacy initiatives that can be used by Kyambogo University academic staff to improve their research publications productivity and also to mitigate the accrued challenges.&amp;nbsp;Results: The paper explored eight initiatives through which data literacy skills could enhance the research productivity of Kyambogo University academic staff. These were awareness and promoting freedom of using open data, engaging students in data literacy activities, pedagogical reflection, datafication of single and joint academic staff publications, visualization of data, storytelling, ethical use of data, and preservation of research data.&amp;nbsp;Conclusions: While this paper relies on the context of the Kyambogo University academic staff, the authors posit that these data literacy skills can be embraced by universities in developing economies; especially those struggling with poor research and publications productivity. The paper further identifies areas where universities in developing economies, in conjunction with their libraries, can improve the academic staff pedagogy and compliance to eScience through polishing their data literacies.
Artificial Intelligence and its Application in Games

This review paper provides a comprehensive analysis of the role of Artificial Intelligence (AI) in computer games, drawing insights from various research papers in the field.The integration of AI techniques in games has revolutionized the gaming industry by offering enhanced gameplay experiences and immersive environments.In the paper, various aspects of AI in games are examined, such as the development of player opponents, adaptive behavior, interactive narrative, and strategic planning.It looks at the improvements in AI approaches and algorithms that have made it possible to create intelligent game systems.These papers, which cover a variety of topics and offer insightful information on the development and use of intelligent gaming systems, advance our understanding of AI techniques in games.The results of these investigations add to the body of AI research in the game industry, allowing for the creation of more immersive and interesting gaming experiences.
The Evaluation of Performance Related to Noise Robustness of VITS for Speech Synthesis

In recent years, the utilization of voice interfaces has gained significant popularity, with speech synthesis technology playing a pivotal role in their functionality. However, speech synthesis technology is susceptible to noise interference in practical applications, which may lead to a decrease in the quality of speech synthesis. In this paper, the noise robustness of the Variational Inference with adversarial learning for end-to-end Text-to-Speech (VITS) model was investigated, which has shown promising results in speech synthesis tasks. This study conducted experiments using six different texts and evaluated the speech synthesis results using three metrics: Mean Opinion Score (MOS), Disfluency Prediction (DIS), and Colorfulness Prediction (COL). The experiments consist of a control group and six experimental groups, which include two types of noise, Additive White Gaussian Noise (AWGN) and real-world noise, at three different signal-to-noise ratios (SNRs). The results demonstrated that both types of noise can significantly reduce the MOS scores of the synthesized speech, with a more severe decrease at lower SNRs. In terms of DIS and COL scores, the VITS model exhibits superior performance with real-world noise compared to AWGN noise, especially at lower SNRs. Moreover, even at an SNR of 3, the VITS model can still generate intelligible speech, which demonstrates its high noise robustness. The findings have important implications for the design of robust speech synthesis models in noisy environments. Future studies may focus on exploring more advanced noise-robust models or investigating the application of these models in practical voice interfaces.
Perception of knowledge sharing among reviewers from the perspective of scientific editors

Introduction/Objective: This work aims to analyze the editors’ perception about the contribution of knowledge sharing among reviewers of scientific articles, aiming at the improvement of the reviews elaboration.Methods:The study is of exploratory type, conducted through bibliographic research, associated with the questionnaire as a technique for data collection. Content analysis was applied to analyze the data obtained. Results: The instrument was sent via e-mail,and 32 editors from various areas of knowledge responded. Most of themarefromtheApplied Social Sciencesareas, and more than half have six or more years of experience. It was found that most respondents believe that sharing knowledge among reviewers contributes to their improvement and to the improvement of their opinions, and although they recognize such contribution, 87.5% claim that there are no initiatives in the journal where they work that propose the exchange of knowledge. Finally, when asked about having knowledge about similar initiatives, even if in other spheres, some respondents claim to knowand cite: the practices proposed by international publishers, which hold webinars and make content available on their websites; the projects and events of the Brazilian Association of Scientific Editors, WhatsApp groups and e-mail lists, and the interaction proposed by the practices of open peer review. Conclusions: Basedon the above, it is understood that editors perceive the relevance of knowledge sharing among reviewers.However, they have not yet put into practice such initiatives in the journals in which they work.
Trusting Critical Open Source Components

Device drivers are an elementary part of the Linux kernel and comprise roughly 2/3 of the project's lines of code. Even though the fraction of device driver code in a conventional operating system (OS) can vary, some of these components are essential for system functioning. In addition, the Linux kernel is used in a wide range of applications, from cloud service providers to embedded systems and supercomputers. If GNU/Linux systems should be trustworthy to justify running them in those environments, then testing the kernel is fundamental. However, since device drivers are designed to interface with hardware, conventional test approaches may not suit the occasions when devices are unavailable at test time. This raises the question: How are device drivers tested?
"WELL-BEING AND ACADEMIC ACHIEVEMENT OF STUDENTS: THE ROLE OF MINDFULNESS"

Besides academic achievement, well-being of students is highly important for both their academic and general life.Although some current research showed the role of mindfulness in general flourishing, the research on the role of mindfulness in academic flow and academic flourishing is limited, while the role of mindfulness for students' achievement is inconclusive.Therefore, this study is focused on understanding the role of specific facets of dispositional mindfulness in students' academic achievement assessed as grade point average and the students' well-being indexed as academic flow, academic flourishing, and general flourishing.The sample consisted of 135 university students at the Faculty of Teacher Education in Zagreb studying preschool education (all female, 22 years on average).Participants completed several questionnaires: the Five-Facet Mindfulness Questionnaire, the Swedish Flow Proneness Questionnaire adapted for assessing academic flow, the Academic Flourishing Scale, and the Flourishing Scale.The correlation analysis showed that of the five facets of mindfulness, only acting with awareness was associated (r = 0.23, p < 0.01) with students' higher academic achievement.Regression analyses showed that facets of mindfulness explained 24% of academic flow with describing (β = 0.22, p < 0.05) and acting with awareness (β = 0.35, p < 0.001) being significant predictors, 34% of general flourishing with describing (β = 0.21, p < 0.05), acting with awareness (β = 0.30, p < 0.001) and non-reactivity to inner experience (β = 0.24, p < 0.001) being significant predictors and 14% of academic flourishing with acting with awareness (β = 0.34, p < 0.001) being a significant predictor.The study results contribute to the limited literature on the role of dispositional mindfulness for students' academic achievement as well as elucidate which specific facets of mindfulness are important for well-being of students.In addition to the theoretical contribution, the results also have practical implications suggesting the great value of practising mindfulness for sake of students' academic achievement, optimal functioning, and well-being.
Research on Key Technologies of Earthquake Emergency Response Based on Multi-Sensor Data

Remote sensing has achieved good application in disaster monitoring, and also exposed some problems in earthquake remote sensing emergency monitoring.This paper mainly introduces the demand for remote sensing data under different geographical environments, existing remote sensing data sources and actual earthquake cases in earthquake emergency remote sensing monitoring and evaluation, especially the use of high-resolution radar remote sensing data to successfully identify a large number of landslides and barrier lakes caused by earthquake disasters, and determine their distribution and scale, Measure the area, length, etc.When remote sensing images before and after earthquakes are from different sources, a post-classification change detection method based on object-oriented combination is proposed to overcome the requirements of traditional change detection for data type and time consistency, and realize multi-sensor data assimilation and information collaborative processing.The rapid development of multi-remote sensing sensing technology provides a timely and effective technical means for earthquake disaster monitoring and disaster assessment.This paper focuses on the research of multi-mode remote sensing image seismic disaster information identification and disaster dynamic change monitoring methods before and after the earthquake, and explores the intelligent and automatic remote sensing earthquake emergency application of multi-mode remote sensing data collaboration.
The improvement of students' academic level and comprehensive quality under the perspective of CTCL (learning technology)

With the development of society, the implementation of technology in education has received more and more attention.However, the implementation effect of technology in education is not ideal, mainly due to the lack of effective management and support for the implementation of technology.Based on the CTCL horizon, this paper discusses how to improve the implementation effect of technology in education to improve learners' academic level and improve their comprehensive quality.Firstly, this paper analyzes the research status of learning technology at home and abroad.Secondly, this paper introduces the implementation effect of technology in education, and analyzes the factors affecting the effect of technology implementation.Finally, this paper summarizes the importance of improving the implementation effect of technology in education, puts forward the method of improving technology implementation effect in education, including establishing effective management mechanism, provide effective technical support, improve teachers 'technology application ability, improve the students' technology use ability, and looks forward to the future development trend.
Study on Inversion of Damage Incentives of High Pile Wharf in Inland River Based on SEResNet

Based on SEResNet neural network algorithm, the inversion model of damage incentives of inland high-piled wharf is constructed.The stress data of pile foundation under the action of damage incentives of high-piled wharf are obtained by using solid element finite element model calculation and indoor model test methods.The parameterized finite element calculation model of high-piled wharf is established by using subprocess in Python program to call MANSYS module, and verified with solid element model.The parameterized simplified finite element model meets the needs of inversion calculation.Based on the stress data samples of the pile foundation of the high-piled wharf obtained from the model test, the inversion analysis of single and multiple damage incentives is carried out.The model can identify the location, size and type of injury causative agent with good generalization ability.
Research on Basic Education Informatization and Educational Leadership in the Information Age

Informatisation in basic education is an extremely complex systemic project of educational and even social change.A cross-temporal perspective on the history, current situation and trends of informatisation in basic education, on the basis of which the mission of educational leaders in the information age is explored [1] .The historical process from computer education to education informatisation in China can be summarised in three stages: the experimentation of computer courses in primary and secondary schools, the application and popularisation of information technology in primary and secondary education, and the deepening development of informatisation in basic education.While affirming the achievements, it reflects rationally on the problems in the process of informatization in basic education, analyzes the root causes of various problems and contradictions in reality, reveals that educational decision makers and managers must complete four transformations in the process of informatization, and enhance the quality and ability of leading informatization in three aspects.Over the past 40 years of reform and opening up, basic education in China has been moving forward in a difficult quest for innovative development.The development of basic education has to meet the requirements of the times, the needs of the public, especially parents, and the laws of education itself (the core of which is the law of healthy physical and mental development of students).Understanding, balancing and managing the above three aspects is the responsibility and mission of basic education policy makers and managers, and is also an important indicator of their ability and competence [2] .In the past 40 years, the themes and grips of the innovative development of basic education in China have been formed precisely in the synthesis of the three, of which the three main themes are: quality education, curriculum reform and education informatization.This article takes this as the general context for reflection on the times, and provides a cross-temporal perspective and analysis of the history, current situation and trends of informatisation in basic education, and on this basis explores the mission of educational leaders in the information age.
FACE EXPRESSION RECOGNIZER DENGAN CONVOLUTIONAL NEURAL NETWORK UNTUK MEMBANTU PENDERITA AUTISME MENGENALI EKSPRESI WAJAH SESEORANG

Autism is a type of mental health disorder that is 80% caused by heredity, and environmental influences cause the rest. People with autism tend to be unable to concentrate and look at the other person when interacting. Since the age of toddlers, people with autism have a shallow response to the surrounding environment. In addition, people with autism also find it very difficult to recognize someone's expression, even though facial expressions or facial expressions are one way that can be used to recognize someone's emotions. In addition, facial expressions indirectly reveal the contents of a person's thoughts. To overcome this, the author wants to build a face expression recognition model to help people with autism recognize someone's facial expressions. The primary purpose of this research is to help people with autism socialize and recognize the facial expressions of the people around them. This face expression recognition model was built by applying Convolutional Neural Network (CNN) intelligence and using the Tensorflow library and the Keras API. The dataset used in this study is a collection of faces from all over the world. In this research and model development process, the output display of the detection of facial expressions is in the form of diagrams and descriptions of the expressions that a person is experiencing.
Neural Methods for Cross-Language Information Retrieval

This half day tutorial introduces the participant to the basic concepts underlying neural Cross-Language Information Retrieval (CLIR). It discusses the most common algorithmic approaches to CLIR, focusing on modern neural methods; the history of CLIR; where to find and how to use CLIR training collections, test collections and baseline systems; how CLIR training and test collections are constructed; and open research questions in CLIR.
Session Search with Pre-trained Graph Classification Model

Session search is a widely adopted technique in search engines that seeks to leverage the complete interaction history of a search session to better understand the information needs of users and provide more relevant ranking results. The vast majority of existing methods model a search session as a sequence of queries and previously clicked documents. However, if we simply represent a search session as a sequence we will lose the topological information in the original search session. It is non-trivial to model the intra-session interactions and complicated structural patterns among the previously issued queries, clicked documents, as well as the terms or entities that appeared in them. To solve this problem, in this paper, we propose a novel Session Search with Graph Classification Model (SSGC), which regards session search as a graph classification task on a heterogeneous graph that represents the search history in each session. To improve the performance of the graph classification, we design a specific pre-training strategy for our proposed GNN-based classification model. Extensive experiments on two public session search datasets demonstrate the effectiveness of our model in the session search task.
A Lightweight Constrained Generation Alternative for Query-focused Summarization

Query-focused summarization (QFS) aims to provide a summary of a document that satisfies information need of a given query and is useful in various IR applications, such as abstractive snippet generation. Current QFS approaches typically involve injecting additional information, e.g. query-answer relevance or fine-grained token-level interaction between a query and document, into a finetuned large language model. However, these approaches often require extra parameters & training, and generalize poorly to new dataset distributions. To mitigate this, we propose leveraging a recently developed constrained generation model Neurological Decoding (NLD) as an alternative to current QFS regimes which rely on additional sub-architectures and training. We first construct lexical constraints by identifying important tokens from the document using a lightweight gradient attribution model, then subsequently force the generated summary to satisfy these constraints by directly manipulating the final vocabulary likelihood. This lightweight approach requires no additional parameters or finetuning as it utilizes both an off-the-shelf neural retrieval model to construct the constraints and a standard generative language model to produce the QFS. We demonstrate the efficacy of this approach on two public QFS collections achieving near parity with the state-of-the-art model with substantially reduced complexity.
Conversational Bibliographic Search

In almost every area of research, it is necessary to find experts and publications on a topic. However, finding experts and publications is a difficult task not only for computers, but also for humans. For example, searching for experts, a user often enters a topic into a search engine, which then checks which people have published on that topic. A problem arises when a user does not make their query specific enough which can happen intentionally, e.g. when the user is doing a navigational search, or unintentionally, e.g., when the user lacks knowledge. As a result, the quality of the search results may not be very high and the best results may not be found. Current and widely used search engines for bibliographic metadata, such as dblp[2], ResearchGate, Google Scholar or Semantic Scholar allow only keyword-based searches. Kreutz et al.[1] presented SchenQL, a query language for bibliographic metadata that allows users to formulate their queries more easily and precisely than SQL. However, it requires training to understand the language and is not as easy for non-experts to use e.g. Google Scholar.
An agent-based secure privacy-preserving decentralized protocol for sharing and managing digital health passport information during crises

The aim of this article is to identify a range of changes and challenges that present-day technologies often present to contemporary societies, particularly in the context of smart city logistics, especially during crises. For example, the long-term consequences of the COVID-19 pandemic, such as life losses, economic damages, and privacy and security violations, demonstrate the extent to which the existing designs and deployments of technological means are inadequate. The article proposes a privacy-preserving, decentralized, secure protocol to safeguard individual boundaries and supply governments and public health organizations with cost-effective information, particularly regarding vaccination. The contribution of this article is threefold: (i) conducting a systematic review of most of the privacy-preserving apps and their protocols created during pandemics, and we found that most apps pose security and privacy violations. (ii) Proposing an agent-based, decentralized private set intersection (PSI) protocol for securely sharing individual digital personal and health passport information. The proposed scheme is called secure mobile digital passport agent (SMDPA). (iii) Providing a simulation measurement of the proposed protocol to assess performance. The performance result proves that SMDPA is a practical solution and better than the proposed active data bundles using secure multi-party computation (ADB-SMC), as the average CPU load for SMDPA is approximately 775 milliseconds (ms) compared to about 900 ms for ADB-SMC.