Reviewerly/exHarmony huggingface dataset:
Digital transformation and economic development: A review of emerging technologies' impact on national economies

This scholarly investigation delves into the transformative influence of digital technologies on national economies, marking a pivotal shift in the global economic landscape. The study's primary objective was to dissect the multifaceted impact of digital transformation, scrutinizing its role in reshaping economic sectors and societal norms. Adopting a qualitative synthesis of peer-reviewed literature, the research meticulously evaluated the economic ramifications of emerging technologies, drawing insights from diverse global contexts and industries. The findings reveal that digital transformation catalyses economic change, with significant variations observed across sectors such as healthcare and manufacturing. The study highlights the critical role of digital technologies in driving innovation, enhancing productivity, and fostering sustainable development. Case studies from different countries provided real-world perspectives on the implementation of digital strategies, underscoring the challenges and opportunities inherent in this digital shift. Conclusively, the research emphasizes the necessity of strategic planning and policy support in navigating the complexities of digital transformation. It advocates for a holistic approach that considers sociocultural, political, and economic factors in technology adoption, stressing the importance of global partnerships in fostering digital economic growth. The study culminates in recommendations aimed at guiding policymakers and business leaders in leveraging digital transformation for sustainable and inclusive economic development. In essence, this paper offers a comprehensive and nuanced understanding of the digital era's impact on national economies, serving as a valuable resource for stakeholders navigating the ever-evolving landscape of digital transformation.
A comparative study on effects of controlled English on the translatability of Technical texts from English to Turkish

Controlled language, as a subset of natural language, refers to the restricted or standardised use of lexicon, grammar and style. It has been hypothesised that the use of controlled language makes technical texts more translatable and therefore more understandable. This paper reports the results of an experimental study designed to test this hypothesis. The results showed that controlled technical language improved the comprehensibility and translatability of technical documentation in terms of accuracy, style and text quality. The study suggests that international companies should employ technical writers and translators who prioritise the language and extralinguistic norms of the target audience, rather than blindly adhering to the source text.
Latent Variable Modeling of Scientific Impact: Estimation of the Q Model Parameters with Structural Equation Models

Statistical modeling of scientific productivity and impact provides insights into bibliometric measures used also to quantify differences between individual scholars. The Q model decomposes the log-transformed impact of a published paper into a researcher capacity parameter and a random luck parameter. These two parameters are then modeled together with the log-transformed number of published papers (i.e., an indicator of productivity) by means of a trivariate normal distribution. In this work we propose a formulation of the Q model that can be estimated as a structural equation model. The Q model as a structural equation model allows to quantify the reliability of researchers’ Q parameter estimates, it can be extended to incorporate person covariates, and multivariate extensions of the Q model could also be estimated. We empirically illustrate our approach to estimate the Q model and also provide openly available code for R and Mplus.
Criminal clickbait: a panel data analysis on the attractiveness of online advertisements offering stolen data

Introduction Few studies have examined the sales of stolen account credentials on darkweb markets. In this study, we tested how advertisement characteristics affect the popularity of illicit online advertisements offering account credentials. Unlike previous criminological research, we take a novel approach by assessing the applicability of knowledge on regular consumer behaviours instead of theories explaining offender behaviour. Methods We scraped 1,565 unique advertisements offering credentials on a darkweb market. We used this panel data set to predict the simultaneous effects of the asking price, endorsement cues and title elements on advertisement popularity by estimating several hybrid panel data models. Results Most of our findings disconfirm our hypotheses. Asking price did not affect advertisement popularity. Endorsement cues, including vendor reputation and cumulative sales and views, had mixed and negative relationships, respectively, with advertisement popularity. Discussion Our results might suggest that account credentials are not simply regular products, but high-risk commodities that, paradoxically, become less attractive as they gain popularity. This study highlights the necessity of a deeper understanding of illicit online market dynamics to improve theories on illicit consumer behaviours and assist cybersecurity experts in disrupting criminal business models more effectively. We propose several avenues for future experimental research to gain further insights into these illicit processes.
Decomposing user-defined tasks in a reinforcement learning setup using TextWorld

The current paper proposes a hierarchical reinforcement learning (HRL) method to decompose a complex task into simpler sub-tasks and leverage those to improve the training of an autonomous agent in a simulated environment. For practical reasons (i.e., illustrating purposes, easy implementation, user-friendly interface, and useful functionalities), we employ two Python frameworks called TextWorld and MiniGrid. MiniGrid functions as a 2D simulated representation of the real environment, while TextWorld functions as a high-level abstraction of this simulated environment. Training on this abstraction disentangles manipulation from navigation actions and allows us to design a dense reward function instead of a sparse reward function for the lower-level environment, which, as we show, improves the performance of training. Formal methods are utilized throughout the paper to establish that our algorithm is not prevented from deriving solutions.
Children Early Nutrition Supplementation and Augmenting Factors in Teaching of Reading in Lungwena, Mangochi District, Malawi

The study investigated the effect of early nutrition supplementation on children’s reading ability and factors that augment reading skills in children at pupil, household and school level. The study followed up on children that were provided with early nutrition supplementation in varying levels of intensity with a standardised reading test that was levelled for children with an equivalent of two years of primary education to determine how the nutrition supplementation affected their ability to read. The study used a multi-level random effects regression to determine effects that were due to children exposure to school, household and individual factors. The variables that were key at each level of effect were determined through a Principal Component analysis, and later regressed to determine those variables that were key to their reading abilities. The study did not find statistical significance on household and school effects but child specific variables. The study found that intensity of nutrition that a pupil takes in early years affect their ability to acquire reading skills. This was augmented by the number of times a pupil practice reading and availability of textbooks to read, including well stocked and utilized libraries available in schools where pupils were enrolled.
The Problem of Dependency

It is a wide-spread assumption that multiple pieces of evidence, whether they involve measurements or testimony, provide stronger evidential support when they are independent than when they are not. The standard view is that non-independence creates redundancy and leads to over-confidence (see e.g., Soll, 1999; Nisbett &amp;amp; Ross, 1980). In keeping with this, fields as diverse as law, statistics, and philosophy have sought to understand the implications of non-independence and set out rules for dealing with it. In this paper, we set out how the challenges posed by non-independence are both far more wide-spread and more challenging than typically assumed. Specifically, we review how Bayesian probabilistic analysis reveals “simple” inference cases that conflict with wide-spread assumptions about the value of independence. These suggest that mere intuition is not a reliable guide in this arena. However, we then show that the same framework cannot (in its current form) be scaled to common real-world situations even in principle. We conclude with a discussion of the more modest, partial, solutions that are currently available and outline possible future directions.
CBEET: Constructing Certificate-based Encryption with Equality Test in the CB-PKS

To maintain the confidentiality of private data, encryption mechanisms have become prevalent. Researchers always strive to design secure and efficient encryption mechanisms in both symmetric and asymmetric key systems. Certificate-based public key systems (CB-PKS) belong to the family of asymmetric key systems. CB-PKS offers solutions to both the key escrow problem present in identity-based public key systems, and the need to construct a public key infrastructure in traditional public key systems. The past saw a wealth of research into the encryption mechanisms in the CB-PKS, called certificate-based encryption (CBE). Indeed, encrypted data (ciphertext) can be used in other applications such as the comparison of personal medical data as two ciphertexts can be compared to determine if they contain the same data (plaintext). However, the equality test of two ciphertexts in the CB-PKS is an open issue since research which has empirically studied is scant. The purpose of this paper is to propose the first certificate-based encryption with equality test (CBEET), and to prove that it is secure under the bilinear Diffie-Hellman (BDH) assumption.
Improving Student Learning Outcomes Using Powtoon Media Apps

21st-century learning requires us to utilize technology to enhance learning. Powtoon is one of the media that can be used. The aim of this research is to demonstrate that the utilization of Powtoon video media can enhance student learning outcomes regarding the excretory system. This research utilizes a pre-experimental method employing a one-group pretest-posttest design. This research involved 80 students who were divided into three groups: the upper group, the middle group, and the lower group. Data collection techniques include observation, tests, and questionnaires. The data analysis technique employs a quantitative analysis approach. The research results show that: 1). the learning outcomes in the top group showed an N-gain of 0.68. 2) The learning outcomes in the medium group had an N-gain of 0.57. 3) The learning outcomes in the top group showed an N-gain of 0.66. Thus, it can be concluded that the use of Powtoon video media can enhance student learning outcomes. We recommend that if Powtoon media is used and created with greater creativity, it can effectively support the learning process.
Using Innovative Tools to Support and Monitor Open Science

The purpose of the article is to examine and describe the operational principles and functional capabilities of modern services and tools designed to monitor, support and analyse dissemination of research outputs published open access, which can be utilized by librarians to promote open science practices. The research methodology is based on a combination of scientific methods, incorporating content analysis of official web pages of tools and services, as well as methods of synthesis, systemic analysis, structural-functional analysis, and information-analytical methods. Scientific novelty. This article reviews modern tools, services and instruments, namely OpenAlex, BISON, COKI Open Access Dashboard, Journal Tracker Tool, OPTIMETA Plugins and others to support open scientific communication. It can be argued that development of open, non-commercial and shared infrastructure has the potential to vastly improve open access implementation. Conclusions. This study identified a number of innovative tools for analyzing and monitoring open access that can be used by librarians, researchers, and all interested parties. These tools are important for expanding and democratizing access to knowledge, overcoming territorial, institutional and disciplinary barriers. Improving open, non-profit infrastructure should be a key task on the path to implementing open science practices. Consequently, it is important for modern librarians to master their skills in working with open tools to monitor, analyze, and support open scholarly communication practices, given the evident potential of online tools in scholarly communication and their ability to disseminate the results of scientific activity.&#x0D; Keywords: open access, open access monitoring, scientific communication, digital tools, open science.
Quantum Computers, Computing, and Machine Learning: A Review

Quantum theory has perhaps had the greatest effect over logical advancement during the last 100 years. It introduced another line of logical ideas, anticipated incomprehensible circumstances, and affected a few spaces of present-day innovations. Over 50 years after its initiation, quantum theory was wed with software engineering, and the new subject of quantum calculation was conceived. Quantum registering combines two logical insurgencies of the last 100 years: quantum physical science and software engineering.
A Comparative Analysis of Image Encoding of Time Series for Anomaly Detection

A novel approach to anomaly detection in time series data is based on the use of multivariate image analysis techniques. With this approach, time series are encoded as images that make them amenable to analysis by pretrained deep neural networks. Few studies have evaluated the merits of the different image encoding algorithms, and in this investigation, encoding of time series data with Euclidean distance plots or unthresholded recurrence plots, Gramian angular fields, Morlet wavelet scalograms, and an ad hoc approach based on the presentation of the raw time series data in a stacked format are compared. This is done based on three case studies where features are extracted from the images with gray level co-occurrence matrices, local binary patterns and the use of a pretrained convolutional neural network, GoogleNet. Although no method consistently outperformed all the other methods, the Euclidean distance plots and GoogleNet features yielded the best results.
This Is Great, but Watch Out: The Effect of Risk and Reward Cues on Target Selection for Account Hijacking

Account hijacking, the unauthorized access to online accounts, poses a significant security threat. The target selection process of this cybercrime is not well understood. This online field experiment investigated how risk and reward cues influence target selection for account hijacking in the context of a hacker forum. Following rational choice principles, we manipulated risk and reward cues in post titles featuring (fake) credential leaks across six credential types. We found that posts in the high-risk condition, which included warnings of potential account monitoring, received fewer views than those in the low-risk condition, while posts with high-reward cues, promising various account benefits, attracted more views than their low-reward counterparts. Similar patterns were observed for post replies and likes. These findings suggest that potential cyber-offenders act rationally when choosing a target for account hijacking. Furthermore, an unexpected law enforcement operation created a natural experiment interacting with our own. This operation triggered post removals and rejections of, specifically, high-risk posts on the hacker forum. This responsive removal continued but slowed over time, suggesting forum administrators adapt to real-life events, temporarily intensifying efforts to reduce perceived detection risks. These findings underscore the rational behavior of hacker forum administrators and their adaptation to changing circumstances.
Zero-Shot Photovoltaic Power Forecasting Scheme Based on a Deep Learning Model and Correlation Coefficient

Accurate photovoltaic (PV) power forecasting is essential for the stable and reliable operation of PV power generation systems. Recently, various deep learning- (DL-) based forecasting models have been proposed for accurate forecasting, but newly built systems cannot benefit from them due to the absence of PV power data. Although zero-shot methods based on single site can be used for PV power forecasting, they suffer from performance degradation problems when the characteristics of the source data and target data are different. To address this issue, we propose a novel zero-shot PV power forecasting scheme that leverages historical data from multiple PV generation systems at different sites. The proposed scheme constructs an individual forecasting model using historical data from each PV generation system. Then, two correlation coefficients are calculated for each forecasting model: one based on the correlation between the input variables of the source data and target data and the other on the correlation between the input variables and output variables of the source data. Lastly, the final forecasting value is calculated as a weighted sum of the predicted values of the constructed forecasting models for the input variables of the target data. In the extensive experiments for diverse DL models for forecasting, correlation coefficient types for weights, and data time intervals, the combination of recurrent neural network, Pearson’s correlation coefficient, and solar-noon time yielded the best prediction performance, with an improvement of up to 34.47% in mean absolute error and up to 15.94% in root mean square error compared to the best single-site zero-shot prediction. In addition, in experiments on PV power data from 9 cities in Korea using this combination, the proposed scheme achieved the best predictive performance in almost all cases and the second-best performance with a very narrow margin only in a few cases.
Implementasi Aplikasi Reservasi Lapangan Futsal Berbasis Android pada King Soka Arena

The reservations of futsal court s at King Soka Arena are still carried out using conventional transactions, namely customers must come directly to King Soka Arena to see and ask for information on the schedule of court s still available for rent, then customers make transactions to order futsal court s at the available time and as desired . The research method used by the author is the Waterfall Method. In implementing this application , the author us es Android Operating System. Considering the problems in the research object, this research is conducted to implement a Futsal Field reservation application, to make futsal court booking transactions at King Soka Arena effective and efficient , making it easier for customers to make Android-based futsal field reservations at the King Soka Arena. With the implementation of the futsal court ordering application at King Soka Arena . The customers only need to access it via an Android smartphone. The results of application testing using Black Box Testing show that the application function s well.
Pengembangan Media E-Flashcard sebagai Media Pembelajaran Bahasa Indonesia Kelas I Sekolah Dasar

Challenges in learning language skills are still often found in Indonesian elementary schools. This affects children's ability to understand the information needed in the knowledge transfer process. One of the reasons is the use of learning media that does not look attractive. Referring to existing problems, the aim of this research is to develop learning media in the form of electronic flashcards and test its suitability as a medium for learning Indonesian for Class I Khairul Uswah Private Elementary School which focuses on reading skills. This research was conducted in 2023. The method used is the Research and Development (RnD) method by utilizing the 4D approach model and using a validity test consisting of two validity tests, namely a validity test by media experts and also a validity test by language experts. using a questionnaire instrument with a Likert scale. The results were in the form of a percentage value of 92.5% for the linguist validity test and 93.3% for the media expert validity test, as well as a value obtained in the presentation of 92.9% for the combined score which was included in the category Very good. This means that the E-flashcard learning media is suitable and can be used as a medium for learning Indonesian for Class I Elementary School.
Analisis Cluster K-Means pada Indikator Indeks Pembangunan Teknologi, Informasi, dan Komunikasi

The information and Communication Technology Development Index (IP-ICT) is used to describe the level of ICT Development, the imbalance and development opportunities of a region from the use of ICT. IP-TIK is composed of 11 indicators , with the calculation conducted by BPS. The analysis is a K-Means Cluster analysis used to group provinces in Indonesia based on the similarity of IP-ICT constituent indicators. Grouping provinces based on IP-TIK indicators will help the government set realistic targets, track and evaluate progress over time to promote development and growth based on the capabilities of each province , s o that the government is able to determine the right policies to assist in ICT development in each cluster. The results of this study obtained 4 clusters with different characteristics each . Cluster 1 and Cluster 2 require improvement in IP-TIK development.
Large gear structure assembly method based on uncalibrated image visual servo guidance

The reliability of the transmission system hinges significantly on the assembly quality of its main component, the large gear structures. However, the traditional approach of employing manual lifting presents a host of challenges, such as high assembly complexity and lowered efficiency, rendering the overall assembly process notably arduous. In this study, a large gear structure assembly method based on uncalibrated image visual servo guidance is proposed. Comprising three modules, the approach involves constructing a task function for projective homography, estimating the image Jacobian matrix, and designing an adaptive servo controller. This methodology facilitates the mapping of changes in gear images to the motion of the end-effector in the parallel mechanism. Consequently, the system dynamically guides the end-effector to achieve the required attitude adjustments in the gear assembly in response to changes in the image features. Experimental results demonstrate that the method proposed surpasses alternative approaches, simultaneously exhibiting a significant enhancement in assembly efficiency. The method has a wide application prospect in the field of automated assembly of large gear structures.