Reviewerly/exHarmony huggingface dataset:
Penggunaan Media Kalender Dan Jam Analog Untuk Meningkatkan Hasil Belajar Matematika Tentang Waktu Pada Siswa Kelas III SD Negeri 09 2x11 Enam Lingkung

Mathematics learning activities in elementary schools must be adapted to the daily lives of students. Mathematics learning that is not related to students' lives will be felt to be less meaningful and less interesting. Learning mathematics requires abstract thinking, one of which is the material of measuring time. Based on interviews with class III teachers, time units are material that is difficult for students to understand. This can be seen from the results of students'daily tests regarding time units (year, week, day and hour) showing that of the 21 students who got a KKM score of 75 only 9 students while the others still did not meet the KKM. This means that 42.88% of students achieved the KKM score. This is because in conveying the concept of a unit of time, the teacher uses the lecture method. The purpose of this research is to improve the results of learning mathematics about units of time through the use of calendar media and analog clocks for class III SD N 09 2X11 Enam Lingkung. This type of research is classroom action research (CAR). The subjects in this remedial research were class III students with 21 students. The data in this study consisted of quantitative data which were analyzed using quantitative analysis techniques with simple statistics in the form of average values and percentages. Based on the results of learning corrective actions that have been carried out, the conclusion of this study is that the application of calendar media and analog clocks can improve mathematics learning outcomes in class III SD N 09 2X11 Enam Lingkung carried out through 2 cycles. The application of calendar media and analog clocks can improve student learning outcomes in mathematics in class III SD N 09 2X11 Enam Lingkung . This is evidenced from the percentage in the initial conditions (pre-cycle) there were 42.86% of students who passed (above the KKM that had been set). After that there was an increase in learning outcomes in cycle I where there were 57.14% of students who had completed and in cycle II learning improvement actions there were 80.95% of students who had completed learning mathematics using the calendar and analog clock media.
Proposal of Vulnerability Assessment Tool for Software Supply Chain Security

Software supply chain attacks have become more prevalent due to attacker's higher skills and sophisticated attack methods. These attacks can go unnoticed for a long time and cause widespread damage. Therefore, monitoring all software products in the supply chain to detect these attacks is crucial. However, the software supply chain is complex, and tracking suspicious changes to third-party products, services, and libraries used to build software products can be challenging. This makes it difficult for developers to identify and confirm vulnerabilities in the software. Even one vulnerable file in the supply chain can have severe consequences if exploited, so regular vulnerability checks are necessary to keep software products safe. To address this issue, we propose a Vulnerability Assessment Tool that identifies newly found vulnerabilities from public databases and efficiently checks them in existing internal products. Our method uses a two-step process. The first step is estimating related products and directly affected products for a given CVE ID. The second step is a detailed check of the vulnerabilities in the system based on the results estimated in Step 1. This assessment method reduces the time the PSIRT Analyst requires to assess the vulnerabilities.
Predictions of Duck Egg Production in 34 Indonesian Provinces Using Backpropagation Algorithm

There are still many duck egg growers who are perplexed and have no idea how many eggs their ducks produce in a year. They can't always predict a figure near to the number of duck eggs produced. Our purpose in doing this research is to assist duck farmers in each region of Indonesia in estimating the duck eggs generated by their ducks and also anticipating them if there are few duck eggs. Then they must take further procedures to ensure that big volumes of duck eggs are produced. Furthermore, it can have a favorable influence on our study as well as future research, resulting in improved outcomes with a high accuracy value. We employ the backpropagation algorithm with the cyclical order weight / bias approach as our study material to forecast the production rate of duck eggs. We acquired an accuracy rate of 91% with an MSE value of 0.00027084 and a margin of error value of 9% for the findings in each province in Indonesia utilizing the 10-20-1 architectural model.
Generalization Ability in Medical Image Analysis with Small-Scale Imbalanced Datasets: Insights from Neural Network Learning

Within the medical image analysis domain, the lack of extensive and well-balanced datasets has posed a significant challenge to traditional machine learning approaches, resulting in poor generalization ability of the models. In light of this, we propose a novel approach to evaluate the efficacy of neural network learning on small imbalanced datasets. The proposed methodology uncovers the relationships between model generalization ability, neural network properties, model complexity, and dataset resizing. This research highlights several key findings: (1) data augmentation techniques effectively enhance the generalization ability of neural network models; (2) a neural network model with a minimal number of each layer type can achieve superior generalization ability; (3) regularization layers prove to be a crucial factor in achieving higher generalization ability; (4) the number of epochs is not a determining factor in enhancing generalization ability; (5) complexity measures exhibit no significant correlation with generalization ability in the described scenarios. The findings from this study offer a practical roadmap for model selection, architecture search, and evaluation of the methods’ effectiveness in medical image analysis.
Service Layers

One of the core concepts for the Enterprise Canvas is the notion of layering. We need to be clear, though, as to what type of layering we mean, and what we don’t, because there are at least four different types of layers that are in common use in the business context:
Power of Fuzzing and Machine Learning in Smart Contract Security Validation

Blockchain technology revolutionized digital payments and transactions by introducing disruptive capabilities. It operates through smart contracts, automated software code facilitating transactions without intermediaries. Smart contracts, written in various languages, have gained popularity but are vulnerable to logic flaws and security threats, potentially leading to financial losses and undermining blockchain integrity. Validating their security is essential, although current tools only detect specific attacks, lacking a comprehensive automated solution. This article presents a two-stage process wherein the initial phase involves the extraction of characteristics from smart contracts through the analysis of abstract syntax trees (ASTs) and control flow graphs (CFGs). In order to improve the precision of our results, we can leverage the distinctive capabilities of the fuzzing technique to label the data prior to its integration into the training model. In the subsequent phase, approach is utilized neural decision tree (NDT) typically combines a decision tree structure with neural network components. The mixed-method approach is utilized, leveraging the distinct advantages offered by neural networks and decision trees. The purpose of this collaborative technique is to enhance the probability of identifying vulnerabilities in smart contracts. The experimental assessment yielded favorable outcomes in the detection of smart contract vulnerabilities, namely those pertaining to Reentrancy, integer overflow, and Block Number Dependency.
A Method for Measuring Node Similarity in Heterogeneous Information Networks

Meta-path-based methods for measuring the similarities between nodes in Heterogeneous Information Networks (HINs) have attracted attention from researchers due to excellent performance. However, these methods suffer from some issues: (1) it is difficult for users to provide effective meta-paths of complex HINs; (2) it is inefficient to enumerate all instances of meta-paths. In order to solve the above issues, this paper proposes a novel method, K-order Neighbors based Heterogeneous Graph Neural Network (KN-HGNN), for measuring node similarity. Firstly, KN-HGNN generates meta-paths based on network schema. Then, KN-HGNN obtains the features of nodes by aggregating itself type feature and its k-order neighbors’ type features under the constraints of meta-paths. Finally, KN-HGNN calculates the similarities between nodes based on the features of nodes. The experimental results on real datasets show that KN-HGNN outperforms the baselines.
Ontology-Driven Computer Systems

This article delves into the evolving frontier of ontology-driven natural language information processing. Through an in-depth examination, we put forth a novel linguistic processor architecture, uniquely integrating linguistic and ontological paradigms during semantic analysis. Distancing from conventional methodologies, our approach showcases a profound merger of knowledge extraction and representation techniques. A central highlight of our research is the development of an ontology-driven information system, architected with an innate emphasis on self-enhancement and adaptability. The system’s salient capability lies in its adept handling of elementary knowledge, combined with its dynamic aptitude to foster innovative concepts and relationships. A particular focus is accorded to the system’s application in scientific information processing, signifying its potential in revolutionising knowledge-based applications within scientific domains. Through our endeavours, we aim to pave theway for more intuitive, precise, and expansive ontology-driven tools in the realm of knowledge extraction and representation.
Application of Regular Grammar in the Syntactic Analysis of Email Addresses

Context: This article proposes the use of regular grammar as a strategy to validate the textual structures of emails. It focuses on the RFC 5321 standard and its syntax, formalizing regular grammars to apply production rules with the aim of validating the syntactic context of each structure of an email address. Method: This article presents a literature review and the development of an email validation model. Related texts focus on the Internet Protocol, along with building automata that apply IPV4 protocol. There are three phases: the development of the model from syntax and regular grammar rules and its construction and application. Results: The result is a functional application that validates email addresses based on regular grammars and existing regulations. When running efficiency tests, our application obtained a higher email validation margin in comparison with JFLAP. The library can work as a great analyzer of grammatical or lexical structures. Conclusions: The email validation tool based on GR regular grammars contributes to the practical use of specialized algorithms in the field of computer science, since it is possible to apply it to the recognition of search patterns such as the analysis of lexical structures (e.g., NITs, alphanumeric codes, and valid URLs).
Mixtures of t$$ t $$ factor analysers with censored responses and external covariates: An application to educational data from Peru

Analysing data from educational tests allows governments to make decisions for improving the quality of life of individuals in a society. One of the key responsibilities of statisticians is to develop models that provide decision-makers with pertinent information about the latent process that educational tests seek to represent. Mixtures of t$$ t $$ factor analysers (MtFA) have emerged as a powerful device for model-based clustering and classification of high-dimensional data containing one or several groups of observations with fatter tails or anomalous outliers. This paper considers an extension of MtFA for robust clustering of censored data, referred to as the MtFAC model, by incorporating external covariates. The enhanced flexibility of including covariates in MtFAC enables cluster-specific multivariate regression analysis of dependent variables with censored responses arising from upper and/or lower detection limits of experimental equipment. An alternating expectation conditional maximization (AECM) algorithm is developed for maximum likelihood estimation of the proposed model. Two simulation experiments are conducted to examine the effectiveness of the techniques presented. Furthermore, the proposed methodology is applied to Peruvian data from the 2007 Early Grade Reading Assessment, and the results obtained from the analysis provide new insights regarding the reading skills of Peruvian students.
Oversea Cross-Lingual Summarization Service in Multilanguage Pre-Trained Model through Knowledge Distillation

Cross-lingual text summarization is a highly desired service for overseas report editing tasks and is formulated in a distributed application to facilitate the cooperation of editors. The multilanguage pre-trained language model (MPLM) can generate high-quality cross-lingual text summaries with simple fine-tuning. However, the MPLM does not adapt to complex variations, like the word order and tense in different languages. When the model performs on these languages with separate syntactic structures and vocabulary morphologies, it will lead to the low-level quality of the cross-lingual summary. The matter worsens when the cross-lingual summarization datasets are low-resource. We use a knowledge distillation framework for the cross-lingual summarization task to address the above issues. By learning the monolingual teacher model, the cross-lingual student model can effectively capture the differences between languages. Since the teacher and student models generate summaries in two languages, their representations lie on different vector spaces. In order to construct representation relationships across languages, we further propose a similarity metric, which is based on bidirectional semantic alignment, to map different language representations to the same space. In order to improve the quality of cross-lingual summaries further, we use contrastive learning to make the student model focus on the differentials among languages. Contrastive learning can enhance the ability of the similarity metric for bidirectional semantic alignment. Our experiments show that our approach is competitive in low-resource scenarios on cross-language summarization datasets in pairs of distant languages.
Machine learning models in trusted research environments -- understanding operational risks

Trusted research environments (TREs) provide secure access to very sensitive data for research. All TREs operate manual checks on outputs to ensure there is no residual disclosure risk. Machine learning (ML) models require very large amount of data; if this data is personal, the TRE is a well-established data management solution. However, ML models present novel disclosure risks, in both type and scale.As part of a series on ML disclosure risk in TREs, this article is intended to introduce TRE managers to the conceptual problems and work being done to address them.We demonstrate how ML models present a qualitatively different type of disclosure risk, compared to traditional statistical outputs. These arise from both the nature and the scale of ML modelling.We show that there are a large number of unresolved issues, although there is progress in many areas. We show where areas of uncertainty remain, as well as remedial responses available to TREs.At this stage, disclosure checking of ML models is very much a specialist activity. However, TRE managers need a basic awareness of the potential risk in ML models to enable them to make sensible decisions on using TREs for ML model development.
Distributed resource governance on a blockchain

This research proposes a novel method for ensuring fair governance of a common resource recorded on a blockchain. It features a self-governing system of stakeholders, managing resources by taking on the roles of auditors and claimants in place of having an overseeing bureaucracy with its accompanying overhead costs. While self-governing can be subject to fraud and collusion, in the proposed governance system, anonymity, a staple of blockchain transactions, is utilized to mitigate these negative effects. This is done by assigning random anonymous auditors to resource claimants. Cheating, along with improper auditing, will result in penalties for both auditor and claimant. Improper auditing consists not only of allowing unlawful resource use but also denying lawful use. The proposed system is a Decentralized Autonomous Organization (DAO) running on a Hyperledger Fabric blockchain. All activities are recorded as immutable public transactions on the blockchain. A simulation and a blockchain game to support the plausibility of the model are presented.
Research on digital education teaching reform under the mode of “equal emphasis on teaching”

In response to the requirements of “accelerating the construction of an educational power and promoting the digitalization of education” pointed out by the 20th National Congress, this paper explores the teaching reform of teaching content, teaching resources, teaching strategies and teaching evaluation. Among them, the reform is supported by digital technology, with the teaching design of “equal emphasis on learning and teaching” as the core, and the purpose is to solve the problems existing in the course and talent training of “Electrical Control Technology”. It has been proved that this teaching model can effectively improve the quality of the classroom, improve the skill level of students, and truly open up the last mile between the classroom and the enterprise.
The Similarity Measurement of Normal Cloud Concept Based on Hellinger Distance and Expectation Curve with Entropy

Uncertainty exists widely in nature and human society. The handling and analysis of these uncertain phenomena have long been a focal point of research in natural sciences, representing a hot and crucial topic. The cloud model uses three numerical characteristics to realize qualitative and quantitative transformation of uncertain knowledge. Normal cloud is one of the more important and common cloud models. The similarity measurement of cloud model is used to judge the similarity of two cloud concepts, and the purpose is to understand the correlation between different cloud concepts, so as to improve the efficiency of classification and clustering. As a deformation extension of f divergence, Hellinger distance has good properties and is suitable for describing the difference between two probability distributions. HECMk and HCCMk algorithms are proposed by combining Hellinger distance and expectation curve cluster with entropy. Compared with previous algorithms, these two algorithms have higher distinguishing ability. Among them, HCCMk algorithm has better comprehensive performance and is feasible in actual similarity measurement.
Research on the Influence of Viewers’ Satisfaction in IMAX Movies Based on SOR Model

With the development of the movie industry, IMAX movies have become popular due to its huge screen, stunning sound effects and immersive viewing experience. Audience satisfaction serves as an important indicator of service quality and movie-going experience in movie theaters. By introducing the theoretical framework of the SOR model, this study systematically explores the relationship between the form factor characteristics (visual factor-sound factor) and content factor characteristics (narrativespectacle) as well as the viewer’s organismic awareness (perceived quality-perceived entertaining) and satisfaction in IMAX movies in order to establish a comprehensive impact model. And based on data from 279 questionnaires from IMAX moviegoers, the study shows: There is a significant positive relationship between visual factors, sound factors, narrative, spectacle and audience perceived quality; There is a significant positive relationship between visual factors, sound factors, narrative, spectacle and audience perceived entertainment; Perceived quality, and perceived entertainment all had a significant positive effect on audience satisfaction. Based on the results of the study, IMAX filmmakers can more accurately grasp the needs of the audience, optimize the viewing experience, and enhance audience satisfaction.
IMPACTS OF POLITICAL INSTABILITY ON BANK PERFORMANCE: (EVIDENCE FROM ETHIOPIAN BANKING SECTOR)

The soundness of the banking sector is very important because they play a pivotal role in economic growth. This article sought to investigate the Impacts of political instability on financial institution performance evidence from Ethiopian banks. The study employed 16 out of 26 banks due to the availability of audited panel data from the National Bank of Ethiopia (NBE) and the ministry of finance and economic development (MoFED), for the period ranging from 2013 to 2021. For the study, the panel regression model was employed. The study used two common measures of performance, proxied by either ROA or ROE as the dependent variables. Eight independent variables are also used. The descriptive and multiple linear regression analyses were done. The results documented that political violence &amp; ethnic conflicts, and inflation have a significant negative effect on ROA and ROE, but non- performing loans have a significant and negative effect on ROA and a significant and positive effect on ROE, while deposit volume, operational efficiency, financial risk, and GDP have a positive and significant effect on ROA and ROE. The finding also reveals liquidity has a positive and insignificant effect on ROA and ROE. Political instability is a serious problem for the sustainable development of the country's banking sector. Thus, the study gives worthy acumens for the Government, regulators, and interested parties for enhancing the performance of the banking sector in Ethiopia.
The effect of electricity reliability on the sustainability of SMEs in Fako Division of Cameroon

Purpose: To investigate the effects of the reliability of electricity supply on the sustainability of SMEs in Cameroon. Research Methodology: This study investigates the effects of the reliability of electricity supply on the sustainability of SMEs in Cameroon. The study used a quantitative approach with the philosophical underpinnings of objectivist ontology and positivist epistemology. Primary data were obtained through closed-ended questionnaires that were responded to by 54 purposively sampled participants in the Fako division. The data were analyzed by Structural Equation Modeling (SEM) using SPSS 23 and SPSS Amos 24 software. Results: The study revealed that sporadic electricity supply interruptions, chronic electricity supply interruptions, and momentary electricity supply interruptions have no significant positive impact on the sustainability of SMEs in the Fako Division of the Southwest Region of Cameroon. Limitations: This study had a limitation in methodology, as it used a quantitative approach only. A better understanding of this study’s phenomenon can be achieved by adopting a mixed research method, where the qualitative aspect will provide a deeper understanding of the effects of other variables that affect SME sustainability. Contribution: Based on the results of this study, business managers will come to the understanding that the sustainability of their businesses is not dependent on the reliability of the electricity supply. This means that other variables responsible for the sustainability of businesses can be studied qualitatively.