Reviewerly/exHarmony huggingface dataset:
Pengaruh Penggunaan Metode Multisensori dan Media Flashcard terhadap Peningkatan Kemampuan Pramembaca Anak

Pre-reading abilities in children can influence their development process and multisensory techniques and flashcard media are the right choices to improve the quality of children s pre-reading. This technique can help children who do not have pre-reading skills to develop better. The research method used is quantitative with a descriptive analysis concept. Based on data obtained from the use of multisensory techniques and flashcard media, the SPPS calculation results have a significance value of 0.006 which means less than 0.05. This means that it is proven that there is an influence on beginner reading skills. Therefore, these two methods have a significant effect on children s pre-reading abilities, so that each method has its own uniqueness, and is able to arouse children s learning enthusiasm while participating in the learning process.
DIACRITIC-AWARE YORÙBÁ SPELL CHECKER

Spell checking and correction is still in infancy for Yorùbá language, and existing tools cannot be applied directly to address the problem because Yorùbá language requires extensive use of diacritics for marking phonemes and tone. We addressed this problem by collecting data from on-line sources and from optical character recognition of hard copy of books. The features relevant to spell checking and correction in this language that marks tones (and underdot) were identified through the review of existing spell checking solutions, analysis of the data collected and consultation with relevant Yorùbá Linguistics textbooks. A conceptual model was formulated as a parallel combination of a unigram language model and a language diacritic model to form a dictionary sub-model that is used by Error Detection and Candidate Generation modules. The candidate generation module was implemented as an inverse Levensthein edit-distance algorithm.&#x0D; The system was evaluated using Detection Accuracy (calculated from Precision and Recall) and Suggestion Accuracy (SA) as metrics.Our experimental setups compared the performance of the component subsystems when used alone with the their combination into a unified model. The detection accuracies for each of the models were 93.23%, 94.10% and 95.01% respectively while their suggestion accuracies were 26.94%, 72.10% and 65.89% respectively. In relation to the size of training corpus, the unified model was able to achieve a increase of 1.83% in detection accuracy and 5.27% in suggestion accuracy for 70% increase in size of corpus. The results indicated that each of the sub-models in the dictionary played different roles while the increase in training data does not give a linear proportional increase in performance of the spell checker. The study also showed that spell checking a Yorùbá text was better when attention is paid to the diacritical aspect of the language
A named entity recognition framework using transformers to identify relevant clinical findings from mammographic radiological reports

Detecting and extracting findings in a radiological report is crucial for text mining tasks in several applications. In this case, a labeled process for the image associated with the radiological report in mammography and Spanish context for a computer vision model is required. This paper shows the methodology and process generated for this goal. This paper presents a Named Entity Recognition (NER) approach based on a transformer deep learning model, using a labeled corpus and fine-tuning process to find three concepts that compose a typical finding in a mammographic radiological report: laterality, location, and the finding. We add another concept in the labeled process, the negation, necessary to identify falses positive inside the text that writes the radiologist. Our model achieves an F1 score of 88.24% classifying the three principal concepts for a finding, product of the labeled and fine-tuning process. The results presented here will be used as input for future training work on a computer vision model.
Lower Bounds for Quasi-Cyclic Codes and New Binary Quantum Codes

This paper considers three kinds of quasi-cyclic codes of index two with one generator or two generators and their applications in quantum code construction. In accordance with the algebraic structure of linear codes, we determine the lower bounds of the symplectic weights of these quasi-cyclic codes. Quasi-cyclic codes with the dual-containing property enable the construction of quantum codes. Defining the coefficient symmetric polynomials of the generator polynomials gives a concise condition for the dual-containing of the quasi-cyclic codes. The lower bound results can significantly reduce the scope of the search for a larger minimum distance of quasi-cyclic codes. With these algebraic results and computer supports, we obtain classical quasi-cyclic codes with better parameters and some new quantum codes under the symplectic construction. In particular, two examples of the new quantum codes [[63,42,6]]2,[[51,35,5]]2 improve the corresponding codes in Grassl’s code table.
Crop cultivation planning with fuzzy estimation using water wave optimization

In a complex agricultural region, determine the appropriate crop for each plot of land to maximize the expected total profit is the key problem in cultivation management. However, many factors such as cost, yield, and selling price are typically uncertain, which causes an exact programming method impractical. In this paper, we present a problem of crop cultivation planning, where the uncertain factors are estimated as fuzzy parameters. We adapt an efficient evolutionary algorithm, water wave optimization (WWO), to solve this problem, where each solution is evaluated based on three metrics including the expected, optimistic and pessimistic values, the combination of which enables the algorithm to search credible solutions under uncertain conditions. Test results on a set of agricultural regions in East China showed that the solutions of our fuzzy optimization approach obtained significantly higher profits than those of non-fuzzy optimization methods based on only the expected values.
Threshold Implementations in Software: Micro-architectural Leakages in Algorithms

This paper provides necessary properties to algorithmically secure firstorder maskings in scalar micro-architectures. The security notions of threshold implementations are adapted following micro-processor leakage effects which are known to the literature. The resulting notions, which are based on the placement of shares, are applied to a two-share randomness-free PRESENT cipher and Keccak-f. The assembly implementations are put on a RISC-V and an ARM Cortex-M4 core. All designs are validated in the glitch and transition extended probing model and their implementations via practical lab analysis.
Automated Categorization of Research Papers with MONO Supervised Term Weighting in RECApp

Natural Language Processing, specifically text classification or text categorization, has become a trend in computer science.Commonly, text classification is used to categorize large amounts of data to allocate less time to retrieve information.Students, as well as research advisers and panelists, take extra effort and time in classifying research documents.To solve this problem, the researchers used state-of-the-art supervised term weighting schemes, namely: TF-MONO and SQRTF-MONO and its application to machine learning algorithms: K-Nearest Neighbor, Linear Support Vector, Naive Bayes Classifiers, creating a total of six classifier models to ascertain which of them performs optimally in classifying research documents while utilizing Optical Character Recognition for text extraction.The results showed that among all classification models trained, SQRTF-MONO and Linear SVC outperformed all other models with an F1 score of 0.94 both in the abstract and the background of the study datasets.In conclusion, the developed classification model and application prototype can be a tool to help researchers, advisers, and panelists to lessen the time spent in classifying research documents.
An Enhanced MCDM Model for Cloud Service Provider Selection

Multi-Criteria Decision-Making (MCDM) techniques are often used to aid decision-makers in selecting the best alternative among several options.However, these systems have issues, including the Rank Reversal Problem (RRP) and decision-making ambiguity.This study aimed to propose a selection model for a Cloud Service Provider (CSP) that addresses these issues.This research used the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) to rank the alternatives.The entropy technique is utilized to determine the weight of the criteria, and Single Valued Neutrosophic (SVN) is employed to address uncertainty.To select the best cloud provider based on Quality of Service (QoS) criteria, we used a dataset from Cloud Harmony for this study.The results indicated that the suggested model could effectively resolve the RRP under conditions of uncertainty.This research is novel and is the first to address both the problem of uncertainty in decision-making and RRP in MCDM.
Optimization of Sentiment Analysis of Program Sembako(BPNT) Based on Twitter

Food Assistance Program (Program Sembako) is a development of the Non-Cash Food Assistance (BPNT) program which has been implemented by the Ministry of Social Affairs since 2017, namely of food assistance in the form of non-cash from the government which is given to Beneficiary Families (KPM) every month through an electronic account mechanism that is used only to buy food in food traders/e-warong in collaboration with banks. Twitter social media has now become one of the places to disseminate information about the Program Sembako/BPNT. This case study uses text mining techniques with the support vector machine (SVM), Naïve Bayes (NB) and K-Nearest Neighbor (k-NN) methods which aims to classify public sentiment towards the Program Sembako/BPNT on Twitter. The dataset used is tweets in Indonesian with the keywords “BPNT” and “Kartu Sembako” with a total dataset of 1,094 tweets. Text mining, transformation, tokenize, stemming and classification, etc. A useful technique for constructing sentiment classification and analysis. RapidMiner and Gataframework are also used to help create sentiment analysis to measure classification values. The results obtained by optimization using Particle Swam Optimization (PSO) using the support vector machine (SVM) algorithm and the accuracy value obtained is 78.02%, with a precision value of 78.73%, a recall value of 82.16%, and an AUC of 0.848
Application of Pregnant Mom's Diet Based on Raspberry PI Using Telegram Chatbot

A healthy and regular diet is needed to maintain the body's immunity, the benefits that can be used for mothers and fetuses to help in the growth and development of the fetus in the mother's womb so that the eating patterns that are consumed must have good quality and nutrition, because good diet can be increase the development of the fetus while in the womb, so it is necessary to make an application with the aim of providing information on a dietary pattern of raspberry-based pregnant women using the telegram chatbot. In this study using the Software Development Life Cycle (SDLC) method where this method has a plan in the form of planning, analysis (analysis), design (design), implementation (implements) and management (maintenance). The results of this study use the telegram chatbot as a medium of information that can be delivered easily because the working principle of the chatbot on the telegram has a button feature that makes it easier for user friendly-based use so as to reduce the system error. Users will get results in the form of a PDF document (information on diet, nutritional content, and examples of foods that can be consumed) or images of good solutions and nutrition so that they can be downloaded easily through mobile phones with a minimum specification in the form of Android and Notebook and Personal Computer (PC).
JQPro:Join Query Processing in a Distributed System for Big RDF Data Using the Hash-Merge Join Technique

In the last decade, the volume of semantic data has increased exponentially, with the number of Resource Description Framework (RDF) datasets exceeding trillions of triples in RDF repositories. Hence, the size of RDF datasets continues to grow. However, with the increasing number of RDF triples, complex multiple RDF queries are becoming a significant demand. Sometimes, such complex queries produce many common sub-expressions in a single query or over multiple queries running as a batch. In addition, it is also difficult to minimize the number of RDF queries and processing time for a large amount of related data in a typical distributed environment encounter. To address this complication, we introduce a join query processing model for big RDF data, called JQPro. By adopting a MapReduce framework in JQPro, we developed three new algorithms, which are hash-join, sort-merge, and enhanced MapReduce-join for join query processing of RDF data. Based on an experiment conducted, the result showed that the JQPro model outperformed the two popular algorithms, gStore and RDF-3X, with respect to the average execution time. Furthermore, the JQPro model was also tested against RDF-3X, RDFox, and PARJs using the LUBM benchmark. The result showed that the JQPro model had better performance in comparison with the other models. In conclusion, the findings showed that JQPro achieved improved performance with 87.77% in terms of execution time. Hence, in comparison with the selected models, JQPro performs better.
Neural Machine Translation for Kashmiri to English and Hindi using Pre-trained Embeddings

Neural Machine Translation (NMT) is one of the advanced approaches of Machine Translation (MT) that has recently gained popularity. A significant amount of parallel corpus is required to achieve a sound translation system, but most languages have a deficit worldwide. Many SoTA NMT systems are available for low-resource langauges that are developed using transfer learning, knowledge transfer, and zero-shot learning mechanisms. Most Indic languages fall into low-resource and zero-resource due to the non-availability of rich parallel and monolingual corpora. Though many Indian border languages have social and economic significance, they lack resources and automated machine translation systems. Kashmiri, one such Indian border language, belongs to the zero-resource category with limited corpora and no significant translation system. This paper uses pre-trained word embeddings to create the first NMT system specifically for Kashmiri-English and Kashmiri-Hindi translation. mBPE pre-trained word embeddings for Kashmiri language are used to develop the NMT system. A pre-trained word embedding model shows +2.58 BLEU improvisation in comparison to Vanilla NMT.
Conclusion: Building Fair Data Cultures in Higher Education

This chapter presents the conclusions of the book Data Cultures in Higher Education: Current Practices and Emergent Challenges. It is divided in three parts. The first part introduces the contributions of the initial chapters in the book, highlighting the idea of complexity in the current landscape of data practices. It also reviews the cases introduced along the several chapters, placing them in a symbolic space which highlights the tensions between reactive and proactive data epistemologies. The second part characterises “the challenge ahead”, basing the reflections on the conceptual lens of data cultures. In fact, building on cultural-historical activity theory, we purport the need for a dynamic approach to data cultures in higher education. This idea is further explored through examples and a case study. To conclude, the third part of the chapter makes some final remarks for future practice and research, emphasising the relevant role of academics’ professionalism, participation and activism to build fair data cultures in higher education.
On-axis deflectometric system for freeform surface measurement

We propose an on-axis deflectometric system for the accurate measurement of freeform surfaces with large slope ranges. A miniature plane mirror is attached on the illumination screen to fold the optical path and achieve the on-axis deflectometric testing. Due to the existence of the miniature folding mirror, the deep-learning method is applied to recover the missing surface data in a single measurement. Low sensitivity to the calibration error of system geometry and high testing accuracy can be achieved with the proposed system. The feasibility and accuracy of the proposed system have been validated. The system is low in cost and simple in configuration, and it provides a feasible way for the flexible and general testing of freeform surfaces, with a significant potential of the application in on-machine testing.
Design of a High-Order Kalman Filter for State and Measurement of A Class of Nonlinear Systems Based on Kronecker Product Augmented Dimension

A high-order Kalman filter for full-dimensional variables is proposed for a class of dynamic systems whose state model and measurement model are both nonlinear. The filter requires Taylor expansion of the system equations, and then performs Kronecker product operation on the linear part in the Taylor expansion. Finally, a linear dynamic model is achieved based on the full-dimensional vector formed by the state variables and the high-order dimension expansion variables. After designing the filter, the Kalman filter for the original state variables estimation was selected through the projection operator. The excellent performance of the novel filter is analyzed from the aspects of the information utilization of the state estimation value and the size of the state estimation error covariance matrix. The numerical verification is carried out by computer simulation.
Deep Monocular Depth Estimation Based on Content and Contextual Features

Recently, significant progress has been achieved in developing deep learning-based approaches for estimating depth maps from monocular images. However, many existing methods rely on content and structure information extracted from RGB photographs, which often results in inaccurate depth estimation, particularly for regions with low texture or occlusions. To overcome these limitations, we propose a novel method that exploits contextual semantic information to predict precise depth maps from monocular images. Our approach leverages a deep autoencoder network incorporating high-quality semantic features from the state-of-the-art HRNet-v2 semantic segmentation model. By feeding the autoencoder network with these features, our method can effectively preserve the discontinuities of the depth images and enhance monocular depth estimation. Specifically, we exploit the semantic features related to the localization and boundaries of the objects in the image to improve the accuracy and robustness of the depth estimation. To validate the effectiveness of our approach, we tested our model on two publicly available datasets, NYU Depth v2 and SUN RGB-D. Our method outperformed several state-of-the-art monocular depth estimation techniques, achieving an accuracy of 85%, while minimizing the error Rel by 0.12, RMS by 0.523, and log10 by 0.0527. Our approach also demonstrated exceptional performance in preserving object boundaries and faithfully detecting small object structures in the scene.
Fuzzy improved firefly-based MapReduce for association rule mining

In order to refine association rules based on frequent patterns, we advised an improved version of firefly algorithm called IFF. We had to eliminate blind mating from the design of GA and replaced it by mating between mature fireflies, while ensuring balanced convergence. The proposed approach uses advanced methods such as controlled genetic operations to manipulate frequent patterns, and the uses of fuzzy logic to control IFF parameters to assure convergence calibration, based on data size, algorithm iterations and temporary local optimum. Also, we executed IFF under Hadoop to get a MapReduce system and ensure the most optimal execution time. To analyse the quality of our proposal, we made simulations on MEDLINE dataset. Results indicate that the proposed approach is superior to existing algorithms with an accuracy of 10% to 50% and save execution time around 36%, while ensuring a good balance between the quality and variety of knowledge.
ETSI ZSM Driven Security Management in Future Networks

This paper presents a security management framework driven by Zero-Touch Network and Service Management (ZSM) paradigm and embedded in the High-Level Architecture (HLA) developed in the INSPIRE-5Gplus project. This project work also included design and implementation of different smart 5G security methods and techniques that are essential for achieving security management in future networks. Moreover, the paper provides a summary of lessons learned and guidelines gathered during the practical validation activities for bringing closed loop and smart security management into Beyond 5G systems. Finally we discuss the key challenges and future work needed to enable integrating closed-loop security management in future networks.
Exploring the Operational Status and Improvements of Village Learning Curriculum – Focusing on Document Analysis and Teachers’ Recognition on Middle School -

이 연구는 마을학습 교육과정이 현행 학교계획서에 어떻게 반영되고 있는지를 분석하고, 교사의 인식을 통해 마을학습 교육과정의 운영 현황과 개선 방안을 탐색하는 데 목적이 있다. 이를 위해 이 연구는 중학교의 학교교육계획서를 분석하고, 설문조사를 통해 마을학습 교육과정에 대한 교사의 인식을 탐색하였다. 이러한 과정을 거쳐 도출된 연구 결과는 다음과 같다. 첫째, 학교교육계획서에 반영된 마을학습 교육과정은 주로 지역사회의 인적·물적 자원을 활용하여 운영되고 있다. 둘째, 중학교 교사들은 마을학습 교육과정이 학교와 지역사회의 공동체 형성을 위해 필요하며 학교와 마을의 소통이 필요하다고 인식하고 있다. 또한, 구나 동보다는 시 단위 그리고 마을의 시설을 활용한 마을학습 교육과정에 높은 인식을 보였고, 교육의 제반 영역에서 운영하는 것이 적절하다고 인식하고 있다. 그리고 교사의 업무 부담 가중과 교육과정의 학습 내용 과다를 마을학습 교육과정 운영의 가장 큰 저해 요인으로 꼽았다. 이상의 결과를 바탕으로 지역중심 마을학습 교육과정에서는 중학교 교과와 연결된 교육과정의 개발이 필요하고 교사에게 업무 부담을 줄여 주며 개발한 교육과정을 효율적으로 홍보하고, 학교 중심 마을학습 교육과정의 활성화를 위해서는 교사역량 강화 연수 및 전문적 학습공동체의 운영이 필요하다고 보았다.The purpose of this study is to analyze how the village learning curriculum is reflected in the current school plan, and to explore the operational status and improvement plans of the village learning curriculum through teachers' perceptions. To this end, this study analyzed school education plans of middle school and explored teachers' perceptions of the village learning curriculum through a questionnaire survey. The research results derived from this process are as follows. First, the village learning curriculum reflected in the school education plan was operated mainly by utilizing the human and material resources of the local community. Second, regarding the operation of the village learning curriculum, middle school teachers recognized that the village learning curriculum was necessary for community formation between the school and the local community and that communication between school and village education was necessary. In addition, regarding the content, high awareness was shown on the contents of the city unit and the village learning curriculum using the village's facilities, and it was recognized that it was appropriate to operate in the creative experiential activity area for the operation method and time. And as for the factors hindering the village learning curriculum, the increased work burden of teachers and excessive learning contents of the current curriculum were found to hinder the village learning curriculum the most. Based on the above results, the following are suggestions for improving the operation of the village learning curriculum. First, the diversification and publicity of the local village learning curriculum should be strengthened. Second, teacher capacity building training and professional learning communities to develop and operate a school-centered village learning curriculum should be activated.