Reviewerly/exHarmony huggingface dataset:
Assessing Acceptance Level of a Hybrid Clinical Decision Support Systems

We present a user acceptance study of a clinical decision support system (CDSS) for Type 2 Diabetes Mellitus (T2DM) risk prediction. We focus on how a combination of data-driven and rule-based models influence the efficiency and acceptance by doctors. To evaluate the perceived usefulness, we randomly generated CDSS output in three different settings: Data-driven (DD) model output; DD model with a presence of known risk scale (FINDRISK); DD model with presence of risk scale and explanation of DD model. For each case, a physician was asked to answer 3 questions: if a doctor agrees with the result, if a doctor understands it, if the result is useful for the practice. We employed a Lankton's model to evaluate the user acceptance of the clinical decision support system. Our analysis has proved that without the presence of scales, a physician trust CDSS blindly. From the answers, we can conclude that interpretability plays an important role in accepting a CDSS.
Euskarazko testuen komunikagaitasun-maila automatikoki sailkatzeko lehendabiziko urratsak

Artikulu honetan HABEren eta IXA ikerketa-taldearen arteko egitasmo baten lehen urratsak azaltzen dira. Proiektuaren helburua komunikagaitasun-mailen araberako testu-sailkatzaile automatiko bat sortzea da, ikasleen ulermen-mailarako egokiak diren idatizko testuak objektiboki hautatzeko aukera emango duena. Horretarako, HEOCeko mailaz mailako adierazpide linguistikoak literaturan erabili izan diren bigarren hizkuntzen jabekuntzako (SLA, Second Language Acquisition) parametroen gisakotzat hartu dira, eta horiekin informatika-aplikazioetarako linguagrama bat sortu.
Perencanaan Sistem Informasi Strategis Menggunakan Model Ward and Peppard (Studi Kasus : Klinik Yulia Asniati)

Purpose: This article aims to make strategic planning of information systems and application requirements in health clinics so that they can provide optimally, integrated, and innovative contributions that can unite all supporting aspects in achieving clinical business strategies to increase their competitiveness value in health services in Pagar Alam City.&#x0D; Research Methodology: This article uses a case study at the Klinik Yulia Asniati in Pagar Alam City using the Ward and Peppard strategic planning model. Based on multi-disciplinary literature, the Ward and Peppard Model uses a strategic planning framework and process that includes the external business environment analysis stages using the PEST method, the internal business environment analysis using the value chain method, internal and external IS/IT environmental analysis.&#x0D; Results: This analysis produces an IS/IT strategic planning using McFarlan Strategic Grid Analysis, this stage produces 13 application portfolios that are arranged based on the priority of business needs and information system strategies.&#x0D; Limitations: This article uses the Ward and Peppard model with PEST analysis methods, Michael's Porter value chain, and McFarlan Strategic Grid analysis. This article only uses data and cases at the Klinik Yulia Asniati, Pagar Alam City
Research on Multiplayer Posture Estimation Technology of Sports Competition Video Based on Graph Neural Network Algorithm

With the explosive growth of the number of sports videos, the traditional sports video analysis method based on manual annotation has been difficult to meet the growing demand because of its high cost and many limitations. The traditional model is usually based on the target detection algorithm of manual features, and the detection of human posture features is not accurate. Compared with global image features such as line features, texture features and structure features, local image features have the characteristics of rich quantity in the image, low correlation between features, and will not affect the detection and matching of other features due to the disappearance of some features in the case of occlusion. Referring to the practice of Deep-ID network considering both local and global features, this paper adjusts the traditional neural network, and combines the improved neural network with the human joint model to form a human pose detection method based on graph neural network, and then applies the algorithm to multiperson human pose estimation. The results of several groups of comparative experiments show that the algorithm can better estimate the human posture in sports competition video, and has a good performance in solving multiperson pose estimation in sports game video.
Benchmarking a New Dataset of Traditional Balinese Carving Ornaments for Image Classification Task

In the framework of the development of an automatic Balinese carving ornament recognition application, a valid image dataset is needed.This paper describes the improved new dataset of traditional Balinese carving ornaments and presents the benchmarking results in an image classification task.The improvement of the new dataset involves the increased number of image samples and involves the validation and addition of the number of ornament classes.Some frequently used feature extraction methods, for example, Gabor Filter, Zoning, Histogram of Gradient, Neighborhood Pixels Weights, and Kirsch edge, were tested to benchmark the image classification task for this new dataset.The benchmark results showed that this new dataset has a fairly high technical challenge for feature extraction methods in the pattern recognition field.The new proposed dataset will support further research steps in building a classification and recognition system for Balinese carving ornaments.
Chatbot integrated with machine learning deployed in the cloud and performance evaluation

Recently human-machine digital assistants gained popularity and commonly used in question-and-answer applications and similar consumer-supporting domains.A class of more sophisticated digital assistants employing longer dialogs follow the trend, and there are several commercial platforms supporting their prototyping such as Google DialogFlow, Manychat, Chatfuel, Amazon Lex, etc.This paper explores cloud deployment of chatbots systems and their performance assessment methodologies.The performance measures includes system response delays and natural language processing capabilities.A case study platform supporting so-called deep-logic chatbots with long cycling capabilities is implemented and used for the assessment.To enable human-like conversations with a chatbot, huge training data, complex natural language understanding models are required and need to be adjusted and trained continuously.We explore implementation formats supporting auto scaling, and uninterrupted availability.In particular, we employ an architecture consisting of separate dialog management, authentication, and Natural Language Understanding (NLU) services.Finally, we present a performance evaluation of such loosely coupled chatbot system.
Secured Paillier Homomorphic Encryption Scheme Based on the Residue Number System

In this paper, we present an improved Paillier Cryptosystem for a secured data transmission based on the Residue Number System (RNS). The current state of Paillier Cryptosystem allows the computation of the plaintext from the cipher text without solving its security assumption of Decisional Composite Residuosity or the knowledge of its private keys under mathematical attacks. The proposed RNS based cryptosystem involving two stages of encryption and two stages of decryption has never been adequately studied before. This paper attempts to solve by introducing two stages of encryption and two stages of decryption. The first stage of the encryption process maintains the traditional Paillier encryption process and the second stage process is the encryption using the recommended moduli set – by the RNS Forward converter. At the first stage of the decryption process, our proposed RNS based reverse converter is adopted and finally, the traditional Paillier decryption process will be used at the second stage of the decryption process. Because the entire encryption technique is randomized, it can withstand chosen bruteforce attacks. The suggested algorithm's security study reveals that it has a wide key space ( , a high level resistance to key sensitivity attacks, and an acceptable level of resilience. In terms of security, it has been discovered that the proposed system outperforms the present algorithm.
Research on Students-centered Teaching in Primary and Secondary School

With the development of education,many teaching methods have sprung up at home and abroad.In the traditional teaching mode,the teacher plays the main function,while students are always considered to learn passively,only learning knowledge from the lessons in the class,the textbooks,rather than learning with initiative.But in the real teaching process,no matter what stage the students are at,there are differences between different students in their interests,knowledge acquired,ability,logical thinking and so on.These differences are not immutable and they will change,which drives to the promotion of a new“Student-centered”teaching mode.This paper focuses on the new teaching mode of“Student-centered”in primary and secondary school,and probes into its effects on cultivating of students’questioning ability,interest in study and their personality.
Data visualization of crime data using immersive virtual reality

Visualization explores quantitative content of data with human intuition and plays an integral part in the data mining process.When the data is big there are different analysis methods and approaches used to find inherent patterns and relationships.However, sometimes there is a need to incorporate human in loop approach to find new patterns and relationships.Immersive virtual reality (VR) offers a human centric approach to visualize data by discovering new relationships that an existing algorithm cannot provide.This paper demonstrates the data visualization tool to visualize Baltimore crime data in immersive environment and non-immersive environment.This paper focuses on VR visualization tool design, development, and usability assessment.A pilot user study was conducted for the VR visualization tool based on the system usability scale.The study results show that the crime data visualization tool is relatively easy to use, and that the application can be considered as a storyteller with removing the noise from the data and highlighting the usefulness of the information.
PUBO$$_i$$: A Tunable Benchmark with Variable Importance

In this work, we present the benchmark generator PUBO $$_i$$ , Polynomial Unconstrained Binary Optimization, that combines subproblems to create instances of pseudo-boolean optimization problems. Any mono-objective pseudoboolean functions including existing classical optimization problems can be expressed with Walsh functions. The benchmark generator can tune main features of problems such as problem dimension, non-linearity degree, and neutrality. Additionally, to be able to create instances with properties similar to those of real-like combinatorial optimization problems, the goal of PUBO $$_i$$ is to introduce the notion of variable importance. Indeed, the importance of decision variables can be tuned using three benchmark parameters. In the version presented here, we consider four subproblems already used in Chook generator for benchmarking quantum computers and algorithms as a basis. We also present the impact of benchmark parameters using a fitness landscape analysis that empirically shows these parameters to significantly impact the variable importance.
Machine Reading at Scale: A Search Engine for Scientific and Academic Research

The Internet, much like our universe, is ever-expanding. Information, in the most varied formats, is continuously added to the point of information overload. Consequently, the ability to navigate this ocean of data is crucial in our day-to-day lives, with familiar tools such as search engines carving a path through this unknown. In the research world, articles on a myriad of topics with distinct complexity levels are published daily, requiring specialized tools to facilitate the access and assessment of the information within. Recent endeavors in artificial intelligence, and in natural language processing in particular, can be seen as potential solutions for breaking information overload and provide enhanced search mechanisms by means of advanced algorithms. As the advent of transformer-based language models contributed to a more comprehensive analysis of both text-encoded intents and true document semantic meaning, there is simultaneously a need for additional computational resources. Information retrieval methods can act as low-complexity, yet reliable, filters to feed heavier algorithms, thus reducing computational requirements substantially. In this work, a new search engine is proposed, addressing machine reading at scale in the context of scientific and academic research. It combines state-of-the-art algorithms for information retrieval and reading comprehension tasks to extract meaningful answers from a corpus of scientific documents. The solution is then tested on two current and relevant topics, cybersecurity and energy, proving that the system is able to perform under distinct knowledge domains while achieving competent performance.
Comparison of Educational Media with Video and Pop-Up Book Towards Knowledge of Balanced Nutrition Guidelines in Elementary School Students

The unbalanced eating behavior of school-age children is influenced by the level of nutritional knowledge. The purpose of this research is to analyze the comparison of media education with videos and pop-up books on the knowledge of balanced nutrition guidelines for elementary school students. This type of research is a quasi-experimental design with a nonequivalent control group design. The total sample used in the study was 90 students who were taken by probability sampling and then divided into two groups, 45 students were given education with video media (P1) and 45 students were educated with pop-up book media (P2). Data were analyzed using the Wilcoxon and Mann-Whitney test with a significant level of 0.05. The results obtained are that there is a difference in the level of knowledge of the pop-up book group before and after education (p=0.0001) and there is no difference in the level of knowledge of the video group before and after education (p=1.00). Pop-up book media is more effective in increasing students' knowledge level of balanced nutrition compared to video, so pop-up book media can be an alternative media used to provide education to elementary school students.
FAIR and Quality Assured Data – The Use Case of Trueness

The FAIR Guiding Principles do not address the quality of data and metadata. Therefore, data collections could be FAIR but useless. In a funding initiative of registries for health services research, trueness of data received special attention. Completeness in the definition of recall was selected to represent this dimension in a cross-registry benchmarking. The first analyses of completeness revealed a diversity of its implementation. No registry was able to present results exactly as requested in a guideline on data quality. Two registries switched to a source data verification as alternative, the three others downsized to the dimension integrity. The experiences underline that the achievement of appropriate data quality is a matter of costs and resources, whereas the current Guiding Principles quote for a transparent culture regarding data and metadata. We propose the extension to FAIR-Q, data collections should not only be findable, accessible, interoperable, and reusable, but also quality assured.
Discussion on Teaching Strategies of College Oral English Course under the Background of Informatization

With the rapid development of the globalization, the economic trade and exchanges between countries are unprecedented close, the relationship between countries is also improving significantly.As the most frequently used language, English has been the most internationally recognized language.Therefore, fluent oral English has undoubtedly become an indispensable skill for college students.This essay mainly analyzes the current situation about the arrangement of college oral English class and provides some feasible measures for the cultivation of the oral English class.
Extraction of Temporal Structures for Clinical Events in Unlabeled Free-Text Electronic Health Records in Russian

The important information about a patient is often stored in a free-form text to describe the events in the patient's medical history. In this work, we propose and evaluate a hybrid approach based on rules and syntactical analysis to normalise temporal expressions and assess uncertainty depending on the remoteness of the event. A dataset of 500 sentences was manually labelled to measure the accuracy. On this dataset, the accuracy of extracting temporal expressions is 95,5%, and the accuracy of normalization is 94%. The event extraction accuracy is 74.80%. The essential advantage of this work is the implementation of the considered approach for the non-English language where NLP tools are limited.
Cultural Diversity Interactive Multimedia to Improve Cultural Literacy and Citizenship of Elementary School Students

The problem in research schools is the lack of interest in reading. This research aims to describe the development of interactive multimedia cultural diversity of my nation, know the feasibility of developing interactive multimedia products of my nation's cultural diversity, knowing the effectiveness of interactive multimedia development of my nation's cultural diversity. The method used is RD (Research and Development) with Hannafin Peck models that have needs analysis, design, development, or implementation stages. The study was conducted on grade I-VI elementary school students. Media expert test results score 81.4% with excellent criteria; experts score 96.25% with perfect measures; linguists score 97.5% with ideal standards. Based on the experts' assessment, this medium is very viable. While the effectiveness test conducted with the t-test with pretest-posttest obtained a score of 0.000 sig lower than 0.05, this proves a significant difference between pretest and posttest results to improved cultural literacy and student citizenship with an average score of 96.47%. So it can be said that interactive multimedia includes very effective criteria as a medium of learning and improves cultural literacy and student citizenship.
Assessing Resolvability and Consistency in OBO Foundry Ontologies: Pilot Study

Ontologies listed in the OBO Foundry are often regarded as reliable choices to be reused but ontology interoperability of them remains unknown. This study evaluated the resolvability of URIs and consistency of axioms in the OBO Foundry library, BFO ontology, and CIDO ontology. All had nonresolvable URIs, but the OBO library and the CIDO had additional interoperability issues regarding the use of incorrect prefixes, mixing up with ontologies, and inconsistency in the use of property. These detected issues reflected the real-world common problems that were not significant from human beings' point of view but hindered the machine-processability of ontologies. The assessment performed in this study was automated and enables scale-up against more metrics over more ontologies, which remains future work.
Interpretability of Machine Learning Methods Applied to Neuroimaging

Abstract Deep learning methods have become very popular for the processing of natural images and were then successfully adapted to the neuroimaging field. As these methods are non-transparent, interpretability methods are needed to validate them and ensure their reliability. Indeed, it has been shown that deep learning models may obtain high performance even when using irrelevant features, by exploiting biases in the training set. Such undesirable situations can potentially be detected by using interpretability methods. Recently, many methods have been proposed to interpret neural networks. However, this domain is not mature yet. Machine learning users face two major issues when aiming to interpret their models: which method to choose and how to assess its reliability. Here, we aim at providing answers to these questions by presenting the most common interpretability methods and metrics developed to assess their reliability, as well as their applications and benchmarks in the neuroimaging context. Note that this is not an exhaustive survey: we aimed to focus on the studies which we found to be the most representative and relevant.
EFEKTIVITAS METODE MENDONGENG (STORYTELLING) DALAM MENINGKATKAN KETERAMPILAN BERBICARA DAN KETERAMPILAN MEMBACA SISWA (Sebuah Studi Kasus di SDN 55 Bengkulu Selatan)

This study aims to find out how much the storytelling method can improve speaking skills and student reading skills and to determine the effectiveness of using storytelling methods in students' speaking skills and reading skills. The population of this study were all students of SDN 55 Bengkulu Selatan. The sample used was 30 students in one class in class III SDN 55 South Bengkulu. This study was classified as quasi experimental with one group pretest posttest design which only used one group for the pretest and posttest design. The instrument of this study was a test, this initial study was conducted pretest, then carried out the application of the storytelling method (treatman) and the final activity was carried out posttest. The results of the study on speaking skills showed that there was an increase in student scores from 38 to 50.83, which included aspects of intonation 31.03%, pronunciation aspects 35.59%, fluency aspects 34.52%, and vocal aspects 33.94%. The results of the reading skills study showed that there was an increase in student scores from 40.66 to 51.83 which included 25.81% pronunciation aspects, 25.42% intonation, 33.32% fluency aspects, and 25.40% sound clarity aspects. This study concluded that: First, the Storytelling method can improve speaking skills and reading skills in students as indicated by an increase in the average score of 33.77%, for speaking skills and an increase in the average score of 27.48%, for reading skills, which covering each of the four aspects of speaking and reading. Second, the Storytelling method gives a significant influence (0.05) in improving students' speaking and reading skills. This study will contribute to teachers as one of the effective strategies in teaching students to speak and read.
Character Recognition Using Pre-Trained Models and Performance Variants Based on Datasets Size: A Survey

The most efficient and beneficial mechanism to the feature of extracting data from an image, has been the Convolutional Neural Network (CNN) and it is used in many fields (Optical character recognition, image classification, object recognition and Facial recognition etc.). In this papier, we studied the character classification problems, using pre-trained models based on Convolutional Neural Network (CNN), and how the performance can change the outcome of dataset that is given. For that, we have used five pre-trained models’ such as VGG16/19, ResNet, Xception et MobileNet. The experiment shows that Xception had the best performance rate compared to other models for all datasets, VGG16/19 performance rate are variants depend on dataset. However, Experiments shows that ResNet achieve the worst accuracy rate compared to other methods.