Reviewerly/exHarmony huggingface dataset:
BLOCKCHAIN IN HEALTHCARE: UNLOCKING THE POTENTIAL OF BLOCKCHAIN FOR SECURE AND EFFICIENT APPLICATIONS FOR MEDICAL DATA MANAGEMENT- A PRESENTATION OF BASIC CONCEPTS

Medical data management presents significant challenges in terms of security, privacy, and efficiency. Blockchain technology has emerged as a promising solution to address these concerns in recent years. This comprehensive review explores the role of blockchain technology in secure and efficient medical data management. By providing a decentralized and immutable ledger, blockchain ensures data integrity, enhances privacy, and facilitates auditable access to medical information. The paper examines various applications of blockchain in medical data management, including electronic health records (EHRs), medical imaging, clinical trials, telemedicine, and drug supply chain management. It highlights the benefits and challenges of implementing blockchain in healthcare settings, discussing interoperability, consent management, scalability, and regulatory considerations. The review encompasses relevant research studies, industry initiatives, and real-world use cases to provide a comprehensive overview of the current state of blockchain technology in medical data management. The paper concludes with a discussion of future directions and potential areas for further research, emphasizing the transformative potential of blockchain in revolutionizing the way of medical data storage, sharing, and utilization.
PENGEMBANGAN MEDIA PEMBELAJARAN BERBASIS GAME TEBAK OPERASI MATEMATIKA YANG BERORIENTASI PADA KEMAMPUAN BERPIKIR KRITIS

This study aims to develop game learning media with the aim of making students enjoy learning mathematics and reducing the level of students' thinking that mathematics is difficult and not fun. In addition to these things, this research also develops oriented math games so that students can think critically in every problem. Study this use method study and development of the proposed 4D model by Thiagarajan which consists of 4 stages namely : (1) define or definition ; (2) designs or planning ; (3) develop or development and (4) disseminate or deployment . Beginning with planning in the selection of media to carry out developments that are in accordance with what is needed for learning mathematics. This research produces math game-based learning media that is suitable for use as a learning medium with the aim of students' critical thinking skills, namely valid and feasible to use as learning media and the results obtained implementation device development learning on class X shows an average increase critical thinking ability . Result _ analysis Post test show Skills think critical student or as much 80 % students complete and 20 % students no complete
An IoT Ecosystem Platform for the Evaluation of Blockchain Feasibility

The Internet of Things (IoT) has demonstrated promising growth, as it is crucial to numerous application domains in smart ecosystems, such as the smart city. Decentralization can help achieve growth, with previous research proposing the use of decentralized blockchain technology in IoT ecosystems as, among others, it can offer cryptographically trustworthy interactions, non-repudiable smart contracts, and interoperability between stakeholders. However, proposals are often theoretical or very specific to IoT ecosystem sub-areas, for instance, healthcare. In both cases, implementation may not be feasible in a larger ecosystem. This paper investigates the performance, particularly throughput, aspect of feasibility. It proposes and demonstrates a platform that uses blockchain technology as a building block in an IoT ecosystem. A permissioned blockchain network is built, performance is measured, and the measurements are interpreted in a smart city context, thus providing valuable insights about real-world implementations and their degree of feasibility.
Document-level Relation Extraction via Separate Relation Representation and Logical Reasoning

Document-level relation extraction (RE) extends the identification of entity/mentions’ relation from the single sentence to the long document. It is more realistic and poses new challenges to relation representation and reasoning skills. In this article, we propose a novel model, SRLR , using S eparate Relation R epresentation and L ogical R easoning considering the indirect relation representation and complex reasoning of evidence sentence problems. Specifically, we first expand the judgment of relational facts from the entity-level to the mention-level, highlighting fine-grained information to capture the relation representation for the entity pair. Second, we propose a logical reasoning module to identify evidence sentences and conduct relational reasoning. Extensive experiments on two publicly available benchmark datasets demonstrate the effectiveness of our proposed SRLR as compared to 19 baseline models. Further ablation study also verifies the effects of the key components.
Conducting Sentiment Analysis on Twitter Tweets to Predict the Outcomes of the Upcoming Karnataka State Elections

This research paper aims to predict how Twitter tweets from a specific politician correlate to their winning a seat in a state election.To understand this effect, sentiment analysis has been conducted on tweets by politicians in Karnataka to help predict who will win in the upcoming 2023 Karnataka Legislative Assembly election.Though previous research has already been done in this area, most studies have only focussed on the sentiment analysis of tweets.This paper goes further as it also looks at other factors, including the number of retweets and comments a tweet garners, which measures the tweet's engagement.A model has been created that weighs each factor to help predict who will win an election for a particular constituency.Through this model, a 72.7% accuracy has been achieved.However, the Twitter API severely limited the quantity and quality of data collected.These results can be expanded to help predict elections for other states.They could potentially help understand the effect of positive and negative sentiment on the winnability of a political candidate.
Achieving Verifiable Decision Tree Prediction on Hybrid Blockchains

Machine learning has become increasingly popular in academic and industrial communities and has been widely implemented in various online applications due to its powerful ability to analyze and use data. Among all the machine learning models, decision tree models stand out due to their great interpretability and simplicity, and have been implemented in cloud computing services for various purposes. Despite its great success, the integrity issue of online decision tree prediction is a growing concern. The correctness and consistency of decision tree predictions in cloud computing systems need more security guarantees since verifying the correctness of the model prediction remains challenging. Meanwhile, blockchain has a promising prospect in two-party machine learning services as the immutable and traceable characteristics satisfy the verifiable settings in machine learning services. In this paper, we initiate the study of decision tree prediction services on blockchain systems and propose VDT, a Verifiable Decision Tree prediction scheme for decision tree prediction. Specifically, by leveraging the Merkle tree and hash function, the scheme allows the service provider to generate a verification proof to convince the client that the output of the decision tree prediction is correctly computed on a particular data sample. It is further extended to an update method for a verifiable decision tree to modify the decision tree model efficiently. We prove the security of the proposed VDT schemes and evaluate their performance using real datasets. Experimental evaluations show that our scheme requires less than one second to produce verifiable proof.
CCNN: An Artificial Intelligent based Classifier to Credit Card Fraud Detection System with Optimized Cognitive Learning Model

Nowadays digital transactions play a vital role in money transaction processes. Last 5 years statistical report portrays the growth of internet money transaction especially credit card and unified payments interface. Mean time increasing numerous banking threats and digital transaction fraud rates also growing significantly. Data engineering techniques provide ultra supports to detect credit card forgery problems in online and offline mode transactions. This credit card fraud detection (CCFD) and prevention-based data processing issues raising because of two major reasons first, classification rate of legitimate and forgery uses is frequently changing, and next one is fraud detection dataset values are vastly asymmetric. Through this research work investigating performance of various existing classifier with our proposed cognitive convolutional neural network (CCNN) classifier. Existing classifiers like Logistic Regression (LR), K-nearest neighbor (KNN), Decision Tree (DT) and Support Vector Machine (SVM). These models are facing various challenges of low performance rate and high complexity because of low hit rate and accuracy. Through this research work we introduce cognitive learning-based CCNN classifier methodology with artificial intelligence for achieve maximum accuracy rate and minimal complexity issues. For experimental data analysis uses dataset of credit card transactions attained from specific region cardholders containing 284500 transactions and its various features. Also, this dataset contains unstructured and non-dimensional data are converted into structured data with the help of over sample and under sample method. Performance analysis shows proposed CCNN classifier model provide significant improvement on accuracy, specificity, sensitivity and hit rate. The results are shown in comparison. After cross-validation, the accuracy of the CCNN classification algorithm model for transaction fraudulent detection archived 99% which using the over-sampling model.
Faster or Slower? Performance Mystery of Python Idioms Unveiled with Empirical Evidence

The usage of Python idioms is popular among Python developers in a formative study of 101 Python idiom performance related questions on Stack Overflow, we find that developers often get confused about the performance impact of Python idioms and use anecdotal toy code or rely on personal project experience which is often contradictory in performance outcomes. There has been no large-scale, systematic empirical evidence to reconcile these performance debates. In the paper, we create a large synthetic dataset with 24,126 pairs of non-idiomatic and functionally-equivalent idiomatic code for the nine unique Python idioms identified in [1], and reuse a large real-project dataset of 54,879 such code pairs provided in [1]. We develop a reliable performance measurement method to compare the speedup or slowdown by idiomatic code against non-idiomatic counterpart, and analyze the performance discrepancies between the synthetic and real-project code, the relationships between code features and performance changes, and the root causes of performance changes at the bytecode level. We summarize our findings as some actionable suggestions for using Python idioms.
Silent Vulnerable Dependency Alert Prediction with Vulnerability Key Aspect Explanation

Due to convenience, open-source software is widely used. For beneficial reasons, open-source maintainers often fix the vulnerabilities silently, exposing their users unaware of the updates to threats. Previous works all focus on black-box binary detection of the silent dependency alerts that suffer from high false-positive rates. Open-source software users need to analyze and explain AI prediction themselves. Explainable AI becomes remarkable as a complementary of black-box AI models, providing details in various forms to explain AI decisions. Noticing there is still no technique that can discover silent dependency alert on time, in this work, we propose a framework using an encoder-decoder model with a binary detector to provide explainable silent dependency alert prediction. Our model generates 4 types of vulnerability key aspects including vulnerability type, root cause, attack vector, and impact to enhance the trustworthiness and users' acceptance to alert prediction. By experiments with several models and inputs, we confirm CodeBERT with both commit messages and code changes achieves the best results. Our user study shows that explainable alert predictions can help users find silent dependency alert more easily than black-box predictions. To the best of our knowledge, this is the first research work on the application of Explainable AI in silent dependency alert prediction, which opens the door of the related domains.
Seated-Walking: A Walking-in-Place Technique for Seated VR

We present Seated-Walking, a footstep locomotion technique designed for use when sitting on a chair. Seated-Walking is driven by users’ forefoot or rearfoot stepping, which is meant to embody the foot motions of real walking while mitigating potential fatigue. We demonstrate Seated-Walking in two applications. The first application delivers a casual virtual showroom experience, where users sitting iOptoma Corporationn a chair are allowed to walk navigating artworks in the virtual environment. The second application is an intense survival shooter experience where users track and fight enemies while avoiding their attacks, all while seated walking.
Transformers for extracting breast cancer information from Spanish clinical narratives

The wide adoption of electronic health records (EHRs) offers immense potential as a source of support for clinical research. However, previous studies focused on extracting only a limited set of medical concepts to support information extraction in the cancer domain for the Spanish language. Building on the success of deep learning for processing natural language texts, this paper proposes a transformer-based approach to extract named entities from breast cancer clinical notes written in Spanish and compares several language models. To facilitate this approach, a schema for annotating clinical notes with breast cancer concepts is presented, and a corpus for breast cancer is developed. Results indicate that both BERT-based and RoBERTa-based language models demonstrate competitive performance in clinical Named Entity Recognition (NER). Specifically, BETO and multilingual BERT achieve F-scores of 93.71% and 94.63%, respectively. Additionally, RoBERTa Biomedical attains an F-score of 95.01%, while RoBERTa BNE achieves an F-score of 94.54%. The findings suggest that transformers can feasibly extract information in the clinical domain in the Spanish language, with the use of models trained on biomedical texts contributing to enhanced results. The proposed approach takes advantage of transfer learning techniques by fine-tuning language models to automatically represent text features and avoiding the time-consuming feature engineering process.
Digital and inclusive pedagogical competences of educators

Teacher training focuses on generic technologies such as the European framework DigCompEdu, but special education students face them without being inclusive or adapted. DIPCE seeks to design inclusive learning activities where scientific and professional disciplines (pedagogy and didactics of the subject) and inclusive practices are added into a coherent whole. It was based on a selection of European countries by geographical location and a descriptive investigation taking samples from more than 8 educational centers and more than 50 teachers from each partner country. A model has been developed to incorporate digital resources and novel pedagogical approaches into teaching practices. A set of tools is being compiled that offers the means to create accessible and inclusive digital learning content to implement digital education practices. The results on how to transform conventional lessons to their digital format are being well received by teachers.
Single-pixel image reconstruction using coherent nonlinear optics

We propose and experimentally demonstrate a novel, to the best of our knowledge, hybrid optoelectronic system that utilizes mode-selective frequency upconversion, single-pixel detection, and a deep neural network to achieve the reliable reconstruction of two-dimensional (2D) images from a noise-contaminated database of handwritten digits. Our system is designed to maximize the multi-scale structural similarity index measure (MS-SSIM) and minimize the mean absolute error (MAE) during the training process. Through extensive evaluation, we have observed that the reconstructed images exhibit high-quality results, with a peak signal-to-noise ratio (PSNR) reaching approximately 20 dB and a structural similarity index measure (SSIM) of around 0.85. These impressive metrics demonstrate the effectiveness and fidelity of our image reconstruction technique. The versatility of our approach allows its application in various fields, including Lidar, compressive imaging, volumetric reconstruction, and so on.
Information Society: Educational Trends and Technical Aspects of Formation (EU Experience)

The purpose of the article is to analyze educational trends and technical aspects of information society development; to investigate the modern education digitization through the prism of global transformations of human society and its opportunities. In the article next theoretical research methods were used: analysis, synthesis, deduction, and induction. The results reveal the modern mutual influences between education and the "knowledge society", the role, place and significance of information competence for modern educational processes. In the conclusions one emphasizes that important aspects of education development will be the acquisition of additional digital competencies, essential measures of which are value-motivational, cognitive, technological, communicative, reflective components.
Automated Detection and Scoring of Tumor-Infiltrating Lymphocytes in Breast Cancer Histopathology Slides

Detection of tumor-infiltrating lymphocytes (TILs) in cancer images has gained significant importance as these lymphocytes can be used as a biomarker in cancer detection and treatment procedures. Our goal was to develop and apply a TILs detection tool that utilizes deep learning models, following two sequential steps. First, based on the guidelines from the International Immuno-Oncology Biomarker Working Group (IIOBWG) on Breast Cancer, we labeled 63 large pathology imaging slides and annotated the TILs in the stroma area to create the dataset required for model development. In the second step, various machine learning models were employed and trained to detect the stroma where U-Net deep learning structure was able to achieve 98% accuracy. After detecting the stroma area, a Mask R-CNN model was employed for the TILs detection task. The R-CNN model detected the TILs in various images and was used as the backbone analysis network for the GUI development of the TILs detection tool. This is the first study to combine two deep learning models for TILs detection at the cellular level in breast tumor histopathology slides. Our novel approach can be applied to scoring TILs in large cancer slides. Statistical analysis showed that the output of the implemented approach had 95% concordance with the scores assigned by the pathologists, with a p-value of 0.045 (n = 63). This demonstrated that the results from the developed software were statistically meaningful and highly accurate. The implemented approach in analyzing whole tumor histology slides and the newly developed TILs detection tool can be used for research purposes in biomedical and pathology applications and it can provide researchers and clinicians with the TIL score for various input images. Future research using additional breast cancer slides from various sources for further training and validation of the developed models is necessary for more inclusive, rigorous, and robust clinical applications.
Exploring the Effectiveness of GPT-3 in Translating Specialized Religious Text from Arabic to English: A Comparative Study with Human Translation

In recent years, Natural Language Processing (NLP) models such as Generative Pre-trained Transformer 3 (GPT-3) have shown remarkable improvements in various language-related tasks, including machine translation. However, most studies that have evaluated the performance of NLP models in translation tasks have focused on general-purpose text, leaving the evaluation of their effectiveness in handling specialized text to be relatively unexplored. Therefore, this study aimed to evaluate the effectiveness of GPT-3 in translating specialized Arabic text to English and compare its performance to human translation. To achieve this goal, the study selected ten chapters from a specialized book written in Arabic, covering topics in specialized religious context. The chapters were translated by a professional human translator and by GPT-3 using its translation Application Programming Interface. The translation performance of GPT-3 to was compared to human translation using qualitative measures, specifically the Direct Assessment method. Additionally, the translations were evaluated using two different evaluation metrics, Bidirectional Encoder Representations from Transformers (BERT) score and Recall-Oriented Understudy for Gisting Evaluation (ROUGE) metric, which measure the similarity between the translated text and the reference text.The qualitative results show that GPT produced generally understandable translations but failed to capture nuances and cultural context. On the other hand, the quantitative results of the study showed that GPT-3 was able to achieve a relatively high level of accuracy in translating specialized religious text, with comparable scores to human translations in some cases. Specifically, the BERT score of GPT-3 translations was 0.83. The study also found that the Rouge score failed to fully reflect the capabilities of GPT-3 in translating specialized text.Overall, the findings of this study suggest that GPT-3 has promising potential as a translation tool for specialized religious text, but further research is needed to improve its capabilities and address its limitations.
Pentingnya Proses Evaluasi Dalam Pembelajaran Di Sekolah Dasar

This article discusses the importance of the evaluation process in elementary school learning. Evaluation plays a crucial role in determining the effectiveness of learning and the achievement of educational goals. Evaluation helps teachers obtain valid data on students' abilities, identify their learning difficulties, and understand their position among their peers. Through evaluation, teachers can make professional decisions in developing their teaching methods. The article emphasizes the significance of understanding and skills in conducting effective evaluation. Evaluation should not only be carried out at the end of a lesson but throughout the entire learning process. By conducting accurate and comprehensive evaluations, educators can gather precise information about students' abilities, enabling them to make informed decisions in their teaching practices. Improving understanding and implementing effective evaluation processes contributes to enhancing the quality of elementary school education. Effective evaluation not only benefits teachers in decision-making but also helps improve self-quality and classroom management.