Reviewerly/exHarmony huggingface dataset:
Blockchain-Based Smart Home Network Security through ML

Abstract Due in large part to the Internet of Things’ (IoT) anticipated enormous scope and extensive implementation, achieving safe and private communications on the IoT is difficult. Recent initiatives have investigated the use of blockchain technology to enable decentralized protection and privacy. Such methods, however, are prohibitive for the bulk of IoT applications due to their high computational and time requirements. We specifically offer a resource-efficient, blockchain-based IoT security and privacy solution in this study. The approach is made achievable by utilizing Deep Extreme Learning Machine in combination with unique computational resource exploitation in a typical IoT context (such as smart houses) (DELM). In the proposed method, the privacy, integrity, and accessibility of the Blockchain based Architecture of Smart Homes are prudently considered while assessing the reliability of the system. The overheads caused this strategy are negligible with respect to the security and privacy benefits, we further underline by presenting simulated findings.
Distributed Fundamentals based Conducting the Web Crawling Approaches and Types (Focused, Incremental, Distributed, Parallel, Hidden Web, Form Focused and Breadth First) Crawlers

Abstract Over the last several years, there has been a significant rise in the number of people getting online and using the internet. Individual hypertext links are available, and any one of them may be used to get access to the resource. There is a variety of hypertext links available. It has been feasible to construct new websites as a result of the growth of crawlers, which has been facilitated by the rise in the number of people who use the internet. Web crawlers are highly evolved search engines that make it simpler for customers to get the information they are searching for on the internet. Web crawlers are also known as web crawlers. In a similar vein, these web crawlers have the potential to be used for more research endeavours in the months and years to come. Furthermore, the information that has been gathered may be used to detect and uncover any connections that are absent, as well as to assess the possibility for expansion inside complicated networks. This can be done by discovering any connections that are missing. The analysis of web crawlers is the primary topic of this study. Topics covered include the architecture of web crawlers, the many types of web crawlers, and the challenges that search engines have while using web crawlers.
Snake Optimization with deep learning enabled disease detection model for colorectal cancer

Abstract Colorectal cancer (CC) is prevalently studied carcinoma and it grows in the colon area of huge intestine. Histopathologist Usually Examine the colon biopsy during surgery or colonoscopy. Initial recognition of CC is useful for maintaining the model of accumulated cancerous cells. In medicinal practices, histopathological study of tissue specimens usually occurs in a traditional method but automatic tools which utilize Artificial Intelligence (AI) systems create effectual outcomes in disease detection efficiency. Deep learning (DL)techniques are demonstrated to generate remarkable outcomes on histopathology images in several studies. This study presents a Snake Optimization with Deep Learning Enabled Disease Detection Model for Colorectal Cancer (SODL-DDCC). The presented SODL-DDCC technique concentrates on the identification of CC on histopathological images. In the preliminary stage, the presented SODL-DDCC technique executes bilateral filtering (BF) approach to remove noise. In addition, the presented SODL-DDCC technique exploits Inception v3 as a feature extracting model with SO algorithm as a hyperparameter maximizing process. For CC classification, the graph convolution network (GCN) model is exploited. The investigation outcome evaluation of the SODL-DDCC approach is evaluated on standard dataset and the outputs are evaluated under distinct features. The empirical outputs highlighted the enhancements of the SODL-DDCC over current approaches.
Formation of schoolchildren’ divergent thinking in the conditions of additional education when working with timelines

Problem statement . One of the most important tasks that digital school teachers face is preparing a graduate, who is ready to use the acquired knowledge in his future life and find non-standard, original solutions, overcoming the usual patterns and established opinions. The study aimed at substantiating the effectiveness of the use of timeline services for the development of divergent thinking in schoolchildren in the conditions of additional education. Methodology . Theoretical and methodological analysis and generalization of fundamental scientific works on the research problem, processing of test results and timelines were applied. The pedagogical experiment involved 130 students of the state budgetary educational institution of Moscow “School No. 1506”. To diagnose and assess the formation of divergent thinking, the method of F. Williams (adapted by E.E. Tunik) was used. Pearson's chi-square test was used as a statistical processing method. Results . The timeline in additional education was carried out when displaying historical facts in chronological order, compiling algorithms, creating biographies, and presenting statistics. The didactic potential of the timeline as a kind of quantitative infographics is revealed, which offers a graphical implementation of digital data in relation to each component in the structure of creativity: flexibility, originality, elaboration, fluency. Statistically significant differences in qualitative changes in the pedagogical system were determined. Conclusion . The use of timeline services in the conditions of additional education contributes to the development of divergent thinking of students due to the possibilities of interactive presentation of events and facts, reuse, multimedia, technical minimalism. Difficulties that complicate the use of virtual timelines are noted: time resource management, the need to develop criteria and indicators for the effective use of web services for the corresponding purpose.
KEMAMPUAN LITERASI NUMERASI SISWA SD INKLUSIF DALAM MEMECAHKAN SOAL CERITA

This study aims to describe the numeracy literacy ability of inclusive students in solving story problems. This type of research is descriptive qualitative research. The subjects of this study were inclusive students at Madrasah Ibtidaiyah Darul Hikmah who were in high class as many as 2 students. Data collection techniques used in this research are technical tests and non-tests. The test technique uses an instrument of description questions to see the numeracy literacy abilities of inclusive students in the form of story questions about simple arithmetic operations. Then the non-test technique used interviews with inclusive teachers and students who were used as research subjects. The data analysis technique uses triangulation techniques, namely the stages of data reduction, data presentation, and conclusion drawing. The results of this study indicate that the numeracy literacy ability of inclusive students is still said to be below. Subject A's responses to the questions are 1) subject A can use contextual understanding to solve mathematical problems but is still not correct in solving them; 2) unable to write the conclusion of the settlement, and 3) subject A has not been able to relate various representations when solving problems. Meanwhile, subject B's response to the question is: 1) can write the process in reaching a solution, but it is not complete and not correct; 2) have also been able to write the conclusions of mathematical results based on the results obtained based on the written completion process but the answers given are not correct, and 3) have been able to use contextual understanding to solve mathematical problems but it is not correct.
Borderless Azerbaijani Processing: Linguistic Resources and a Transformer-based Approach for Azerbaijani Transliteration

Reihaneh Zohrabi, Mostafa Masumi, Omid Ghahroodi, Parham AbedAzad, Hamid Beigy, Mohammad Hossein Rohban, Ehsaneddin Asgari. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.
FastRAT: Fast and Efficient Cross-lingual Text-to-SQL Semantic Parsing

Pavlos Vougiouklis, Nikos Papasarantopoulos, Danna Zheng, David Tuckey, Chenxin Diao, Zhili Shen, Jeff Pan. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Retrieval Augmented Generation with Rich Answer Encoding

Wenyu Huang, Mirella Lapata, Pavlos Vougiouklis, Nikos Papasarantopoulos, Jeff Pan. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
Emerging Challenges in Personalized Medicine: Assessing Demographic Effects on Biomedical Question Answering Systems

Sagi Shaier, Kevin Bennett, Lawrence Hunter, Katharina Kann. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers). 2023.
The Language Model, Resources, and Computational Pipelines for the Under-Resourced Iranian Azerbaijani

Marzia Nouri, Mahsa Amani, Reihaneh Zohrabi, Ehsaneddin Asgari. Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 2: Short Papers). 2023.
DEVELOPMENT OF A LEARNING MANAGEMENT SYSTEM FOR PHYTON PROGRAMMING LANGUAGE

An Electronic Learning System is an educational platform that takes place over the Internet. Due to its convenience and flexibility, learning through an Electronic Learning Management System (LMS) results in effective learning experiences. The purpose of this research is to design and develop a LMS for programming education. Collaborative, microlearning and scaffolding programming learning approach were incorporated into the design and development of the learning management system for programming language. The software was developed using waterfall model, and evaluated by analyzing user’s feedback. The tools used in building the learning management system are visual studio code, Hyper Text Markup Language (HTML), cascading style sheet, javascript, python, django, Django rest framework, Jason, ajax, git, tinyMCE. The evaluation of the newly implemented online learning platform based on user feedback was overwhelmingly positive. Users appreciated the platform's intuitive interface, which allowed for easy navigation and quick access to resources. The clear and concise instructions provided were also highly commended, as they helped to ensure that users understood the system's various features and capabilities. Users also noted the platform's support for various types of learning content, including text, audio, video, and interactive materials, which helped to create a more engaging and dynamic learning experience. Furthermore, the platform's effective tools for monitoring student progress and managing course completion were praised for their efficiency and ease of use. The platform's support for collaborative learning was also highly valued, with users citing the ability to work together and exchange ideas as a valuable aspect of the platform. In conclusion, the newly implemented online learning platform is a valuable tool that can significantly enhance the learning experience of students. The positive feedback from users suggests that the platform's support for micro-learning and collaborative learning can effectively promote engagement, collaboration, and successful learning outcomes. These findings underscore the importance of designing and implementing effective online learning platforms that support the needs of both students and instructors.
The Effectiveness of Using Video in Teaching Speaking of the Seventh Grade Students’ at SMP Negeri 17 Makassar

Technological developments are also growing rapidly. Its development is almost sectoral, economic, political, and educational have implemented technology. One of them is the use of video. Video can be used as a solution to overcome the low ability of students to understand a concept. This study aims to determine the effect of WhatsApp groups on the ability to write narrative texts of eighth grade students of SMP Negeri 17 Makassar. This study uses a quantitative type of experimental method, there are two classes, namely the experimental class using video and the control class not using video. The sample of this study was class VII students consisting of 32 students for the experimental class and 32 students for the control class. The data collection technique in this study uses pre-test and post-test to collect data by giving text to write. The results of this study indicate that the post-test experimental class has a mean value of 74.69 and the post-test control class has a mean value of 72 ,66. The results of the data analyzed using independent sample t-test, showed that Sig.(2-tailed) was 0.000 lower than 0.05 so that the alternative hypothesis (Ha) was accepted and the null hypothesis (H0) was rejected so that there was an effect of using video in teaching speaking to students after using the video. Based on the results of this study, it can be concluded that video media is effective in improving the speaking ability of seventh grade students of SMP Negeri 17 Makassar.
Experience of Involving Future it Professionals in the Development of Learning Tools as an Element of Digital Transformation of Education

The development of digital educational resources is currently the primary task of IT specialists, which, in turn, is a component of the digital transformation of education in general.Since the digital transformation of education consists of the development of significantly new electronic resources for educational purposes, we have considered specific examples of the development of digital educational resources by students and analyzed their feasibility of development and use within the framework of the digital transformation of education.The article also shows how the involvement of students of IT majors in the development of such digital educational resources contributes to the improvement of their professional and linguistic competencies.At the same time, students reflect exactly their vision of learning a foreign language with the help of such tools, and all this contributes to the digital transformation of higher education.
Developing a Mobile Learning Virtual Nursing Diagnosis (VND) Media for Medical Surgical Nursing Course

Link of Video Abstract: https://youtu.be/4zHCrIC1IGE Background: The development of innovative and technology-aligned learning media is a necessary solution for nursing students. Students require access to interactive, practical, and easily accessible learning resources. An application that can facilitate students' learning in nursing diagnosis for surgical nursing, such as Virtual Nursing Diagnosis (VND), is needed. This study focuses on the development of the VND application specifically designed for the Surgical Nursing Care course. Patients and methods: The research is a Research and Development (R&amp;D) study involving 253 nursing students from Aceh Health Polytechnic. A needs analysis was conducted to identify the difficulties faced by students in applying nursing diagnoses to topics related to surgical nursing, particularly the cardiovascular system. Based on the findings of the needs analysis, it was discovered that students face difficulties in determining nursing problems. Therefore, the VND application was developed with features such as assessment forms, assessment tools, disease pathology, and nursing diagnoses. Additionally, the application is equipped with competency tests on interesting nursing cases that are user-friendly for students. Results: The research results indicate that the VND application has received positive feedback from 253 students who have used it in real classroom environments. The application is user-friendly, efficient in performing nursing diagnoses, and provides available nursing case questions. VND application provides an interactive, innovative, and effective learning tool for understanding nursing diagnoses while enhancing accessibility and flexibility through mobile-based learning. The application equips students with the necessary skills to apply nursing diagnoses in real-world surgical medical practice. Conclusion: the VND application significantly contributes to the development of nursing education. It not only improves understanding of nursing diagnoses but also provides an engaging and interactive learning experience. It is hoped that the use of the VND application in surgical nursing education can be an effective and innovative solution to enhance competency in applying nursing diagnoses in clinical practice.
Quarantine in Motion: A Graph Learning and Multi-Agent Reinforcement Learning Framework to Reduce Disease Transmission Without Lockdown

Exposure notification applications are designed to help trace disease spreading by alerting exposed individuals to get tested. However, false alarms can cause users to become hesitant to respond, making the applications ineffective. To address the shortcomings of slow manual contact tracing, costly lockdowns, and unreliable exposure notification applications, better disease mitigation strategies are needed. In this paper, we propose a new disease mitigation paradigm where people can reduce infection spreading while maintaining some mobility (i.e., Quarantine in Motion). Our approach utilizes Graph Neural Networks (GNNs) to predict disease hotspots such as restaurants, shops and parks, and Multi-Agent Reinforcement Learning (MARL) to collaboratively manage human mobility to reduce disease transmission. As proof of concept, we simulate an infection using real-world mobility data from New York City (over 200,000 devices) and Austin (over 36,000 devices) and train 10,000 agents from each city to manage disease dynamics. Through simulation, we show that a trained population suppresses their reproduction rate below 1, thereby mitigating the outbreak.
CMSI: Carbon Market Sentiment Index with AI Text Analytics

Climate change is an increasing environment concern, carbon markets are received attention because they can reduce emissions by carbon trading. With the rising in sustainable finance demand, there need some innovative tools to study investors' attitude. Although there has been an increasing amount of literature on market indicator and sentiment analysis, there is a limited research focus on carbon market. Therefore, this research purpose is to develop carbon market sentiment indicator. The research method contains three mainly phase. By using pretrained models like BERT, GPT, and so on, the research can obtain sentiment analysis results from news and social media data. Then, taking a specific carbon market as the object, and the selected sentiment indicators, the carbon market response for a period can be obtained at last. Due to complexity and the lack of transparency in the institutional and financial infrastructure for carbon market transactions, there has the problem of market volatility. The contribution of research is that provides a comprehensive sentiment index for carbon market. By this way, the research expects to provide valuable insights into sustainable finance and help investors to make better decision.
The Influence of Interreflections on Shape from Fluorescence

Fluorescence is an optical phenomenon, specific to certain materials that absorb light with higher energy and re-emit it at lower energy, with unnoticeable time latency. Due to this so-called Stokes shift, fluorescent materials pose several challenges in image capture, where usually a filtering setup is required at the illumination and/or sensing ends. Nevertheless, fluorescence emission is diffuse, which was previously used in shape from photometry models. In this work, we target the shape from fluorescence method for a specific category of materials: those with overlapping reflectance and fluorescence signals. In particular, we investigate how the self-interreflections (light bounces off a surface that get re-reflected by the surface itself) change the appearance of scenes with such fluorescent materials and how this affects the shape estimation with a photometric stereo model. To avoid instrumental artifacts inherent in real image capture setups, we perform our analysis on a synthetic dataset of multi-light images, generated with a physically-based spectral renderer that supports fluorescence.
Review to unfold the role of Machine Learning Algorithms in Natural Language Processing

This research review has been written to explore the role of machine learning algorithms in natural language processing (NLP), a computer field that offers human-like comprehension of virtual text. The main motive of the article is to reveal the participation of machine learning (ML) in NLP and its scope in coherent fields. In this study, the researchers have used a systematic literature review approach to explore the role of ML algorithms in NLP. Highlighting the techniques of ML algorithms as supervised, unsupervised, semi-supervised, and reinforcement methods, this study discloses the connectivity with morphological, semantic, syntactic, pragmatic, and discourse analysis in NLP. The article describes that ML/NLP has immense applications in different fields where several tools of ML/NLP are utilized. The researchers have studied the use of chatbots, text summarization tools, web scrapping, sentimental analysis in the social media stock market, medical field disease detection, and fraud detection. ML’s role and contributions to the progressions are detained in current studies. A distinct comparison has been made between the past present and future of ML in NLP. ML algorithms and applications with examples are disclosed in the classification of logistic regression, SVM, Naive Bayes, K-Nearest Neighbor, and decision tree. Data distortion, interpretation of the research findings, and contextual ambiguity are visibly determined as challenges and discussed in the study. This writing opens up to the recent advancements and advantages in multiple areas correspondingly. The discussions and findings expose the role of ML in opening the gateway to the profound revolutionary search engines, algorithms, and multiple techniques through the development of technology and its evolution to adapt to new language differences, slang, and changes in language use.
Annual Forecast of Photovoltaic Power Generation Based on MLP Artificial Neural Networks

The intermittency of solar energy resources presents a significant challenge in balancing power generation and load demand. To enhance system consistency, forecasting photovoltaic solar energy is crucial. Among numerous techniques, Artificial Neural Network (ANN) is an efficient tool that can help simplify this problem and predict photovoltaic power generation based on various inputs such as weather data and panel characteristics. In this paper, we present the results of an annual forecast of photovoltaic power generation based on Multilayer Perceptrons (MLP), which provides valuable insights into the potential of MLP ANN for accurate and reliable prediction of photovoltaic power generation, thereby improving the efficiency and reliability of photovoltaic systems. The results were obtained based on data collected over a year and validated with data from the following year. Mean Squared Error (MSE) was utilized to quantify the error between the predicted and measured photovoltaic solar energy generation. The analysis demonstrated that this annual forecast of photovoltaic power generation is highly accurate.