Reviewerly/exHarmony huggingface dataset:
ANKA

For the purpose of enabling, democratizing, and reducing the fees of peer-to-peer energy trading for battery-powered devices, we propose ANKA as a fully decentralized energy marketplace for peers with battery-powered devices. ANKA utilizes state-of-the-art technologies, namely blockchain, smart contracts, and decentralized applications. Within this marketplace, users who possess surplus energy actively offer their excess energy for trading. Concurrently, consumers can readily explore the energy options available and make purchases according to their individual preferences while taking into consideration the location of the offered energy and voltage compatibility. In addition, we provide a comparison between a centralized traditional market and our proposed solution, identifying that the cost of deploying and operating ANKA is less than the centralized approach.
Towards a Rollup-Centric Scalable Architecture for Decentralized Physical Infrastructure Networks

Decentralized physical infrastructure network (DePIN) is an emerging research topic in Web3 and blockchain. By combining blockchain, IoT and tokenomics, DePINs are expected to disrupt existing IoT business models and enable Web3 communities to build innovative, machine-driven and decentralized IoT networks and applications. Due to the characteristics of DePINs such as the large number of smart devices and network scale as well as the interactions with blockchain, scalability remains to be one of the key challenges. In this position paper, we outline the core ideas and components for building a rollup-centric scalable architecture for DePINs. The proposed architecture takes a modular design approach and leverages off-chain computing and zero-knowledge proofs to address the scalability challenge. This work is expected to highlight the importance of this new research direction and shed some light on potential solutions.
Preserving Privacy of Vulnerable Users across Heterogeneous Sensitive Sensor Data Streams using Smart Contracts

This paper is concerned with helping people who are vulnerable during important transitions in life, such as 'coming out' as LGBTQIA+, experiencing serious illness, undergoing relationship breakdown etc. Rich sensor streams derived from so-called 'smart' Internet of Things (IoT) devices can be highly beneficial, for example in ensuring the safety of such individuals during their sensitive life transitions, or in providing functionality that can mitigate some of the difficulties faced by them. However, the data that needs to be extracted to provide these benefits can itself be highly sensitive and needs to be processed with safeguards to protect privacy. We develop scenarios that highlight issues arising from having to merge data streams from multiple devices, including data governance issues that are relevant when the sensors are owned by multiple individuals. We propose a "Transition Guardian" architecture that leverages "Smart Experts" written as smart contracts operating on homomorphically encrypted sensor data streams to provide real-time protection without disclosing their sensitive information. We have also implemented a proof-of-concept on the Ethereum protocol to validate our proposed solution.
IV Curve Characterization Methods for Photovoltaic Panels: An Applicative Review

The characterization/reconstruction of the IV curve of the photovoltaic (PV) panel or array involves obtaining strategic sampling points, regardless of the test or measurement condition. These measured points require an adequate processing strategy to optimize and validate the results obtained (sample points). This article presents a brief review of the different methods reported in the literature based on the application and scope of each method. Implementation variations and PV panel technology are considered. Also, an approach is presented with the main considerations of a post-processing strategy, based on unprocessed data.
AI Methods Used for Spam Detection in Social Systems - An Overview

Over the past few years, AI has emerged as a formidable asset in the battle against spam. AI approaches have demonstrated their potential in improving spam detection in social systems. The importance of AI methods lies in their flexibility and ability to identify patterns, rendering them highly suitable for addressing the ever-changing characteristics of spam. In this paper, we provide an overview of all AI methods and algorithms used for spam detection in social systems, and evaluation of their performance in terms of used metrics such as accuracy, recall, precision, and F1-score. Also, we provide a comparative analysis of the used AI methods, challenges and limitations, emerging trends and future research directions, and privacy concerns in AI-driven spam detection in social systems. This paper emphasizes the significance of AI Methods in the realm of spam detection and investigates a range of AI-driven strategies that have been suggested and put into practice to combat spam with efficacy.
Learning Interferometric Equipment through Integration of Game Methods into Mobile Application

The article focuses on the research and development of a mobile application for basic and advanced study of interferometric equipment. Based on modern approaches to education and actual needs of students, the peculiarities of operation with high-precision interferometric equipment were investigated. The article examines the problems associated with studying the interferometry as a discipline: the difficulty of understanding the terminology, the uniqueness of the application base, and the limited number of students. To solve these problems, it is proposed to involve game-based learning methods, which makes the process more immersive and effective. The application, developed on the basis of the Unity platform, aims to make the study of interferometry accessible, interesting and effective, combining theoretical knowledge with practical tasks and game elements.
The dynamic relationship between BTC with BIST and NASDAQ indices

Abstract The significance of digital investment has grown substantially, enabled by advancing technology, which provides digital monitoring of investment instruments. Consequently, analyzing these instruments has become imperative. In particular, investors are inclined to compare new investment opportunities with well-established global stock markets, seeking to capitalize on their advanced financial literacy. This study aims to employ econometric analysis to explore the dynamic relationship between Bitcoin and the BIST100 and NASDAQ 100 indices. The time frame for this investigation spans from January 1, 2017, to March 10, 2022. Stationarity was confirmed through unit root tests (ADF, PP, KPSS, ZA, FADF, and FFFFF ADF) for the subsequent utilization of Autoregressive Conditional Variance Models. Additionally, Generalized Autoregressive Conditional Variance and Dynamic Conditional Correlation Tests were conducted. Results from the Dynamic Conditional Correlation Test model revealed no statistically significant dynamic conditional correlation between Bitcoin and BIST 100. Conversely, a negative and significant dynamic conditional correlation emerged between Bitcoin and NASDAQ 100. Investors should not only monitor the market but also review academic studies before making investment decisions. In this regard, this study holds significant importance. The study is limited to the BTC, BIST, and NASDAQ indices. Researchers interested in the topic can increase the dataset to further enrich the study.
Introduction

As the amount of content available on the World Wide Web (WWW) continues to grow, so does the need for efficient tools to support Web search and navigation. This chapter provides an introduction and overview of this book.
Library Service Management to Enhance Students' Reading Interest at State Senior High School 1 Secanggang, Langkat Regency

Library service management is the process of optimizing the contribution of human resources, materials, and budget to achieve the goals of the library or library management process based on management principles and theories. Meanwhile, reading interest is the ability to read and encourages the growth of reading culture and habits. Through reading, students can broaden their insights, sharpen their ideas, and enhance creativity. This research aims to explore the planning, implementation, and assessment of library service activities at State Senior High School 1 Secanggang, Langkat Regency. This study uses a qualitative descriptive research method. Data collection is conducted through observation, interviews, and documentation. The primary data sources for the research are interviews with the school principal, library head, library staff, teachers, and students. Secondary data consists of documents related to library service management in enhancing students' reading interest at State Senior High School 1 Secanggang, Langkat Regency. Data analysis techniques include data reduction, data presentation, drawing conclusions, and checking data validity by comparing interview data with the research object. The results of the research show that library service management in enhancing reading interest at State Senior High School 1 Secanggang, Langkat Regency, is well-established. The planning activities begin with meetings attended by the school principal, library head, library staff, teachers, and students. The implementation of library services is carried out by two library staff members with their respective schedules and is supervised by the library head. Assessment is conducted for the school principal, library head, library staff, teachers, and students.
Combating economic cybercrime using artificial intelligence (AI)

Combating economic cybercrime using AI can be a new powerful approach. AI technologies have the potential to detect and respond to cyber threats in real-time, identify patterns and anomalies in large data sets, and automate various security processes. The basic ways of using artificial intelligence to combat economic cybercrime are Threat Detection, Behavioral Analysis, Fraud Prevention, Phishing and Malware Detection, Vulnerability Management, Incident Response and Threat Hunting, Predictive Analytics or Security Automation. However, it should be noted that while AI can significantly improve cybersecurity operations, it is not a standalone solution. It should be used in conjunction with other security measures such as regular software updates, employee training, and strong access controls to create a robust defense against economic cybercrime
Hyperparameter Tuning for Address Validation using Optuna

Public institutions generally share personal information on their websites. That allows the possibility to find personal information when performing internet searches quickly. However, the personal information that is on the internet is not always accurate and can lead to misunderstandings and ambiguity concerning the accessible postal address information. That can be crucial if the information is used to find the location of the corresponding person or to use it as a postal address for correspondence. Many websites contain personal information, but sometimes as people change the web address, information is not up to date or is incorrect. To synchronize the available personal information on the internet could be used an algorithm for validation and verification of the personal addresses. In the paper, a hyperparameter tuning for address validation using the ROBERTa model of the Hugging Face Transformers library. It discusses the implementation of hyperparameter tuning for address validation and its evaluation to achieve high precision and accuracy.
Histopathological Image Analysis Using Deep Learning Framework

Breast cancer has the highest mortality rate. Therefore, histologic imaging evaluations must detect breast cancer early. Traditional methods are time-consuming and limit pathologists’ skills. Breast cancer histopathology picture segmentation is neglected by existing HIAs because of its complexity and lack of historical data with exact annotations. Histopathology breast cancer images are classified using graph-based segmentation. Graph-segmented images retrieve relevant features. Using recursive feature removal, breast cancer photographs are categorized. Breast cancer symptoms can be detected by appropriately classifying breast histopathology scans as abnormal or normal. Modern medicine diagnoses and predicts diseases, including cancer, using histopathological image analysis. Due to picture identification and feature extraction, deep learning can automate and improve histopathological image analysis. This study extensively analyses deep learning frameworks in histopathology image analysis. Starting with histopathological image interpretation’s challenges, this study emphasizes the intricate patterns, cell structures, and tissue anomalies that demand professional attention. It then examines CNNs, RNNs, and their variants’ design and ability to catch subtle features and patterns in histopathological images. We examine tumour detection, grading, segmentation, and prognosis using deep learning in histopathology. For each problem, this article evaluates cutting-edge deep learning models and approaches to demonstrate their accuracy and efficiency. While training deep learning models for histopathology image analysis, this study tackles data collection, preprocessing, and annotation. We also analyse automated clinical systems’ ethical and regulatory ramifications. Deep learning-based histopathological image processing case studies show patient care and applications. Multi-modal data fusion, transfer learning, and explainable AI may increase the accuracy and interpretability of histopathological image analyses.
Literature Review

The literature review chapter begins by defining the main concepts on which the current project is based. The concepts include computer-mediated communicationComputer-Mediated Communication, machine translation, instant messaging, English as a foreign languageEnglish as a Foreign Language (EFL), foreign language willingness to communicateForeign language (FL)FLWTC, and opportunities to communicateOpportunities To Communicate (OTC) in a foreign language. The definition of concepts is followed by a detailed presentation of IM, focusing on IM users, IM language, and IM clientsInstant messagingIM clients currently available in the market. The IM clients we have introduced in this section include QQ International, WeChatWeChat, Skype Translator, LringoLringo, ChatLingualChatlingual, VoxOxVoxOx, and SendbooSendboo.
Conclusion

The first part of the Conclusion summarizes the main research questions, hypotheses, and methodology adopted in this project. It further recapitulates the findings obtained and the implications of the findings for the conceptual framing of WTC, FL learning, and translation (theoryTranslationtheory and practiceTranslationpractice). The second part of the chapter summarizes the limitations of the methodology adopted, the contribution of the project to knowledge and theory formation, and avenues for further research.
Assessing Consumers’ Acceptance of AR Wayfinding for Indoor Shopping Navigation in Singapore

This study investigates consumers’ acceptance of Augmented Reality (AR) Wayfinding for indoor shopping navigation toward consumer behavioral intention. This study suggested a conceptual model investigating major determinants of users’ behavioral intention through the UTAUT model. In this study, 175 respondents were selected using a purposive sampling technique, and a survey method distributed via Google form was used to collect data, then analyze the collected data from the respondents via SmartPLS (Partial Least Squares Structural Equation Model. The results revealed that two determinants have a positive and significant relationship with behavioral intention to use the indoor AR wayfinding system application; they are facilitating conditions and performance expectancy. Furthermore, Effort expectancy (EE), social expectancy (SE), and privacy risk (PR) were found to have an insignificant relationship with the behavioral intention of adopting an AR wayfinding system. Software development in Singapore has reliable, secure technologies and policies that protect personal information, which would lower consumers’ perceived privacy risks.
Lessons learned supporting access to streaming videos in Women Studies courses

In this case study, two librarians and a faculty member evaluate the use of videos in four courses at a small Canadian liberal arts university. The authors analyzed the types of videos used and access to the course videos, including restrictions, and unavailable videos. The benefits and drawbacks of subscription versus freely available content were considered through analysis of the sources of streaming videos. Geoblocking emerged as a barrier for students living abroad in accessing some content. This article concludes with recommendations for further research and how education and advocacy might address some of the difficulties in accessing videos that support teaching and learning.
Using Cluster Analysis for Author Classification of Albanian Texts: A Study on the Effectiveness of Stop Words

Cluster analysis is a statistical approach that identifies uniform clusters within data. The closeness of data is measured quantitatively using distance functions. Specifically for text data mining, clustering serves as a method of categorization of words based on the similarity of their occurrence within texts and classifying texts by topics or author. Hierarchical clustering is a powerful technique for identifying natural groupings within datasets, which can be especially useful for unsupervised text classification. This paper aims to utilize cluster analysis to establish Albanian texts clusters by authors. Using agglomerative hierarchical clustering we classify Albanian texts by authors according to the similarity of their word frequency. The similarity of texts is evaluated using cosine and Euclidean distances. Considering two study cases, respectively with and without Albanian stop words we conclude that the best clustering by authors of the Albanian documents is achieved with 87% accuracy using Ward’s method with cosine distance in the case of study by removing stop words.
College Management Chatbot System

Chatbots are basically bots that are used to have faster and more accurate conversations with people. Nowadays, with chatbots very much attention in different areas. Chatbots have embedded knowledge to identify sentences and provide a response Questions.This Work proposes a chatbot to be used for school administration, the user only needs to log in to Chatbot for any college related activities. A student need not be present to know about any college related activity physically there. College Management Chatbot is developed using artificial intelligence algorithms like Porter Stemmer and word order similarity, which analyzes user queries and understands user messages to provide an accurate response this College Management Chatbot, various natural language processing techniques will be used such as parsing, stemming, Tokenization and filtering
The Internal Network Structure that Affects Firewall Vulnerability

Sharing extensive healthcare information is essential for the advancement of medicine and the formulation of effective public health policies. However, it often contains sensitive or personal information, or trade secrets. Certain safety measures are needed to strike a balance between the sharing of data and the protection of such information. A firewallFirewall is one of the major safety measures designed to prevent the delivery of protected information by severing harmful connections or limiting the formation of new connections between relevant parties in an information exchange network. Although very simple models suggest firewallFirewall vulnerabilities, such models often oversimplify real-world scenarios, neglecting factors like internal connections among nodes and the influence of other information held by nodes. Therefore, we propose several improved models and use them to explore some of the reasons why firewalls fail. Our study finds that firewallsFirewall are less effective as the number of network nodes increases, and that both high- and low-degree nodes pose non-negligible risks. The study also raises awareness about the role of internal monitors in preventing leaks. The effectiveness of information leakage control could be increased with the monitor's proximity to the information source. This necessitates a greater focus on internal monitoring, perhaps using information and communication technology.
Real-Time Outlier Detection and Removal in Data Collection with LabVIEW using Peirce’s Criteria

Testing and experimental processes in industries and research institutes play important roles in understanding systems and developing accurate models. Experimental uncertainties in variables introduce deviations (errors) in measured data, which can stem from various factors, one of which is the presence of outliers. Outliers impact the accuracy of measurements significantly during analysis and lead to inaccurate conclusions. Outliers can be due to environmental changes, drift in measurements, etc., during assessment. Identifying outliers and eliminating them is a crucial step during data analysis. This research aims to investigate real-time outlier detection and removal using LabVIEW. Peirce Criteria are assessed for detecting and eliminating outliers from displacement data obtained through LVDT sensors. The research demonstrates that Peirce’s criteria are particularly well suited for small datasets. By employing LabVIEW and Peirce criteria, this study presents a practical approach to accurately detect and remove outliers in real time to enhance the reliability of data analysis.
Enhancing Teaching and Learning through Virtual Reality: A Focus on Textile Materials

Virtual reality (VR) is transforming education by enhancing the teaching and learning experiences. This study addresses the application of VR and focuses on understanding textile structures using a semester project as an example. VR immerses learners in 3D environments, going beyond traditional microscopy. In this project, textiles and partial textiles from vintage cars from the 1950s were converted from 2D microscope data into virtual reality, bringing together students of communication design, apparel technology, conservation, and excavation. Advanced digital microscopy was used to examine textile surfaces, transform the results into dynamic 3D data, and visualize textiles. This interdisciplinary collaboration promoted knowledge exchange, innovative teaching methods, and transformative thinking.
Development of communication tools for deaf and mute people using design thinking method

In the post-COVID-19 pandemic, society was required to prepare and adapt in the face of post-pandemic conditions. Adjustment in society is a major problem experienced by people with disabilities, especially for people who are deaf and mute due to the difficulty of communicating. With design thinking method a communication tool for the deaf-mute people idea was obtained from this research. Smart Gloves for Deaf and Mute People (Smart Gloft) is a product in the form of a glove where there is a device that uses Arduino technology with the principle of converting visual text into audio and converting audio into visual text. This tool also contains sentences that are often used by users, making it easier for them to ask or answer something quickly. In this study, the researchers presented the design ideas in the form of 3D designs and prototype devices.