Reviewerly/exHarmony huggingface dataset:
The MLAT-Elementary

The chapter first revisits the major developments of MLAT and its variants based on Stansfield & Reed (2019), and then turns to focus on the MLAT-Elementary test battery that was designed particularly for long-neglected groups of young L2 learners, including the history of MLAT-E, its applications and validations over the past five decades, and finally, and outline of its future.
Bitcoin’s Governance Challenges

This paper delves into the governance challenges faced by Bitcoin, the pioneering decentralized cryptocurrency introduced in 2009 by an anonymous entity known as Satoshi Nakamoto. Despite its growing popularity and record-breaking value, Bitcoin's decentralized structure poses unique governance challenges that threaten its long-term stability. This paper explores the historical evolution of currency and governance, examining the role of governments and central banking systems. It highlights the advantages of decentralized cryptocurrencies, including enhanced security, anonymity, and accessibility, while acknowledging the challenges faced by Bitcoin, such as decentralized governance and concentration of mining power. The paper also addresses regulatory obstacles and the varying approaches taken by governments worldwide. Regional examples, such as the collaboration between the UK Financial Conduct Authority and the Treasury Committee, and the Indian government's stance on Bitcoin, shed light on potential solutions. Ultimately, the paper concludes that overcoming these challenges and controversies necessitates regulatory reform, technological innovation, and widespread user education. Collaboration among governments, regulators, and the cryptocurrency community is crucial to establish clear regulations, enhance security measures, and promote mainstream adoption, thereby unlocking the potential benefits of digital currencies in the evolving landscape.
CorCA: An Automatic Program Repair Tool for Checking and Removing Effectively C Flaws

Embedded systems are present in many devices, such as the Internet of Things, drones, and cyber-physical systems. The software security of these devices can be critical, depending on the context they are integrated and the role they play (e.g., water plants, vehicles). C is the core language used to develop the software for these devices and is known for missing the bounds of its data types, which leads to vulnerabilities such as buffer overflows. These vulnerabilities, when exploited, can cause severe damage and put human life in danger. One of the concerns with vulnerable C programs is to correct the code automatically and adequately, employing secure code that can remove the existing vulnerabilities and avoid attacks. However, such a task faces some challenges, namely determining what code is needed to remove them and, at the same time, ensuring the correct behaviour of the program, where to insert it, and verifying that the correction applied is secure and effectively removes the vulnerabilities. Another challenge is to accomplish all these elements in an automated manner. This paper presents an approach that automatically, after discovering and confirming potential vulnerabilities of an application, applies code correction to fix the vulnerable code of those confirmed vulnerabilities and validates the new code. We implemented the approach, resulting in the CorCA [1] tool, and evaluated it with a set of tests and real applications. The experimental results showed that the tool was capable of detecting vulnerabilities and fixing them correctly.
AI is a game-changing technology: how to test and robustify Machine-Learning software?

The recent release of ChatGPT conversational agent has been a surprise to me, and to many of my colleagues from the software engineering community. Progress goes extremely fast, while it appears to be a true "game-changing" technology that can even generate programs and fix bugs. Machine Learning (ML) provides engineers with the prospect of producing data-driven software, with little manual code writing. These ML-enabled software bring us to a new era where systems’ logic is automatically produced from data, with a small amount of human-written code. Would we trust such software mixing ML and regular code, would you rely on it and under which conditions? This is still too early to answer these questions, and a challenging direction to explore.This radical change questions the way software are engineered, validated, secured, deployed and maintained. The overall challenge is thus to automate these activities accounting for the statistical nature of ML-enabled software.Taking a software engineering perspective, and starting from a concrete case from the finance industry, the presentation will focus on testing and robustifying a ML model which is integrated in a larger software system that takes as input domain objects (e.g. financial transaction, malware, network traffic). One traditional way to robustify a ML model consists in generating adversarial inputs, e.g. leading to a misclassification, and retraining the model. Indeed, despite their impressive performance, ML models are sensitive to small perturbations in the input. The resulting adversarial inputs raise multiple questions about the robustness of such systems, especially in safety- and business-critical domains. However, the generation of feasible, exploitable adversarial test examples is challenging, as they must satisfy the business logic constraints over the feature space. We analyse the limitations of current adversarial approaches and explore new algorithms that combine multi-objective search with constraint-solving techniques. While the attack part is the offensive weapon, we also consider the challenge to efficiently shield (e.g. repair) the systems against such threats, and finally end the seminar by mentioning other research directions to deploy robust ML-enabled systems.
DaViTPose: An Enhanced Vision Transforme- Based Architecture for Accurate Human Pose Estimation

Human keypoint detection from a single image is exceedingly challenging due to occlusion, blur, illumination, and size differences. We present DaViTPose, an encoder-decoderbased architecture consisting of a Vision Transformer-based encoder and an attentional decoder for constructing accurate localisation heatmaps for keypoint identification, in this paper. Even when there are occlusions in the input image, our model outperforms previous algorithms and can predict the human posture with high accuracy. We conducted extensive tests on the MS COCO benchmark dataset and shown that our model outperforms other state-of-theart models of comparable size. On the MS-COCO benchmark dataset, the suggested model gets an average accuracy (AP) score of 73.2 and an average recall (AR) of 76.8.
Teamwork through time: collective intentions in the voting process

Voting is a collective activity: it requires more than one person to win a vote. In a corporation, voting allows the winning idea to become an intention of the corporate group once the vote is concluded. In this paper, argue that unlike in corporate boards, in a democratic election, the voting process does not create a group intention. The difference between the two processes is an oft-overlooked moment directly after the corporate vote in which members on the losing side ratify the decision of the group and move on with the group into the future. In state elections, because of the high cost of renouncing membership, there is typically no ratification moment. While different groups do have collective intentions prior to an electoral vote, the vote itself does not manifest the intention of a group agent such as ‘the People.’ This conclusion has implications for minority rights.
3D Printing Technique to Make Information inside an Object Rewritable: Effect of Amount of Filament on Readability

This paper presents a technique for embedding information into a 3D object. The technique can rewrite the information non-destructively. Information can be rewritten into the object using magnetization and demagnetization from the outside of the fabricated object. We are studying the factors that can make more clear readability and effective rewritability in embedding data into 3D objects. One factor is the amount of magnetic filament that we use for representing the information in each position. We found that the strength of the magnetic field varies in accordance with the amount of filament. In this way, we can control the size, frequency, and capacity of future embedded information.
Differentiating Digital Printing Through Physical and Chemical Analyses

Over the past decade, the trade of counterfeit goods has increased. This has been enabled by advancements in low-cost digital printing methods (e.g., inkjet and laserjet) that are an asset for counterfeit production methods. However, each printing method produces characteristic printed features that can be used to identify not only the printing method, but also, uniquely identify the specific make and model of printer. This knowledge can be used for determination of whether or not the analyzed item is counterfeit. During the first phase of this research, chemical and physical analyses were performed on printed documents and ink samples for two types of digital printing: inkjet and laserjet. The results showed that it is possible to identify the digital method used to print a document by its unique features. Physical analysis revealed that the laserjet prints have a higher image quality characterized by sharper feature edge quality, brighter image area, and a thicker ink layer (10 micron average thickness) than in inkjet documents. Chemical analysis showed that the inkjet and laserjet inks could easily be distinguished by identifying the various ink components. Ink jet inks included (among others) water, ethylene glycol while laserjet inks presented styrene, methacrylate, and sulfide compounds.
Navigating the landscape of research paradigms: An overview and critique

The aim of this study is to investigate the different research paradigms, including conventional and alternative paradigms, and to critically examine their underlying assumptions and implications for research design, data collection, and analysis. The methodology involves a critical examination of different research paradigms and their underlying belief structure. The main findings of this study indicate that different research paradigms have different assumptions about the nature of reality, the role of the researcher, and the goals of the research. These assumptions have implications at the fundamental level. This review article contributes to the existing literature by providing a comprehensive overview of research paradigms and their implications for research design, data collection, and analysis, practically. The study also emphasizes the importance of choosing the most appropriate research paradigm that aligns with the research questions and goals, leading to more accurate and relevant findings that contribute to the advancement of knowledge in the scholarly fields.
The Efficacy of Social Media as an Online Learning Medium during the COVID-19 Period

The goal of this study was to determine the efficacy of social media as an alternative to using Online Learning Media to add new knowledge and information and assist with assignments, particularly for technical matters, at Allodia Vocational High School Bekasi City during the COVID-19 pandemic, which is currently underway. This study employs a research and development strategy, with research instruments consisting of a validation questionnaire and a product attractiveness rating questionnaire delivered in the form of a Google form. The findings revealed that students' use of social media as a learning medium was quite beneficial. This could be because practically all students have social media profiles and are therefore highly familiar with them. According to the study findings, the practicality of teaching materials uploaded on social media is very possible, according to 75% of media presentation experts, 82% of material experts, and 70% of linguists. As a result, adopting social media as a learning medium throughout the COVID-19 period is highly beneficial.
Random Color Image Encryption Using the Genetic Algorithm

This paper implements a combined pseudo-random sequence generator based on neural networks and chaotic, random variable color images based on an algorithm for chaotic encryption, secure image storage, and transmission. The generator that controls the operation of the encryption algorithm: the arrangement of pixel positions, the use of random color images of the AES algorithm to achieve encryption, creation algorithms, and neural networks by dynamically updating control parameters and numbers to increase the generated chaotic sequence randomness by using simulation MATLAB for getting output. The chaotic function's iteration of the neural networks. The experimental results are presented in the form of a pixel correlation coefficient and security analysis to prove the security and effectiveness of the proposed chaos-based ANN encryption.
Thank You to Reviewers

Journal of Adolescent & Adult LiteracyVolume 66, Issue 6 p. 391-393 Thank You to Reviewers Thank You to Reviewers First published: 25 May 2023 https://doi.org/10.1002/jaal.1287Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume66, Issue6May/June 2023Pages 391-393 RelatedInformation
Adaptive Phishing Detection: Harnessing the Power of Artificial Intelligence for Enhanced Email Security

The rapid evolution of phishing attacks poses a significant threat to email security, with traditional detection methods struggling to keep up with the constantly changing landscape.This study investigates the use of Artificial Intelligence (AI) for developing adaptive phishing detection techniques capable of identifying and preventing sophisticated phishing attempts.By harnessing the power of AI, the proposed approach aims to enhance email security and protect users from the evolving tactics employed by attackers.This paper explores various machine learning algorithms, including deep learning and natural language processing techniques, to create an intelligent system capable of detecting phishing emails in real time.This system is designed to learn from a diverse dataset of phishing and legitimate emails, dynamically updating its detection capabilities in response to emerging trends and attack patterns.Additionally, the study examines the role of AI in identifying social engineering tactics, such as the use of personalized content and psychological manipulation, which are frequently employed in targeted phishing campaigns.
Classification of Arabic Social Media Texts Based on a Deep Learning Multi-Tasks Model

The proliferation of social networking sites and their user base has led to an exponential increase in the amount of data generated on a daily basis.Textual content is one type of data that is commonly found on these platforms, and it has been shown to have a significant impact on decision-making processes at the individual, group, and national levels.One of the most important and largest part of this data are the texts that express human intentions, feelings and condition.Understanding these texts is one of the biggest challenges that facing data analysis.It is the backbone for understanding people, their orientations, and making decisions in many cases and thus predicting their behavior.In this paper, a model was proposed for understanding texts that written by people on social media platforms, and hence knowing people's attitudes within specific topics, the emotion of those people, positivity, negativity, and neutrality.Also, it extracts emotion of those people.In this context, the system solves many tasks in natural language processing therefore it uses many techniques including topic classifier, sentiment analyzer, sarcasm detector and emotion classifier.CNN-BiLSTM was used for topic classifier, sentiment analyzer, sarcasm detector, and emotion classifier where (f-measure, accuracy) were (97,97.58)%, (84,86) %, (95,97) %, and (82,81.6)% respectively.
Topography of Research on the History of Medicine in the Modern Korea, 1876-1945: Including Research on the Body, Hygiene, Infectious Diseases, Chronic Diseases, Medical Care, Medical science, Traditional Medicine, and Drugs

This review article aims to examine the overall aspects and characteristics of research on the history of medicine in the modern Korea, 1876-1945. This review article takes an approach that quantitatively grasps the overall research landscape, instead of selecting only the necessary parts for discussion and making qualitative interpretations. By adopting this method, it is possible to review research results that were not included in the previous review papers, such as internal research in the medical field and research in Korean literature. This article collects as many papers and books as possible, numbering 1,645 (1,067 research papers, 263 books, 167 master's and doctoral theses, 83 papers in the editorial books, 62 biographical documents, and 3 lecture notes). Here, the number of 167 master's and doctoral theses shows that beginner scholars have shown a lot of interest in this field, and the number of 263 books means that veteran researchers who are enough to publish books are also active. By field, 38% are related to Western medicine, 23% are traditional Korean medicine, 32% focus on non-medical fields such as body, hygiene, quarantine, and infectious disease, and the rest 7 percent cover multiple fields. All theses and books are reviewed nine categories: body, hygiene, infectious disease, disease excluding acute infectious disease, medical care, medical science, traditional Korean medicine, drug, and comprehensive review including two or more categories. The category of comprehensive review includes discussions of general history, bibliographic research, institutions, general thesis, etc. The category of body includes articles on thoughts about the body, body discipline, health, athletic, eugenics, sexuality, children, disability, and death. The category of hygiene includes theory, institution, hygienics, various fields, urban hygiene, culture, and folk beliefs. The category of infectious disease includes theory of infectious diseases and epidemic prevention and control. The category of disease includes diseases in general, culture of diseases, leprosy, tuberculosis, sexually transmitted diseases, mental diseases, and others. The category of medical care includes general history, institutions, medical profession, hospital, activities of doctors, branch of medical care, dentistry, veterinary medicine, nursing, and medical culture, and consuming of medicine. The category of medical science includes medical education and scientific research. The category of traditional Korean medicine includes it’s general aspect and Sasang medicine as a specialized branch of it. he category of drug includes regulation of the sale of drug, medicine, ginseng, consumption of drugs, and drug-related culture. These 1645 papers and books revealed many aspects of history of Korean medicine in modern period and suggest future tasks in those fields.
Blockchain and IPFS: A Permanent Fix for Tracking Farm Produce

Blockchains typically employ IPFS for off-chain storage of user information.Centralized management, muddled data, inaccurate data, and the simplicity of building information enclaves plague traditional traceability systems.In this research, blockchain technology is used to record and access data on Non-Perishable (NP) agricultural commodities in the distribution chain to solve the challenges above.The blockchain and IPFS both store public and private data encrypted.This lessens the burden on the blockchain and enhances information search.Blockchain technology enhances farmer-customer relationships and food supply chains by tracking food back to its source.Its secure data storage enables datadriven farming.By storing encrypted files IPFS hashes in smart contracts, IPFS secures agricultural data and addresses the blockchain storage problem.Being deployed in association with connects makes it possible for rapid financial transactions to occur with any changes made to the blockchain's data.This article analyses performance and simulates implementation in Ethereum testnets.The results show that our system protects sensitive data, supply chain data, and real-world applications by increasing the throughput and latency.
Bimbingan Belajar Vocabulary MI &amp; SD Di Desa Nglaban Jombang

Vocabulary is important in communication the students who are capable in mastering vocabulary will be easy to understand English text. they will easily understand the contents of the text. However, many students do not master it, including students who live in Nglaban. This guidance aims to help those students, and the results of the guidance can be concluded that the assistance in learning English for class II MI/SD students in Nglaban using drill method, and game techniques and picture is very effective and has a positive impact, the students vocabulary seems increased significantly, because it can be seen from the comparison of pretest and posttest values with the paired sample test that the results are t count &gt; t table (6, 689 &gt; 2, 16037, this means the learning is successful.