Reviewerly/exHarmony huggingface dataset:
Recommender Systems with Pandas, Surprise, and PySpark

In this chapter, we explore a new area of supervised learning, that of recommender systems. Even though recommender systems fall under supervised learning, they do not typically fall under either regression (Chapters 3 – 6 ) or classification (Chapters 7 – 12 ). They are considered a distinct area within machine learning called collaborative filtering.
k-Means Clustering with Pandas, Scikit-Learn, and PySpark

In this chapter, we delve into the process of building, training, and evaluating a k-means clustering algorithm for effective data segmentation. Clustering is a commonly used technique in segmentation analysis to group similar observations together based on their characteristics or their proximity in the feature space. The result is a set of clusters, with each observation assigned to a specific cluster. By organizing data into clusters, we can gain a deeper understanding of complex datasets and identify key subgroups.
Discovering Mathematical Expressions Through DeepSymNet: A Classification-Based Symbolic Regression Framework

Symbolic regression (SR) is the process of finding an unknown mathematical expression given the input and output and has important applications in interpretable machine learning and knowledge discovery. The major difficulty of SR is that finding the expression structure is an NP-hard problem, which makes the entire process time-consuming. In this study, the solution of expression structures was regarded as a classification problem and solved by supervised learning such that SR can be solved quickly by using the solving experience. Techniques for classification tasks, such as equivalent label merging and sample balance, were used to enhance the robustness of the algorithm. We proposed a symbolic network called DeepSymNet to represent symbolic expressions to improve the performance of the algorithm. DeepSymNet has been proven to have a strong representation ability with a shorter label compared to the current popular representation methods, reducing the search space when predicting. Moreover, DeepSymNet conveniently decomposes SR into two smaller subproblems, which makes solving the problem easier. The proposed algorithm was tested on artificially generated expressions and public datasets and compared with other algorithms. The results demonstrate the effectiveness of the proposed algorithm.
PENGENALAN BASSIC ENGLISH VOCABULARIES WITH PICTURES MEDIA KEPADA SISWA KELAS V SDN 137/III KOTO SALAK

Abstract Based on observations of students in class V SD No. 137/III Koto Salak, teaching English is not a must, only as a local content and not all elementary schools make English as mulok. Some include Iqra' subjects, regional culture and skills as local content (mulok). So that there are educational institutions in elementary schools that provide English lessons but not a few do not. Even if given, the activities carried out are singing in English, not in the form of two-way conversations. For that, we need a forum that can provide alternative problem solving, namely the existence of free English learning activities, but not ignoring the quality of the results and the beauty of the process. In planning this media, the lecturer provides the media, then the media is used in teaching English vocabulary. The results of this service indicate that the use of Pictures Media is very helpful in the process of introducing vocabulary and the occurrence of real communication processes between teachers and students. Learning activities that seem to play make students not burdened with the material being taught, even students' motivation increases because of the reward hunting activity on the toys island. Keywords: Vocabularies, Pictures Media, Elementary School, Learning
Super-resolution with generative adversarial networks for improved object detection in aerial images

Purpose Data quality and data resolution are essential for computer vision tasks like medical image processing, object detection, pattern recognition and so on. Super-resolution is a way to increase the image resolution, and super-resolved images contain more information compared to their low-resolution counterparts. The purpose of this study is analyzing the effects of the super resolution models trained before on object detection for aerial images. Design/methodology/approach Two different models were trained using the Super-Resolution Generative Adversarial Network (SRGAN) architecture on two aerial image data sets, the xView and the Dataset for Object deTection in Aerial images (DOTA). This study uses these models to increase the resolution of aerial images for improving object detection performance. This study analyzes the effects of the model with the best perceptual index (PI) and the model with the best RMSE on object detection in detail. Findings Super-resolution increases the object detection quality as expected. But, the super-resolution model with better perceptual quality achieves lower mean average precision results compared to the model with better RMSE. It means that the model with a better PI is more meaningful to human perception but less meaningful to computer vision. Originality/value The contributions of the authors to the literature are threefold. First, they do a wide analysis of SRGAN results for aerial image super-resolution on the task of object detection. Second, they compare super-resolution models with best PI and best RMSE to showcase the differences on object detection performance as a downstream task first time in the literature. Finally, they use a transfer learning approach for super-resolution to improve the performance of object detection.
TYPES OF INFORMATION IN LAW-MAKING PROCESS

This article analyzes the concepts of “information,” “information support” associated with the process of law making. The normative legal acts of the Republic of Uzbekistan and the opinions of scientists are discussed, author’s definitions for the notions have been given. As part of the study, the normative legal acts have been studied in detail. In addition to legal research, the author developed scientifically proved definitions of several legal terms such as “Information for law-making,” “Legal information” and “Informational support of law-making activity.” Information principles and classification criteria have been projected by the author. By analyzing the literature on law making, the author developed the classification of information submitted to the primary and secondary legislators. Approaching the issue from a theoretical and legal point of view, it should be noted that the classification issue of information provided for law-making activities based on appropriate criteria has been sufficiently studied in this paper.
(Nothing But) Many Eyes Make All Bugs Shallow

Open source package repositories have become a crucial component of the modern software supply chain since they enable developers to easily and rapidly import code written by others. However, low quality, poorly vetted code residing in such repositories exposes developers and end-users to dangerous bugs and vulnerabilities at a large scale. Such issues have recently led to the creation of government-backed verification standards pertaining to packages, as well as a significant body of developer folklore regarding what constitutes a reliable package. However, there exists little academic research assessing the relationships between recommended development practices and known package issues in this domain. Motivated by this gap in understanding, we conduct a large-scale study that formally evaluates whether adherence to these guidelines meaningfully impacts reported issues and bug maintenance activity across the most widely utilized npm packages (encompassing 7,162 packages with over 100K weekly downloads each), which unveiled wide disparities across package-level metrics. We find that it is only recommendations pertaining to a broad notion of scrutiny that provide strong and reliable insights into the reporting and resolving of package issues. These findings pose significant implications for developers, who seek to identify well-maintained packages for use, as well as security researchers, who seek to identify suspicious packages for critical observation.
Estimating Security Risk Through Repository Mining

As open-source projects are becoming ever more present throughout the modern computing stack, it is crucial to be able to quickly spot problematic, poorly maintained, or otherwise "risky" projects. The OSSF Scorecard has been introduced to address this need for fast security risk assessment by mining software repository metadata. We can reasonably expect a project with a CI system, code-reviews, and lots of users to be less risky than a project without those qualities. Since measuring security risk directly is difficult, we test a proxy hypothesis that these factors should also correlate with a reduction of bugs in software and test our hypothesis by scanning thousands of popular C & C++ projects with static analysis tools.
Scalable Policies for Supply Chain Security

Most of the discussion around how to apply policies in the domain of Software Supply Chain Security is focused on how to meet the policy goals of a specific project. The solutions often assume the project owners are highly motivated and can invest significant time in writing the detailed rules that ensure their policy goals are met. While this may be true of people interested in software supply chain security today, we must also develop solutions that are easy to adopt by project owners who are less interested in security and who may see new requirements as a roadblock to achieving the goals of their project. In addition, the solutions must still meet a high security bar and be easy to reason about across the entire industry's software development lifecycle (SDLC)
The Knockout of the Century: Cyber Resiliency through the Arts

Join us for a captivating keynote that will take you on a unique journey where the worlds of technology, martial arts, and open-source software community empowerment converge. Discover the power of partnership within the tech industry as we unveil innovative collaborations that have reshaped the cybersecurity landscape. Learn how principles borrowed from martial arts philosophy find practical application in the ever-evolving battle against cyber threats. Be inspired by the fusion of tech, martial arts wisdom, open-source innovation, and inclusivity for a resilient cyber world. Don't miss "The Knockout of the Century" in cybersecurity!
An Extensive Survey on Investigation Methodologies for Text Summarization

Natural language processing (NLP) is a fast-expanding field, and text summarization has recently gained a lot of research interest. The necessity for automatic summarizing approaches to effectively digest massive amounts of textual data has grown in importance, due to the plethora (excessive amount of something) of information available in the digital age [18]. By automatically producing succinct and educational summaries of extensive materials, NLP-based text summarizing systems have the potential to revolutionize the way humans consume and process information. This review paper offers a thorough examination of the text summarizing research approaches. The process of creating a concise and useful summary of a text document is called text summarization. Even for cutting-edge natural language processing (NLP) systems, it is a difficult task. It was carried out using a thorough analysis of the most recent text summarizing research. The evaluation revealed a variety of research approaches that have been employed in the creation and assessment of text summarizing systems. This study's key discovery is that there are numerous different investigative approaches that can be used for text summarizing. These methods can be roughly divided into two groups: • Extractive text summarization • Abstractive text summarization During the review we found that extractive summarization is a fairly simple method as it selects the key phrases from a text and extracts them to create a summary while abstractive summarization presents data in a clearer, more informative fashion by producing a summary. This review was important because it gives a thorough overview of the research approaches utilized for text summarizing, this article is significant. Researchers and programmers can utilize this data to create brand-new, improved text summarizing systems. [20]
Boosting legal case retrieval by query content selection with large language models

Legal case retrieval, which aims to retrieve relevant cases to a given query case, benefits judgment justice and attracts increasing attention. Unlike generic retrieval queries, legal case queries are typically long and the definition of relevance is closely related to legal-specific elements. Therefore, legal case queries may suffer from noise and sparsity of salient content, which hinders retrieval models from perceiving correct information in a query. While previous studies have paid attention to improving retrieval models and understanding relevance judgments, we focus on enhancing legal case retrieval by utilizing the salient content in legal case queries. We first annotate the salient content in queries manually and investigate how sparse and dense retrieval models attend to those content. Then we experiment with various query content selection methods utilizing large language models (LLMs) to extract or summarize salient content and incorporate it into the retrieval models. Experimental results show that reformulating long queries using LLMs improves the performance of both sparse and dense models in legal case retrieval.
Reconstructing Hand Shape and Appearance for Accurate Tracking from Monocular Video

A virtual animatable hand avatar capable of representing a user’s hand shape and appearance, and tracking the articulated motion is essential for an immersive experience in AR/VR. Recent approaches use implicit representations to capture geometry and appearance combined with neural rendering. However, they fail to generalize to unseen shapes, don’t handle lighting leading to baked-in illumination and self-shadows, and cannot capture complex poses. In this thesis, we 1) introduce a novel hand shape model that augments a data-driven shape model and adapt its local scale to represent unseen hand shapes, 2) propose a method to reconstruct a detailed hand avatar from monocular RGB video captured under real-world environment lighting by jointly optimizing shape, appearance, and lighting parameters using a realistic shading model in a differentiable rendering framework incorporating Monte Carlo path tracing, and 3) present a robust hand tracking framework that accurately registers our hand model to monocular depth data utilizing a modified skinning function with blend shapes. Our evaluation demonstrates that our approach outperforms existing hand shape and appearance reconstruction methods on all commonly used metrics. Further, our tracking framework improves over existing generative and discriminative hand pose estimation methods.
Enabling Lattice-Based Post-Quantum Cryptography on the OpenTitan Platform

\sisetupgroup-separator = ,, group-minimum-digits=3, The first generation of \glspqc standards by the \glsnist is just around the corner. The need for secure implementations is therefore increasing. In this work, we address this need and investigate the integration of lattice-based \glspqc into an open-source silicon \glsrot, the OpenTitan. \glsplrot are important security building blocks that need to be future-proofed with \glspqc. The OpenTitan features multiple cryptographic hardware accelerators and countermeasures against physical attacks, but does not offer dedicated support for lattice-based \glspqc. Thus, we propose instruction set extensions for the \glsotbn to improve the efficiency of polynomial arithmetic and sampling. As a case study we analyze the performance of signature verification of digital signature scheme Dilithium. Our implementation verifies signatures within νm997722 cycles for security level II, pushing this \glsrot functionality below \SI10 \milli\second for the OpenTitan's target frequency of \SI100 \mega\hertz. With an overhead of \SI242 \kilo\nothing GE, our hardware extensions make up only about \SI5 \percent of the total \glsrot area. All our extensions integrate seamlessly with countermeasures against physical attacks and comply with the adversary model chosen by the OpenTitan project.
In Search of Trust: 30 Years of Evolution of Trusted Computing and Hardware Security

Hardware has been long considered as the source and root of trust in computing, but the nature of its trust characteristics and views on trust technologies have evolved over time. The infancy of hardware security and trust, decades ago, emerged in the days when security attacks were limited in number and sophistication, the computing environment was relatively simple and homogeneous, the population of connected users was small, and the use cases where computing played a key role were very far from ubiquitous. Today, the situation is diametrically different from the early days of hardware trust and trusted computing. The talk will provide an overview of the evolution of technology and use cases in this area, examine persistent challenges, analyze mistakes we made along the way, look into the intersections with adjacent areas of research and practice and finally outline some promising directions for the next generation of trusted technologies.
Entangled Clouds: Measuring the Hosting Infrastructure of the Free Contents Web

Free content websites (FCWs) are a critical part of the Internet, and understanding them is essential for their wide use. This study statistically explores the distribution of free content websites globally by analyzing their hosting network scale, cloud service provider, and country-level distribution, combined and per the content category they provide, and by contrasting these measurements to the characteristics of premium content websites (PCWs). Our study further contrasts the distribution of these websites to general websites sampled from the Alexa top-1M websites and explores their security attributes using various security indicators.
CDGeB: Cloud Data Geolocation Benchmark

Cloud computing has revolutionized data processing and management, offering flexible and scalable infrastructure for the distribution of content, computing power, and services across the globe. Dynamic, flexible, and transparent reallocation of resources increases cloud-based services' use and effectiveness. As rates of cloud adoption soar, privacy regulations, and geopolitical security introduce new challenges, which include the assessment, validation, and enforcement of data geolocation. However, currently, there is no standardized benchmark for this research domain. Therefore, this paper presents a novel dataset of measurements specifically designed to evaluate cloud data geolocation algorithms. In addition to its beneficial role in evaluating data geolocation algorithms, our dataset can be used for other data geolocation subtopics.
FOBOS 3: An Open-Source Platform for Side-Channel Analysis and Benchmarking

The lightweight cryptography (LWC) standardization process by the National Institute of Standards and Technology (NIST) of the US is the latest example of competitions that require benchmarking and side-channel leakage evaluation of hardware implementations of a multitude of candidate algorithms. A common hardware application programming interface (API) streamlines the development of a test harness. However, no existing platform is directly compatible with the LWC algorithms' hardware interface. Hence, a significant effort is needed to evaluate and benchmark a large number of candidates.
Identifikasi pohon keputusan hipertensi dengan sistem RapidMiner dan metode klasifikasi

At present health is very important to support all activities, both work and sports. Sometimes people ignore it in health checks, especially related to blood health. Early detection is very important in controlling the health of the body in everyday life. One of the factors that people often complain about is high blood pressure, which hinders all activities. High blood pressure is a disease caused by increased blood pressure. This study aims to identify the decision tree for high blood pressure for residents around the Bekasi area with sample data used, namely data on age, weight, blood type, and susceptibility to high blood pressure by distributing questionnaires. This study uses the RapidMiner system and data classification methods through questionnaires as respondents and health service test samples. The findings of this study are 6 people who allow for high blood pressure of various ages. The potential for high blood pressure is detected from the age of over 34 with a total of 3 people from blood groups B and O. The results of this study based on an accuracy rate of 77.78% show that of the 32 people in the high blood questionnaire data, 3 people are aged over 34 and 3 people aged 3 people.
Research on the Reform of the “Dual-Mode Dual-Track” Talent Cultivation Model for Architecture Majors in Higher Vocational Colleges under the Background of “Quality Improvement and Excellent Cultivation”

Under the background of “quality improvement and excellent cultivation”, the talent training mode of higher vocational colleges has aroused the attention of the society. As the key specialty of many higher vocational colleges, the construction specialty can explore a new professional and technical talent training mode through the implementation of “dual-mode dual-track” talent training mode, and promote the further development of the construction industry. By analyzing the reasons for the reform, this paper draws the reform strategy, hoping to promote the long-term development of higher vocational colleges.
Non-fungible tokens in healthcare: a scoping review

Introduction Non-Fungible Tokens (NFTs) are digital assets that are verified using blockchain technology to ensure authenticity and ownership. NFTs have the potential to revolutionize healthcare by addressing various issues in the industry. Method The goal of this study was to identify the applications of NFTs in healthcare. Our scoping review was conducted in 2023. We searched the Scopus, IEEE, PubMed, Web of Science, Science Direct, and Cochrane scientific databases using related keywords. The article selection process was based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). Results After applying inclusion and exclusion criteria, a total of 13 articles were chosen. Then extracted data was summarized and reported. The most common application of NFTs in healthcare was found to be in health data management with 46% frequency, followed by supply chain management with 31% frequency. Furthermore, Ethereum is the main blockchain platform that is applied in NFTs in healthcare with 70%. Discussion The findings from this review indicate that the NFTs that are currently used in healthcare could transform it. Also, it appears that researchers have not yet investigated the numerous potentials uses of NFTs in the healthcare field, which could be utilized in the future.