Reviewerly/exHarmony huggingface dataset:
Pedatren: Educational Administration Applications for Simplifying Paiton Probolinggo's Nurul Jadid Islamic Boarding School's Management

Nurul Jadid, one of the biggest Islamic boarding schools in Indonesian with 10,154 students, faces difficulties in running Islamic boarding schools' educational administration. The validity of student data is crucial to the ongoing process of education. If complex and large amounts of data are not managed properly, data validation issues may arise. If there are issues with validating student data, islamic boarding schools will struggle to obtain information on the growth of their students. In the end, it will be challenging for Islamic boarding schools to decide on policy directions for the growth of Islamic boarding schools. The Nurul Jadid Islamic Boarding School Paiton Probolinggo created the Pedatren (Islamic Boarding School Data Management) application to address this issue. The Nurul Jadid Islamic Boarding School developed the application Pedatren with the goal of bridging the data interests of all institutions and organizations. Every user who can log in to Pedatren is given access rights according to their respective interests by using the jami’ wal mani’ principle to provide levels of use, or access rights. The goal of this study is to provide a description of the Pedatren application and how it aids in the management of the Nurul Jadid Islamic Boarding School in Paiton Probolinggo. The data for this study were collected through in-depth interviews, extensive observations, and trials on the Pedatren application. Parenting programs, application-based student permits, application-based student attendance and ustadz, as well as the islamic boarding school database are all included in the Pedatren application. The Node.Js programming language, PM2 vue js clustering framework, and Rest-Api are all used in the management of this islamic boarding school's data. Pedatren can be an effective and efficient management solution for Nurul Jadid Islamic Boarding School with indicators, maximum service quickly, simplify searches, become a comprehensive Islamic boarding school database, become policy considerations, and become the main data in inter-institutional systems and local government.
Extracting Implicit Twitter Replies to News Topics

With the increase in the number of users on social networking services (SNSs) in the recent times, such services have been widely adopted to gather information. In general, users on SNSs typically use keyword searches to find information on news topics. However, multiple representative keywords are often related to any given topic. Therefore, comprehensively searching for opinions and reactions to news using only keyword search is typically difficult. In this study, we focus on a function implemented on Twitter, as a representative popular SNS, which allows users to reply to others' posts. We propose a method to discover tweets that express opinions and reactions to news topics by using posted news articles and replies as training data for a machine learning model. The model learns the relationship between news articles and replies, which do not necessarily include typical keywords. The proposed model can extract tweets that implicitly reply to news topics without directly replying to any specific post. This work contributes to the literature on social media in terms of understanding news topics and their associated discourse.
Building a Recommendation System of Consumers' Preferable Choice Using a Collaborative-Filtering

Nowadays, one of many challenges in e-Commerce is to target the right customer by recommending relevant products or services that suit their preference. To overcome this challenge, various sources have been utilised including customer comments and reviews from social media to analyse their preference based on their interests and sharing. Numerous large e-Commerce web sites are now investing in recommender systems to ease their customers into recommending the relevant products that suit their preferences. However, there are still limitations within the existing recommender systems that are lacking in discovering the real sentiment values of how customers perceived the products or services offered. This study aims to develop a product recommender system that incorporates the sentiment values from customer post on social media and predict the top five recommended products using the Collaborative-Based filtering technique. The tweets were collected from top global and local retailers, Amazon and Shopee to understand the customer response from social media followed by the development of a recommender system for the customer. The findings of this study highlighted a total of 8 clusters such as electronics, entertainment, and books from the clustering experiment. The top 5 best seller product predictions for each category also showed a score of 0.59, which is higher than the threshold score in the evaluation stage.
CIA-based Analysis for E-Leaming Systems Threats and Countermeasures in Malaysian Higher Education: Review Paper

In Malaysia, the early days of e-learning implementations can be traced back to the late 1990s.However, the development and use of such systems are fraught with security issues, concerns about the security of personal data, and content-related security flaws, confidentiality, integrity, availability,
Exploring the Use of Facial Attributes in Personality-Driven Recommendation Systems (FABaRS): A Survey

Modern computing systems are designed to provide a personalised experience for the user. They use a variety of techniques, such as machine learning and data analytics, to tailor their interactions and results to the user's needs and preferences. This paper aims to provide an updated survey of the state of facial attribute-based personality-aware computing used specifically in recommender systems, with a focus on recent developments. The objective of this study is to outline the current themes and directions of research in the field of facial attribute-based personality-aware computing for recommenders and to provide insights into potential future developments in this area.
Detection of Synthesized Satellite Images Using Deep Neural Networks

The technology of generative adversarial networks (GAN) is constantly evolving, and synthesized images can no longer be accurately distinguished by the human eyes alone. GAN has been applied to the analysis of satellite images, mostly for the purpose of data augmentation. Recently, however, we have seen a twist in its usage. In information warfare, GAN has been used to create fake satellite images or modify the image content by putting fake bridges, buildings and clouds to mislead or conceal important intelligence. To address the increasing counterfeit cases in satellite images, the goal of this research is to develop algorithms that can classify fake remote sensing images robustly and efficiently. There exist many techniques to synthesize or manipulate the content of satellite images. In this paper, we focus on the case when the entire image is forged. Three satellite image synthesis methods, including ProGAN, cGAN and CycleGAN will be investigated. The effect of image pre-processing such as histogram equalization and bilateral filter will also be evaluated. Experiments show that satellite images generated by different GANs can be easily identified by individually trained models. The performance degraded when model trained with one type of GAN samples is employed to determine the originality of images synthesized with other types of GANs. Additionally, when histogram equalization is applied to the images, the detection model fails to distinguish its authenticity. A four-class universal classification model is proposed to address this issue. An overall accuracy of over 99% has been achieved even when pre-processing has been applied.
Extended Addressing Machines for PCF, with Explicit Substitutions

Addressing machines have been introduced as a formalism to construct models of the pure, untyped lambda-calculus. We extend the syntax of their programs by adding instructions for executing arithmetic operations on natural numbers, and introduce a reflection principle allowing certain machines to access their own address and perform recursive calls. We prove that the resulting extended addressing machines naturally model a weak call-by-name PCF with explicit substitutions. Finally, we show that they are also well-suited for representing regular PCF programs (closed terms) computing natural numbers.
Distributed Learning of Pure Non-IID data using Latent Codes

There has been a huge increase in the amount of data being generated as a result of the proliferation of high-tech, data-generating devices made possible by recent developments in mobile technology. This has rekindled interest in creating smart applications that can make use of the possibilities of this data and provide insightful results. Concerns about bandwidth, privacy, and latency arise when this data from many devices is aggregated in one location to create more precise predictions. This research presents a novel distributed learning approach, wherein a Variational Auto Encoder is trained locally on each client and then used to derive a sample set of points centrally. The server then develops a unified global model, and sends its training parameters to all users. Pure non-i.i.d. distributions, in which each client only sees data labelled with a single value, are the primary focus of our study. According to our findings, communication amongst the server and the clients takes significantly less time than it does in federated and centralised learning setups. We further demonstrate that, whenever the data is spread in a pure non-iid fashion, our methodology achieves higher accuracy than the federated learning strategy by more than 4%. We also showed that, in comparison to centralised and federated learning systems, our suggested method requires less network bandwidth.
Large scale multi-output multi-class classification using Gaussian processes

Abstract Multi-output Gaussian processes (MOGPs) can help to improve predictive performance for some output variables, by leveraging the correlation with other output variables. In this paper, our main motivation is to use multiple-output Gaussian processes to exploit correlations between outputs where each output is a multi-class classification problem. MOGPs have been mostly used for multi-output regression. There are some existing works that use MOGPs for other types of outputs, e.g., multi-output binary classification. However, MOGPs for multi-class classification has been less studied. The reason is twofold: 1) when using a softmax function, it is not clear how to scale it beyond the case of a few outputs; 2) most common type of data in multi-class classification problems consists of image data, and MOGPs are not specifically designed to image data. We thus propose a new MOGPs model called Multi-output Gaussian Processes with Augment &amp; Reduce (MOGPs-AR) that can deal with large scale classification and downsized image input data. Large scale classification is achieved by subsampling both training data sets and classes in each output whereas downsized image input data is handled by incorporating a convolutional kernel into the new model. We show empirically that our proposed model outperforms single-output Gaussian processes in terms of different performance metrics and multi-output Gaussian processes in terms of scalability, both in synthetic and in real classification problems. We include an example with the Ommiglot dataset where we showcase the properties of our model.
Improved Evolution Performance for Genetic Programming with Method to Search Numbers of Trees

In evolution, Genetic programming (GP) is proposed to obtain suitable action rules for a target problem. Because action rules are expressed in a tree structure, their meaning is easily understandable. In addition, GP with multiple tree structures has been proposed for agent learning, and a method has been proposed to decide the number of multiple trees that must be set to individuals for each target problem during evolution. In this study, we focused on an algorithm to search for the suitable number of multiple trees in evolution and introduced a method for generating individuals with conditional probability to improve performance.
Constituency-Informed and Constituency-Constrained Extractive Question Answering with Heterogeneous Graph Transformer

Large neural language models are achieving exceptional performance in question answering and other natural language processing tasks. However, these models can be costly to train and difficult to interpret. In this paper, we propose to investigate whether incorporating explicit linguistic information can boost model performance while improving model interpretability. We present a novel constituency-informed and constituency-constrained question answering model called SyHGT-CN. The linguistics-informed model integrates the symbolic information contained in constituency trees with the statistical knowledge of a neural language model. The integration of the linguistics graphic structures with the transformer-based neural language model is achieved by the adjunction to the latter of a heterogeneous graph neural network, in which the former is encoded. We comparatively and empirically show, with the SQuAD2.0 benchmark, that the proposed approach is more accurate than a constituency-oblivious BERT and the constituency-informed SyHGT-C model.
Pixel Rarity Score: Rarity Learned Directly From Pixel Data

Conventional rarity scores rely on the meta-data of an NFT collection (the traits) to determine relative rarity within a collection. Such data is provided by the developers of the collection. In this paper, we discuss pixel-based rarity, which utilizes on raw pixel data to determine rarity. More specifically, we apply an algorithm that captures the pixels in the NFTs and assigns a rarity score to each pixel. It weighs the scores across all pixels within a set of NFTs to give an overall rarity score. We argue that by extracting the pixel data directly from the picture we could generate a richer and more systematic rarity computation. We compare rarity scores to pixel-based rarity scores and discuss their respective merits. Our results show that both scores, on average, show similar performance in predicting relative prices. A surprising result is that rarity and pixel rarity are weakly correlated. We argue that pixel rarity could be a viable metric in the case that the trait data does not properly reflect the visual features of the NFTs. Additionally, pixel-based rarity can be used to compare NFTs across multiple collections and could be a viable substitute for rarity scores where there is limited or no feature data available to compute conventional rarity scores.
Backgrounds

To make this book self-satisfied, we introduce some preliminaries in this chapter, including some introductions on process algebra and true concurrency.
Introduction

Parallelism and concurrency are the core concepts within computer science. There are mainly two camps in capturing concurrency: the interleaving concurrency and the true concurrency. This chapter gives an introduction to the interleaving concurrency and the true concurrency, especially true concurrency.
Calculus and optimization

We start with a review of differential calculus. We highlight a few key results in calculus including the extreme value theorem, the mean value theorem, and the chain rule, which will play an important role for data science and machine learning. We also discuss a number of optimization results and machine learning methods including logistic regression, support vector machines, and neural networks.
Development of the formalism

We begin this chapter by introducing 4-vectors and the index notation. Raising and lowering of indices are then presented, along with Einstein's summation convention. We then study quadratic scalars. Then we study the continuity equation and deduce that charge density and the three components of the current density form a 4-vector, the 4-current. The example of a rectangular current loop in motion is studied as an example. The concept of 4-potential of the electromagnetic field and the electromagnetic field tensor is also introduced. 6-Vectors, as equivalent to antisymmetric tensors of rank 2, are introduced and their transformations derived.
Vertical Approach Anomaly Detection Using Local Outlier Factor

Detection of anomalies based on smart meter data is crucial to identify potential risks and unusual events at an early stage. In addition anomaly detection can be used as a tool to detect unwanted outliers, caused by operational failures and technical faults, for the pre-processing of data for machine learning, to detect concept drift as well as enhancing cyber-security in smart electrical grid operations. It is known that anomalies are defined through their contextual appearance. Hence, anomalies are divided into point, conceptual and contextual anomalies. In this work the contextual anomaly detection is examined, through a novel type of load forecasting known as vertical approach. This chapter explores the use of anomaly detection in the relevant learning systems for machine learning in smart electrical grid operation and management through data from New South Wales region in Australia. The presented vertical time approach uses seasonal data for training and inference, as opposed to continuous time approach that utilizes all data in a continuum from the start of the dataset until the time used for inference. It is observed that Local Outlier Factor identifies different local outliers given different vertical approaches. In addition, the local outlier factor score vary vertically. An anomaly is defined as a deviation from an established normal pattern. Spotting an anomaly depends on the ability to defy what is normal. Anomaly detection systems aim at finding these anomalies. Anomaly detection systems are in high demand, despite the fact that there is no clear validation approach. These systems rely on deep domain expertise.
Life cycle analysis of medical robotic manipulators

Sustainability and environmental effect have rapidly become crucial issues in the manufacturing business and the consciousness of green manufacturing has driven engineers to be increasingly more environmentally cognizant. Thus, this study assessed life cycle analysis of medical robotic manipulators. A literature survey on the life cycle analysis of robotic manipulators was done to get a deeper understanding of the topic. For the purposes of the study, it was assumed that the robotic manipulator will be manufactured in Asia and used in North America. The analysis was done for two types of materials namely, Kevlar 53% and Carbon Fiber. Power consumption of the robotic manipulator was determined using the power rating of the servo motors, which was 11.11 kWh using Kevlar 53% as the material and 71.37 kWh using Carbon Fiber. Findings, revealed that carbon foot print produced by the robotic manipulator is 3700 kg and has the highest impact on the environment compared to other factors such as air acidification. Furthermore, findings showed that, recyclable material like Kevlar 53% generate less carbon foot print compared to nonrecyclable material. Overall, it is crucial to know how medical robots will affect the lives of people they aim to save during their operational life.
Future of intelligent robots for testing specimens

A lot of research is being done on how to address COVID-19 and similar pandemics in future, the findings and discussions of this book are of paramount importance in this regard. The main objectives of the projects were achieved, that is, the initiation to use Artificial Intelligence and robotics to fight against pandemics likes COVID-19 was achieved. Unfortunately, some challenges where experienced during research and in this chapter the authors have summarized what they recommend for further study and future work. A detailed review on how to pursue further studies is given through introducing each potential area of further study. Overall, Artificial Intelligence and robotics research are important in battling pandemics similar to COVID-19 especially if their downsides are addressed.
The classification of the investigations and punishment for crime syndicates that relate to intrusions against IoT-based healthcare systems

Studies on crime syndicates have shown the emerging issue of penal offenses that correlate with organized intrusions against IoT-based healthcare systems. However, empirical facts that have exhaustively clarified, formulated, and categorized the investigations and the punishment for organized cyber offenders in the above healthcare systems are inadequate. Accordingly, for intrusions of this sort, some prosecutors may erroneously overemphasize penal sanctions over rehabilitative therapeutic interventions and constructive approaches to help and reshape the organized intruders to conform to social standards and lawful behavior globally expected from people in the usage of and interaction with the IoT-based healthcare systems. Thus, we recruit and interview 46 experts from the IT, social work, mental healthcare, nursing, strategy, and data analytics fields for the survey. Quantitative analysis of certain logs of Snort-IDS on public trace files, clustering, and C ++ programming techniques furnish the participants with potential cyber offenses that may correlate with organized intrusions on the above healthcare systems. Subsequent qualitative and thematic analyses of the data identify simple and complex forms of organized cybercrimes on IoT-based healthcare systems. Furthermore, punitive interventions like economic strangulation, detention, and imprisonment statistically correlate with cybercrimes with severe impacts on the victims. Such crimes include organized distributed denial of service (DDoS) attacks, password guessing, and unlawful leakages of healthcare data in the above settings. The study believes that erroneous publication, incorrect presentations, and unlawful sharing of official matters on the above healthcare information via social media, radio, and television statistically correlate to offenses that require corrective therapeutic interventions like temporary isolation, psychotherapy, and occupational and speech therapies.
Blockchain for medical insurance: Synthesizing current knowledge and problematizing it for future research avenues

Blockchain-based applications are now becoming prevalent in every aspect of human life for promoting security, trust, and reliability. Healthcare is one of the most important basic human needs, where trust, security, and privacy are crucial. Healthcare services involve medical insurance, which is beneficial for emergencies and general medical services. People avail themselves of various healthcare facilities using medical insurance by placing legitimate claims. Similarly, the service must provide the necessary facilities upon that claim. However, similar to other multiparty trading problems, medical insurance also suffers from fraudulent claims from customers. Customers can also face untoward delays and administrative complexity regarding insurance claims. Motivated by these trust and reliability problems, this chapter leverages blockchain technology as a service enabler, and will comprehensively outline the current situation of blockchain usage in medical insurance. Firstly, this chapter presents blockchain fundamentals to provide the reader with a sound background on blockchain. Later, this chapter briefly describes the outline of blockchain usage in healthcare and various features of blockchain. Furthermore, this work comprehensively describes the benefits of using blockchain in medical insurance with the help of a recent literature synthesis. Finally, future research avenues are presented in a tabular format along with research gaps.