Reviewerly/exHarmony huggingface dataset:
Spam/Ham Email Classification Using Machine Learning

Email stands for Electronic mail which means it is the way to distribute messages by electronic means from a computer source to one or more by means of network. It is one of the fastest method of distributing messages between sender’s and receiver’s computer system via Internet. In these days, email has become the most frequent communication system and a significant portion of the population depends on accessible email or texts from strangers. One of the biggest issues email consumers confront is the increase of spam communications. The tools that determine if an email is spam or not are known as spam filters. Currently there are many spam filter tools available on internet. Identifying these spammers is one of the hot topic of research and arduous tasks. In this paper we will look into the spam filter techniques using machine learning. Logistic regression algorithm and the spam emails will be categorized using the Natural Language Toolkit. The dataset utilized comes from the public datasets of Apache Spam Assassin and includes samples of spam and ham. The accuracy is defined on the basis of score of cross validation, precession and recall.
Types of Annotated Bibliography Used by Introvert and Extrovert Students in UIN SU Medan

Writing is highly essential in school, especially in the English Department, since they must master the fundamentals of writing. Unfortunately, writing is considered as the most challenging of the four English language abilities. Numerous researches discovered that EFL students also experienced a va
Revolutionizing the Creative Process: Exploring the Benefits and Challenges of AI-Driven Art

This paper examines how Artificial intelligence (AI) has revolutionized the creative process by enabling machines to create art and explore new mediums, technologies, and techniques in artistic expression. The research methodology employed includes a literature review, interviews with leading art professionals, and case studies. The results suggest that AI can be used to expand human creativity by allowing the artist to explore new techniques and technologies as well as to create unique works of art. However, ethical and practical considerations must be considered when working with this type of technology, such as the cost of creating AI-driven art and the imperfect output that can be generated by algorithms. Additionally, AI-driven art may not be accepted into major institutions as its medium, meaning that consideration must be taken to ensure that AI art is presented professionally and respectfully. This paper pursues to provide insight into the impact of AI technology on the creative process and explore the potential benefits and challenges associated with using AI to create artwork.
Evaluation of Capture The Flag-Based Online Cyber Security Learning Media in Terms of Ethical Hacking and Gamification

This study intend to (1) determine the suitability of the components of online learning media for network and computer security to suitability with the ethical hacking process, (2)conduct a comprehensive review of cyber security learning media. This research is a qualitative research with a grounded theory research approach. The data sources of this study include Capture The Flag websites, ethical hacking aspects and gamification aspects. Sampling techniques are carried out using application search strategies based on certain keywords in accordance with the relevance of online applications of cyber security learning media. Data analysis in this study uses qualitative data analysis, as for data analysis techniques that are adjusted to the stages in the study (Perry, Lunde, and Chen 2016). The first thing that is done is to search for research data sources using predetermined keywords. Filtering is carried out on search results so that search results are unique and duplication does not occur. Filtering is carried out to find inappropriate online applications, then removed from the list. Scoring of data that is in accordance with the ethical hacking aspects and gamification aspects. Second, the obstacle experienced in the research process is that there are several obstacles that cannot be accessed due to closed access or developers who do not update the CTF-based cyber security website so that it cannot be run. Third, the maker or owner or developer of this CTF-based cyber security website needs to consider the gamification side in the creation and preparation of this CTF-based cyber security website. This is necessary to increase the interest of participants with the presence of gamification aspects that are implemented
DEVELOPMENT OF A VIRTUAL-BASED SKILL COMPETENCY TEST (UKK) PRACTICUM MODEL IN VOCATIONAL SCHOOLS DEPARTMENT OF COMPUTER NETWORKING ENGINEERING

Abstract This study aims to determine: (1) the stages of developing a virtual-based UKK practicum model in the Network Computer Engineering Laboratory at SMK Negeri Labuang. (2) Development of a virtual-based UKK practicum model majoring in computer network engineering is valid, practical and effective as a result of development. The research method used is the method of research and development or Research and Development. The process of developing a virtual-based UKK practicum model refers to the ADDED development model which is a new development model resulting from descriptive analysis, critical analysis, and comparative analysis. The ADDED model stages include: (1) the Analyze stage, namely the analysis of the need for a computer / laptop as hardware, the availability of internet services, a computer laboratory while the application needs as software. (2) The Design Stage, namely the sequence or steps of making a design for the development of a practicum model, namely the required applications. (3) Development stage, namely product development in the form of a practicum model with a simulation model design specification. (4) Evaluate and Revise stage, namely steps in assessing and testing the validity, practicality, and effectiveness; (5) Dissemination and Implementation Stage, namely the process of disseminating and implementing the practicum model development. Assessment and testing is carried out by competent experts or experts, namely media and network computer techniques, while the testing is carried out through limited individual and group trials. The results of the assessment show that the development of a virtual-based UKK practicum model has fulfilled its validity by covering aspects of display, virtual and programming, the test results show that it is practical and effective. The development of the virtual-based UKK practicum model is said to be practical because it is in the very good category, it is said to be effective because the results of the evaluation carried out have met or reached the predetermined passing standard value. The development of a virtual-based UKK practicum model that was developed met the criteria of being valid, practical, and effective. For this reason, it is suggested that the virtual-based UKK practicum model can be used as a media for learning simulation for students majoring in network computer engineering in dealing with UKK SMK as well as a reference for tutors to conduct a practice orientation period, practice using practical tools. Keywords: Development, Model, Virtual UKK Practicum
Hierarchical Local Differential Privacy

The local differential privacy metric has attracted attention due to its quantitative nature, and many mechanisms have been studied for satisfying local differential privacy based on data formats and use cases. Local differential privacy mechanisms generally target a certain data space and perturb it sufficiently to provide indistinguishability of the data on that space. Therefore, individual data tends to be greatly disturbed so that even relatively simple tasks require a large amount of data to equalize the noise caused by the mechanism. In this paper, we define hierarchical local differential privacy, which is an extension of local differential privacy, and propose a mechanism to satisfy both local differential privacy and hierarchical local differential privacy. Hierarchical local differential privacy views a data space hierarchically as a set of smaller spaces, and instead of abandoning the privacy of data contained in different spaces, the amount of noise can be reduced. In this paper, we further design a hierarchical local differential privacy framework and achieve a privacy guarantee based on local differential privacy for all the data in the framework. Finally, we experimentally evaluate the proposed framework using image data. The framework allows control over the amount of information that can be disclosed, and furthermore, maintains a higher degree of utility than applying a simple local differential privacy mechanism.
On the Documental Basis of A. Blok’s Essay “Poslednie dni Imperatorskoi vlasti” (“The Last Days of Imperial Power”)

This work is devoted to issues related to commenting on the first chapter of the essay “The Last Days of Imperial Power” — “The State of Power.” The introduction briefly outlines the main stages of Block’s work in the Extraordinary Investigative Commission “to investigate the illegal actions of former ministers, chief executives and other senior officials of both civil and military and naval departments.” The paper presents an analysis of individual portraits of government officials, derived in the first chapter, and notes the sources of hidden and direct quotations. Characteristics from “The Last Days…” are compared with Blok’s portrait sketches from his notebooks, diaries and notes based on the protocols of interrogations of former representatives of the tsarist government. The article also presents unknown archival materials from the Manuscript Department of the Pushkin House.
A Steel Wire Rope Defect Detection Method for Gantry Crane in Gravity Energy Storage Systems

Gravity energy storage is a sustainable energy storage technology widely used in power systems and the energy industry. Steel wire ropes are critical components of gravity energy storage systems, and their safety and reliability are vital for the operation of the energy storage system. In recent years, significant progress has been made in defect detection in the field of deep learning-based object detection methods. This study presents an enhanced YOLOv7 model-based approach for wire rope defect detection. Firstly, this paper applies mosaic data augmentation to a portion of the image data, followed by mixup data augmentation on a subset of the mosaic-augmented data, to strengthen the resilience and generalizability of the enhanced model. Additionally, this paper adopts feature-level cascaded reuse in the backbone of the YOLOv7 model to address the issue of information loss from lower-level networks to higher-level networks. Experimental results demonstrate that in contrast to the original YOLOv7 model, the enhanced model achieves a 4.3% increase in mAP@0.5 and improves the detection of small defects in steel wire ropes.
The case for the curve: Parametric regression with second- and third-order polynomial functions of predictors should be routine.

Polynomial regression is an old and commonly discussed modeling technique, though recommendations for its usage are widely variable. Here, we make the case that polynomial regression with second- and third-order terms should be part of every applied practitioners standard model-building toolbox, and should be taught to new students of the subject as the default technique to model nonlinearity. We argue that polynomial regression is superior to nonparametric alternatives for nonstatisticians due to its ease of interpretation, flexibility, and its nonreliance on sophisticated mathematics, like knots and kernel smoothing. This makes it the ideal default for nonstatisticians interested in building realistic models that can capture global as well as local effects of predictors on a response variable. Low-order polynomial regression can effectively model compact floor and ceiling effects, local linearity, and prevent inferring the presence of spurious interaction effects between distinct predictors when none are present. We also argue that the case against polynomial regression is largely specious, relying on either misconceptions around the method, strawman arguments, or historical artifacts. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
Research On Task Allocation Algorithm For Heterogeneous Robot Group Target Searching

In this paper, a collaborative target searching system of heterogeneous robot group is developed, which can understand the searching target according to human natural language instructions and carry out the searching until the target is found. The system is relatively complex, and the technology mainly involves network communication, natural language understanding, task allocation, motion planning and target recognition. This paper focuses on the optimization algorithm of target searching task allocation based on ant colony algorithm. The algorithm considers not only the distance of the robot from the target object but also its structural characteristics and ability, so that the searching task is allocated to the most suitable robots, resulting in a fast and good searching task.
Investigating the interplay between morphosyntax and event comprehension from the perspective of intersecting object histories.

In a series of sentence-picture verification studies we contrasted, for example, "… choose the balloon with "… inflate the balloon" and "… the inflated balloon" to examine the degree to which different representational components of event representation (specifically, the different object states entailed by the inflating event; minimally, the balloon in its uninflated and inflated states) are jointly activated after state-change verbs and past participles derived from them. Experiments 1 and 2 showed that the initial and end states are both activated after state-change verbs, but that the initial state is considerably less accessible after participles. Experiment 3 showed that intensifier adverbs (e.g., completely) before both state-change verbs and participles further modulate the accessibility of the initial state. And in Experiment 4, we ruled out the possibility that the initial state is accessible only because of the semantic overlap. We conclude that although state-change verbs activate representations of both the initial and end states of their event participants, their accessibility is graded, modulated by the morphosyntactic devices used to describe the event. (PsycInfo Database Record (c) 2023 APA, all rights reserved).
What does it mean to trust a technology?

A survey published in October 2023 revealed what seemed to be a paradox. Over the past decade, self-driving vehicles have improved immeasurably, but public trust in the technology is low and falling. Only 37% of Americans said they would be comfortable riding in a self- driving vehicle, down from 39% in 2022 and 41% in 2021. Those that have used the technology express more enthusiasm, but the rest have seemingly had their confidence shaken by the failure of the technology to live up to its hype.
The Transformation of a Science by a “Technology”: The Case of Astrophysics and the Introduction of Machine Learning

Abstract This article explores the transformations of an area of scientific research provoked by the introduction of big data and the possibilities to treat them by Artificial Intelligence. We study these transformations in contemporary astrophysics as it has developed over the last thirty years. These transformations extend from cognitive work procedures to information processing, systems of collaboration between scientists, epistemology and terrain of questions.
Imbalanced Regression Evaluation Under Uncertain Domain Preferences

In natural phenomena, data distributions often deviate from normality. One can think of cataclysms as a self-explanatory example: rarely occurring events differ considerably from common outcomes. In real-world domains, such tail events are often the most relevant to anticipate, allowing us to take adequate measures to prevent or attenuate their impact on society. However, mapping target values to particular relevance judgements is challenging and existing methods do not consider the impact of bias in reaching such mappings—relevance functions. In this paper, we tackle the issue of uncertainty in non-uniform domain preferences and its impact on imbalanced regression evaluation. Specifically, we develop two methods for assessing the volatility of model performance when dealing with uncertainty regarding the range of target values that are more important to the underlying problem. We demonstrate the importance of our proposed methods in capturing the impact of small changes in relevance assessments of target values and how they may impact experimental conclusions.
Design of JKN Claim Information System for Outpatient BPJS Patients with SDLC Method

BPJS claim is the submission of all BPJS patient care costs by the Hospital to the BPJS Health, carried out collectively and billed to the BPJS Health every month through the verification process. BPJS Health is a legal entity established to administer the Health Insurance program. Health Insurance is a guarantee in the form of health protection so that participants obtain health maintenance benefits and protection in meeting basic health needs provided to everyone who has paid contributions or whose contributions are paid by the government. This study aims to design a medical record management system to support JKN claims for outpatients at Muhammadiyah Bandung Hospital. The research method used in this research is qualitative with a descriptive approach. The data collection techniques used were interview, observation and literature study. The results showed that there were problems that occurred, namely unclear resumes, illegible SEPs, missing referral letters, and there was still 1 outpatient period that was mixed up so that coding officers found it difficult to read it and became an obstacle to claiming. Based on this research, it can be concluded that there is a need for a quality audit of specific medical resume writing and incomplete BPJS patient requirement files.
Students’ Problems in Writing Academic Article: A Case Study in Academic Writing Class

This research was conducted to find students’ problems in writing academic articles. The participants were 36 students in Academic Writing Class. The authors used a questionnaire and an interview to gain the data. The results of this study were explained by descriptive analysis. The data shows that students experience problems while writing an article. The main problems are lack of knowledge (organization, vocabulary, and grammar) and motivation. Furthermore, students also experience anxiety during the article-writing process.
Application of Agile Methodology in Managing the Healthcare Sector

The healthcare sector has gradually embraced effective project management for improved healthcare outcomes. This paper explores the application of the agile methodology for managing the healthcare sector. This study significantly contributes to the healthcare sector by providing insights into the application of Agile methodology, potentially enhancing healthcare management and patient outcomes. In this article, the author reviews ten peer-reviewed articles, analyses the findings, and generates three themes: Agile methodology development and implementation in the healthcare sector, the healthcare sector's readiness factors/levels for applying the agile methodology, and how agile methods improve healthcare outcomes. The findings across the three identified themes reiterate the crucial role of organizational leadership, flexible structures, and advanced technology in enhancing agility within the healthcare sector. Moreover, the studies highlight the potential of agile principles to enhance customer-centricity, customer satisfaction, and overall adaptability in healthcare organizations. The study recommends healthcare institutions priorities Agile competency development through training programs and cultivate a culture of adaptability to support Agile methodology adoption. Furthermore, it suggests quantitative research to validate readiness factors' influence, focusing on patient-centered outcomes and comparative studies between Agile and traditional healthcare approaches to bridge literature gaps.
Topic Model with Contextual Outlier Handling: a Study on Electronic Invoice Product Descriptions

E-commerce has become an essential aspect of modern life, providing consumers worldwide with convenience and accessibility. However, the high volume of short and noisy product descriptions in text streams of massive e-commerce platforms translates into an increased number of clusters, presenting challenges for standard model-based stream clustering algorithms. This is the case of a dataset extracted from the Brazilian NF-e Project containing electronic invoice product descriptions, including many product clusters. While LDA-based clustering methods have shown to be crucial, they have been mainly evaluated on datasets with few clusters. We propose the Topic Model with Contextual Outlier Handling (TMCOH) method to overcome this limitation. This method combines the Dirichlet Process, specific word representation, and contextual outlier detection techniques to recycle identified outliers aiming to integrate them into appropriate clusters later on. The experimental results for our case study demonstrate the effectiveness of TMCOH when compared to state-of-the-art methods and its potential for application to text clustering in large datasets.
FAST: Adopting Federated Unlearning to Eliminating Malicious Terminals at Server Side

The emergence of the right to be forgotten has sparked interest in federated unlearning. Researchers utilize federated unlearning to address the issue of removing user contributions. However, practical implementation is challenging when malicious clients are involved. In this paper, we propose FAST, i.e., Adopting <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">F</u> ederated unle <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">A</u> rning to Eliminating Maliciou <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">S</u> <underline xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">T</u> erminals at Server Side, a framework with three main components: 1) eliminating contributions of malicious clients: the central server records and subtracts updates of malicious clients from the global model, 2) judging unlearning efficiency: we model a mechanism to assess unlearning efficiency and prevent over-unlearning, and 3) remedying unlearning model performance: the central server utilizes a benchmark dataset to remedy model bias during unlearning. Experimental results demonstrate that FAST can achieve 96.98% accuracy on the MNIST dataset with 40% malicious clients, offering a 16x speedup to retraining from scratch. Meanwhile, it can recover model utility with high efficiency, and extensive evaluations of four real-world datasets demonstrate the validity of our proposed scheme.