Reviewerly/exHarmony huggingface dataset:
Crowd Detection, Monitoring and Management : A literature Review

The rapid increase in the global population has led to the emergence of large crowds during public events across various domains such as sports, music festivals, religious gatherings, and political campaigns. If these events are not properly organized and controlled, they have the potential to result in disasters. Tragically, stampedes occur every year, causing fatalities, disappearances, and injuries for many individuals. Therefore, crowd identification, monitoring, and control are problems that will be addressed and discussed in this paper in order to lessen casualties and prevent such catastrophes. The objective of this study is to present a thorough review of technologies and methods relevant to crowd management, planning, behaviour analysis, and counting. Furthermore, it aids researchers' future progress by examining recent technology developments in the field of crowd planning and monitoring.
CREATION OF THE RESIT INFORMATION SYSTEM FOR PREDICTION OF RIVER POLLUTION UNDER EMERGENCY SITUATIONS

This article describes developed RESit information system, that created, as improvement to RESit software. System parts are described, such as database, server, interaction between server and possible water flow measurement sensors, learning utility, administration application and user application. RESit advantages are given in relation to using similar systems possibility in emergencies. A practical implementation of such developed methods is described: • pollution forecasting results adjusting method based on a neural network working based on the regression problem; • pollution level forecasting method between specific points using interpolation and recursion methods; • pollution source determining method based on a filtering and sorting algorithm that works on the basis of facts database; • system information with sensors measuring current water flow interaction mechanism. Thus, an information system acquires information technology for forecasting river pollution in emergencies features.
USING SLURM WORKLOAD MANAGER FOR MANAGING SUPERCOMPUTERS AND LINUX CLUSTERS

The article is dedicated to the use of the Slurm Workload Manager for managing supercomputers and Linux clusters and highlighting the importance and advantages of using the Slurm Workload Manager in resource management on multi-user systems. Within the research, existing workload management systems and their limitations were analyzed. Based on this analysis, it was found that Slurm is one of the most widely used and efficient solutions in the field of resource management on multi-user systems. The article provides a detailed examination of the functionality of Slurm, including its core functions such as resource scheduling, task distribution, and system monitoring. Slurm allows users to efficiently utilize computational resources, distribute tasks among cluster nodes, ensure optimal CPU time usage, and control the system load. These features enable high productivity and efficiency in resource utilization. The advantages of Slurm compared to other resource management systems are presented in the article. Slurm is noted for its flexibility and the ability to configure various types of resources, as well as support for different scheduling algorithms. Limitations and challenges associated with using Slurm are also mentioned, providing readers with a comprehensive understanding of its capabilities and potential considerations for implementation. This article will provide readers with a detailed overview of the Slurm Workload Manager, its core functions, advantages, and limitations. With the comprehensive analysis and description of Slurm's core functions, the article will serve as an invaluable source of information for professionals working with large-scale computing clusters and supercomputers. It will also be beneficial for those interested in exploring best practices in resource management on multi-user systems and learning effective strategies for utilizing the Slurm Workload Manager.
Penerapan Metode K-Means Clustering Untuk Mengelompokkan Data Kelayakan Penerima Bantuan Renovasi Rumah

The government provided one form of assistance, namely the renovation of houses in the Kuala Bangka Village area in Kualuh Hilir District, North Labuhan Batu Regency from 2015 until now. However, with this assistance, Kuala Bangka Village sometimes has problems in determining the feasibility of receiving assistance from the government, therefore the author will conduct research by categorizing the eligibility of recipients of house renovation assistance by applying the K-Means algorithm. The K-Means Clustering algorithm is an algorithm that can classify data accurately according to the previous problem. The grouping aims to determine cluster 0 and cluster 1 as recipients of house renovation assistance, cluster 0 is feasible and cluster 1 is not feasible. The attributes used in this research are number of family members, employment, housing conditions, and income. The results obtained from 170 data were cluster 0 with 91 data and cluster 1 with 79 data. From these results, 91 people were eligible to receive home renovation assistance and 79 were not eligible to receive home renovation assistance
Cybercrime

First, the paper will define and analyze cybercrime, computer crimes, and internet fraud. Then, the paper will examine how criminals use the Internet to commit crimes anonymously. Lastly, the paper will offer insight into the prevention and how to combat cybercrime, internet fraud, and computer crimes.
Concept Emergence from Complex Sensory Data: A Connectionist Model

This paper presents a first approximation to a connectionist implementation of the Bateson-inspired concept generation model previously introduced by de-Miguel &amp;amp; Sancho (https://arxiv.org/abs/2307.08087v2)
Penilaian Sistem Informasi Akuntasi Siklus Penjualan Kamar di Hotel Garden Palace Surabaya

Accounting information system is one of the instruments used by companies to achieve company goals. A good information system is an information system that is supported entryway an effective internal control. The company's internal controls must be periodically reviewed to be effective. The Garden Palace Hotel already has a computerized accounting information system, but internal control is not effectively linked to access control activities. Increase the effectiveness of inside control with five COSO components. The results of the research were: climate control, especially hierarchical construction, did not work as expected. This affects the adequacy of internal controls in the cycle of credit transactions. Doubled posting bets and error values ​​were still reasonable and the control measure that susceptible was access control.
The Growth of Industrial Revolution 4.0 and the Preparation of Software Engineers in Digital Transformation at Makers Institute Indonesia

The transformation of business models using increasingly big data analytics is one of the most significant changes in today's modern corporate culture. The emphasis in contemporary business has switched from a product to a platform. Human resources as Full Stack Engineers are required to respond to changes in business with the dimensions of the Internet of Things, Big Data, Augmented Reality, and Artificial Intelligence. Efforts to develop Full Stack Engineers are necessary since this is a worldwide and national stage for Software Engineers in the Industrial Revolution Era 4.0. The purpose of this research is to establish the hard skills of prospective Software Engineer graduates from an information technology institute of engineering and technology and the skills needs of the business world in the real world. Makers Institute Bandung's experience in managing Full Stack Engineers has revealed that there is a gap between what they learn in college and what is required. One of the gaps is that what is taught in the classroom does not apply to what is required in the workplace. There isn't truly a relationship. One of the issues that prospective Software Engineers confront is the lack of a robust model of thinking when it comes to constructing device integration.
Developing Android-Based Applications To Improve Reading Skills | تطوير التطبيقات المستندة الأندروئيدية لتحسين مهارة القراءة

The use of learning applications represents an innovative development in educational media aimed at enhancing the quality of learning. The development of learning applications for Class VII at MTsN 1 Karanganyar is driven by the limited availability of technology-based teaching materials and a lack of proficiency in Arabic reading skills among students. This study aims to create an Android-based learning application and assess its impact on improving reading abilities among Class VII students at MTsN 1 Karanganyar. This research follows the ADDI method, comprising four stages: needs analysis, design, development, and implementation. Data collection techniques included observation, interviews, tests, questionnaires, and expert validation. The study's results indicate a significant improvement in learning achievement when using an Android-based learning application, with no students scoring below the class's minimum passing grade. The feasibility test results for the learning application showed approval from various experts: material experts at 78% (good), design experts at 75% (good), Arabic language studies teachers at 93% (very good), and student responses at 91% (very good).
The Utilization Of Audio Visual Media In Increasing Students Listening Skills

The study aimed to address the low listening ability of grade VII students in Arabic Listening Skill at SMP Muhammadiyah 2 Gisting. Educators previously used audio-visual media, but the results were not optimal because the media was only used once. The research used Classroom Action Research (CAR) with the Kurt Lewin model to answer two questions: (1) How is the implementation of the use of audio-visual media in improving listening skills (maharah istima') for grade VII students of SMP Muhammadiyah 2 Giting? (2) Can the use of audio-visual media improve the listening ability of grade VII students at SMP Muhammadiyah 2 Giting? The results of the study showed that the use of audio-visual media can improve the listening ability of grade VII students at SMP Muhammadiyah 2 Gisting.
The Utilization of Digital-Based Chain Picture Game Using Gartic Phone Application to Improve Students' Mastery of Arabic Vocabulary

Digital-Based Chain Picture Game using the Gartic Phone application to improve students' mastery of Arabic vocabulary. The research employed a Quasi-Experimental Design with a Nonequivalent Control Group Design. The participants consisted of students from Grade VIII at MTs N 1 Klaten. The experimental group utilized the Digital-Based Chain Picture Game with the Gartic Phone application as the intervention, comprising 30 participants. Meanwhile, the control group did not receive any intervention and consisted of an equal number of 30 students. Data on the mastery of Arabic vocabulary were collected before and after the intervention using a vocabulary test. Additionally, students' perceptions regarding their experience using the Digital-Based Chain Picture Game with the Gartic Phone application were collected through a questionnaire employing the Technology Acceptance Model (TAM) instrument. Data analysis was conducted using appropriate statistical methods to compare the differences in vocabulary mastery between the experimental and control groups. The findings of this study are expected to provide a better understanding of the effectiveness of using the Digital-Based Chain Picture Game with the Gartic Phone application in enhancing students' mastery of Arabic vocabulary. It also serves as a recommendation for Arabic language instructors to incorporate the Gartic Phone application as one of the media to improve Arabic vocabulary mastery.
TEXT poetry and prose October 2023

New graphic prose, poetry, prose and script works by Ross Watkins, Timothy Loveday, David Thomas Henry Wright, Andrew Leggett, Kathryn Hummel, Katrina Finlayson, Ola Kwintowski, Julia Prendergast, Gayelene Carbis, Megan Anning, Nadia Mead, Amy Lilwall and Robert Loydell (introduced by Eion Murray) and Jessica Faulkner.
Intelligible Coding Using TypeScript

The main goal of TypeScript is to increase productivity when creating intricate applications. Enabling IDEs to have a richer environment to identify frequent problems while you type the code is one way this occurs.This gives your projects an extra layer of safety. When modifications are done manually, developers no longer need to verify that there are no mistakes. Additionally, since TypeScript essentially adds static typing to JavaScript, it can assist you in avoiding common mistakes.This makes code rewriting easier without harming it much because it catches faults for you. with capabilities like generics, method overloading, tuples, abstract classes, interfaces, and type aliases. Using this programming language could result in more reliable software that can still be deployed anyplace a JavaScript application would run Index Terms- Type Script, Java Script, Tuples, Interfaces
Urban Flood And Its Correlation With Built-Up Area In Semarang, Indonesia

The expansion of urban areas is closely related to environmental problems such as changes in land use, flooding, and land subsidence. Semarang is a city with reasonably rapid development and a high land change experiencing floods and land subsidence. This paper will discuss land transformation caused by urban growth and its implications. It uses a combination of geospatial techniques and cloud computing Google Earth Engine (GEE) to carry out mapping over a large area without being constrained by computer capabilities. This study found that the built-up area in 2010 occupied 36.27% of the city, and it went up to 59.79% in 2021, in eastern, south-eastern, and northern parts of Semarang City in the last six years. Most of the built-up areas, especially those located in coastal areas, are located in areas with a high rate of land subsidence and urban flooding. The built-up area also significantly increases residential areas compared to other land cover types such as vegetation, open land, and water bodies. Built-up area growth also contributes to the rate of land conversion in Semarang City, especially in Mijen Regency; the vegetation cover was transformed into industrial areas and housing, which heavily stressed the land and environment. This leads to the increasing subsidence on the land of that area, which resulted in increasing tidal floods or rob.
Bio-Heuristics Inspired by Fauna (Review)

In recent years, there has been a noticeable increase in publications devoted to new metaheuristics for the effective solution of optimization problems for which classical methods do not provide acceptable results with reasonable time and resources. The review focuses on bio-heuristics inspired by fauna, which account for about half of the publications of meta-heuristics in the last ten years, and which model patterns of behavior of individuals of the animal world. The review is organized taking into account the biological classification of fauna and the simulated mechanisms of animal behavior. The analysis of the mechanisms used to ensure a balance between the convergence rate of the algorithm and the diversification of the solution search space is also given.
DETERMINASI PRESTASI BELAJAR SISWA PADA SEKOLAH BERBASIS PESANTREN

The learning achievement of students majoring in fashion at SMK Syubbanul Wathon has been satisfactory, this is evidenced by the semester scores of students majoring in fashion having reached the KKM (Minimum Completeness Criteria), there are only a few students who still get scores below the KKM (Minimum Completeness Criteria). The purpose of this study was to determine the variables that affect student achievement. Student achievement can be influenced by factors such as classroom management and teacher professionalism. In this study, quantitative research methods were used using primary data. The sample used in this study was 133 randomly selected. The data analysis used multiple linear regression. The results showed that all the independent variables simultaneously significantly affected the learning achievement variable. Partially, all independent variables have a significant effect on student achievement.
Reminder Bag: Designing Smart Bag for New Parents

Taking care of a newborn requires a lot of concentration on the part of the parent, who has to remember a lot of different things. It is challenging for new parents to remember and pack every item needed when bringing the baby out the door. In this paper, we present a reminder bag design that utilizes a pressure sensor to give a lighting alert when anything is missing from the bag. We carried out a pilot user study with 12 participants (9 mothers and 3 fathers) and a formal user study with 13 participants (8 fathers and 5 mothers) to understand their experiences with this reminder bag through interviews. According to the findings, the lighting feedback reminder works as an eye-catcher for parents to ensure they have packed all of the items needed for their babies outside trip, which can help them with stress reduction and practicality. This work transfers the focus from newborns to new parents in order to enable them to adapt to the new role with less burden. We discuss design implications and design guidelines for designing reminder systems to facilitate new parents’ daily lives.
Happily Error After: Framework Development and User Study for Correcting Robot Perception Errors in Virtual Reality

While we can see robots in more areas of our lives, they still make errors. One common cause of failure stems from the robot perception module when detecting objects. Allowing users to correct such errors can help improve the interaction and prevent the same errors in the future. Consequently, we investigate the effectiveness of a virtual reality (VR) framework for correcting perception errors of a Franka Panda robot. We conducted a user study with 56 participants who interacted with the robot using both VR and screen interfaces. Participants learned to collaborate with the robot faster in the VR interface compared to the screen interface. Additionally, participants found the VR interface more immersive, enjoyable, and expressed a preference for using it again. These findings suggest that VR interfaces may offer advantages over screen interfaces for human-robot interaction in erroneous environments.
The Effect of Human Prosody on Comprehension of TTS Robot Speech

The ability to interact verbally with humans is a key requirement of many social robots. It is common however for robot speech to lack contextual human-like prosody, making it intelligible but seeming inexpressive and cold. We investigated the effect that applying human-like prosody to synthetic speech had on aural comprehension during human-robot interaction. A text-to-speech system was used to generate synthetic sentences in two conditions: ‘default’ and ‘human’(informed by voice actor). A speech-in-noise experiment was then performed that required participants to transcribe perceived sentences spoken by a robot in both test conditions. Overall, we found no significant difference in comprehension between sentences spoken using the synthetic voice with prosody and the unaltered synthetic voice, however significant differences in comprehension were detected for shorter sentences (n=50), and among participants that learned English in a different country to the native dialect of the voice actor (n=26). In both of these cases, participants found the voice with human-like prosody harder to comprehend. These findings suggest that introducing human-like prosody to synthetic speech in human-robot interaction, under certain circumstances, may lead to the voice becoming less intelligible. This motivates further research and adds to the growing body of literature on the multifaceted role that voice plays in human-robot interaction.
Scale Development of Anxiety Toward Robots in Consumer Robotics: An Approach Using Item Response Theory

Anxiety toward robots is a pre-conditioned emotion that might inhibit the formation of positive attitudes about interactions with robots. In the context of consumer robotics, this study operationalizes anxiety toward robots as consumers’ anxious feelings and fears about robots in frontline retail encounters. The study evaluates item quality of the anxiety toward robots construct and showcases the benefits of using Classical Test Theory (CTT) and Item Response Theory (IRT) for developing and assessing a scale measurement. The findings of this study suggest that the five items comprising the anxiety toward robots scale demonstrate high measurement quality, indicating their accurate representation of the intended construct. By integrating CTT and IRT analytic techniques, the study successfully builds a reliable scale for measuring anxiety toward robots in a and contributes to the use of an alternative statistical method for facilitating the construction of valid research instruments in consumer robotics.
Creation and Testing of Synthetic Datasets for Training Road Scenes Algorithms

Deep learning models require large amounts of data to be trained to fulfill their potential. To solve this problem, we propose a novel method for creating high-quality photorealistic synthetic training data and compare its performance to real data for object detection. We introduce the RealStreet and SynthStreet datasets, which were designed to enhance a safety analysis for road object detection. The objective of the project is to provide a useful synthetic environment for learning and building a road traffic experience by imitating the real environment as nearly as possible. This will improve the safety of road users and enable testing and planning before actual, dangerous events arise. The RealStreet data-set was collected in real-world scenarios of an urban city, while the SynthStreet closely recreates the RealStreet scenes: field of view, road objects, such as pedestrians, cyclists, vehicles, and background information buildings are matched. We study the performance and behavior of a network model trained on real and synthetic data-sets and both with various ratio mixes. Our approach is to evaluate data-set performances using a state-of-the-art method for object detection tasks in learning from synthetic data. We also compare the performance of each dataset, which is evaluated on real-world data, to determine the possibility of synthetic data-set benefits and analyze the effect of limited real-world data. However, it is important to note that synthetic data-sets may not always accurately represent the variability and complexity of real-world environments and may not generalize well to real-world scenarios. Therefore, it is important to carefully evaluate the performance of deep learning models trained on synthetic data and validate their results using real-world data.
A Randomized Ensemble Based on Distinguishing Output Labellings]{A Randomized Ensemble Based on Distinguishing Output Labellings

Abstract This article proposes and analyzes a novel classification model trained to distinguish correctly from incorrectly labeled instances. The proposed method transforms any classification problem from a task of producing the correct label to a task of deciding if a given label is correct. To do so, a new dataset is created in which an attribute is added with a randomized label suggestion for each instance. The model then learns if the suggested labels are correct or not.The method builds an ensemble of decision trees trained on different randomized versions of this classification task.The proposed ensemble is validated with several experiments with the aim of giving some insights on the algorithm performance, especially its hyper-parameters, and to compare its performance with benchmark models. The experiments, carried on a variety of datasets, real-world and synthetic,shows that the proposed model achieves competitive results with respect to state-of-the-art learning models of the literature such as Random Forest.