Reviewerly/exHarmony huggingface dataset:
Enhancing iteration performance on distributed task-based workflows

Task-based programming models have proven to be a robust and versatile way to approach development of applications for distributed environments. They provide natural programming patterns with high performance. However, execution on this paradigm can be very sensitive to granularity –i.e., the quantity and execution length of tasks. Granularity is often linked with the block size of the data, and finding the optimal block size has several challenges, as it requires inner knowledge of the computing environment. Our proposal is to supplement the task-based programming model with a new mechanism –our SplIter proposal. At its core, the SplIter provides a transparent way to split a collection into partitions (logical groups of blocks, obtained without any transfers nor data rearrangement), which can then be iterated. Tasks are linked to those partitions, which means that SplIter breaks the dependency between block size and task granularity. The evaluation shows that the SplIter is able to achieve performance improvements of over one order of magnitude when compared to the baseline, and it is either competitive or strictly better (depending on application characteristics) to the competitor alternative. We have chosen different applications covering a wide variety of scenarios; those applications are representatives of a broader set of applications and domains. The changes required in the source code of a task-based application are minimal, preserving the high programmability of the programming model. Two different state-of-the-art task-based frameworks have been evaluated for all the applications: COMPSs and Dask, showing that the SplIter can be effectively used within different frameworks.
Quantum error correction beyond the toric code: dynamical systems meet encoding

We construct surface codes corresponding to genus greater than one in the context of quantum error correction. The architecture is inspired by the topology of invariant integral surfaces of certain non-integrable classical billiards. Corresponding to the fundamental domains of rhombus and square torus billiard, surface codes of genus two and five are presented here. There is significant improvement in encoding rates and code distance, in addition to immunity against noise.
Similar failures of consideration arise in human and machine planning

Humans are remarkably efficient at decision-making, even in "open-ended'' problems where the set of possible actions is too large for exhaustive evaluation. Our success relies, in part, on efficient processes of calling to mind and considering the right candidate actions for evaluation. When this process fails, however, the result is a kind of cognitive puzzle in which the value of a solution or action would be obvious as soon as it is considered, but never gets considered in the first place. Recently, machine learning (ML) architectures have attained or even exceeded human performance on certain kinds of open-ended tasks such as the games of chess and go. We ask whether the broad architectural principles that underlie ML success in these domains tend to generate similar consideration failures to those observed in humans. We demonstrate a case in which they do, illuminating how humans make open-ended decisions, how this relates to ML approaches to similar problems, and how both architectures lead to characteristic patterns of success and failure.
The Effect of The Use of Android Applications on Increasing the Motivation of SMA Students on Plantae Materials

The use of android applications as Plantae teaching materials is currently a trend in science education research. This study aims to determine the effect of using android applications as teaching materials in increasing the motivation of high school students on Plantae material. The research design used is posttest-pretest-control group design. The subject of the study was SMAN 1 Panombeian Panei, precisely in Simalungun Regency, which consisted of one experimental class and one control class. Collecting data using a learning motivation questionnaire with a Likert. The data obtained were analyzed for the gain to determine the increase in learning motivation of high school students. The results showed that the use of android applications as Plantae teaching materials can have a significant effect on increasing high school students' learning motivation
Using WhatsApp Group in the Online English Learning Process at University During the Covid-19 Pandemic

There are many benefits to using WhatsApp as a medium for distance learning, including the fact that it excites students about learning even from a distance and encourages concentration when using groups. However, in this current study, the usefulness of WhatsApp Groups in online learning during the Covid-19 pandemic of the English education study program at Baturaja University is investigated. The present writers utilized data gathered using a questionnaire and a Google Form as a bridge program. The results revealed that the benefits of WhatsApp media in the pandemic-related teaching and learning process WhatsApp makes it simple to send assignments, whether in the form of direct comments (chat groups), images, videos, PPTs, or other soft files, and features of. Moreover, the students enjoy learning activities. Out of all the replies respondents gave, most indicated that they would answer with the majority of statements they believed to be true for each item. This claims that during the COVID-19 Pandemic, using WhatsApp or creating WhatsApp Groups was incredibly helpful for their online learning process. The study's findings revealed that most participants thought that WhatsApp Groups are an effective medium.
Inclusive normalization of face images to passport format

Face recognition has been used more and more in real world applications in recent years. However, when the skin color bias is coupled with intra-personal variations like harsh illumination, the face recognition task is more likely to fail, even during human inspection. Face normalization methods try to deal with such challenges by removing intra-personal variations from an input image while keeping the identity the same. However, most face normalization methods can only remove one or two variations and ignore dataset biases such as skin color bias. The outputs of many face normalization methods are also not realistic to human observers. In this work, a style based face normalization model (StyleFNM) is proposed to remove most intra-personal variations including large changes in pose, bad or harsh illumination, low resolution, blur, facial expressions, and accessories like sunglasses among others. The dataset bias is also dealt with in this paper by controlling a pretrained GAN to generate a balanced dataset of passport-like images. The experimental results show that StyleFNM can generate more realistic outputs and can improve significantly the accuracy and fairness of face recognition systems.
Hotspot Network Security System From Brute Force Attack Using Pfsense External Firewall (Case Study of Wifi-Ku.Net Hotspot)

Many activities that are often carried out on the internet network that users without realizing threaten their privacy. From the several cybercrime attacks, one of them is the brute force method, where the brute force method is a method that aims to gain access rights to enter a system by force by trying all possible usernames and passwords that the attacker has prepared in a wordlist. The purpose of this study is to find out how to implement a hotspot network security system from brute force attacks using PFSense external firewall on a wifi-ku.net hotspot. The research method used is experimental research methods, namely testing a variable against other variables objectively, systematically and controlled by predicting the causes and consequences of the problems that the writer examine on the hotspot network security system from brute force attacks using PFsense external firewall. The results of the research that was carried out before applying Pfsense external firewall, it is found that the brute force successfully entered Mikrotik router, and after applying Pfsense external firewall attack with Brute Force method is no longer able to enter Mikrotik router because Pfsense's external firewall blocked attacks from brute force.
STUDENT’S DIFFICULTIES IN LISTENING TOEIC TEST OF SEMESTER II (MECHANICAL ENGINEERING STUDENTS) AT THE STATE POLYTECHNIC BENGKALIS

Toeic test is one of the requirements to graduate at State Polytechnic Bengkalis. To pass in Toeic test, some of students get difficulties to pass the passing grade. The aim of this study is to find out students’ difficulties in listening to Toeic tests and how to overcome the problem. This study found that students’ ability in listening comprehension on Toeic test was average category from 11 students by 36.7%. Then students ‘difficulties in Toeic tests Picture description (used similar sound answered agree by 50%), question response (the speech rate answered agree by 53.3%), short conversation (unfamiliar word answered strongly agree by 53.5%), short talks (can easily miss the text answered agree by 43.3%).
To Whom are You Talking? A Deep Learning Model to Endow Social Robots with Addressee Estimation Skills

Communicating shapes our social word. For a robot to be considered social and being consequently integrated in our social environment it is fundamental to understand some of the dynamics that rule human-human communication. In this work, we tackle the problem of Addressee Estimation, the ability to understand an utterance's addressee, by interpreting and exploiting non-verbal bodily cues from the speaker. We do so by implementing an hybrid deep learning model composed of convolutional layers and LSTM cells taking as input images portraying the face of the speaker and 2D vectors of the speaker's body posture. Our implementation choices were guided by the aim to develop a model that could be deployed on social robots and be efficient in ecological scenarios. We demonstrate that our model is able to solve the Addressee Estimation problem in terms of addressee localisation in space, from a robot ego-centric point of view.
ST-DBSCAN Algorithm Implementation At Riau Province Forest Fire Points (2015-2022)

The forest conditions in Indonesia require more serious attention as they are constantly disturbed, including incidents of forest fires. Clustering or grouping using the ST-DBSCAN algorithm will group forest fire points based on distance and time. This data can be obtained from the FIRMS (Fire Information for Resource Management System) website, which utilizes MODIS sensor data. The research implements the ST-DBSCAN algorithm using the R language, focusing on a case study in the Riau Province from 2015 to 2022. The parameters used in this research for the ST-DBSCAN algorithm are Eps1 = 0.7, Eps2 = 2, and MinPts = 2. The algorithm generates several types of clustering patterns, including Stationary, Reappearing Regular, Irregular, Occasional, and Tracks. The fire point data used in this research covers the years 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022 in the Riau Province. The results obtained from this research include 1 Reappearing Regular pattern, 5 Tracks patterns, 1 Reappearing Irregular pattern, and 1 Stationary pattern. Within the time frame of 2015, 2016, 2017, 2018, 2019, 2020, 2021, and 2022, the highest occurrence of forest fire spots happened in the month of November 2015, reaching a total of 573 fire spots.
Website Design Of Junior High School (Smp) Negeri 21 Bengkulu As Information Media And Promotion

In cyberspace there is a lot of information that is needed, especially in the world of education, therefore in this Global network SMP Negeri 21 Bengkulu Tengah which is one of the educational institutions in Bengkulu Province, especially in Central Bengkulu, needs to make breakthroughs and innovations in promoting its existence by creating a web site. With the creation of a Web site SMP Negeri 21 Bengkulu Tengah is very helpful for students and teachers in getting the information they need, all information needs about activities in the teaching and learning process and extracurricular activities can be obtained and seen on this website. The information is presented in a series of systems in the form of attractive web pages and designs using Xampp, Mysql and Dreamweaver Cs4 programs.The process of running the program to enter the website page the first time a person will enter on the initial display there are several menu tobols. The profile page is information at a glance about the school and activities at the school while our Services page has several menus including menus of school facilities, study rooms, school buildings, and sports, then on the contact us page there is complete school address information. The next menu is the guest book, in this guest book there is a column that can be filled in by web site visitors in the form of suggestions and responses that are to advance the Website of SMP Negeri 21 Bengkulu Tengah. The next menu is an information menu containing a location plan and road direction, this aims to provide information to website visitors in order to find out the location plan of SMP Negeri 21 Bengkulu. From the information above, it can be concluded that website programming using Dreamweaver CS4 macromedia can produce a website that looks attractive, and helps provide information and as a means of promotion increases competitiveness in accepting new students, and this website can be updated.
Application Of Data Mining Using The Naïve Bayes Classification Method To Predict Public Interest Participation In The 2024 Elections

A big data processing process using a Data Mining technique that will be used in a study in the Application of Data Mining Using the Naïve Bayes classification to predict the participation of the Public Interest in the 2024 election. The data was obtained from the General Election Commission (KPU). The data was tested using the Naïve Bayes classification method with Weka Tools and 7 predetermined attributes. The dataset was taken as much as 96.67% of 11,406 training data, namely 2014 election data and 99.90% of 11,908 testing data, namely 2020 election data. Results It is known that the number of participants in Central Bengkulu Regency for the 2024 election based on participant data in 2020 and the 2014 election results is likely to increase by up to 3.23%, from 96.67% per 11,406 participants to 99.90% per 11,908 participants and the results predictions are likely to increase.
Implementation Of Mapping Temporary Landsfills In Bengkulu City Based On Android

Garbage is a disaster that can cause a lot of harm both in terms of the environment and health. In this incident there were many factors that affected the surrounding community, such as population density and insufficient public awareness. These factors are the main reasons for illegal waste disposal. By utilizing a Geographic Information System (GIS) implemented with Google Maps in the chaos of an Android-based temporary landfill. Data collection methods are by interview, and observation. The data obtained is to facilitate system analysis and design. The results of this study are that it can make it easier for the people of Bengkulu City to find the nearest TPS.
Interactive Multimedia Introduction To 3D Animation-Based Body Management Procedures For Muhammadiyah 1 Elementary School Students Bengkulu City

This research was conducted in order to be able to further assist teachers in providing material related to procedures for managing corpses based on 3D animation. In this study the authors used empirical methods, in which data collection was carried out in several steps, namely: Observation and Library Studies. In addition, the system design has 2 (two), namely: First Marker Check (Washing the Body) and Initialization. Then there are 4 (four) paths in application design, namely: Interface Design, Splashscreen Menu, Main Menu, Prayer Movement AR Menu. And the last one is testing, where testing of the process book augmented reality application for children is carried out. Before the application is tested on users, it is first carried out using the white box testing method, testing the application on devices with the Android operating system. From this study it can be concluded that the stages involved in developing a 3D multimedia in the form of augmented reality consist of the process of modeling, texturing, ringing, animation, rendering, making APKs. The results of the animation making process are then arranged in a single unit using vuforia and unity so as to produce a 3D animation-based corpse management movement guide application.
Modeling Time-Varying User Attitudes in Social Media

The underlying text classification setting generally used to model user attitudes in social media merely captures current attitudes whenever a text is generated. Under such constraints, the evolving historical attitudes are omitted and no anticipation of subsequent attitudes could be made. To alleviate this limitation, we propose a time-dependent approach that takes advantage of historical user comments to learn temporal descriptors that explain their evolving attitudes. We demonstrate that our approach could be used to predict user attitudes at subsequent times ahead. The experimental results on Tweets data, during the Covid-19 pandemic, corroborate our claim.
Deconstructing Internet Memes through Semiotic Analysis: Unveiling Myths and Ideologies in Visual and Verbal Signs

This paper will examine the meanings embedded in memes through semiotic analysis. Semiotics, as proposed by Chandler (2007), is the study of signs and symbols and their meanings, particularly in the context of communication and representation. Internet memes are culturally significant artifacts that consist of visual and verbal signs which convey a shared experience or cultural reference within a community. The objective of this study is to explore the myth and ideology that is conveyed through the signs used in these memes, and to examine the message that is being communicated to the audience. The data were collected by using observation method which involves the selection of a sample of widely shared Internet memes on social media platforms. This study used qualitative method in analyzing the data. These memes were analyzed using semiotic analysis to identify the signs used in the Internet memes and their associated meanings. The findings suggest that memes are a form of communication that uses signifiers to convey a message, and the connotative meanings of these signifiers can reveal the myth and ideology behind the message. Memes convey cultural values and beliefs and can challenge or reinforce dominant cultural ideologies. Understanding them can shed light on cultural reproduction in society, with implications for media, cultural, and sociology studies in the digital age.
Evaluating the Student and Faculty MyMU Portal Interface at Methodist University

The Methodist portal, myMU, is a platform used at MU that allows students, faculty, and parents to access different resources and information.The portal serves both undergraduate and graduate students, which also includes online students.Many of our peers have mentioned that the interface could be daunting for first-time users and lacks instructions on how to navigate each tab.Recently, there have been updates to the student portal and one of the goals they were able to achieve was downsizing the student portal from 18 to 8 tabs.However, the faculty portal has not been revised; we hope to help streamline it.This research aims to evaluate both the student and faculty portals to enhance user experience through user surveys/focus groups and by incorporating the International Standards Organization (ISO) standard ISO 9241, which addresses the accessibility of interactive systems to meet the needs of users.
Optimization of Breast Cancer Prediction using Optimaze Parameter on Machine Learning

At present, a very common cancer disease in women is breast cancer. This cancer develops in the female breast tissue and is the cancer with the highest mortality rate. This needs great attention. Forecasting breast cancer has been studied by a number of researchers and is considered a serious threat to women. Clinical difficulties in creating treatment approaches that will help patients live longer, due to the lack of solid predictive models that can predict outcomes at an early stage by analyzing patient history data. Because it can affect women all over the world. Early detection of breast cancer is crucial in determining the path of action. Cancer types can be distinguished into two types: benign and malignant. this research aims to provide information and science to medical professionals and also cancer patients to know the classification of the two types of cancer. The research project aims to also leverage data mining techniques using several algorithms on Machine Learning (ML) such as Decision Tree(DT), Random Forest (RF), K-Nearest Neighbors (KNN), Logistic Regression (LR), Support Vector Machine (SVM), and Gradient Boosting Tress (XGBoost). The results of this algorithm will determine the prediction of the most common types of cancer. The study used 683 samples of breast cancer patients, including 10 characteristics. This test is measured through mammography and biopsy tests. Using K-Fold Validation operators, then the sresults of the study showed that the K-Nearest Neighbor (KNN) algorithm produced the highest accuracy of 96.87% compared to the other five algorithms. Then, as a comparison again, the researchers also optimized the accuracy value using the parameter optimize operator. Where the number produced becomes more overwhelming. The highest accuracy result after calculated with the parameter optimize is the Random Forest (RF) algorithm. Where the result is 100% accurate compared to other ML algorithms.
Planning Guideline for Failure Handling in Onboard CBTC Device Using RCM (Reliability Center Maintenance) Analysis

Nowadays, the need for public transportation keeps on growing rapidly.One of the globally rapidly growing transportation modes is the Train.Train operation has something to do with the signaling system.Communications-based train control (CBTC) is a modern type of signaling system based on two-way communication using radio waves between Wayside and Onboard devices.CBTC System is a fully automated system that requires robust maintenance of many components in order to prevent failure.Even after the maintenance, a CBTC failure may still occur.Focused on the Onboard CBTC device, a Reliability Centered Maintenance (RCM) method is adopted as an effort to discover the severity level of failure and what kind of maintenance should be given to the Onboard CBTC device.FMEA and FTA methods are also included in the RCM method to conduct the evaluation.The evaluation result indicates that Remote Input Output (RIO) is the most vulnerable equipment to experience failure, in severe conditions, it's resulting in an emergency brake and threatening the safety of passengers.It also indicates that the frequently unclear reports from Mainline officers and different directions from the control center significantly impact poor handling, resulting in delayed traveling.In addition to the need for maintenance, this research aims to produce guidelines in form of a Standard Operating Procedure (SOP) for dealing with Mainline failure during operating hours.It is expected that any different direction from the control center and engineer's action can be eliminated in dealing with the Mainline failure.