Reviewerly/exHarmony huggingface dataset:
Communication-Efficient Device Scheduling for Federated Learning Using Stochastic Optimization

Federated learning (FL) is a useful tool in distributed machine learning that utilizes users' local datasets in a privacy-preserving manner. When deploying FL in a constrained wireless environment; however, training models in a time-efficient manner can be a challenging task due to intermittent connectivity of devices, heterogeneous connection quality, and non-i.i.d. data. In this paper, we provide a novel convergence analysis of non-convex loss functions using FL on both i.i.d. and non-i.i.d. datasets with arbitrary device selection probabilities for each round. Then, using the derived convergence bound, we use stochastic optimization to develop a new client selection and power allocation algorithm that minimizes a function of the convergence bound and the average communication time under a transmit power constraint. We find an analytical solution to the minimization problem. One key feature of the algorithm is that knowledge of the channel statistics is not required and only the instantaneous channel state information needs to be known. Using the FEMNIST and CIFAR-10 datasets, we show through simulations that the communication time can be significantly decreased using our algorithm, compared to uniformly random participation.
Big Multimodal Data-driven Medical Image Processing for the COVID-19 Diagnosis

Diagnosis using medical images is extremely labor-intensive and could even be subjective. With the exciting progress of artificial intelligence (AI) in the last decade, it has been increasingly realistic and promising to develop automatic diagnoses from medical images. In this chapter, we give comprehensive advances on recent data-driven AI technologies for the COVID-19 diagnosis based on medical images. Moreover, rather than using a massive volume of data to train a network, it is also valuable to take multi-modal multi-view data into a unified learning framework to improve the accuracy of diagnosis. To this end, two categories of representation learning methods are proposed to deal with this variety and variability of big medical image data. One is an average approximate hashing (AAH) method for searching large-scale multimedia databases, which projects data into different semantic spaces but shares a unified hash code. The other focuses on nonnegative matrix factorization-based clustering models for multi-view data. Experiments justify the effectiveness and efficiency of the proposed methods.
The Study of Fashion Items Generation Based on Big Data and Deep Learning

Since the Third Industrial Revolution, computer network information technology has made continuous progress, and the era of big data and Artificial Intelligence (AI) has arrived. The rapid progress of big data and AI will not only be able to promote the continuous improvement of computer network information technology, but also improve the level of economic development and make positive contributions. In this chapter, we propose a style-based fashion items generator for clothing design with big data and deep learning. Designing a clothing item is complicated, time-consuming, and challenging for clothes designers and clothing industry; however, recent improvements in conditional image generation provide a feasible solution. With a desired fashion category, the proposed framework will generate a nonexistent fashion item which cannot be distinguished from the real ones. Generative adversarial networks (GANs) make it possible to perform our clothing design with semi-supervised conditions. Our method can generate clothing items conditioned on 15 fashion categories. Furthermore, due to the multimodality of the clothes images, we employ a style-based generator with disentangled networks and adopt a multistage discriminator to improve the results of image generation. The effectiveness of our approach is well demonstrated through quantitative experiments. Our method will spark inspirations for fashion designers in their work.
Penerapan Metode Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) Dalam Menentukan Bintara Polri Di Sumatera Utara

Selection of candidates for police officers is carried out by police institutions throughout Indonesia, with the aim of improving the quality of personnel. In this recruitment, the North Sumatran National Police prepare a large quota for the National Police NCO. The criteria sought in this recruitment are to meet the aspects of capability, potential ability, creativity, basic character and sincerity in carrying out their duties. The North Sumatra Regional Police is one of the government institutions that utilizes the development of technology and information, namely the creation of a system for the selection of non-commissioned officers. The problem that is often found in the North Sumatra Regional Police is that the selection of BINTARA so far is still considered less than optimal. The problem was because the examiners at the time of selection were still using the manual system. The manual system in question is that an examiner must first go to the office to take a list of the scores of the selection participants and then take it to the test location which is quite far from the SUMUT POLDA office. After that, the examiner must fill in the list of participants' scores at the test location and return it to the SUMUT POLDA office. The list of values will then be processed by the admin on duty in the office, then after processing, the results of the exam will be obtained. To determine how to select candidates for the National Police, the Decision Support System can be used. The research method used is the Multi-Objective Optimization On The Basis Of Ratio Analysis (MOORA) method. The result of this study is to find out that Academic, Psychology, Health and Equality (Physical) have a very large influence in determining the selection of the National Police officer candidate in North Sumatra.
Penerapan Monitoring Locator Dies pada Perusahaan Stamping Part Otomotif

PT. Ganding Toolsindo is an automotive stamping parts company that has been established since 1998. This design is illustrated by conducting research at the kruing plant which is placed in the production division to assist the audit process at first and collect customer data, then get a mandate from the managerial to create an information system that is integration and innovation in the area of return and use of dies. Then look for accurate data with various methods so that the implementation of this system goes well, on this occasion the design was made with the concept of a dies locator information system that tracks the existence of dies for production and contains the identity of dies data in the production process. The problem that occurs is that the operator often places the dies not according to the existing shelves and in the end the dies become unorganized and neat when the production process is running. The method of developing this system is to use SDLC (system development life cycle), namely Waterfall. This information system uses mysql and codeigniter, then to run this system using xampp. The whole of this information system is about tracking the dies whether they are in production or on the shelf, if they are being repaired, the status of the dies will change as well as the status of the dies when they are being used in production. In this system feature there are customer data, part data, shelf data, user data and the duration of use of the dies. In the end, this system generates a report to the production supervisor to be seen and in what dies data is being used in production so that there will be no more misplaced shelves.
Analisis Vulnerability Management Pada Container Docker Menggunakan Opensource Scanner Berdasarkan Standar Cyber Resilience Review (CRR)

One of the most widely used container technologies to provide IT services is Docker. The vulnerability in container technology, namely Docker, requires special management. Management of this vulnerability can be done technically with a software vulnerability scanner and standard Cyber Resilience Review (CRR) guidelines. Experiments were carried out with Aquasec and Anchore scanners that performed vulnerability scanning on two Docker Images systems. The two vulnerable systems have different versions, namely version – 1 and version – 2. The software elements in version – 2 have a higher versioning level than version – 1. Experimental data in the form of vulnerability reports are analyzed based on Cyber Resilience Review (CRR) which focuses on four stages namely Define a Strategy, Develop a Plan, Implement the Capability, Assess and Improve the Capability. So that the results of Category Vulnerability are obtained, namely 30 Closed Vulnerability, 10 Open Vulnerability, and 13 Newly Vulnerability. Continuation of this research can use aspects of Patch Management with more varied software tools.
Analisis Data Mining Klasifikasi Berita Hoax COVID 19 Menggunakan Algoritma Naive Bayes

The rapid dissemination of information along with the rapid development of technology along with the massive speed of electronic media and the internet. But the rapid spread of news cannot guarantee that the information and news that we get can be validated from valid sources. Based on data released by Kominfo at the end of 2021, there were 1773 hoax news that were successfully clarified from the hoax news. Then during the Covid-19 pandemic itself, there were various hoaxes circulating in the community. Throughout 2021, the Ministry of Communications and Informatics discovered as many as 723 hoaxes about Covid-19. Based on the background above, the researchers and previous studies have discussed hoax detection in various fields. Such as, fraud detection in online writing style [1], classification of hoax news based on machine learning [3] and the application of nave Bayes and PSO algorithms for classification of hoax news on social media [4]. From here the researchers tried to carry out experiments on the nave Bayes classification algorithm to classify hoax covid 19 news. Based on the results of research that has been done, the nave Bayes model and cross validation can classify hoax news well, the resulting accuracy is 86.3% where 80-90% included in the good classification criteria. The data that is predicted to be incorrect is also not too much from a total of 300 datasets, only 41 are declared incorrect in labeling less than 2% of the total dataset, so it can be concluded that this model can be used as a reference if you want to proceed to a more complex prediction model, for example the model prediction using web-based machine learning.
Rancang Bangun Aplikasi Midesk Berbasis Website Menggunakan Metode User Centered Design

Assurance is a non-bank financial institution that carries out an agreement between the insurer and the insured to provide protection or protection suffered by the insured for financial losses arising from unexpected and uncertain events. Insurance consumers come from various circles, insurance has an agent who plays a role in marketing insurance products owned by the company. The problem with insurance users is that they often have difficulty reporting problems. Reporting problems at the Manulife insurance company still cannot help optimally, as evidenced by the use of Notepad and Microsoft Excel. Based on these problems, this research creates an application called MiDesk to help the problems faced by consumers. This application uses the user-centered UCD method so that all these processes produce products that meet user needs. The result achieved using the UCD method is a Web-based Helpdesk application. The result of this research is a web-based MiDesk application that can be used by the Manulife Helpdesk IT agent team.
Seleksi Fitur untuk Prediksi Hasil Produksi Agrikultur pada Algoritma K-Nearest Neighbor (KNN)

Agriculture is one of the largest economic driving sectors in Indonesia. The Central Statistics Agency (BPS) in 2021 recorded that 37.02% of Indonesia's population worked in the agricultural sector. The problem faced by farmers today is the decline in yields, both in quantity and quality due to unpredictable weather, making it difficult for farmers to choose the types of plants that are suitable for planting. The application of data mining techniques has problems related to the complexity of weather parameters and natural conditions that support agricultural production, so it is very important to do feature selection, namely to form the most relevant features. This study conducted an experiment to determine the effect of implementing the Principal Component Analysis (PCA) selection feature on the performance of the K-Nearest Neighbor (KNN) algorithm which produces the highest accuracy of 99.64% in this study.
Analisis Sentimen Produk Kecantikan Jenis Serum Menggunakan Algoritma Naïve Bayes Classifier

The increased consumption of beauty products as a lifestyle has increased public opinion on the beauty products used. Generally, reviews are given through posts on social media. This study discusses the classification of sentiment analysis on the use of serum beauty products on Twitter using the Naïve Bayes Multinomial algorithm. Sentiment analysis of serum beauty products is carried out to provide information and preferences to the public regarding the quality of a product. The results of the information and preferences become a reference for consideration in choosing the appropriate serum beauty product. The data used in this study were 27,587 tweets using three keywords, namely "serum," "face serum", and "beauty serum". Tweet data is divided into training data and test data with the number of training data as much as 22,070 tweets and test data as much as 5,518 tweets. The data is categorized using the lexicon senticnet 7 dictionary based on polarity values. The results of the analysis of positive sentiment are 35%, negative sentiment is 63.8%, and neutral sentiment is 1.2%. The classification results using Naïve Bayes Multinomial obtain the highest accuracy value of 80%. The Confusion Matrix results get the highest precision value of 88%, the highest recall of 81%, and the highest f1-Score of 86%.
Vulnerability Management Pada Vulnerable Docker Menggunakan Clair Scanner Dan Joomscan Berdasarkan Standar GSA CIO-IT Security-17-80

Vulnerabilities in Docker need to be managed considering that this vulnerability is one of the potentials for exploitation, this can happen because Docker is a container related to application and system security. This study analyzes the vulnerability management process in Docker Images and Docker Images Applications using the GSA CIO-IT Security-17-80 standard. This vulnerability search uses two scanning tools, namely Clair Scanner and JoomScan. Vulnerabilities in Docker Images and Docker Images application version - 1, were overcome by creating a new system, namely version - 2 which upgrades the Docker Images software and Docker Images application. The test scenario is run by scanning for vulnerabilities in two versions of the trial system, in the form of a vulnerability report. The data was analyzed using the GSA CIO-IT Security Standard-17-80 which was limited to the stages of Scanning Capabilities, Vulnerability Scanning Process, Vulnerability Scan Reports, Remediation Verification, and Re-Classification of Known Vulnerabilities. The result is the fastest scanning time is in version - 2, the results of the comparison of vulnerabilities obtained are 44.45% on Docker Images and 77.78% on Joomla. So that the contribution that can be given is to provide an overview of the use of the GSA CIO-IT Security-17-80 standard as a guide for managing the security of an IT asset based on the stages carried out. Continuation of research can be in the form of using the 6 stages of GSA with the support of adequate vulnerability data from the right scanner software.
Analisis Sentimen Vaksinasi Booster Berdasarkan Twitter Menggunakan Algoritma Naïve Bayes dan K-NN

Covid-19 or the corona virus has spread throughout the world, one of which is Indonesia. There have been many problems due to this virus for 2 years in Indonesia, and various efforts and policies have been made by the government to control the impact that does not become worse by this corona virus, these efforts are vaccination actions against the Indonesian people, and in early 2022 the government started a new program, namely booster vaccination. Many people are pro and contra to the program on social media Twitter. This study was conducted with the aim of knowing the sentiment of Indonesians towards booster vaccination in Indonesia.The data obtained as many as 2000 tweets obtained from the keyword "booster vaccine" on Twitter. Then the data is divided into training data and test data (training) then made into three different portions, namely 60/40, 70/30, and 80/20. The test results are that the best performance is found in testing a portion of 80% of the training data 20% of the test data using the K-NN algorithm, the test produced the highest value results, namely 78.62% accuracy and AUC 0.845 and categorized as good classification. The results show that the K-NN algorithm model with an 80% portion of training data is the best in the classification of booster vaccination sentiment analysis. The sentiment results in the test data were positive with 303 tweet data and negative sentiment totaled 93 tweet data. The results of more positive sentiments show that booster vaccinations in Indonesia are acceptable and get a lot of support from the Indonesian people on social media Twitter.
RPGPref: A Planning Heuristic That Uses Playstyle Preferences to Model Player Action and Choice

Recent work extending planning algorithms that reason about action and change has been successful at supporting game design, player modeling, and story generation. Incorporating agent preferences over actions and propositions into a planning process allows for a more accurate prediction of what a human might do when solving a problem like playing through a game level. This paper presents the preference-based planning heuristic RPGPref which uses relaxed plan graphs (RPGs) and preference sets to guide a planner toward a preference-conforming path to its goal. A human subjects evaluation confirms that RPGPref successfully guides the planning process toward solution plans that recognizably match and differentiate player playstyles.
FarmQuest: A Demonstration of an AI Director Video Game Test Bed

In AI director research, it is not straightforward for researchers to understand how each algorithm affects the player experience. This demo introduces PWR, which is a new fully developed video game test bed to evaluate AI directors. This demo includes 3 different AI director algorithms in order to help researchers improve their intuition for understanding the differences between potential algorithms, and also provides insight on the framework required to author a new AI director. This test bed can support future AI director research by allowing for direct comparisons of new algorithms.
On the Challenges of Generating Pixel Art Character Sprites Using GANs

We pose the problem of generating pixel art character sprites facing one side (e.g., right), given their images facing another one (e.g., front), as an image-to-image translation task and investigate the use of the Pix2Pix architecture to solve it. Aiming to improve the results over unseen data, we propose and investigate two architecture modifications: (a) representing images using color palettes and (b) adding a histogram loss term to the generator. We compared the results qualitatively and quantitatively using FID and L1 distances between the generated and target images. Results indicate that representing images with color palettes encourages overfitting, and the histogram loss leads to slightly improved results.
Step: A Highly Expressive Text Generation Language

Games often generate text from human-authored templates and adapt that text to relevant context. Many systems have been developed to aid this process using tech-niques such as context-free grammars, randomization, logic programming, global state, and HTN planning. In this paper, I present Step, a novel programming language for text generation. So far as I can determine, previous techniques can all be implemented within Step using a few lines of code. This allows designers to mix-and-match features as needed, without having to write an interpreter for a new language. While extremely expressive as a programming language, Step is also intended to allow writers to add new text to an existing system with minimal markup and little knowledge of programming. In a head-to-head comparison, the Step implementation was more compact than, and used less markup than, a Prolog implementation of the same generator.
Reasoning with Ontologies for Non-player Character’s Decision-Making in Games

In most games, the decision-making of non-player characters (NPCs) is usually constructed using variants of state machines, behaviour trees, utility-based AI or planning. These methods are relatively simple to implement, but have drawbacks in that it can be difficult to create complex non-hard-coded behaviour for many agents and to maintain the algorithms, especially when scaling up. Game designers usually think of their games with rules that closely resemble logic rules. A methodology is introduced to design both general and modular behaviour using a logic reasoner with hierarchical ontologies. This approach is combined with the well-founded semantics (WFS) to solve the problem of representation and reasoning despite the lack of NPC knowledge.
Systematic Literature Review: Characteristics of Teachers in Aspects of the Industrial Revolution 4.0

In the era of IR 4.0, the education system needs a transformation that can adapt to such rapid change.The role and characteristics of teachers have also changed in terms of meeting the requirements of the Industrial Revolution 4.0 aspect.This study aims to examine the trends of systematically published studies involving the role and characteristics of 4.0 teachers in the last five years, which are from 2018 to 2022.Qualitative methods are used by analyzing content and screening articles using PRISMA guidelines.Article searches are through Google Scholar database sources.A total of 24 articles were found after being screened according to the set criteria.The results of the analysis of this study found that 4.0 teachers need to meet digital literacy skills, lifelong learning skills, leadership skills, and personal attitudes.It is hoped that this study can provide guidelines to the Ministry of Education Malaysia (MOE) in formulating skills training programs for educators towards producing a useful generation in the era of IR 4.0.In addition, this study can help researchers to see research trends related to the characteristics and role of teachers in aspects of Industrial Revolution 4.0.
Venda Mentilia 21016174 Model Project Based Learning dalam Pembelajaran Bahasa dan Sastra Indonesia yang Kreatif dan Inovatif.

this artical about project based learning
Mobile application as a learning object in the teaching of a programming language

The development of this prototype consists of a mobile application (app) under the Android operating system platform that aims to teach the essential bases of the structure of a programming language and promote programming logic in students, this app is oriented to engineering students in the area of ​​computational sciences, since today most students who study these technologies use smartphones on a daily basis and necessary for their training, so the idea is to create an educational app that allows greater interaction between the user and the thematic content that promotes greater dynamism in the teaching-learning process, the app offers practical programming exercises based on the connectivist educational model to generate the knowledge and skills to program in Python for being an accessible and easy to learn language.