Reviewerly/exHarmony huggingface dataset:
IMPLEMENTASI APLIKASI JUAL BELI MOBIL BEKAS DENGAN METODE ANALYTICAL HIERARCHY PROCESS DAN NAIVE BAYES

One area of life that is affected by the rapid development and advancement of technology is the automotive sector. Information technology bridges various groups who participate in automotive transaction activities. Therefore, the physical condition and performance of an automotive are determining factors for a buyer who wants to make a purchase.Not all types of automotive offered meet the buyers' standards, nor do they have a selling price in accordance with the quality described by the seller. The Analytical Hierarchy Process (AHP) method helps determine the type of automotive that suits the buyers' wishes and the Naïve Bayes method helps determine the selling price according to the quality of the automotive offered.The Analytical Hierarchy Process (AHP) method is a decision support model that describes a complex multi-factor or multi-criteria problem into a hierarchy, which in turn can organize complex problems into a more orderly and systematic manner. The Naïve Bayes method is a classification using probability and statistical methods that predict future opportunities based on past experiences. Naïve Bayes calculates a set of probabilities by adding up the frequency and value combinations from the given dataset. Based on the comparison of the calculation results with the tests carried out, the AHP method obtains an accuracy rate of 100% in displaying the criteria for cars being sold. Meanwhile, the Naïve Bayes method obtains an accuracy rate of 100% in determining whether a dealer is interested in buying a car or not.
ReinforceBug: A Framework to Generate Adversarial Textual Examples

Adversarial Examples (AEs) generated by perturbingining examples are useful in improving the robustness of Deep Learning (DL) based models. Most prior works generate AEs that are either unconscionable due to lexical errors or semantically and functionally deviant from original examples. In this paper, we present ReinforceBug, a reinforcement learning framework, that learns a policy that is transferable on unseen datasets and generates utility-preserving and transferable (on other models) AEs. Our experiments show that ReinforceBug is on average 10% more successful as compared to the state-of the-art attack TextFooler. Moreover, the target models have on average 73.64% confidence in wrong prediction, the generated AEs preserve the functional equivalence and semantic similarity (83.38%) to their original counterparts, and are transferable on other models with an average success rate of 46%
Unsupervised blind-deconvolution with optimal scaling applied to astronomical data

Many blind deconvolution algorithms have been proposed for image deblurring when the instrumental point spread function (PSF) is unknown. Blind deconvolution can be stated as an inverse problem whose unknowns are the object of interest and the PSF. In that case, the direct model is bilinear and has an intrinsic scaling degeneracy: scaling one of the components can be compensated by inversely scaling the other one. Provided that homogeneous regularization functions are chosen for the object and for the PSF, the scaling degeneracy of the direct model can be exploited to reduce the number of hyper-parameters in the problem. Using this property, we propose an instance of a blind deconvolution algorithm that amounts to alternately estimating and scaling the two components of the bilinear model. Our algorithm is insensitive to the scaling of the initial estimate of the component (PSF or object) used to start the iterations. We show that this yields much faster convergence and, in practice, reduces the odds of being trapped in a bad local minimum. These features make our algorithm suitable for being embedded into a simple procedure to automatically tune the remaining hyper-parameter(s) and obtain a fully unsupervised method. We also propose a homogeneous version of an edge-preserving regularization to be used by our algorithm. Using Stein's Unbiased Risk Estimator (SURE) as a criterion to automatically tune the hyper-parameter(s), we assess the advantages of our algorithm for empirical astronomical images compared to other methods.
Penggunaan 3D Grapher Dalam Pembelajaran Matematika Geometri

The very rapid development of technology affects various sectors of human life, including the field of education which is marked by the emergence of various learning media.This study aims to see students' responses in learning mathematics geometry using learning media, namely the 3D Grapher application.The subjects of this study were students of class VIII in a junior high school in Garut Regency.This research uses a descriptive qualitative approach.The results of this study indicate that students enjoy learning mathematics, have enthusiasm, and the will to learn when using the 3D Grapher and 3D Grapher can also make it easier for students to understand the learning material.So that the 3D Grapher application can be used as a media in learning geometry mathematics in the schools.
The Limits of Morality in Strategic Games

An agent, or a coalition of agents, is blameable for an outcome if she had a strategy to prevent it. In this paper we introduce a notion of limited blameworthiness, with a constraint on the amount of sacrifice required to prevent the outcome. The main technical contribution is a sound and complete logical system for reasoning about limited blameworthiness in the strategic game setting.
On Verifying Expectations and Observations of Intelligent Agents

Public observation logic (POL) is a variant of dynamic epistemic logic to reason about agent expectations and agent observations. Agents have certain expectations, regarding the situation at hand, that are actuated by the relevant protocols, and they eliminate possible worlds in which their expectations do not match with their observations. In this work, we investigate the computational complexity of the model checking problem for POL and prove its PSPACE-completeness. We also study various syntactic fragments of POL. We exemplify the applicability of POL model checking in verifying different characteristics and features of an interactive system with respect to the distinct expectations and (matching) observations of the system. Finally, we provide a discussion on the implementation of the model checking algorithms.
On the First-Order Rewritability of Ontology-Mediated Queries in Linear Temporal Logic (Extended Abstract)

We argue that linear temporal logic LTL in tandem with monadic first-order logic can be used as a ba- sic language for ontology-based access to tempo- ral data and obtain a classification of the resulting ontology-mediated queries according to the type of standard first-order queries they can be rewritten to.
Business Cybersecurity. Case study in Peruvian and Mexican SMEs

Currently, organizations present a gap between business processes and information technology areas that translates into economic loss when they are victims of cyberattacks and affect the continuity of their business. For this reason, it is relevant to know how business cybersecurity is in the environment of a Peruvian and a Mexican SME considering ISO 27032. The results of the research show the importance of applying regulations in business cybersecurity.
The State of the Art in Deep Learning-based Recommender Systems

Recommender systems are tools and techniques for effective information filtering that have become more common as a result of increased Internet access, personalization tendencies, and changing computer user habits. Existing recommender systems are capable of making acceptable recommendations, but they face issues in recommending accurate items, suffer scalability problem with big data and cold start. In recommendation systems, deep learning, a recently created sub-domain of machine learning technique, is used to improve the feature of anticipated output. Deep learning is utilized to generate suggestions, and the research issues that come with employing deep learning for recommendation systems are also discussed. The core terminology, fundamental principles of a recommendation engine, and a comprehensive overview of deep learning models used in recommender systems are discussed in this paper.
KLASIFIKASI EMPLOYABILITY MAHASISWA PENERIMA BEASISWA DI UNIVERSITAS TARUMANAGARA DENGAN GRAPH THEORY (MINIMUM SPANNING TREE)

The application for classifying the employability of scholarship recipients with Graph Theory is a method for the classification of student employability. This method was made for Tarumanagara University which is used to replace the Tarumanagara University method which is still manual. There are 2 programming languages used to create this application, namely Visual Studio and Python. Visual Studio for the user interface and python for calculations. Testing is carried out by User Acceptance Testing (UAT) and amount testing. UAT test to check buttons and features and calculate testing to check whether the results of the manual method are the same as the K-Nearest Neighbor (K-NN) method before making it in a graph. From the two tests carried out it can be seen that the results of the mixed test data testing with an average accuracy of 92.5%, whereas for all scholarship test data with an average accuracy of 97.5%
Motion Fall Detection System for the Elderly Based on Multi-feature Fusion Robot

With the improvement of people's living standards, countries around the world are facing the serious problem of aging in their own countries.Unexpected events such as falls in the elderly have a serious and inestimable impact on the health of the elderly.The purpose of this paper is to study the elderly fall detection system based on multi-feature fusion robot.This article first introduces a fall detection algorithm.Second, it improves the way of feature fusion.It uses feature weighted kernel functions to achieve the weighted fusion of acceleration and angular velocity features to build a fall recognition model based on support vector machines.Then, it analyzes the key parts of the fall recognition model.To verify the generalization performance of the algorithm.Finally, a fall detection system based on the recognition model constructed in this paper is implemented on the Android phone platform, and the practical application effect of the fall detection algorithm is verified.The experiments show that the feature fusion fall monitoring algorithm constructed in this paper has a sensitivity of 90.67%, a specificity of 92.96%, and an accuracy of 92.14%, which verifies the detection performance of the detection algorithm constructed in this paper.The robustness and stability of the drop detection software based on the Android platform.
PENERAPAN ALGORITMA MAX-MINER UNTUK ANALISIS POLA BELANJA KONSUMEN (STUDI KASUS KAFELOAJA)

This application is used to produce combination meal for Kafeloaja in order to give the owner advices to make decision in business. In this application the method used are Max-Miner and Market Basket Analysis. Max-Miner is used to find frequent maximal itemset and the result is used to formulate association rule in Market Basket Analysis. The result of this program are menu combinations that can be used by owner to be added as new combination meal.
PERANCANGAN SISTEM REKOMENDASI PENJURUSAN PADA SEKOLAH MENENGAH KEJURUAN SANTO PAULUS MENGGUNAKAN METODE K-MEANS CLUSTERING

Santo Paulus Vocational School in Jakarta, is one of the Private Schools in Central Jakarta which holds majors for grade X (ten) students who will continue their studies to grade XI (eleven). The obstacle that is often found in the majors in SMK Santo Paulus Jakarta is the difficulty in determining which students meet the criteria to occupy certain majors. This is because the majors process is still done manually, so it requires quite a long time and is considered inappropriate. This study, purpose to the K-Means Clustering algorithm for the decision support system of student majors at SMK Santo Paulus Jakarta. K-Means Clustering algorithm can group data that has attributes and has a large amount of data, so that it can be utilized in the determination of student majors in accordance with students' academic abilities.Based on the results of the implementation of the K-Means Clustering algorithm for the majors decision support system, the conclusion that can be drawn is the K-Means-Clustering algorithm can be applied in the student majors decision support system.
PENERAPAN ALGORITMA C4.5 UNTUK PENERIMAAN KELAYAKAN KREDIT PADA KOPERASI (STUDI KASUS:KOPERASI XYZ)

In this developing era, competent human resources are still needed. Therefore, human resources is one of the factor in determining the successful implementation of organizations and companies. Despite having facilities and infrastructure as well as human resources with expected performance, the activities cannot be completed as they should. Based on this, a company, organization and cooperative must be able to manage existing human resources so that they can be obtained as needed.C4.5 algorithm is a group of decision tree algorithms that have input in the form of training samples and samples. Training samples are sample data that is used to build a tree that has been tested for correctness. While samples are data fields which will be used as parameters in classifying data. C4.5 algorithm aims to reduce and minimize errors in the provision of credit worthiness in cooperatives to the wider community.In this process we need an algorithm that is used to manage credit worthiness in cooperatives in order to facilitate the credit worthiness process. With this algorithm C4.5, it can be easier for admin to assess creditworthiness quickly and accurately.
SISTEM PENUNJANG KEPUTUSAN PEMILIHAN KARYAWAN DENGAN MENGGUNAKAN ANALYTICAL HIERARCHY PROCESS

Decision support systems are made to facilitate users in determining a decision that will be made with the help of a system. The purpose of this study is to facilitate Porence owner in selecting prospective teachers who want to apply as a teacher at Porence. Porence is a course institution engaged in English education. Data collection for this system is collected by using interviews with Porence owner. This system uses the Analytical Hierarchy Process method to determine the decisions made. This system is implemented in the form of a website so as to facilitate program accessibility by using a common browser such as Google Chrome and Mozilla Firefox. The results of the Analytical Hierarchy Process-based Decision Support system are displayed directly to the Porence owner after the data entered has met the standard method used. The results of the decision support system can be used to provide recommendations to applicants who can be accepted and who will be considered.
PEMBUATAN DASHBOARD PENJUALAN DAN PEMBELIAN HEVINDO SPORT

Dashboards are used to present quality information on sales and purchases made available to managers or owners of a business. The purpose of this thesis is to assist shop owners in knowing the state of the sale and purchase of Hevindo Sport. Hevindo Sport is a shopping center engaged in sports, especially badminton. Dashboard uses the data warehouse Extract, Transform, and Load (ETL) method for the importing process. Importing ETL with the process of making dimension tables and fact tables. The dimensions of the table consist of the importing master table in SQL Server 2018. The fact table will be created after all dimension tables are finished and then create a dashboard. The results will be displayed in graphical form with the Tableau tools. The results of the dashboard are used to make decisions on the sale of goods reached in the safe zone and monitor the entry and exit of existing goods.
Effects of Teaching Geometry Using Mobile App Instructional Technique on Students Interest in Bayelsa State

The study investigated the effect of teaching geometry using mobile application instructional technique on senior secondary students' interest in Geometry in Bayelsa State. A quasi-experimental, non-equivalent control group design was used in the study. All the 506 SS2 mathematics students in the three federal unity schools formed the population of the study. One hundred and seventy nine (179) students from two schools formed the sample of the study. Intact classes were assigned by flipping of coin to either experimental or control group; and separately taught by their regular mathematics teachers who had earlier been trained for the purpose. All the groups were pre and post-tested. Geometry Interest Inventory (GII) was used as instruments for both the control and experimental groups. Three research questions and three hypotheses guided the study. The mean and standard deviation were used to answer the research questions, while Analysis of Covariance (ANCOVA) was used in testing the hypotheses at .05 levels of significance. The result showed that the use of mobile application instructional technique as a medium of teaching improves students' interest in geometry. Also there was no significant effect between teaching method, gender and choice of discipline on students' interest in Geometry. The study recommended that the use of mobile application should be integrated into the teaching and learning of mathematics generally.
Research Analysis of IoT Healthcare Data Analytics Using Different Strategies

In the twenty-first century, it’s important to communicate with each other to stay connected with everyday events. IoT has become most prominent technology in many applications over the past years. Especially in medical applications, IoT is used to exchange the collected information from IoT smart devices to the server. In healthcare monitoring system, it’s important to maintain the patient health record in server for monitoring and tracking patient health information without any privacy issues. Maintaining a secured of electronic health record (EHR) is one of the major concerns in this vast society. To improve the security in healthcare monitoring system, many algorithms have been implemented. In this paper, the security of IoT healthcare data with some data analytics techniques such as RFID, deep learning, machine learning and blockchain is analyzed. Apart from many issues in healthcare monitoring system, this analysis aims to discuss and exhibits how these analytics techniques impact the prediction and decision making based on the performance, security, data transportation efficiency and accuracy.
B-Spot: Blockchain and Steganography based Robust and Secure Photo Transmission Mechanism

In this paper, a Steganography and Blockchain based robust and secure photo transmission mechanism, named B-Spot, is proposed. At the sender side, firstly, a 3-3-2 LSB image steganography algorithm is used to hide a secret photo into a cover photo. Therefore, the existence of the secret photo is concealed. Then the stego-image is broken down into pixels and embedded into blocks. Blocks are connected by the hash values forming a blockchain. Any modifications on the blocks are reflected by the breakage of the chain, which makes the mechanism tamper-evident. Another copy of the blockchain is stored in a hash table for the latter recovery process. The blockchain and the hash table can be transmitted via any network. When the receiver receives the data, it firstly executes the verification process to check integrity of the blockchain. Then the lost and tampered blocks are recovered by referring to the hash table, which makes the mechanism more robust to noises. Finally, the stego-image is reconstructed from the recovered blockchain. Then the receiver can obtain the secret photo following the extraction algorithm. The simulation results show that the proposed mechanism has high data capacity, better imperceptibility, reasonable computing time, and strong robustness to noise. This mechanism adds an extra layer of security and robustness to the existing schemes.
Multiple intelligences for the development of listening skills in adult learners

: Intelligence is a multidimensional phenomenon that is present at multiple levels of our brain, mental and bodily systems. There are various forms of intelligence, in which people know themselves and the world around them. It is important to create a guide to improve listening skills based on multiple intelligences. At present, in the Ecuadorian Amazon region, studies on multiple intelligences for the development of listening skills have been insipient. In this sense, seeing the need to know a descriptive reality of multiple intelligences, the main objective of this study was to analyze the conceptual arguments of the theory of multiple intelligences, as well as to diagnose listening skills and socialize the results of this study. In order to respond to the objectives, a bibliographic review of the scientific literature was carried out, accompanied by field work (interviews). The results indicate that it is necessary to strengthen reading comprehension and innovate in the methodologies used by teachers. It is also important to mention that each student has a capacity that dominates, therefore not everyone will learn in the same way, the teacher plays a primary role so that the student can recognize their type of intelligence and thus develop their listening skills.
Empirical linkage learning for non-binary discrete search spaces in the optimization of a large-scale real-world problem

Empirical linkage learning (ELL) is a new type of problem decomposition techniques proposed recently for Black-box optimization. ELL techniques are proven never to report false gene dependencies (denoted as false linkage), which is their main advantage. The linkage learning based on local optimization (3LO) (the first ELL technique proposed) was shown to bring a significant potential of reaching high-quality results. However, 3LO applies only to binary search spaces, this paper examines the 3LO for discrete non-binary search spaces (3LO-nb). 3LO-nb applies to any problem using a discrete encoding. On the base of NP-hard, large-scale, real-world problem, we show that using 3LO-nb may lead to outstanding results.
Using graph neural networks as surrogate models in genetic programming

Surrogate models have been used for decades to speed up evolutionary algorithms, however, most of their uses are tailored for problems with simple individual encoding, like vectors of numbers. In this paper, we evaluate the possibility to use two different types of graph neural networks to predict the quality of a solution in tree-based genetic programming without evaluating the trees. The proposed models are evaluated in a number of benchmarks from symbolic regression and reinforcement learning and show that GNNs can be successfully used as surrogate models for problems with a complex structure.
SUPPORT FOR SCHOOL LEADERS AND DIGITAL COORDINATORS IN THE FIELD OF LEADERSHIP AND PERSONAL DEVELOPMENT

SUPPORT FOR SCHOOL LEADERS AND DIGITAL COORDINATORS IN THE FIELD OF LEADERSHIP AND PERSONAL DEVELOPMENT
REFLECTION OF DIVERSITY IN THE OBJECT OF SOCIAL WORK. STUDENT’S VIEWS AND PRACTICAL EXPERIENCES

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 9901-9907ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.2385Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
EPISTEMOLOGY AND EDUCATION IN THE DIGITAL AGE – A POSTPHENOMENOLOGICAL VIEW

Appears in: EDULEARN22 Proceedings Publication year: 2022Page: 4315 (abstract only)ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.1031Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain
INTEGRATED SCIENCE (IS) FOR MIDDLE SCHOOL VIA INTERACTIVE DIGITAL BOOK

Appears in: EDULEARN22 Proceedings Publication year: 2022Pages: 9032-9041ISBN: 978-84-09-42484-9ISSN: 2340-1117doi: 10.21125/edulearn.2022.2175Conference name: 14th International Conference on Education and New Learning TechnologiesDates: 4-6 July, 2022Location: Palma, Spain