Reviewerly/exHarmony huggingface dataset:
LANGUE DES JEUNES: THE USE OF GREETINGS BY E-SPORTS PLAYERS

One of the media used by teenagers to greet each other is electronic sports. Through e-sports, teenagers wave greetings and express their thoughts in words or sentences. The greeting is closely related to the name and designation used to address people. This qualitative descriptive study discusses e-sports players' greetings, especially those on the E-sport Podcast YouTube channel. The data were collected from five transcribed videos by note-taking. Then, they were analyzed using the theory of greeting according to the theory of Crystal (1997) and Kridalaksana (1980). Finally, the data were presented in tabulation for easy understanding. Based on the data analysis, the greetings used by e-sport players include personal names, pronouns, other nouns, kinship, and other words to show annoyance or anger.
Few-Shot Learning of Compact Models via Task-Specific Meta Distillation

We consider a new problem of few-shot learning of com-pact models. Meta-learning is a popular approach for few-shot learning. Previous work in meta-learning typically assumes that the model architecture during meta-training is the same as the model architecture used for final deployment. In this paper, we challenge this basic assumption. For final deployment, we often need the model to be small. But small models usually do not have enough capacity to effectively adapt to new tasks. In the mean time, we often have access to the large dataset and extensive computing power during meta-training since meta-training is typically per-formed on a server. In this paper, we propose task-specific meta distillation that simultaneously learns two models in meta-learning: a large teacher model and a small student model. These two models are jointly learned during meta-training. Given a new task during meta-testing, the teacher model is first adapted to this task, then the adapted teacher model is used to guide the adaptation of the student model. The adapted student model is used for final deployment. We demonstrate the effectiveness of our approach in few-shot image classification using model-agnostic meta-learning (MAML). Our proposed method outperforms other alternatives on several benchmark datasets.
Bent &amp; Broken Bicycles: Leveraging synthetic data for damaged object re-identification

Instance-level object re-identification is a fundamental computer vision task, with applications from image retrieval to intelligent monitoring and fraud detection. In this work, we propose the novel task of damaged object re-identification, which aims at distinguishing changes in visual appearance due to deformations or missing parts from subtle intra-class variations. To explore this task, we leverage the power of computer-generated imagery to create, in a semi-automatic fashion, high-quality synthetic images of the same bike before and after a damage occurs. The resulting dataset, Bent & Broken Bicycles (BB-Bicycles), contains 39,200 images and 2,800 unique bike instances spanning 20 different bike models. As a baseline for this task, we propose TransReI3D, a multi-task, transformer-based deep network unifying damage detection (framed as a multi-label classification task) with object re-identification. The BBBicycles dataset is available at https://tinyurl.com/37tepf7m
Motion Capture Systems and Their Use in Educational Research: Insights from a Systematic Literature Review

Motion capture is gaining significance in educational research. Different motion capture systems (MCSs) are used in educational research for different purposes and in different ways, which creates a diversity that is difficult to monitor. The aim of this article is to provide an overview of MCSs and their uses in educational research addressing primary and secondary school education. We conducted a systematic review focusing on the types of MCSs and the concrete systems being used, looking at how they are used, the purposes of their use, the aims and the research questions under investigation, the subjects/topics addressed in the studies, the locations/places of use, the potential benefits of using MCSs perceived by the researchers, and the underlying theories of movement. Based on n = 20 studies focusing on primary and secondary school education identified in our database search, we found that these studies mainly used optical MCSs. Furthermore, we identified three main purposes of their use: to directly support the learning process of students, as analysis tools, or for developing tutoring systems that support students in their learning. This paper gives insights into the use of MCSs in educational research and provides a springboard for further research using MCSs in educational research.
Trajectory Tracking and Collision Avoidance on Smart Wheel Chair

There have been many developments of wheelchairs as mobility aids, including electric wheelchairs. Wheelchairs sometimes still require manual steering. Therefore, in this research, a smart wheelchair is developed that can move automatically to the destination position from a predetermined position with a trajectory tracking system. The system deploys the odometry method, orientation angle using the output of the IMU-9DOF sensor with Kalman filter, and collision avoidance to avoid collisions with obstacles in front of it. The use of the Kalman filter improves the angle output that is close to the reference. In the trajectory tracking test, the wheelchair can approach the given reference position with a maximum error of 20 cm for the x-axis and 2 cm for the y-axis. The wheelchair collision avoidance test has been able to avoid collisions, as it successfully detects an obstacle less than 40 cm and avoids the collision correspondingly.
Why is the prediction wrong? Towards underfitting case explanation via meta-classification

In this paper we present a heuristic method to provide individual explanations for those elements in a dataset (data points) which are wrongly predicted by a given classifier. Since the general case is too difficult, in the present work we focus on faulty data from an underfitted model. First, we project the faulty data into a hand-crafted, and thus human readable, intermediate representation (meta-representation, profile vectors), with the aim of separating the two main causes of miss-classification: the classifier is not strong enough, or the data point belongs to an area of the input space where classes are not separable. Second, in the space of these profile vectors, we present a method to fit a meta-classifier (decision tree) and express its output as a set of interpretable (human readable) explanation rules, which leads to several target diagnosis labels: data point is either correctly classified, or faulty due to a too weak model, or faulty due to mixed (overlapped) classes in the input space. Experimental results on several real datasets show more than 80% diagnosis label accuracy and confirm that the proposed intermediate representation allows to achieve a high degree of invariance with respect to the classifier used in the input space and to the dataset being classified, i.e. we can learn the metaclassifier on a dataset with a given classifier and successfully predict diagnosis labels for a different dataset or classifier (or both).
DYNAMICS OF THE NON-PERFORMING LOANS IN AZERBAIJAN: EMPIRICAL ANALYSIS

Bank loans are the primary source of financing for businesses in the countries with non-developed financial markets like Azerbaijan.Thus, analysis of the dynamics of non-performing loans has u ermost importance in preventing banking crises in recession scenarios.This study aims to analyze the dynamics of non-performing loans in Azerbaijan.Employing three different estimation methods (FMOLS, DOLS and CCR), the study reveals a significant negative link between non-performing loan rates and macroeconomic factors like inflation and interest rates.There is no statistically significant relationship between nonperforming loan rates and real GDP growth in Azerbaijan.The lack of a statistical database on government support to the financing of businesses is a restriction of research.As a policy recommendation, it is proposed that government should improve regulations on collateralization of loans and banks' loan policies.This study's theoretical and methodological results can serve as a scientific source for further research and can be used in the monetary policy decision-making process.
Effective Tamil Character Recognition Using Supervised Machine Learning Algorithms

Computational linguistics is the branch of linguistics in which the techniques of computer science are applied to the analysis and synthesis of language and speech. The main goals of computational linguistics include: Text-to- speech conversion, Speech-to-text conversion and Translating from one language to another. A part of Computational Linguistics is the Character recognition. Character recognition has been one of the active and challenging research areas in the field of image processing and pattern recognition. Character recognition methodology mainly focuses on recognizing the characters irrespective of the difficulties that arises due to the variations in writing style. The aim of this project is to perform character recognition for of one of the complex structures of south Indian language ‘Tamil’ using a supervised algorithm that increases the accuracy of recognition. The novelty of this system is that it recognizes the characters of the Predominant Tamil Language. The proposed approach is capable of recognizing text where the traditional character recognition systems fails, notably in the presence of blur, low contrast, low resolution, high image noise, and other distortions. This system uses Convolutional Neural Network Algorithm that are able to exact the local features more accurately as they restrict the receptive fields of the hidden layers to be local. Convolutional Neural Networks are a great kind of multi-layer neural networks that uses back-propagation algorithm. Convolutional Neural Networks are used to recognize visual patterns directly from pixel images with minimal preprocessing. This trained network is used for recognition and classification. The results show that the proposed system yields good recognition rates.
Representing Technical Standards as Knowledge Graph to Guide the Design of Industrial Systems

Technical standards help software architects to identify relevant requirements and to facilitate system certification, i.e., to systematically assess whether a system meets critical requirements in fields like security, safety, or interoperability. Despite their usefulness, standards typically remain vague on how requirements should be addressed via solutions like patterns or reference architectures. Thus, software architecture design remains a time-consuming human-centered process. In this work, we propose an approach on how to use knowledge graphs for supporting software architects in the design of complex industrial systems. We discuss how project-generic knowledge (e.g., technical standards) and project-specific knowledge like the description of a concrete system can be modeled as knowledge graph. Making the architectural knowledge, which is currently present in technical standards and other resources, machine-readable, enables the support of the software architect through expert systems and therefore, improve the quality of the overall system design. However, since architectural knowledge is currently presented in many different formats, the transformation to a uniform, machine-readable form is required. We demonstrate the applicability of our approach with a representative example of an industrial client-server architecture and outline research challenges for future work.
Obtaining Difference Equations for Glucose Prediction by Structured Grammatical Evolution and Sparse Identification

Diabetes is one of the most common and difficult non-communicable diseases to deal with in our days. People with diabetes need to keep their glucose levels within a certain range to avoid health complications. Some patients must inject insulin to regulate their glucose levels, and estimating the necessary dose is not an easy task. In this paper, we investigate how to obtain expressions that predict glucose levels using variables such as previous glucose values, food ingestion (in carbohydrates), basal insulin dosing, and dosing of bolus of insulin. This paper proposes the combination of structured grammatical evolution and sparse identification to obtain difference equations governing the dynamics of the glucose levels over time. Glucose prediction serves as a tool for deciding the most convenient insulin dosing. Our technique produces promising results that provide explainable equations and use information usually managed by people with diabetes.
A data-centric way to improve entity linking in knowledge-based question answering

Entity linking in knowledge-based question answering (KBQA) is intended to construct a mapping relation between a mention in a natural language question and an entity in the knowledge base. Most research in entity linking focuses on long text, but entity linking in open domain KBQA is more concerned with short text. Many recent models have tried to extract the features of raw data by adjusting the neural network structure. However, the models only perform well with several datasets. We therefore concentrate on the data rather than the model itself and created a model DME (Domain information Mining and Explicit expressing) to extract domain information from short text and append it to the data. The entity linking model will be enhanced by training with DME-processed data. Besides, we also developed a novel negative sampling approach to make the model more robust. We conducted experiments using the large Chinese open source benchmark KgCLUE to assess model performance with DME-processed data. The experiments showed that our approach can improve entity linking in the baseline models without the need to change their structure and our approach is demonstrably transferable to other datasets.
RANCANG BANGUN APLIKASI GAME MULTIPLAYER SEBAGAI ALAT BANTU PROSES PEMBELAJARAN BERBASIS WEBSOCKET

The development of technology information is growing rapidly. For example in theeducation world, the purpose of interactive learning media is increasingly felt. With that purpose,an ICT-based learning process (Information Communication Technology) can be developedwhich has the concept of computer and multimedia learning. Computers can be used as asubstitute for books and also multimedia can be used as a substitute for teachers whenexplaining the material. In this study, to give an innovation in the learning models that usequizzes in the learning process, so it would be build a plan of a web socket based of multiplayergame application as a tool for learning process. To making this system, the advantage of websocket is to give a speed in exchanging information on the game system. Finally, the result ofthis application are to produce a web socket based of multiplayer game system design and howweb sockets work when used in a game. The results of the research produce a system designgame multiplayer based websocket. Based on BlackBox Testing and websocket testing. thebasic needs of the game are functionally fulfilled and the use of the system websocket providesa shorter lag time for requests and responses.
Machine Unlearning Method Based On Projection Residual

Machine learning models (mainly neural networks) are used more and more in real life. Users feed their data to the model for training. But these processes are often one-way. Once trained, the model remembers the data. Even when data is removed from the dataset, the effects of these data persist in the model. With more and more laws and regulations around the world protecting data privacy, it becomes even more important to make models forget this data completely through machine unlearning.This paper adopts the projection residual method based on Newton iteration method. The main purpose is to implement machine unlearning tasks in the context of linear regression models and neural network models. This method mainly uses the iterative weighting method to completely forget the data and its corresponding influence, and its computational cost is linear in the feature dimension of the data. This method can improve the current machine learning method. At the same time, it is independent of the size of the training set. Results were evaluated by feature injection testing (FIT). Experiments show that this method is more thorough in deleting data, which is close to model retraining.
Graph Fusion in Reciprocal Recommender Systems

Unlike traditional user-item recommendation tasks (e.g., movie or consumer-product recommendation), reciprocal recommender systems (RRSs) (e.g., online dating services and job-recruitment sites) must consider the interests of both two users. Pair matching prediction can improve the efficiency with which RRSs match potential partners. Graph Neural Networks (GNNs) are powerful models for learning representations of attributed graphs and information circulation between nodes. GNNs greatly facilitate link prediction in the area of user-item recommender systems but have not been extensively applied to RRS. In this study, we present a novel method for pair matching prediction that learns the reciprocal information circulation between users: not only side information about them but also structural information about their behavior histories. In contrast to earlier RRSs, which focus on response prediction, ours predicts both send and reply signals. Moreover, we introduce negative sample mining to explore the effect of different types of multiple samples on recommendation accuracy in real applications. Testing our method on data provided by an online dating service, we achieved an AUC of 73.15% (an absolute improvement of over 3.20% point above baseline) and an AP of 26.01% (an absolute improvement of over 2.79%) on send prediction; an AUC of 68.95% (an absolute improvement of over 1.74%) and an AP of 23.02% (an absolute improvement of over 0.70%) on reply prediction; an AUC of 71.26% (over 4.35% point absolute improvement) and an AP of 23.95% (over 0.30% point absolute improvement) on fusion reciprocal prediction.
Optical Information Cryptosystems Based on Structured Phase Encoding

The structured phase mask (SPM) is a diffractive optical element. It mainly comprises a radial Hilbert phase mask (RHPM), a Fresnel zone plate phase mask (FZPM), a spiral zone plate phase mask (SZPM), and a chaotic spiral phase mask (CSPM). It is easier to align with setup axis and it is itself a multiple-key device. Optical cryptosystems based on RHPM and FZPM in the gyrator transform domain have been presented. In these cryptosystems, the construction parameters are used as encryption keys. An asymmetric information cryptosystem based on SZPM and optical interference principle has been studied. Finally, asymmetric multiple-information cryptosystem based on CSPM and random spectrum decomposition has been discussed. In asymmetric cryptosystems, the construction parameters are used as decryption keys. Hybrid optoelectronic systems can be used for the implementation of cryptosystems. Numerical simulations demonstrate the efficiency and viability of the optical methods.
Food and Beverage Recommendation in EatAja Application Using the Alternating Least Square Method Recommender System

EatAja is a startup in Indonesia that provides a mobile application-based food and beverage ordering solution for restaurants. The EatAja application uses transaction data to recommend food and beverage menus to customers. Previous studies have developed recommender systems using the Apriori and Collaborative Filtering methods. However, there are shortcomings in the recommendation system using both methods, i.e., the lack of personalization factors and low scalability. The learning method with matrix factorization can overcome the problem. In this study, we improve the food and beverage product recommender system in the EatAja application using the Alternating Least Square (ALS) matrix factorization method on Apache Spark. We will compare the results of the recommender system using the ALS method with the Collaborative Filtering method. The comparison uses the Mean Absolute Error (MAE) evaluation method. The results showed that the MAE value decreased by 0.07 with the ALS Matrix factorization method.
Penerapan Metode Waterfall Dalam Rancangan Sistem Informasi Manajemen Kartu Induk Retribusi (KIR) Berbasis Website

The UPTD of the Department of Transportation (Dishub) has one task, namely to carry out motorized feasibility tests or what is often called the KIR Test. This is done to ensure that motorized vehicles are roadworthy and ensure safety. In the operational activities in the service at the UPTD, there is a problem in the input process for the Retribution Master Card which is still being carried out using Microsoft Access. This, of course, slows down the work of managing user retribution card data. With these problems, the authors provide a solution that can help the relevant agencies in managing the levy master card into an integrated system based on the website. In this study the author uses the waterfall method in software development. The waterfall method is considered capable of solving a complex problem. Then in modeling the structure of the software the author uses UML (Unified Modeling Language) modeling. The result of this research is to create an information system that can facilitate UPTD in managing the master card when conducting motorized feasibility testing
The Implementation of STEM-Based Lipid E-Book on Student Learning Outcomes

The purpose of the study was to analyze the effect of using lipid e-books using the STEM approach on student learning outcomes. The method uses pre-experimental research with one group pretest posttest design. This research was conducted at one of the State Universities in Bengkulu with the subject consisting of 26 students who took the Organic Chemistry 2 course. The instrument used in this study was a multiple choice test with 4 meetings in the form of pretest and posttest questions. Data analysis techniques include the average value (mean), normality test, and hypothesis testing using a t-test using the SPPS for windows version 23 computer program. The results showed the average score of the learning outcomes before using the lipid organic chemistry e-book was 54.90, while after using the e-book the average value is 79.81. The learning outcomes data were normally distributed and the t-test value showed Significant (2-tailed) 0.00. These results indicate that the developed STEM-based teaching materials can improve student learning outcomes in particular and are able to improve the quality of learning for organic chemistry 2 courses in general.
Improved Decission Tree Performance using Information Gain for Classification of Covid-19 Survillance Datasets

&lt;p class="AbstractL-MAG"&gt;One of the most feared infectious diseases today is COVID-19. The transmission of this disease is quite fast. Patients also sometimes do not have the same symptoms. Overcoming the spread of the pandemic has been widely carried out throughout the world. Apart from the medical method, there are also many other methods, including computerization. Data mining is a discipline that can project data into new knowledge. One of the main functions of data mining is classification. Decision tree is one of the best models to solve classification problems. The number of data attributes can affect the performance of an algorithm. This study uses information gain to select the attribute features of the Covid-19 surveillance dataset. This study proves that there is an increase in the accuracy of the decision tree algorithm by adding information gain feature selection. Previously, the decision tree only had an accuracy rate of 65% for the classification of the Covid-19 surveillance dataset. After pre-processing using information gain, the accuracy rate increased to 75%.&lt;/p&gt;