Reviewerly/exHarmony huggingface dataset:
Ball tracking and trajectory prediction system for tennis robots

Abstract Recently, as the service robot market has grown, robots have emerged in various fields such as industry, service, and sports. In the field of sports, robots that can play with humans have been developed. We proposed a novel vision system for measuring the trajectory of a tennis ball and predicting its bound position, which can be utilized in the development of tennis robots. In this paper, we introduce a ball detection algorithm using an artificial neural network and a ball trajectory prediction algorithm using stereo vision. Our approach involved the use of a net vision system and a robot vision system to accurately detect and track the ball as it moves across the court. By combining these two systems, we were able to predict the trajectory and bound position of the tennis ball with high accuracy. As a result, the accuracy of the neural network for ball detection in actual tennis images reaches 81.4%. The ball trajectory prediction error in Gazebo simulation is 29.6 cm in the x-axis, 7.2 cm in the y-axis, and 11.7 cm in the z-axis on average.
A novel explainable AI-based situation recognition for autonomous robots with partial unlabeled data

Rise of unmanned vehicles and autonomous robots has been accompanied by study of path planning, navigation, and decision-making algorithms. Current state-of-the-art employs deep neural nets to extract the required features. This technology, though successful in most cases, fails where the training has not been done for unseen conditions. For such unlabeled training data, transfer learning approaches have been proposed. A major drawback of using transfer learning approaches is that the actions and/or state spaces are reactive only to present circumstances. A truly intelligent autonomous operation has to consider a subordinate-to-a-human approach for its mission risks that vary with topography, path planning as well as mission goals. To address these complex combinatorial problems, DARPA has initiative a novel Explainable AI (XAI) technology in the past few years. In XAI, machine learning is paired with human intervention to make decisions by generating textual explanations of all the available relevant information / decision. In this paper, we propose to use available information along with human intelligence in a feedback loop for helping the unlabeled data to be trained and generate cost functions which were previously not programmed. We study this context/situation awareness to generate list of decision available from explanations on combinatorial tasks. Moreover, we employ this approach to a quadruped robot to learn its environment and the AI model starts in its infancy to mimic human cognitive architecture. We show that the learning process can be improved in a way that suits a particular mission in mind.
The TRP Tutoring System in Education: System-Forming Factors

The problem of research and justification of its relevance. Introduction. The TRP in the USSR has not only become a historical chronicle in the upbringing and inculcation of physical qualities, but the ideological basis of the spiritual and moral development of society. There is a stage of continuity of the TRP of the USSR and the TRP of Russia in an updated conceptual framework. The mechanism of implementation of TRP in Russia is being improved in accordance with the digitalization of society, the creation of online services, taking into account the development of the region, mass physical culture and sports. The introduction of a new administrative unit of a tutor-teacher of the TRP of Russia in 2014 marked new approaches to the implementation of the complex and the creation of a tutoring system. The TRP tutoring system differs from tutoring in education as a whole, therefore, it requires the allocation of system-forming factors that will help identify problems and will contribute to the development of strategic and tactical tasks for the implementation of the TRP complex, health promotion, spiritual and moral forces in social development.&#x0D; The social function of a student as a tutor in TRP is presented. Students of the Kuban State University of Physical Culture, Sports and Tourism not only personally take the TRP, but also promote it through various competitions, festivals, participation in the evaluation of TRP standards in secondary schools, regional testing centers. Regional peculiarities of the tutoring system development are shown on the example of Sochi. The authors note that in 2014, with the return of the TRP, a new administrative unit tutor appears on a legal basis, which made it possible to start building a tutoring system in the TRP complex, taking into account system-forming factors. The problem of the prospects for the development of the TRP is raised, taking into account its socio-orienting function in society.
MinUn: Accurate ML Inference on Microcontrollers

Running machine learning inference on tiny devices, known as TinyML, is an emerging research area. This task requires generating inference code that uses memory frugally, a task that standard ML frameworks are ill-suited for. A deployment framework for TinyML must a) be parametric in the number representation to take advantage of the emerging representations like posits, b) carefully assign high-precision to a few tensors so that most tensors can be kept in low-precision while still maintaining model accuracy, and c) avoid memory fragmentation. We describe MinUn, the first TinyML framework that holistically addresses these issues to generate efficient code for ARM microcontrollers (e.g., Arduino Uno, Due and STM32H747) that outperforms the prior TinyML frameworks.
The use of SQ6R method in increasing EFL students' reading comprehension

This study was carried out to overcome the difficulties in reading comprehension encountered by high school students. The SQ6R method was offered as the appropriate method to solve the problem in this school. It is aimed at assisting students to get reading information and its meaning. This research utilized quantitative method, i.e. experimental research to find the answer as to whether the SQ6R method could increase students' reading comprehension. The sample of this study was 30 students, equally distributed in both experimental group (class IX3) and control group (class IX2), of a junior high school in Central Aceh, Indonesia. The data of the study was gathered by conducting two tests (pre and post with the treatment in between) which was further analyzed by using t-test. The results indicated that although both classes increased their scores in the post-test, the scores of experimental class who experienced the SQ6R method in their learning increased significantly (39 – 84) and outperformed those in the control class (33 – 63). Furthermore, the result of t-test was found to be 13.53 which was higher than t-table of 1.4 at a significance level of 0.05. This signified that the hypothesis (Ha) was accepted and the hypothesis (H0) was rejected. Thus, SQ6R method could increase the students’ reading comprehension. Based on the results, the teachers are recommended to apply this method in their teaching and learning process.
What Is the Problem?

The information technology we rely upon is a tremendous enabler and also… imperfect. Unplanned exceptions and incidental impairments exist in the underlying and interconnected technology, deviating its actual functionality away from the ideal. These imperfections expose attractive perforations perfectly accommodating for an unintended user. This is the problem of cybersecurity.
Qualitative Modeling to Extract Knowledge for Problem Structuring

In this chapter, we address applying qualitative meta-synthesis and conducting qualitative modeling to extract knowledge from data and information during the problem-solving process. Two situations are considered. One situation refers to context with a small-scale size of available texts. Under this context, we illustrate the application of two technologies, CorMap and iView, for qualitative meta-synthesis for idea or assumption generation for further verification and validation. Another situation refers to context with open and complex systems problems, such as various societal problems highlighted and discussed in the online media. We describe qualitative modeling by outlining the complex problem-evolving process and provide two approaches for storyline generating. For both situations, visualized perspective toward the concerned problems reflected by the texts is emphasized.
RoBERTweet: A BERT Language Model for Romanian Tweets

Developing natural language processing (NLP) systems for social media analysis remains an important topic in artificial intelligence research. This article introduces RoBERTweet, the first Transformer architecture trained on Romanian tweets. Our RoBERTweet comes in two versions, following the base and large architectures of BERT. The corpus used for pre-training the models represents a novelty for the Romanian NLP community and consists of all tweets collected from 2008 to 2022. Experiments show that RoBERTweet models outperform the previous general-domain Romanian and multilingual language models on three NLP tasks with tweet inputs: emotion detection, sexist language identification, and named entity recognition. We make our models ( https://huggingface.co/Iulian277/ro-bert-tweet ) and the newly created corpus ( https://huggingface.co/datasets/Iulian277/romanian-tweets ) of Romanian tweets freely available.
An NLP-Empowered Virtual Course Assistant for Online Teaching and Learning

In this paper, we present a Natural Language Processing (NLP)-empowered virtual course assistant solution that supports online teaching and learning in the context of the COVID-19 pandemic. We leverage advanced technologies of pre-trained language models in NLP to construct several fundamental functionalities for the virtual course assistant. The assistant is designed to answer general course enquiries to reduce time-consuming and repeated human responses, to answer course-related knowledge questions by understanding both queries and teaching materials, and to analyze students' feedback via sentiment analysis. Additionally, we have constructed the course-related database and cross-platform virtual assistants for both website and mobile applications. Different pre-trained models are utilized to fine-tune the dataset in each type of model. By comparing different datasets and analyzing their performance, the best performance model is selected for the virtual assistant. Empirically, adopting NLP-empowered virtual course assistants in class improves teaching and learning experiences: With the help of an NLP-empowered virtual course assistant, the teaching team could devote more effort and time to answering complex questions; For students, an immediate response increases their motivation to study. Thus, the online system could give an excellent user experience to a wide variety of users. Our code and dataset are released at https://github.com/Heriannan/NLP-for-educationVirtualAssistant.
Understandings of Ethics in DevOps Teams: A Preliminary Investigation

Ethics are essential in DevOps for promoting transparency, trust, security, quality, continuous improvement, and user-centricity, as they guide individuals and organizations to make responsible choices and develop high-quality, secure, and reliable software applications. To consider the ethics of the DevOps teams, there is a need to develop standards and a code of conduct. The objective of this research work is to develop robust guidelines to assist software development organizations to consider ethics in the DevOps teams. The empirical study will be performed with industry practitioners, and legislation experts to explore the important areas and practices that need to be followed by the software organizations' work ethics for the DevOps teams. We believe the developed robust guidelines will assist the organization to follow the work ethics while working in a DevOps culture.
A Recommendation on How to Teach K-Means in Introductory Analytics Courses

We teach K-Means clustering in introductory data analytics courses because it is one of the simplest and most widely used unsupervised machine learning algorithms. However, one drawback of this algorithm is that it does not offer a clear method to determine the appropriate number of clusters; it does not have a built-in mechanism for K selection. What is usually taught as the solution for the K Selection problem is the so-called elbow method, where we look at the incremental changes in some quality metric (usually, the sum of squared errors, SSE), trying to find a sudden change. In addition to SSE, we can find many other metrics and methods in the literature. In this paper, we survey several of them, and conclude that the Variance Ratio Criterion (VRC) is an appropriate metric we should consider teaching for K Selection. From a pedagogical perspective, VRC has desirable mathematical properties, which help emphasize the statistical underpinnings of the algorithm, thereby reinforcing the students’ understanding through experiential learning. We also list the key concepts targeted by the VRC approach and provide ideas for assignments.
Ontologies and Linked Data

chapter Share on Ontologies and Linked Data Author: Dimitris Bilidas View Profile Authors Info & Claims Geospatial Data Science: A Hands-on Approach for Building Geospatial Applications Using Linked Data TechnologiesJune 2023Pages 53–66https://doi.org/10.1145/3581906.3581911Published:14 June 2023Publication History 0citation0DownloadsMetricsTotal Citations0Total Downloads0Last 12 Months0Last 6 weeks0 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteGet Access
Overview of Deep Learning Models for Banknote Recognition

This paper offers a comparative analysis of various deep learning models, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and Deep Belief Networks (DBNs), applied to the task of banknote recognition. Accurate banknote recognition is indispensable in financial transactions, vending machines, and security systems, and the development of reliable recognition systems is essential to mitigate fraud and ensure seamless operation. In this study, we employed the Banknote dataset, containing a balanced mixture of genuine and forged banknotes, to train and test these models. Our ex-perimental results demonstrate that the CNN model, specifically with ResNet-50 architecture, outperforms the others in terms of accuracy, precision, recall, and F1 score. The paper encapsulates the performance metrics and considerations for applying these models to banknote recognition, and aims to assist researchers and practitioners in choosing the most effective methods for future development of banknote recognition systems.
Overview Of Vanet Network Security

This article provides an overview of the security of VANET, which is a vehicle network. When reviewing this topic, publications of various researchers were considered. The article provides information security requirements for VANET, an overview of security research, an overview of existing attacks, methods for detecting attacks and appropriate countermeasures against such threats.
Research on Data Interaction and System Integration of Heterogeneous Systems Based on Web Services

At present, the mainstream DCOM and other middleware technologies cannot be used for the communication between heterogeneous systems. To solve this problem, a heterogeneous data interaction and integration scheme based on Cobra and WebService is proposed. Firstly, the overall framework combining WebService and XML file technology is designed. Then the data type mapping technology is used to integrate the data types of various data sources. At the same time, a platform independent and language independent middle layer is established to complete the encapsulation design of CORBA resource attributes and resources. Finally, we test the performance of such integration scheme through a data service component example. The experimental results show that the system can improve the utilization and coverage of heterogeneous data, which also has optimal performance and scalability
Design and implementation of Braille learning machine based on Internet of Things technology

In order to meet the needs of the visually impaired to learn Braille and improve the efficiency of teachers teaching Braille, this paper uses Internet of Things technology and embedded technology to design a new type of Braille learning machine to optimize the existing Braille teaching. The Braille teaching system is designed through the MQTT protocol to realize the network control of multiple Braille learning machines. A micro motor is used to control the lifting of a single braille bump, and a dual CPU control architecture is used to control the lifting of the braille bump, which improves its reusability compared with traditional braille boards. We tested the accuracy of Braille conversion, and the experimental results were in line with expectations. This research can better serve the visually impaired and bring convenience to their Braille learning.
Enhancing students' knowledge and competency through Accurate software training: SMK Muhammadiyah 9 in South Jakarta

The advancement of technology and the increasingly complex challenges in the working world have prompted accounting vocational school students to acquire sufficient knowledge and competency in using Accurate software. The purpose of this activity is to enhance the competence and hard skills knowledge of students at SMK Muhammadiyah 9 in South Jakarta. Through this community service activity, it is expected to improve students' abilities in using Accounting Software, specifically Accurate version 5, to generate financial reports. The training was conducted over 2 days on February 14 and 15, 2022, with a total of 59 student participants using Google Meet. It started with an introduction by the community service team, followed by completing a pre-test, database creation using basic setup, and practical exercises solving case studies using Accurate software specifically for trading companies. The outcome of this community service is an increased understanding, ability, and competency of the training participants in using Accurate software, which will assist teachers and students in the learning process at school and serve as preparation for their future entry into the workforce
OMA-DSS: Ontology Based Multi Agent Decision Support System in Healthcare Domain to Prevent Cardiovascular Diseases

It is one of the cumbersome tasks to extract patient’s relevant information from a cluster of web documents and traditional hospital systems and presenting those search results to users in a meaningful form. This is done by carrying out traditional approaches that help in predicting infections and diseases including heart attack and many more. But the saddest part is that these traditional methods failed to tell about early signs of human health system. The solution to this problem lies in employing agent based integration approach with the help of ontologies. The paper deals with ontological based agents data integration proposed system in which external data related to human health (unstructured or structured) is coordinated and semantically integrated by multiple agents. After the integration of data, new knowledge is inferred on basis of a set of rules thus making it multi agent decision support system. The proposed model is able to predict health status on the basis of the activities performed by humans. The approach presented in the paper is detailed in context of human day to day activities which helps in enhancing cardiovascular soundness of humans.
Spectral-Spatial MLP Network for Hyperspectral Image Super-Resolution

Many hyperspectral image (HSI) super-resolution (SR) methods have been proposed and have achieved good results; however, they do not sufficiently preserve the spectral information. It is beneficial to sufficiently utilize the spectral correlation. In addition, most works super-resolve hyperspectral images using high computation complexity. To solve the above problems, a novel method based on a channel multilayer perceptron (CMLP) is presented in this article, which aims to obtain a better performance while reducing the computational cost. To sufficiently extract spectral features, a local-global spectral integration block is proposed, which consists of CMLP and some parameter-free operations. The block can extract local and global spectral features with low computational cost. In addition, a spatial feature group extraction block based on the CycleMLP framework is designed; it can extract local spatial features well and reduce the computation complexity and number of parameters. Extensive experiments demonstrate that our method achieves a good performance compared with other methods.
Integrating Hybrid Feature Extraction Techniques with Support Vector Machine for Efficient Facial Emotion Recognition

Facial emotion recognition is a vital piece of brain science, criminology, and web-based media. In every one of these fields, a serious level of unwavering quality of order is basic. Presently human judgment is utilized in these fields however people are not generally precise. Subsequently, there is a requirement for a solid and speedy method of distinguishing human feelings. The new advances in AI and example acknowledgment have offered a few calculations to perceive human feelings like Local Binary Pattern (LBP), Convolutional Neural Network (CNN), GLCM for feature extraction, and SVM for Classifier. A facial emotion recognition software that detects five basic emotions of a face is implemented in this paper. We had learned many machine learning technologies and algorithms. Along with that, we learned technologies like sci-kit-learn, Keras, OpenCV that were used in the implementation of our paper. In this paper, we used technologies like LBP, GLCM, and Gabor filter to extract aspects of images. For training the model, we used the CK + dataset that contains 24,282 images in the training set. In this, Support Vector Machine (SVM) is applied for the classification of emotions using the features extracted from the above technologies.