Reviewerly/exHarmony huggingface dataset:
ANALISIS TINDAK TUTUR ILOKUSI PADA KUMPULAN CERPEN SENYUM KARYAMIN DAN IMPELEMENTASINYA SEBAGAI MEDIA PEMBELAJARAN DI SMP KELAS IX

This study discusses the problems encountered in the use of language and learning Indonesian. Indonesian is often used with many mistakes, especially in the world of education. Students often experience obstacles in understanding and using language properly and correctly. One of the language skills that students are less interested in is reading, due to the lack of awareness of the importance of reading in increasing knowledge. Another problem encountered in learning Indonesian is understanding the meaning of speech and the use of learning media. Understanding the meaning of utterances is often disturbed due to context and indirect disclosure. Meanwhile, the use of deep learning media is still lacking, especially in the use of technology. Many teachers experience obstacles in using learning media, so that student learning motivation becomes low. This study aims to understand the problems associated with language use and learning Indonesian. The results of this study are expected to provide useful insights and recommendations for educators and students in improving Indonesian language learning.
Migration of adversarial example in image classification

Neural network technology has made remarkable achievements in computer vision, speech recognition, natural language processing, and other fields. However, the problem of the interpretability of the neural network model makes its application in real situations have potential security risks. In recent years, many studies have pointed out that using Adversarial example technology to make extremely weak perturbations of the input sample can mislead most mainstream neural network models, such as fully connected neural networks and convolutional neural networks, to make wrong judgments. This phenomenon reveals that the existing neural network technology lacks security and robustness. The study of adversarial example technology is of great significance to improve the safety and robustness of neural network models and to promote the researcher's understanding of the learning process of neural network models deeply. Studying adversarial examples of migration is an important research field in adversarial attacks. Researchers attempt to summarize the rules of adversarial attacks by exploring the migration of adversarial examples, thus establishing a robust model in the deep learning area. In this paper, the migration of adversarial examples in image classification is studied to provide analytical data for summarizing the characteristics of adversarial examples.
Graph convolutional network with position and global information improves relation extraction

With the continuous improvement of the related relation extraction model, researchers focused on optimizations of the input of the sentence expression structures, such as dependency trees, and slight improvements and integrations of basic models applied to this task. This paper utilizes position-aware attention mechanisms to highlight critical information and makes good use of global information generated by the bidirectional LSTM, absorbing characteristics and advantages of those related models to propose a new Position-Aware Graph Convolutional Network (PA-GCN) model and describe it in three steps. Moreover, this paper trials the model on the RACRED dataset to evaluate its performance and suitability. The experiment results demonstrate that the PA-GCN model outperformed others listed in this paper, achieving an F_1 score of 66.6%, which verifies an improvement of the model and the effectiveness of adding an attention-aware mechanism.
Comparison of image compression techniques using Huffman and Lempel-Ziv-Welch algorithms

Image compression technology is very popular in the field of image analysis because the compressed image is convenient for storage and transmission. In this paper, the Huffman algorithm and Lempel-Ziv-Welch (LZW) algorithm are introduced. They are widely used in the field of image compression, and the compressed image results of the two algorithms are calculated and compared. Based on the four dimensions of Compression Ratio (CR), Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), the applicable conditions of the two algorithms in compressing small image files are analysed. The results illustrated that when the source image files are less than 300kb, the Compression Ratio (CR) of Huffman algorithm was better than that of LZW algorithm. However, for Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR) and Bits Per Pixel (BPP), which are used to represent the compressed images qualities, LZW algorithm gave more satisfactory results.
SeeCC: An Online Cross-Cultural Communication Aid to Improve Communication and Cooperation Performance

In this research, we designed an online cross-cultural communication aid SeeCC, which can provide individual-level cultural portrait and real-time communication tips. We conducted an experiment to test the effectiveness of SeeCC in promoting cross-cultural skills and performance. Sixty participants from multiple cultural backgrounds were recruited to complete online collaboration tasks. The results showed that (1) SeeCC significantly improved users’ skills in online cross-cultural communication, and (2) the expert-evaluated outcome quality was better with SeeCC than without it, confirming the effectiveness of SeeCC in improving cross-cultural communication and collaboration performance. The results indicated great potential for SeeCC to be used in cross-cultural training and global collaboration support.
Machine Learning Methods and Words Embeddings in the Problem of Identification of Informative Content of a Media Text

The central problem solved in the article is the task of automating the process of revealing the information content of the media text. This problem is relevant, since its solution allows you to identify texts that do not carry useful information, but try to influence the reader in order to encourage him to take various actions. First, we present a modification of the CBOW algorithm and describe the process of training a neural network to build word embedding. Next, we describe the Russian word embeddings available on the site https://rusvectores.org/ru/models/ . Here we also show how ready-made word embeddings can be used to solve problems of thematic classification of texts. To do this, we use a set of news data from the resource https://lenta.ru . Finally, to solve the problem of information content of texts, we train the embedding of words based on the given set of texts and conduct testing. The quality check of the model is carried out on the basis of a dataset prepared by the employees of the Volgograd State University.
Research on RGB-d-Based Pilot Hand Detection in Complex Cockpit Environment

With the rapid development of the aviation industry, human error has replaced mechanical failure as the main cause of aviation accidents, which makes “human factors” increasingly popular. In human factors research, hand detection is very important for analyzing pilot behavior. However, in the complex environment of the cockpit, common hand detection methods have poor accuracy because of light and background. In this paper, we propose a new hand position detection and contour extraction method based on RGB-D information for cockpit scenes. In the pre-processing stage, this method uses depth images of the unobstructed cockpit scene for background modeling and the median filtering method is used to process depth images. After that, the result of subtracting the depth background from the depth image and image segmentation using the threshold are applied to extract the contour of the pilot's arm. Next, we use the region growing algorithm on the depth image to find the location of the top of the pilot's arm, which is the location of the hand, and use it as the seed point for using the region growing algorithm on the RGB image. Finally, the region growing algorithm combining RGB and YCbCr color spaces is used for the extraction of hand contour. The results show that the method proposed in this paper can effectively reduce the influence of lights and background in the cockpit and achieve the goal of hand localization and hand contour extraction in complex scenes.
KidCadTech STEM Module Solution

This study aimed to study the effectiveness of this STEM module solution as a learning kit for Reka Bentuk Teknologi Subject and to suggest ways to improve on how to engage more while learning by involving students and teachers at school while using teaching aids, especially in STEM Subject. 40 secondary school teachers and 10 secondary school students participated in this study and were interviewed. STEM Module Solution is a Teaching Aid specifically for teachers and students who teach and learn RBT Technology Design subjects in secondary schools. This kit also has Augmented Reality as a medium to convey information more comprehensively and give maximum impact.
AI/ML Based Sensitive Data Discovery and Classification of Unstructured Data Sources

The amount of data produced every day is enormous. According to Forbes, 2.5 quintillion data is created daily (Marr, 2018). The volume of unstructured data is also multiplying daily, forcing organizations to spend significant time, effort, and money to manage and govern the data assets. This volume of unstructured data also leads to data privacy challenges in handling, auditing, and regulatory encounters thrown by governing bodies like Governments, Auditors, Data Protection/Legislative/Federal laws, regulatory acts like The General Data Protection Regulation (GDPR), The Basel Committee on Banking Supervision (BCBS), Health Insurance Portability and Accountability Act (HIPPA), The California Consumer Privacy Act (CCPA) etc. Organizations must set up a robust data protection framework and governance to identify, classify, protect and monitor the sensitive data residing in the unstructured data sources. Data discovery and classification of the data assets is scanning the organization’s data sources both structured and unstructured, that could potentially contain sensitive or regulated data. Most organizations are using various data discovery and classification tools in scanning the structured and unstructured sources. The organizations cannot accomplish the overall privacy and protection needs due to the gaps observed in scanning and discovering sensitive data elements from unstructured sources. Hence, they are adapting to manual methodologies to fill these gaps. The main objective of this study is to build a solution which systematically scans an unstructured data source and detects the sensitive data elements, auto classify as per the data classification categories, and visualizes the results on a dashboard. This solution uses Machine Learning (ML) and Natural Language Processing (NLP) techniques to detect the sensitive data elements contained in the unstructured data sources. It can be used as a first step before performing data encryption, tokenization, anonymization, and masking as part of the overall data protection journey.
Semantic space of the subject area of research on communicative practices in a virtual environment

Introduction. The relevance of studying communicative practices in virtual environments is related to the complex nature of internet communication, which requires a comprehensive phenomenological characterization. Structural modeling of the subject area of virtual practice research will allow for the identification of common and differing trends in English and Russian publications on this issue. The purpose of the study: constructing a semantic space of the subject area of the study of communicative practices in virtual environment. Materials and methods. The object of the study was a corpus of articles on communication in virtual space from the scientometric databases eLibrary.Ru and Scopus (period from 2015 to 2022, with a volume of 4000 documents). The methods used to construct the semantic space included: 1) content analysis for describing the semantic dimensions of the research subject area; 2) automated data extraction methods using the bibliometric analysis program "VOSviewer"; 3) descriptive statistics methods and clustering analysis (k-means method). The results of the study. In foreign research, communicative practices in virtual spaces are studied in the context of computer-mediated communication (Occurrences = 403, Total link strength = 220). The leading topics are related to professional activities on the internet, the effects of social presence on virtual communication, and studies of telemedicine practices (Occurrences = 290, Total link strength = 181). The leading directions of Russian research are related to the study of communicative practices in social networks (Occurrences = 2063, Total link strength = 1068), in the context of internet communication genres (Occurrences = 1479, Total link strength = 1244), from the perspective of cyber-socialization (Occurrences = 1463, Total link strength = 1175), and electronic media practices (Occurrences = 796, Total link strength = 673). Conclusion. The obtained results can help to understand the trends in studying communicative practices in virtual space and determine further directions for analysis and evaluation. The results can also be useful in working with high school students and university students in the process of shaping their information culture and understanding the specifics of interactions with a highly technological information environment.
BUILDING AND STRENGTHENING THE ENTREPRENEURIAL UNIVERSITY - TAPPING INTO THE POTENTIAL OF LIBRARIES

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 4979-4985ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1308Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
POSSIBILITIES OF DEVELOPING THE TECHNICAL CREATIVITY OF STUDENTS IN SECONDARY SCHOOLS

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 3839-3844ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.1041Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
WHEN STUDENTS TAKE THE ROLE OF INSTRUCTIONAL DESIGNERS

1 University of Zagreb, Faculty of Humanities and Social Sciences, (CROATIA)2 University of Zagreb (CROATIA)
WORKSHOP MACHINE LEARNING KLASIFIKASI TUMOR OTAK PADA CITRA MRI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN SUPPORT VECTOR MACHINE

The brain is one of the organs that very important role for humans. Brain tumors can be a threat for humans. So that The researchers developed a CNN method that was tested effective for detecting brain tumors. CNN is a method that is quite popular, in its application it is used for image classification and several other image processing cases. CNN can be used to detect and recognize objects in an image better on an Artificial Neural Network. In addition, many researchers also use the SVM method, SVM can be applied to perform pattern recognition in the case of image processing. Brain tumors can be caused by the spread of cancer in parts of the other body. According to a report by the World Health Organization (WHO) brain cancer accounts for less than 2% of other cancers, but the severe morbidity and resulting complications are enormous. Brain cancer requires multidisciplinary treatment, so a professional standard policy is needed for optimal treatment. This activity proposes a Machine Learning Workshop on Brain Tumor Classification in MRI Imagery using CNN and SVM, in CNN activities can be divided into several parts such as CNN modeling, data preprocessing, building, and implementing CNN models in The SVM teaches how to build a hyperplane. This activity was delivered by expert speakers in their fields from alumni of the University of Muhammadiyah Malang.
FROM THE UNIVERSITY TO THE WORKPLACE – LOVE AT FIRST SIGHT OR DELIBERATE CHOICE?

Appears in: EDULEARN23 Proceedings Publication year: 2023Pages: 630-633ISBN: 978-84-09-52151-7ISSN: 2340-1117doi: 10.21125/edulearn.2023.0262Conference name: 15th International Conference on Education and New Learning TechnologiesDates: 3-5 July, 2023Location: Palma, Spain
OUTCOMES OF THE USE OF VIRTUAL UNIVERSE PRO AS A SUPPORT FOR TEACHING IN AUTOMATION: FIRST STEPS TOWARDS IMMERSIVE EXPERIENCES

OUTCOMES OF THE USE OF VIRTUAL UNIVERSE PRO AS A SUPPORT FOR TEACHING IN AUTOMATION: FIRST STEPS TOWARDS IMMERSIVE EXPERIENCES
EDUCATIONAL ROBOTICS AS A TOOL FOR THE ACQUISITION OF TECHNICAL AND SOFT SKILLS IN UNIVERSITY EDUCATION

EDUCATIONAL ROBOTICS AS A TOOL FOR THE ACQUISITION OF TECHNICAL AND SOFT SKILLS IN UNIVERSITY EDUCATION
MultiBranch-Mono3D: Lightweight improvement of FCOS3D

Significant progress has been made in monocular 3D detection technology, but it still faces challenges. One key challenge is accurately estimating the 3D coordinates and velocity of objects due to the lack of depth and motion information from monocular cameras. Additionally, monocular 3D detection suffers from lower accuracy caused by positioning errors, scale uncertainty, and occlusion. In this study, we propose a lightweight and enhanced monocular FCOS3D detection algorithm, called MultiBranch-Moni3D. The algorithm incorporates improvements such as the integration of the SE module EfficientNet and the replacement of the FPN structure with BiFPN for improved feature representation. Furthermore, we modify the prediction approach by adopting direct prediction instead of binary prediction, reducing the need for positive sample determination. Experimental evaluations on the NuScenes dataset demonstrate promising results. Our proposed algorithm achieves a notable reduction in inference time from 79ms to 27ms on a single NVIDIA RTX3090 video card. Although there is a slight decrease in mAP by 1.71%, other metrics, such as mATE, mASE, mAAE, mAVE, and mAOE, experience increases of 2.57%, 4.4%, 6.72%, 11.1 %, and 27.5%, respectively. Overall, our proposed enhancements aim to enhance the efficiency and accuracy of monocular 3D detection. While there is a minor trade-off in detection accuracy, the algorithm's improved processing speed makes it a promising approach for real-world applications in the field of monocular 3D detection.
The missing mathematical story of Bayesian uncertainty quantification for big data

The missing mathematical story of Bayesian uncertainty quantification for big data New machine- and statistical learning methods are being developed to process the ever-increasing amount of available information. However, these methods often behave like black-box procedures without any theoretical underpinning. In this project, I will derive theoretical guarantees but also limitations of such procedures and, based on their mathe-matical understanding, increase their accuracy in complex models.
The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria

The prediction of crime rates has been a challenging problem for law enforcement agencies and policymakers. The main challenge in developing a predictive model for crime rate is the unavailability and quality of data. Crime data is often incomplete, inconsistent, and biased, which has always affected the accuracy of the scholars’ designed model. For example, the use of traditional methods such as manual crime analysis and statistical analysis are often limited by the volume and complexity of the data. The need for a more accurate and efficient predictive model that can handle large and complex datasets is increasing. This research developed a predictive model using the KNN algorithm that is into accurate prediction of crime rates and helps policymakers to make better decisions. The KNN algorithm was implemented using Python programming language with scikit-learn libraries. The primary dataset was collected from security units of Adekunle Ajasin University, Akungba Akoko and Akungba police station. The Algorithm was implemented on a Laboratory Local Area Network at the Information and Communication Technology Application Centre (ICTAC), Adekunle Ajasin University, Akungba Akoko. The performance evaluation was based on precision, recall, and F1 score. The results show that the KNN algorithm can accurately predict crime rates with an average accuracy of 88%. The model demonstrates that the KNN algorithm can be a useful tool for predicting crime rates. In addition, the model is hoped to enhance crime prevention strategies, reduce crime rates, and ultimately improve the quality of life in university communities. Keywords: Crime, Crime Rate, Prediction, K-Nearest Neighbour, Public Universities. Akinwumi, D.A. (2023): The Use of K-Nearest Neighbour Approach for Crime Rate Prediction in Public Universities: Case Study Adekunle Ajasin University, Akungba–Akoko (AAUA), Ondo State, Nigeria. Journal of Advances in Mathematical &amp; Computational Science. Vol. 11, No. 2. Pp 11-24. dx.doi.org/10.22624/AIMS/MATHS/V11N2P1. Available online at www.isteams.net/mathematics-computationaljournal.
Visualization of all two-qubit states via partial-transpose moments

Efficiently detecting entanglement based on measurable quantities is a basic problem for quantum information processing. Recently, the measurable quantities called partial-transpose (PT)-moments have been proposed to detect and characterize entanglement. In the recently published paper [L. Zhang \emph{et al.}, \href{https://doi.org/10.1002/andp.202200289}{Ann. Phys.(Berlin) \textbf{534}, 2200289 (2022)}], we have already identified the 2-dimensional (2D) region, comprised of the second and third PT-moments, corresponding to two-qubit entangled states, and described the whole region for all two-qubit states. In the present paper, we visualize the 3D region corresponding to all two-qubit states by further involving the fourth PT-moment (the last one for two-qubit states). The characterization of this 3D region can finally be achieved by optimizing some polynomials. Furthermore, we identify the dividing surface which separates the two parts of the whole 3D region corresponding to entangled and separable states respectively. Due to the measurability of PT-moments, we obtain a complete and operational criterion for the detection of two-qubit entanglement.
Multiple Dynamic Impact Signal Identification Method Based on Lightweight Neural Network With Acceleration Sensor

Multiple dynamic impact signals are widely used in many engineering scenarios. In the process of penetrating multilayer hard targets, the overload signals from multiple dynamic impacts are difficult to identify due to “signal adhesion” problems caused by nonlinear strong disturbances. For this problem, a customized lightweight neural network framework is proposed here. First, the characteristics of multiple impact signals and their disturbances are analyzed by dynamics modeling and continuous wavelet transform. Second, considering these characteristics with SqueezeNet, a simplification rule for a network redundancy structure is proposed. A customized residual feedback structure and attention mechanism are nested within the structure, thus improving feature recognition performance with less computational complexity. Third, experimental results show that the proposed method maintains comparable excellent feature recognition results to the complex GoogleNet under all test conditions with varying impact speeds, while its metrics for computational complexity are even lower than those of SqueezeNet and other classical lightweight networks by 23%–61.6%. Therefore, the proposed method is of great practical value for penetrating ammunition and other weak hardware application platforms that require accurate identification of multiple dynamic impact signals.