Reviewerly/exHarmony huggingface dataset:
Contribution to the Quasigroup Based Error-Detecting Code

In the last years we have developed few error-detecting codes based on quasigroups. One of them is the code which is a subject of this paper. The previous analyses of the code shows that the code has very high probability of detecting transmission errors. We have previously identified so-called best class of quasigroups of order 4, with the highest probability of detecting errors when the coding process is performed with a quasigroups of order 4. But, there is one more class of quasigroups of order 4, second-best class, whose quasigroups give approximately equal probability of undetected errors as the quasigroups from the best one. Therefore, we proceeded with the examination of the code when it uses quasigroups from this second-best class of quasigroups of order 4. In this paper we will analytically obtain the second important parameter of every error-detecting code, i.e. the number of errors that the code surely detects when for coding it uses a quasigroup from this second-best class of quasigroups of order 4. At the end we will conclude whether the quasigroups from these top two classes have overall equal ability to detect errors with this code.
Exploring Interaction Techniques for Navigating Medical Images Using a Motion-Tracked Pen

Medical imaging modalities like computer tomography or magnetic resonance imaging produce vast amounts of imaging data. For radiologists, whose tasks is to evaluate these images, work becomes increasingly burdensome, leading at times to occupational diseases like repetitive strain injury. Interacting with 3D patient data derived from the original 2D images is another challenge in human-computer-interaction. To simplify the handling of both 2D and 3D medical imaging, a motion-tracked pen-like input device is presented to explore novel interaction modes for medical imaging.
MENTORING AS A TOOL FOR PERSONNEL ADAPTATION AND DEVELOPMENT

In today's rapidly changing world, professional development has become an integral part of personal growth. Improving existing skills, learning new competencies, or setting career goals can make it difficult for a new employee to adapt and develop. The use of mentoring will contribute to the gradual inclusion of an employee in the work process in new professional, psychophysiological, socio-psychological, organizational-administrative, economic, sanitary-hygienic and living conditions of work and recreation. However, very often many HR specialists confuse the concepts of «mentoring» and «mentoring». This article will consider the concept of «mentoring», highlight its advantages and disadvantages, consider the system of motivation for a mentor, global trends in mentoring, as well as analyze the experience of Russian organizations where this tool for personnel development is already used, as well as how it can help in the development of other organizations, methods are proposed to evaluate the results and effectiveness implementation of mentoring in organizations.
Design of a virtual simulation interaction system based on enhanced reality

&lt;abstract&gt;&lt;p&gt;Traditional virtual simulated interaction systems experience data fragmentation during the process of converting from two-dimensional to three-dimensional information, resulting in reduced realism and an inability to meet the teaching requirements of computer courses. Therefore, the integration of augmented reality (AR) technology into the educational environment remains an urgent and unresolved issue. To address the aforementioned issues, this paper investigates the data throughput limitations present in virtual simulation interaction systems. In response to this problem, an application solution utilizing AR technology is proposed, specifically a design concept for a virtual simulation interactive system tailored to computer-related courses. This system achieves its objectives through the collaborative interaction of AR hardware and supplementary software algorithms. The AR hardware is subdivided into framework design and functional hardware design, while the software components encompass AR models, virtual interaction models, and fusion methods. Through testing and comparison of the data throughput of this system with two other virtual simulation interaction systems, it was found that the virtual simulation interactive system optimized using AR technology can effectively enhance data throughput and address the issue of reduced realism in virtual interaction scenes caused by data fragmentation. This design system provides a more realistic and efficient mode of interaction for teaching computer-related courses.&lt;/p&gt;&lt;/abstract&gt;
A Federated Learning Method with DNN and 1DCNN Feature Fusion for Multiple Working Conditions Fault Diagnosis

Under multiple working conditions, the sample size of each client is small and it is not easy to obtain data from other working conditions, making it difficult to establish an effective deep learning fault diagnosis model. Federated learning is a distributed training method that can accomplish collaborative training of multiple clients without directly sharing client data. This paper proposes a federated learning method with DNN and 1DCNN feature fusion. This method designs a feature fusion network based on 1DCNN and DNN, and establishes a federated learning architecture for the feature fusion network in order to better extract fault features and improve the accuracy of multiple working conditions fault diagnosis. The efficiency of the proposed method is demonstrated by utilizing the Case Western Reserve University bearing data set.
Image processing based on neural networks

This article integrates convolutional neural network (CNN) and graph convolutional network (GCN) techniques. Performance is improved by the suggested architecture's use of crucial methods such as dropout, batch normalization, and rank-based random pooling. The network was trained and tested on a sizable amount of breast lesion imaging data, and its precision was evaluated in comparison to other methods. The findings showed a significant increase in accuracy, resulting in high rates of malignant lesion diagnosis with few false positives. The effective fusion of CNN and GCN methods highlights the potential for improving the detection of malignant breast lesions and provides a viable path for further study in this area.
The current status and prospects of transformer in multimodality

At present, the attention mechanism represented by transformer has greatly promoted the development of natural language processing (NLP) and image processing (CV). However, in the multimodal field, the application of attention mechanism still mainly focuses on extracting the features of different types of data, and then fusing these features (such as text and image). With the increasing scale of the model and the instability of the Internet data, feature fusion has been difficult to solve the growing variety of multimodal problems for us, and the multimodal field has always lacked a model that can uniformly handle all types of data. In this paper, we first take the CV and NLP fields as examples to review various derived models of transformer. Then, based on the mechanism of word embedding and image embedding, we discuss how embedding with different granularity is handled uniformly under the attention mechanism in multimodal scenes. Further, we reveal that this mechanism will not only be limited to CV and NLP, but the real unified model will be able to handle tasks across data types through pre-training and fine tuning. Finally, on the specific implementation of the unified model, this paper lists several cases, and analyzes the valuable research directions in related fields.
CoMoPAR: A Comprehensive Conceptual Model for Designing Personalized Augmented Reality Systems in Education

A challenging undertaking that involves an in-depth comprehension of the educational objectives, the target population, and the available technology is the design and development of personalized augmented reality systems for utilization in education. This paper introduces CoMoPAR, a high-level conceptual model that can be used as a starting point for designing and developing such systems, as a solution to this critical requirement. The User Interface Layer, Augmented Reality Layer, Personalization Layer, Content Layer, Analytics Layer, and Backend Layer are the five layers that constitute CoMoPAR. The combination of these layers enables an efficient and successful method for developing educational systems that address the problems of traditional educational systems. The indicated conceptual model presents a solid framework for developing and developing individualized augmented reality educational environments, that hold the potential to revolutionize the field of education while offering students a more engaging and efficient learning environment. Teachers, programmers, and researchers interested in creating customized augmented reality learning environments may utilize this conceptual framework.
FUZZY LOGIC FOR OPTIMIZING ROOM SALES: SUGENO METHOD AND MAPE EVALUATION

The Indonesian hospitality industry has grown rapidly over the past decades and is one of the most important sectors of the national economy. The focus is on determining the number of rooms sold using the Sugeno method's fuzzy logic. This study optimizes room sales by developing a fuzzy logic-based system that can effectively determine the number of rooms sold considering availability, best available rates, and revenue target. The Sugeno method is a type of fuzzy inference system that determines the relationship between input variables (room availability, best available rate, revenue target) and output variables (number of rooms sold). Modeled by using linguistic variables and fuzzy rules, the Sugeno method can provide a quantitative output based on specified input conditions. To evaluate the accuracy of the proposed fuzzy logic model, the mean absolute percentage error (MAPE) is used as a performance measure. Target data 175,000,000 to 245,000,000, BAR standard room 225,000 to 335,000, BAR superior room 285,000 to 425,000, available standard room 68 rooms/day, superior room 10 rooms/day, model accuracy measurement result is 1,80% very accurate interpreted. As such, the proposed system is useful for decision-making related to optimizing room sales in the hospitality industry.
Comparison of Classification Algorithm in Predicting Stroke Disease

ABSTRAK- To prevent stroke, we need a way to predict whether someone has had a stroke through medical parameters. With the influence of technology in the medical world, stroke can be predicted using the Data Science method, which starts with Data Acquisition, Data Cleaning, Exploratory Data Analysis, Preprocessing, and the last stage is Model Building. Based on the model that has been made, it is concluded that the algorithm with the best performance, in this case, is XGBoost with a precision value of 0.9, a recall value of 0.95, an f1 value of 0.92, and a ROC-AUC value of 0.978 after receiving five folds of cross-validation. With these results, the model created can be used to make predictions in real-time. Kata kunci : Machine Learning, Logistic Regression, Random Forest, XGBoost, Stroke
Local trajectory planning for mobile robot in cluttered environment based on Model Predictive Control

The task of local trajectory planning for an autonomous wheeled robotic platform in cluttered indoor environment is considered. Such environment might include narrow passages, which width is less than the length of the platform. Therefore, it is not possible to apply standard approach, when the obstacles are inflated with the maximum radius of the platform. We propose a novel approach based on numerical solution of nonlinear model predictive control task. Oblong shape of the platform is approximated with a high-order ellipse. We define differentiable sigmoid po-tential function, which may be computed for any point of the workspace given position and orientation of the plat-form. This function is small far from the platform, and very high inside the platform; it increases when moving to-wards the robot. The value of this potential function is computed for the set of the support obstacle points and add-ed to the cost function. This function serve as a penalty for collision with obstacles or coming too close to them. We develop an algorithm for the mapping support points onto occupancy grid, which provide collision avoidance. We apply Acados open library, which implement numerical solution of nonlinear model predictive task with sequential quadratic programming. Our approach is implemented as a local planner for the collaborative mobile platform. The experiments were made in artificial maze and in real office environment with narrow passages. Proposed ap-proached allowed the robot to come through the passages that were 10-20 cm wider than the platform. Computa-tion time was around 20 milliseconds.
Autonomous Learning in English Class

In the Indonesian educational system, autonomy has been demanded to be promoted in the teaching and learning process of recent curriculum implementation. Unfortunately, it cannot be denied that learner autonomy still poses a challenge to be implemented in Indonesia since the teaching-learning process still was dominant. Therefore, it is essential to explore students’ problems with learner autonomy. This study used descriptive quantitative study through the questionnaire as the students’ data collection method, to explore students’ problems with autonomous English Learning. Ninety one students from one of MAN in Agam were involved to elicit their problems on autonomous English learning. Furthermore, this finding showed that the students have problems with autonomous English learning. This study found problems of students in autonomous English learning which were (1) developing the target language into a separate reference system and being willing to revise and reject hypotheses and rules that do not apply, (2) willing to take risks, (3) and good guessers. On other hand, the study showed four indicators were low problematic. There were (1) The students should have a tolerant and outgoing approach to the target language, (2) The learners take an active approach to the learning task at hand, (3) Autonomous learners have insights into their learning styles and strategies, and (4) The learners try to be proactive in developing their ability in English by seeking external knowledge. The learners who try to be proactive in developing their ability in English by seeking external knowledge are the least students who answered.
Aspect Based Sentiment Analysis using Various Supervised Classification Techniques: An Overview

The Sentiment Analysis (SA) work is concerned with identifying aspect terms and categories and categorising emotions (positive, negatively, conflict, and neutral) in ratings and reviews. When it comes to subjectivity, it's typical to divide sentences into objective phrases that include accurate information and subjective statements that include express ideas, beliefs, and perspectives on a given topic. Various existing researchers have already done a lot of work in sentiment analysis with various methods, including aspect extraction. This paper proposed a systematic literature analysis of numerous sentiment analysis using supervised and unsupervised classification techniques. We investigate a few features extraction Natural language Processing (NLP) techniques used to identify aspects of machine learning for the detection of sentiment. An extensive experiment analysis, we discuss the findings of the study, challenges of the current and define the problem statement for the future direction
Fraud Detection Using Machine Learning and Blockchain

In the 21st century financial fraud is on the rise in many institutions. Newly released Federal Trade Commission data shows that consumers reported losing nearly $8.8 billion to fraud in 2022, an increase of more than 30 percent over the previous year. The main goal for us is to develop an efficient fraud detection system and utilize blockchain to create a decentralized banking application. Our team has collaborated on various cutting-edge technologies, such as machine learning and blockchain, to create a sophisticated fraud detection system. We have implemented three machine learning algorithms, namely Logistic Regression, Decision Tree, and Random Forest, which have been used to improve the accuracy of the model for detecting fraudulent activities. As for fraud aversion, we have used the Blockchain technology which provides a tamper-proof system that can securely record and track financial transactions, ensuring transparency and security. This feature makes it an ideal solution for fraud prevention, as it guarantees that all transactions are legitimate and free from any manipulations. By combining the power of machine learning and blockchain technology, our team is confident in providing an innovative solution that will benefit all stakeholders involved
Critical Studies on Lesion Segmentation in Medical Images

In medical images, lesion segmentation is used to locate and isolate abnormal structures. It is an essential part of medical image analysis for precise diagnosis and care. However, obstacles exist in medical image lesion segmentation owing to things like image noise, shape and size fluctuation, and poor image quality. Automated lesion segmentation methods include conventional image processing techniques, deep learning (DL) models and machine learning (ML) algorithms to name a few. Thresholding, region growth, and active contour models are examples of conventional methods, while decision trees, random forests, and support vector machines are examples of ML techniques. DL models particularly convolutional neural networks (CNNs), have shown extraordinary performance in lesion segmentation because to their innate potential to autonomously collect high-level characteristics. The objective of the research is to study lesion segmentation in medical images and explore different methods for accurate and precise diagnosis and care.The research focuses on the obstacles faced in lesion segmentation in medical images, such as image noise, shape and size fluctuation, and poor image quality. The research also highlights the need for evaluation metrics, such as sensitivity, specificity, Dice coefficient, and Hausdorff distance, to assess the performance of lesion segmentation algorithms. Additionally, the research emphasizes the importance of annotated datasets for training and evaluating the performance of segmentation algorithms.
An Innovative Approach for Predicting Software Defects by Handling Class Imbalance Problem

From last decade unbalanced data has gained attention as a major challenge for enhancing software quality and reliability. Due to evolution in advanced software development tools and processes, today’s developed software product is much larger and complicated in nature. The software business faces a major issue in maintaining software performance and efficiency as well as cost of handling software issues after deployment of software product. The effectiveness of defect prediction model has been hampered by unbalanced data in terms of data analysis, biased result, model accuracy and decision making. Predicting defects before they affect your software product is one way to cut costs required to maintain software quality. In this study we are proposing model using two level approach for class imbalance problem which will enhance accuracy of prediction model. In the first level, model will balance predictive class at data level by applying sampling method. Second level we will use Random Forest machine learning approach which will create strong classifier for software defect. Hence, we can enhance software defect prediction model accuracy by handling class imbalance issue at data and algorithm level.
Exploration of Deep Learning Models for Video Based Multiple Human Activity Recognition

Human Activity Recognition (HAR) with Deep Learning is a challenging and a highly demanding classification task. Complexity of the activity detection and the number of subjects are the main issues. Data mining approaches improved decision-making performance. This work presents one such model for Human activity recognition for multiple subjects carrying out multiple activities. Involving real time datasets, the work developed a rapid algorithm for minimizing the problems of neural networks classifier. An optimal feature extraction happens and develops a multi-modal classification technique and predicts solutions with better accuracy when compared to other traditional methods. This paper discussing on HAR prediction in four phases namely (i) Depthwise Separable Convolution with BiLSTM (DSC-BLSTM); (ii) Enhanced Bidirectional Grated Recurrent Unit with Long Short Term Memory (BGRU-LSTM); (iii) Enhanced TimeSformer Model with Multi-Layer Perceptron Neural Networks classification and (iv) Filtering Single Activity Recognition are described.In comparison to previous efforts like the DSC-BLSTM and BGRU-LSTM classifications, the experimental result of the ETMLP classification attained 98.90%, which was more efficient. The end outcome revealed that the new model performed better in terms of accuracy than the other models.
Evaluating Text-to-Image GANs Performance: A Comparative Analysis of Evaluation Metrics

Generative Adversarial Networks (GANs) have emerged as powerful techniques for generating high-quality images in various domains but assessing how realistic the generated images are is a challenging task. To address this issue, researchers have proposed a variety of evaluation metrics for GANs, each with its own strengths and limitations. This paper presents a comprehensive analysis of popular GAN evaluation metrics, including FID, Mode Score, Inception Score, MMD, PSNR, and SSIM. The strengths, weaknesses, and calculation processes of these metrics are discussed, focusing on assessing image fidelity and diversity. Two approaches, pixel distance, and feature distance, are employed to measure image similarity, while the importance of evaluating individual objects using input captions is emphasized. Experimental results on a basic GAN trained on the MNIST dataset demonstrate improvement in various metrics across different epochs. The FID score decreases from 497.54594 at Epoch 0 to 136.91156 at Epoch 100, indicating improved differentiation between real and generated images. In addition, the Inception Score increases from 1.1533 to 1.6408, reflecting enhanced image quality and diversity. These findings highlight the effectiveness of the GAN model in generating more realistic and diverse images with training progression. However, when it comes to evaluating GANs on complex datasets, challenges arise, highlighting the need to combine evaluation metrics with visual inspection and subjective measures of image quality. By adopting a comprehensive evaluation approach, researchers can gain a deeper understanding of GAN performance and guide the development of advanced models.