Reviewerly/exHarmony huggingface dataset:
Detection Of Breast Cancer Based on Fuzzy Logic

The most dangerous illnesses in medical history are said to include breast cancer. People die more frequently because breast cancer, one of the leading causes of death worldwide, is more common. However, early detection of this cancer helps to save lives. This is complicated because mammography detection is difficult in many ways. A system for early detection and diagnosis of breast cancer is being developed for physicians using Machine Learning (ML) techniques, which has the potential to significantly improve survival rate of patients. Using machine learning algorithms, fuzzy-based breast cancer detection is demonstrated in this analysis. This analysis proposes two machine learning methods for the Wisconsin (Diagnostic) Data Set: the Support Vector Machine (SVM) and Decision Tree (DT) algorithms. In consideration of the parameters of precision, accuracy, specificity, and recall, the combined model outperforms other individual Machine Learning models. Fuzzy based SVM and DT classification technique efficiently detects the breast cancer diseases with high Accuracy as 98.2%, Precision, 97.6%, Recall 96.5% and Specificity 97.8%.
Ontology-based Car Recommender System Using Functional Requirements Interaction

The automotive industry continues to evolve every year, especially in the field of cars. Manufacturers from various brands are competing to introduce cutting-edge innovations with technological sophistication and unique features. A recommender system for cars has been developed but only based on technical specifications. However, cars have complex specifications, so many users do not familiar with the technical specification. Users find it easier to express their needs in terms of the functional aspects of a product rather than its technical features. To address this issue, we propose a conversational recommender system (CRS) to assist in interacting with users in order to elicit user need preferences. Therefore, we created a conversational recommendation system (CRS) designed to interact with users and capture their specific preferences. CRS engages users in conversations like sales professionals, facilitating natural and efficient interactions to uncover their specific desires. The model combines ontology structures and algorithms to effectively capture user preferences, taking into account explicit product information and temporal data to build domains and user profiles. The evaluation of this system focuses on two main parameters: system performance and user satisfaction. The system performance evaluation yielded an impressive overall accuracy rate of 87.84%, accompanied by positive feedback from users regarding satisfaction. These results demonstrate the effectiveness of the user-centered recommendation system in providing accurate recommendations and assisting users in selecting their desired automotive products.
Video Game Recommender System Using Deep Reinforcement Learning

The gaming industry has seen ramatic shifts in recent decades. The variety of games available and the number of people who play them have exploded due to the growth of online communities dedicated to this kind of entertainment. The difficulty of keeping up with the always-changing preferences of players and the constant stream of new video games is a challenging problem. Although several video game recommender systems have been presented, the algorithm used has difficulties recording changes in user's preferences, leading to a repetition of recommended games. Therefore, a recommender system that actively learns from user behavior and personalizes its recommendations accordingly. We propose developing video game recommendations based on Deep Reinforcement Learning (DRL). The deep reinforcement learning algorithm uses data from Steam platform and user past interactions, such as playtime forever and playtime in the past 2 weeks, to provide future rewards for video game recommendations. We compared the experiment on DRL variance with Singular Value Decomposition (SVD) and Logistic Regression (LR). The results indicate that the proposed method consistently assesses the item’s importance and relative ranks based on user’s past interactions.
Smart Waste Management: Waste Segregation using Machine Learning

Abstract In the digitized era, the role of smart mechanisms plays a vital role and one among them is the segregation of waste. To make use of proper disposal and waste management techniques, the segregation of wastes is essential. In the existing systems, drones are used for identifying waste using image processing, and deep learning and use GPS, and GSM methods to identify and send locations to the authorities. The enhancement achieved is to analyze and implement waste segregation with the help of image classification and multi-object detection. Waste management may therefore be done more efficiently with an accuracy of 95% with a mean average of 87.4% which in turn helps significantly to reduce labor costs.
Application of kalman filter for radar target tracking.

Abstract In this paper, the principles of recursive tracking and prediction filters are used to track a single maneuvering aerial target and implemented using instantaneous noisy measurements. Kalman filter is used to estimate (predict) the system state (bearing, range, target course, and speed) as they cannot be measured directly from measurements. Furthermore, after mathematical formulation of the equations for the Kalman filter fitting the application, it was then applied and evaluated. Monte Carlo Simulation technique was applied to quantify the results and the simulation results are obtained using a python program.
A self-organized shape formation in swarm robots

Abstract This work proposes a functional method-based approach for shape formation. The core principle in swarm intelligence is that simple swarm behaviour can be established with assigning the rules. To keep all the agents in the given shape and to build the set of motion, desired locations must be assigned to govern the leader and the other followers. Given the restrictions of communication, collision avoidance, and computation challenges, the proposed work is ideal for swarm robots shape formation. The proposed dynamic shape formation uses the shortest path method and the obstacle avoidance algorithm. Natural swarm inspired method based on the Particle Swarm Optimization (PSO) model is used to form the robots in the assigned position. A set of simulation analysis demonstrate that the convergence and scalability of our proposed technique. The effectiveness of pattern formation is increased further by adjusting specific parameters in the PSO model with the aid of the MATLAB simulator.
A viability analysis of the ISO/IEC 29110 project management process: a case study in an automotive ecuadorian company

Software process standards such as the ISO/IEC 29110 have been implemented by Very Small Entities (VSE) in order to develop quality software and achieve improvements in terms of time, budget, documentation, and other subjects. The generic profile group established in such a standard includes a set of profiles with situational factors of occurrence applicable to some VSEs. However, documented case studies of the ISO/IEC 29110 implementation in Latin America companies are limited, causing uncertainty about the viability of such a standard in VSEs established in the region. Different conditions of each environment—company type, size, country, etc.—can influence the implementation of a software project. Such a fact can affect the possibility of quality improvement by using the standard. In this paper we aim to implement the project management process included in the basic profile of the ISO/IEC 29110 in an automotive Ecuadorian company. We intend to provide VSEs a preliminary judgment in terms of time and cost overruns when developing a first-time implementation of the project management process. Besides, we summarize a set of lessons learned acquired during such an implementation. Finally, the findings of this study will allow VSEs to decide whether is convenient to implement this standard in such an environment.
Developing English App Based Mobile Learning as the Innovation of English Learning Media

Abstract. This research aims to develop learning media as the innovation English app in enhancing the process of teaching-learning English in 4.0 Era. Through this app, it can be integrated by hybrid learning model that is prepared to deal with post-pandemic era challenge. This research was conducted based on the results of needs analysis of current learning that cannot be carried out fully face-to-face, as well as needs students and educators towards effective learning media. The objectives of this research are to elaborate the process of designing and developing learning media English App based M-Learning and examine the feasibility of that media in learning process of General English Courses in University of Billfath. The research design used is Research and Development (R&amp;D) by applying the ADDIE model with five stages: analyze phase; design; development; implementation; evaluation. In further, the result of this research points out that in analyze phase found 71,3 % college students requires specific learning media-based M-learning that can be applied in hybrid learning model. While in the process of design and development English App as has been validated by experts that describe material validation 88,5 % and media validation 89% that can be said that the product is valid and deemed to be implemented. The stage of implementation is conducted seven meeting by hybrid learning. Furthermore, in evaluation stage indicates that the product has been feasibility to be applied in English learning due to the result obtained 80,6% (valid product). Hence, Mobile English App that is developed for college students in learning English has been succeed in fostering students ‘motivation and ability in General English Course.
Real-Time Detection of Surface Floating Garbage Based on Improved YOLOv7

Cleaning up surface floating garbage is of great significance to improve the ecological environment of the waters, of which the detection of surface floating garbage is a crucial step. It is difficult to distinguish the background and garbage and solve the problem of small targets of the existing target detection methods. Therefore, an improved YOLOv7 network is proposed to achieve efficient and high-precision detection of surface floating garbage. The newly proposed DS method is applied to original YOLOv7 network for multi-scale feature fusion. Meanwhile, CBAM attention mechanism module is applied to network backbone to improve detection precision. The public data set FLOW is used to validate the method in this paper. The simulation test results show that the proposed method significantly improves the detection precision without reducing too much detection speed, which proves the effectiveness of the proposed method. Finally, the proposed method is deployed to an NVIDIA Jetson device and applied to real-time surface floating garbage detection, which proves its practicability.
Formation of the Ability For Personal Self-Development in Future Education Learners

The article presents a study on the issues of personal and professional self-development in future higher education seekers. Based on theoretical analysis and systematic organization of scientific sources, the essential characteristics of the concepts of "personal and professional self-development" and "self-education" have been determined, and their interpretations by domestic and foreign researchers have been analyzed. The key role of personal and professional self-development of education seekers in the context of constant changes, digital technology transformation, and rapid adaptation of future professionals to professional activities has been highlighted. Various types of educational activities that contribute to purposeful formation of personal and professional self-development of education seekers have been identified: application of training methods (group discussions, role-playing games, case method, etc.); performance of professional-oriented tasks of varying complexity; completion of non-standard professional-oriented cases and assignments; organization of extracurricular work for education seekers, involving an individual approach to students. It has been established that practical activities aimed at self-regulation of behavior and self-improvement of professional and personal qualities are among the key conditions for the self-development of future professionals. A summary interpretation of the definition of "self-education" as a gnosiological tool for personal and professional self-development, which ensures the accumulation of cognitive resources, enhancement of knowledge, skills, and abilities, and formation of professional competence of professionals at all stages of professional development, has been provided. Promising methods of self-education that enable gnosiological activities aimed at acquiring knowledge about the profession, independent development of professional skills and abilities have been outlined. Through a survey of education seekers in pedagogical specialties, a study of the process of self-development of the future professional'spersonality has been conducted
Path Planning for Muck Removal Robot of Tunnel Boring Machine

The muck removal operation in tunnel boring machine (TBM) construction seriously restricts the safety and efficiency of tunnel engineering. To meet the requirements of muck removal at the bottom of the tunnel, a mechanism design and path planning algorithm were proposed to address the complex structure and frequent interference of the robot operating environment. Firstly, a robot cleaning plan and the robot body structure were proposed, the kinematic model of the robot was designed. Secondly, an improved RRT algorithm suitable for TBM and robot working environment was proposed to meet the requirements, which conclude obstacle avoidance, short time-consuming, and path smoothness. A collision detection strategy based on distance judgment, a random point generation strategy under goal-oriented constraints, a redundant path point deletion strategy, and a path smoothing processing strategy based on cubic B-splines were proposed to optimize the final path. Finally, the simulation platform has been built for traditional RRT and improved RRT algorithm were used for simulation. The results showed that the improved algorithm reduces the path cost by 27.13%, which verifies the effectiveness of the method.
MLP Neural Network-Based Precise Localization of Robot Assembly Parts

Traditional visual servoing encounters challenges in achieving target localization during the assembly process of riveted components when target features out of the field of view or when image feature motion extends beyond the image boundaries, resulting in feature loss and inability to accomplish visual servoing positioning. To address these issues, this paper first proposes a target localization method based on the ORB feature matching algorithm to solve the problem of the target features out of the field of view. A multi-layer perceptron neural network is applied to estimate the relative error between the current camera attitude and the desired attitude through the target image features obtained by the camera at the initial pose and the preset reference image features. The robot reaches the intermediate posture according to the predicted motion vector, Then the adaptive visual control method is used for fine-tuning, which can realize the accurate positioning and grasping tasks of the assembly with fewer iterations than the traditional visual servo.
Use of E-Resources as an Effective Means of Organizing an Educational Quest in An Institution of Higher Education

The article explores the possibilities of using e-resources for organizing an educational quest in higher education institutions. Presented different approaches to defining the essence of the concepts of «quest», «online quest», «web quest» (as a technique, means, method, approach, learning technology) in the scientific, pedagogical and methodological literature. The article defines online quest as a game-based pedagogical technology and reveals its role in the formation of students' key, professional, linguistic and communicative competencies, acquisition of skills of independent research and improvement of group interaction skills. The authors have identified and characterized the main stages of implementing an online quest, as well as revealed the principles of selecting educational resources and identified aspects of creating tasks. The structure of an online educational quest using digital tools is described. Main attention is paid to the group work of the participating teams on the Learningapps online platform and the Padlet online board, especially their potential during the interactive online quest «Language Marathon» at Borys Grinchenko Kyiv University is substantiated. Showed examples of linguistic, literary and creative tasks are presented, evaluation criteria are highlighted and the principles of interpretation of the results are formulated. The role of the online quest in the formation of students' cultural and linguistic personality is substantiated and the advantages of this approach are identified, in particular, increasing students' motivation and acquiring a number of general professional and linguistic competencies. The relevance and expediency of organizing an educational quest using electronic resources in higher education institutions are empirically confirmed.
Written Corrective Feedback Scientometric Review (1987–2022)

Written corrective feedback is widely used in language learning and teaching, and related studies have attracted significant attention in recent years. Through analysis and visualization of 497 articles indexed in the Web of Science core collection over the past 35 years using CiteSpace, this scientometric review aims to explore the developmental stages, involved disciplines, geospatial fingerprint, main research streams, and emerging research trends of written corrective feedback. The following stages were identified based on the timeline of publications and citations: the sprouting phase (1987–2005), the fluctuating phase (2006–2016), and the explosive phase (2017–2022). The interdisciplinary trend toward written corrective feedback becomes increasingly prominent throughout each stage, and its primary disciplines range from linguistics and cognitive science to psychology and computer science. The primary research contexts for written corrective feedback research are ESL and EFL; countries with multiple coexisting language variants have prioritized research in this field. Existing studies have witnessed a shift from quantitative to qualitative research, and case studies focusing on individual differences are emerging as a newer research frontier. As one of the first few scientometric reviews of written corrective feedback since the phrase first appeared as a combined index term, this study is significant as a reference for comprehensively understanding the intellectual background, dynamics, and evolution of this research field.
Unsupervised Image to Image Translation With Additional Mask

With the development of deep learning, the performance of image-to-image translation is also increasing. However, most of the image-to-image translation models depend on the implicit method which does not explain why the models alter specific parts of the original input images. In this work, we assume that we can control the extent to which the models translate the input images using an explicit method. We explicitly create masks that will be added to the input images, aiming to highlight the difference between the inputs and the translated images. Since limiting the area of the masks directly affects the shape of the translated images, we can adjust the model through a simple regularization parameter. Our proposed method demonstrates that a simple regularization parameter, which regularizes the generated masks, can control where the model needs to change and remain. Furthermore, by adjusting the degree of the regularization parameter, we can generate diverse translated images from one original image.
Temporal Topology Density Map

Spatiotemporal data visualization is of paramount importance in several applications, especially for analyzing urban mobility data encoded on topologies. This paper introduces a novel solution, called Temporal Topology Density Map (TTDM), to represent 2D discrete spatial data with temporal variations into a 2D continuous spatial space constrained by a topology. The proposed approach combines topological density maps with Change Frequency Heatmap (CFH) to convey visual information regarding temporal changes, leading to a new visualization method. Two case studies related to the analysis of response time associated with emergency services and walkability changes over time in specific areas of interest demonstrated the effectiveness of TTDM in challenging scenarios. The proposed solution provides an intuitive visualization for supporting the accurate analysis of spatiotemporal data changes over time using topology density maps.
Discrete Time Signal Localization Accuracy in Gaussian Noise at Low Signal to Noise Ratios

Convolution and matched filtering are often used to detect a known signal in the presence of noise. The probability of detection and probability of missed detection are well known and widely used statistics. Oftentimes we are not only interested in the probability of detecting a signal but also accurately estimating when the signal occurred and the error statistics associated with that time measurement. Accurately representing the timing error is important for geolocation schemes, such as Time of Arrival (TOA) and Time Difference of Arrival (TDOA), as well as other applications. The Cramér Rao Lower Bound (CRLB) and other, tighter, bounds have been calculated for the error variance on Time of Arrival estimators. However, achieving these bounds requires an amount of interpolation be performed on the signal of interest that may be greater than computational constraints allow. Furthermore, at low Signal to Noise Ratios (SNRs), the probability distribution for the error is non-Gaussian and depends on the shape of the signal of interest. In this paper we introduce a method of characterizing the localization accuracy of the matched filtering operation when used to detect a discrete signal in Additive White Gaussian Noise (AWGN) without additional interpolation. The actual localization accuracy depends on the shape of the signal that is being detected. We develop a statistical method for analyzing the localization error probability mass function for arbitrary signal shapes at any SNR. Finally, we use our proposed analysis method to calculate the error probability mass functions for a few signals commonly used in detection scenarios. These illustrative results serve as examples of the kinds of statistical results that can be generated using our proposed method. The illustrative results demonstrate our method’s unique ability to calculate the non-Gaussian, and signal shape dependent, error distribution at low Signal to Noise Ratios.