Reviewerly/exHarmony huggingface dataset:
Leveraging Neural Networks to Guide Path Planning: Improving Dataset Generation and Planning Efficiency

Path planning seeks to enable a robot to reach its goal while avoiding collisions with obstacles. Machine learning has been leveraged in recent years to improve the efficiency of path-planning approaches, particularly those based on Rapidly-exploring Random Tree (RRT). The general idea is to train a neural network using an extensive dataset consisting of solutions obtained by RRT for problems generated at random, and then leveraging the trained neural network to improve the sampling and exploration conducted by RRT on new problems.This paper makes the case that the time to generate the datasets can be drastically reduced by using probabilistic roadmaps (PRM) instead of RRT. Roadmaps make it possible to capture the connectivity of the environment and solve multiple path-planning problems efficiently. We use the datasets to train neural networks based on U-NET. The trained networks are then leveraged to guide exploration. Specifically, we develop two path planners, namely Neural-Network-Guided PRM and RRT, denoted as NNG-PRM and NNG-RRT. Experiments are conducted using a series of challenging obstacle-rich environments where the robot has to wiggle its way through numerous narrow passages. The results show that using PRM instead of RRT drastically reduces the time to generate the datasets from hours to minutes. When comparing the planners on new problem instances, results show that NNG-PRM is faster than PRM, and NNG-RRT significantly outperforms RRT.
Peculiarities of formation of inclusive culture of students of higher education institutions

Therefore, in the preparation of future social workers, the establishment of an inclusive culture as a complex multifaceted holistic education, which ensures the successful performance of professional functions, takes on special importance. The purpose of the article is research and generalization of foreign and domestic experience in the formation of inclusive culture in the educational environment of higher education institutions. The foreign experience of forming an inclusive culture among students of institutions of higher education is determined by the institution's ability to use all the values of inclusion, which affect the ability of the student of higher education to cultural self-determination and self-realization in activities, regardless of signs of psychophysical development or social status aimed at positive adaptation and integration into society . The formation of inclusive culture at national institutions of higher education aimed at the complex application of inclusive values and principles of inclusive culture: ensuring equal access to opportunities for personal educational, scientific, creative growth and self-realization; opportunity to get new skills of socialization and integration; сприятия толеранным стосункам; preservation and restoration of health; active cultural and educational activities. In the aspect of forming an inclusive culture in the system of higher education, the author identifies the values of inclusion (recognition of diversity, equality and justice, cooperation, participation, community), which affect the ability of the student's personality to self-determination, self-realization in various types of educational, future professional and creative activities, which it promotes positive socialization and integration into society.
New Hybrid Data Preprocessing Technique for Highly Imbalanced Dataset

One of the most challenging problems in the real-world dataset is the rising numbers of imbalanced data. The fact that the ratio of the majorities is higher than the minorities will lead to misleading results as conventional machine learning algorithms were designed on the assumption of equal class distribution. The purpose of this study is to build a hybrid data preprocessing approach to deal with the class imbalance issue by applying resampling approaches and CSL for fraud detection using a real-world dataset. The proposed hybrid approach consists of two steps in which the first step is to compare several resampling approaches to find the optimum technique with the highest performance in the validation set. While the second method used CSL with optimal weight ratio on the resampled data from the first step. The hybrid technique was found to have a positive impact of 0.987, 0.974, 0.847, 0.853 F2-measure for RF, DT, XGBOOST and LGBM, respectively. Additionally, relative to the conventional methods, it obtained the highest performance for prediction.
Progressive Web Application (PWA) Development for Outfit Management System

Due to the development of information technology, changes in fashion lifestyle, and the current trend of human consumptive behavior, people often find it difficult or need more time to choose what kind of clothes they are going to wear on certain days. Therefore, the development of this Progressive Web Application is meant to assist the users to manage their clothing. This research begins with data collection. This stage consists of several methods, such as questionnaires, literature studies, and comparison of similar applications. The next stage is to design the system, user interface, database, and the application using scrum as the development method. The result of this research is a Progressive Web Application (PW A) that stores the user's clothing data, help the users to combine a set of clothes into an outfit, and schedule the outfit as needed. Other than the main features mentioned, this application can display the weather and temperature based on the user's location as one of the considerations point in choosing suitable clothing style that they are going to wear. This application also provides a way for users to be able to keep track of their clothing usage and to consult with fashion advisors regarding their fashion style.
A system for multimedia data retrieval using imagecolour feature

Nowadays video data mining plays an important role in information retrieval process. Creation of motion image information is simple for many users. From this, retrieving the needed content especially the unstructured data sets is a complex task. Various reasons pertaining to this complexity may be due to videos taken by unprofessional people, poor camera, light and much more. Number of algorithms had been proposed to retrieve effective video content, but none of them produced expected results. It still continues to be the most challenging field for researchers. In this paper, a new video image extraction technique with the help of image feature value is proposed. Initially, the image is grouped from which, unwanted image frames are removed using the image input extract feature for specified information. Experimental outcomes prove that the suggested method is good for all types of motion images.
SINGLE MR IMAGE SUPER-RESOLUTION USING GENERATIVE ADVERSARIAL NETWORK

Spatial resolution of medical images can be improved using super-resolution methods. Real Enhanced Super Resolution Generative Adversarial Network (Real-ESRGAN) is one of the recent effective approaches utilized to produce higher resolution images,
A Deep Learning Method for Sentence Embeddings Based on Hadamard Matrix Encodings

Sentence Embedding is recently getting an accrued attention from the Natural Language Processing (NLP) community. An embedding maps a sentence to a vector of real numbers with applications to similarity and inference tasks. Our method uses: word embeddings, dependency parsing, Hadamard matrix with spread spectrum algorithm and a deep learning neural network trained on the Sentences Involving Compositional Knowledge (SICK) corpus. The dependency parsing labels are associated with rows in a Hadamard matrix. Words embeddings are stored at corresponding rows in another matrix. Using the spread spectrum encoding algorithm the two matrices are combined into a single unidimensional vector. This embedding is then fed to a neural network achieving 80% accuracy while the best score from the SEMEVAL 2014 competition is 84%. The advantages of this method stem from encoding of any sentence size, using only fully connected neural networks, tacking into account the word order and handling long range word dependencies.
Review-Based Curriculum Learning for Neural Machine Translation

For Neural Machine Translation (NMT) tasks with limited domain resources, curriculum learning provides a way to simulate the human learning process from simple to difficult to adapt the general NMT model to a specific domain. However, previous curriculum learning methods suffer from catastrophic forgetting and learning inefficiency. In this paper, we introduce a review-based curriculum learning method, targetedly selecting curriculum according to long time interval or unskilled mastery. Furthermore, we add general domain data to curriculum learning, using the mixed fine-tuning method, to improve generalization and robustness of translation. Extensive experimental results and analysis show that our method outperforms other curriculum learning baselines across three specific domains.
Application of Contextual Learning-based Interactive Learning Media for Class V Students in Civics Subjects at SDN 250/VI Sinar Gading II

Media is an important means of learning in constructing students' understanding by relating students' real lives.Interactive learning media can be used offline and online so that they can be accessed anywhere.This study examines the application of contextual-based interactive learning media in Civics Class V subjects.The research method uses a mixed-method by describing the data qualitatively and analyzing the data quantitatively.The results of the application of the media by looking at the results of the pretest and post-test obtained a total pretest score of 1435 with an average value of 57.40 and a total post-test score of 2035 with an average value of 81.40 and by testing the N-Gain score obtained 56.3 or 56% with quite an effective category.So it is proven that the interactive learning media that is applied is effective in learning Civics for the fifth-grade students of SDN 250/VI Sinar Gading II.
"Broad-Spectrum" Innovation and Entrepreneurship Education System Framework and Theoretical Value Analysis of Design Specialty in China's Higher Education

In Chinese higher education, "broad-spectrum" innovation and entrepreneurship education for design majors is a new teaching model for design majors, with creative entrepreneurship as the core, combining innovation and entrepreneurship content, and with certain special, professional, and extensive integration characteristics. It is a new way for China's innovation and entrepreneurship education to be professionalized and systematized in design majors. Therefore, this paper briefly analyzes and elaborates on the system construction of "broad-spectrum" innovation and entrepreneurship education in Chinese higher education design majors and the corresponding theoretical values, hoping that it will have some guiding effect on the further development of specialized innovation and entrepreneurship education in colleges and universities.
Transferable Takagi-Sugeno-Kang Fuzzy Classifier With Multi-Views for EEG-Based Driving Fatigue Recognition in Intelligent Transportation

The safety monitoring system of intelligent transportation provides driving fatigue warning and risk control. Electroencephalogram (EEG) signals can directly reflect the neuronal activity of the brain. The detection and early warning of driving fatigue using EEG signals has important practical significance. However, because of the non-stationarity and timeliness of EEG signals, the single feature detection method is significantly impacted by data distribution differences. In this paper, in the framework of multi-input multi-output (MIMO) Takagi-Sugeno-Kang (TSK) fuzzy system, transferable TSK fuzzy classifier with multi-views (T-TSK-MV) is developed for EEG-based driving fatigue recognition in intelligent transportation. First, in view-specific consequent parameter learning, the view-specific consequent regularizer is designed based on technologies of ridge regression, maximum mean discrepancy (MMD), and manifold regularization, which becomes the bridge to transfer the discriminative information from the related domain to the target domain. In addition, the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_{2,1} $</tex-math> </inline-formula> -norm sparse constraint on consequent parameters is used to simplify fuzzy rules. Then multi-view learning is integrated into the consequent parameter learning, in which T-TSK-MV explores the view-shared consequent regularizer and adaptively assigns weights to each view. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\ell_{2,1} $</tex-math> </inline-formula> -norm sparse constraint on view-shared consequent regularizer can effectively exploit the local structure of multi-view data. Finally, the fuzzy classifier is constructed on view-specific regularizers and view weights. The experiment on real-word datasets shows that the proposed fuzzy classifier can significantly improve the driving fatigue recognition performance.
Enabling digital transformation with cloud native architecture

With the advent of high-speed internet, better connectivity, cloud computing, organizations of today or even individuals looking to transform themselves to leverage the benefits. Digital Transformation could loosely be defined as the changes brought forward with applying digital technologies in organizations or all aspects of human society. Cloud Computing and Cloud-Native Architecture play an important role in enabling digital transformation across organizations. Cloud-Native Architecture can be defined as the blueprint or architecture for applications and services designed to reside specifically in the cloud. Micro Services from the base of the cloud-native application architecture. Netflix began moving their customer-facing parts of the website to AWS as early as 2010. Where, Cloud Native architecture completely leverages the advantages of distributed, scalable, and flexible nature of the public cloud. In this paper, we have presented different cases of enabling digital transformation with cloud architecture. The study will prove beneficial for enterprises moving towards digital transformation and help them adopt a cloud-native/ micro services-based approach.
A Task Offloading Strategy for Compute-Intensive Scenarios in UAV-Assisted IoV

With the development of the low-altitude digital economy and the further improvement of the Internet of Vehicles (IoV), the IoV assisted by Unmanned Aerial Vehicle (UAV) has been promoted in many fields. Under task-intensive scenarios, UAV-assisted Mobile Edge Computing (MEC) has been extensively studied due to its flexibility and efficiency. However, with random distribution of computing data, the deployment of UAV and offloading strategy are important issues to be solved. In this context, this paper proposes a UAV-assisted offloading strategy, considering fixed and mobile edge nodes respectively, to meet the requirements of low latency and high reliability of vehicle users. It has been experimentally verified that our system reduces the delay by 30%.
The Teaching Analysis and Design of the Construction of C Language Multi-Document Project Based on Keil

The establishment of C language multi-file engineering is a difficult point to deepen the learning of C language, and is also the basis for learning STM32 programming.This article uses the project creation process of 8 LED lights as an example to establish the source program files and header files of each function and hardware.Guide students from the modularization of functions to the modularization of source files, teaching analysis and design of the establishment of multi-file engineering, so that more students can deeply understand and master the creation of multi-file engineering, laying a solid foundation for the deepening of embedded learning.
An Effective Training Strategy for Upper-limb Rehabilitation Robots Based on Visual-haptic Feedback Using Potential Field

Visual and haptic feedback are crucial to enhance the effectiveness of robot-assisted rehabilitation. To improve the performance of clinical rehabilitation training for patients with motor dysfunction, we propose an effective training strategy based on visual and haptic feedback. Haptic feedback is generated by a designed artificial potential field, which allows patients to perceive the correct training direction. The effectiveness of the proposed training strategy is initially verified, by recruiting three healthy subjects to perform circle drawing tasks on an upper limb rehabilitation robot. Experimental results showed that higher training accuracies were obtained using visual-haptic feedback compared to those with unimodal feedback. The proposed strategy can enhance the users' perception of the training process and corrects the incorrect movements in real-time, simultaneously. The current training strategy can be applied to commercial rehabilitation robots and meet the rehabilitation training needs of the users with impaired vision or the vision is unavailable.
Ml-based Offensive Tweet Accuracy Detector on Social Media

Global access to the internet has surprisingly changed how we see the arena.SM is one of the children of the internet, which can be found in many bureaucracies: online gaming platforms, dating apps, boards, online information services, and social networks with extraordinary functions.On Twitter and Facebook, you can share opinions make business contacts on LinkedIn, share photos on Instagram, send videos on YouTube, and court someone on Meetic.However, they all have one thing in common: they aim to attach people.Social networks have such great potential that by the year 2021, it is projected that there will be 3.02 billion active social media users globally.The rapid rise of social networks and microblogging sites has caused direct communication between people of diverse cultures and mentalities, leading to an increasing number of "cyber" conflicts.As a consequence, hate speech is used more and more, to the point where it has become a serious problem invading these public spaces.Hate speech is defined as the use of competitive, violent, or abusive words directed towards a specific group of people who share a common set of assets, whether that set of assets includes their gender, ethnic group, race, or beliefs and faith.While most internet social networks and microblogging services prohibit hate speech, the sheer size of these networks and websites makes controlling all of their content extremely difficult.As a result, there is a need to detect such speech automatically and remove any content that contains hateful or inciting language.
The iBox Technology-Enabled Teaching and Learning in Pre- and Post-COVID Ghana

Prior to Covid-19, some young private Ghanaian technologists, inspired by COL’s APTUS dongle, undertook a project toward solving the high cost of internet connectivity and the challenges related to equity, access, quality and gender. Their research produced an offline system called the iBox along with a corresponding online iCampusgh.com for learners, teachers, parents and school managers. It was adopted in 2014 by Ghana’s Ministry of Education through the Centre for National Distance Learning and Open Schooling (CENDLOS). Appropriate e-content was also developed by selected teachers. The product became a national solution during the Lockdown. // The system offers lessons in text, quiz, video, audio and laboratory simulation formats. Its unique learner management system (LMS) generates individualized assessment report sheet along with other support features on a dashboard. It supports learning at one’s own pace, is gender sensitive and is convenient for some persons with disabilities (PWD). // This paper considers how adapted and localized technologies can help bridge the digital divide in lower income communities, using the case of the iBox system. It explores the spirit of collaboration within the Pan-Commonwealth education in areas of technology, pedagogy and training.
The Readiness of Mobile Learning: A Study amongst Politeknik Kota Bharu Students

Mobile learning has become a crucial component of electronic learning due to the quick development of mobile technologies. It has made learning possible at any time and anyplace with the usage of wireless internet and mobile equipment, hence it is seen as a potentially significant learning aid. However, learners' readiness should be taken into account before designing and putting into place a replacement learning system. Despite its obvious benefits, Malaysian polytechnics have done a very little study on mobile learning. This quantitative study seeks to investigate how prepared Malaysian polytechnic students are. Purposive sampling was used to pick 285 students from Politeknik Kota Bharu to get a questionnaire for this reason. The three key preparedness areas equipment readiness, technological skill readiness, and psychological readiness were the focus of the questionnaire. The overall conclusions, the vast majority of students exhibit a high level of preparation for the use of mobile learning. The type of gadget utilized during the Mobile Learning session strongly correlates negatively with the students' readiness. The results of this study's data have given educators and curriculum designers important information about how to promote mobile learning in their teaching and learning processes.
E-Magazine sebagai Media Pembelajaran Teks Fiksi dan Nonfiksi (Studi Kasus pada Siswa SMP Islam Al-Azhar 38 Wonosari)

This study aims to explain the use of e-magazine media (electronic magazines) in learning fiction and nonfiction text material in Al-Azhar 38 Islamic Middle School Wonosari. The use of e-magazine media is intended to facilitate students in accessing as well as being a stimulant in reading comprehension of a text. This research is a qualitative descriptive study. The approach in research uses a case study approach. The type of case study in this study is in-depth case studies. Data collection techniques using observation and in-depth interviews. The results show that e-magazine is more effective and efficient in learning in fiction and nonfiction material networks, and is able to increase students' motivation in aspects of reading skills. Students are more interested in reading fiction and nonfiction texts presented in e-magazines than textbooks.
Shoe Print Retrieval Algorithm Based on Improved EfficientnetV2

The retrieval of incomplete and fuzzy shoe prints is a difficult problem in shoe print retrieval algorithm. In order to solve this problem. We improve the feature extraction network by using a new module called Dilated-MBConv on efficientV2. The new module is constructed by introducing multi-scale dilated convolution, and the original MBConv is replaced by Dilated-MBConv in stage 5–7 of the network; At the same time, in order to make the feature extraction network pay attention to the location information of shoe prints, this paper uses coordinate attention to replace the original squeeze and excitation module of the network. The improved network has achieved a better retrieve results in CSS-200 and CS-Database.