Reviewerly/exHarmony huggingface dataset:
Lightweight bidirectional feedback network for image super-resolution

Super-resolution has attracted academic attention recently, for its capabilities of image restoration and image enhancement. To generate informative high-level features for a better reconstruction performance, most super-resolution networks have many parameters, which limit their application in resource-constrained devices. Feedback networks can generate informative high-level features with few parameters by feeding high-level features back to previous layers. In this paper, we propose a lightweight bidirectional feedback network for image super-resolution (LBFN), which consists of two feedback procedures connected in reverse. Bidirectional feedback architecture further improves the perceptual abilities of feedback networks by fusing different level features sufficiently. We propose a residual attention block to enhance the detail expression ability of feature maps, which are cross-learned in feedback block. Finally, we propose a SR regression loss to supervise the training of our network. Extensive experiments demonstrate that our method has an outstanding performance while taking up little computing resources.
Machine Learning based Approach for Indian Sign Language Recognition

Sign language recognition is becoming crucial these days. To bring down the communication barrier between speech and hearing-impaired people automatic sign language recognition systems are being constantly developed and enhanced for accuracy. In this paper, we are going to compare the accuracy of two feature extraction techniques namely Oriented FAST and Rotated BRIEF (ORB) and Scale Invariant Feature Transform (SIFT). The novel model for the system is canny edge detection, feature extraction (ORB vs. SIFT), and Bag of Words technique. Because the controlled environment dataset is unfeasible, a new dataset for Indian Sign Language is produced in order to find the optimum feature extraction approach in an uncontrolled setting. Support Vector Machine (SVM) is used as a classification method.
Improved hybrid resampling and ensemble model for imbalance learning and credit evaluation

A clustering-based undersampling (CUS) and distance-based near-miss method are widely used in current imbalanced learning algorithms, but this method has certain drawbacks. In particular, the CUS does not consider the influence of the distance factor on the majority of instances, and the near-miss method omits the inter-class(es) within the majority of samples. To overcome these drawbacks, this study proposes an undersampling method combining distance measurement and majority class clustering. Resampling methods are used to develop an ensemble-based imbalanced-learning algorithm called the clustering and distance-based imbalance learning model (CDEILM). This algorithm combines distance-based undersampling, feature selection, and ensemble learning. In addition, a cluster size-based resampling (CSBR) method is proposed for preserving the original distribution of the majority class, and a hybrid imbalanced learning framework is constructed by fusing various types of resampling methods. The combination of CDEILM and CSBR can be considered as a specific case of this hybrid framework. The experimental results show that the CDEILM and CSBR methods can achieve better performance than the benchmark methods, and that the hybrid model provides the best results under most circumstances. Therefore, the proposed model can be used as an alternative imbalanced learning method under specific circumstances, e.g., for providing a solution to credit evaluation problems in financial applications.
Effective deep learning for oral exfoliative cytology classification

Abstract The use of sharpness aware minimization (SAM) as an optimizer that achieves high performance for convolutional neural networks (CNNs) is attracting attention in various fields of deep learning. We used deep learning to perform classification diagnosis in oral exfoliative cytology and to analyze performance, using SAM as an optimization algorithm to improve classification accuracy. The whole image of the oral exfoliation cytology slide was cut into tiles and labeled by an oral pathologist. CNN was VGG16, and stochastic gradient descent (SGD) and SAM were used as optimizers. Each was analyzed with and without a learning rate scheduler in 300 epochs. The performance metrics used were accuracy, precision, recall, specificity, F1 score, AUC, and statistical and effect size. All optimizers performed better with the rate scheduler. In particular, the SAM effect size had high accuracy (11.2) and AUC (11.0). SAM had the best performance of all models with a learning rate scheduler. (AUC = 0.9328) SAM tended to suppress overfitting compared to SGD. In oral exfoliation cytology classification, CNNs using SAM rate scheduler showed the highest classification performance. These results suggest that SAM can play an important role in primary screening of the oral cytological diagnostic environment.
DEVELOPMENT OF CREATIVITY OF FUTURE SPECIALISTS AT FOREIGN LANGUAGE CLASSES: CONDITIONS AND OPPORTUNITIES. SUBJECTIVE WELL-BEING

The article deals with the importance of development of future specialists’ creativity at foreign language classes. The concept of creativity has been disclosed; the conditions and opportunities for its development have been revealed. The problem of subjective well-being has been considered. The vision of creativity by researchers from different countries of the world has been represented: as the ability to create original and adaptive products; applicability and usefulness in resolving problems at hand; changeability over time and due to cultural and socio-economic realities; as a source of new technologies, methods and techniques of social work and productive ways of their application. Methods and techniques for development of creativity at foreign language classes have been suggested: asking open-ended or challenging questions; encouraging students to talk freely on various topics; finishing unfinished stories; creative writing in the form of self-invented stories, poems, plans, inventions; development of projects, etc. The methods and tools for improving and promoting future specialists’ creativity have been represented. The impact of creativity on subjective well-being at foreign language classes has been stated and considered. The effect of creativity on subjective well-being has been disclosed. The concept of well-being has been revealed as the state of being or doing well in life; happy, healthy, or prosperous condition; moral or physical welfare of a person or community. The connection of these aspects of well-being with the acquisition of a foreign language have been stated, which, apart from the cognitive gains, can contribute to person's social and emotional well-being.
Analysis of the Status and Perception of Elementary and Secondary School Students on the Metaverse in Education

Objectives The purpose of this study is to research the status and perception of elementary and secondary school students on the metaverse in education.&#x0D; Methods A survey on 9,523 elementary and middle school students in the Chungcheongbuk-do Province was conducted for the research. As a result analysis method, frequency analysis, descriptive statistics, and T/F test were performed. The survey was composed of questions regarding the personal use of metaverse, the state of the metaverse in educational use, and the recognition of it.&#x0D; Results The main results of the study are as follows. First, of all students who participated in the survey, 28.1% use the metaverse for an average of 1.6 hours at home. The students were having fun creating, exploring, and talking in the metaverse. Second, regarding the state of the metaverse in educational use, 6.1% of all students use the metaverse for an average of 1.48 hours in class. Classes utilizing the metaverse are increasingly becoming integrated to different subjects, mainly consisting of activities such as creation, coding, and lectures. Students suggested pranks from classmates, excessive components of entertainment, and difficulties in handling as challenges of education using the metaverse. Third, a total of 60.8% of students wished to use the metaverse in class, in regards to the recognition of education using the metaverse. Students wanted to incorporate the metaverse in creative experience activities, science, and art, to participate in learning activities of creation, experimentation and practice, and coding. Students had a positive perspective on the educational effects of the metaverse.&#x0D; Conclusions The following endeavors are required to stimulate education using the metaverse. First, educational on knowledge for students is required for education involving the metaverse. Second, developing of instructional design competencies is required of teachers. Third, the establishment of a metaverse platform and environment to support education using the metaverse is required.&#x0D; &#x0D;
Biomechanical Analysis of Volleyball Players’ Spike Swing Based on Deep Learning

Deep learning is to learn the inherent laws and representation levels of sample data. The information obtained during these learning processes is of great help in the interpretation of data such as text, images, and sounds. Through the deep learning method, the image features are learned independently, and feature extraction is realized, which greatly simplifies the feature extraction process. It uses deep learning technology to capture the motion of volleyball players and realizes the recognition and classification of motion types in the data. It finds the characteristics and deficiencies of the current volleyball players' spiking skills by comparing the test data of 8 volleyball players' spiking skills and biological analysis. The results show that the front and rear spiking balls with double-arm preswing technology have very obvious technical differences. In the take-off stage, there was no significant difference in the buffering time, the kick-off time, and the take-off time in the front and rear row spikes of the A-type. The buffer time of the B-type spike is 0.26 s in the front row and 0.44 s in the rear row. The range of motion of the front row spike is greater than the range of motion of the back row spike. In the air hitting stage, the range of action of the back row spiking is larger than that of the front row spiking, but the range of action of the back row is greater than that of the front row spiking.
Online Loan Default Prediction Model Based on Deep Learning Neural Network

With the rapid development of Internet loans and the demand for Internet loans, Internet-based loan default prediction is particularly important. P2P online lending is based on Internet technology. With the popularization of personal PCs and mobile terminals, the borrower's financing cost has been reduced to a large extent, and the efficiency of the borrower's capital utilization has also been improved to a considerable level. Making full use of the existing data of the online lending platform, integrating third-party data, and predicting the default behavior of users are the major directions of future development. This paper mainly studies the network loan default prediction model based on DPNN. This paper first analyzes the problems and risks of the P2P online lending platform, then introduces the principle and characteristics of BPNN in detail, and determines the credit risk rating process for online lending based on BPNN. With the help of data analysis and processing software, after cleaning and variable selection of credit customer data provided by lending clubs, a set of corresponding online lending default risk assessment models are established through BPNN. This paper simulates the network loan default assessment model of the BPNN model and compares it with the support vector machine and regression model. The experimental results show that the highest accuracy rate of the BPNN model is 98.01% and the highest recall rate is 99.82%, which is better than the other two models; the AUC value of BPNN is 0.79, which is significantly higher than that of support vector machine and regression model. The above results show that the online loan default prediction model based on DPNN has high application value in practice. Predicting the probability of customer default risk in advance will help reduce the risk of P2P companies and lenders, improve the competitiveness of P2P lending institutions, and promote the development of domestic P2P platforms to be more stable.
Fuzzy Logic based Vegetable Price prediction in IoT

Agriculture is an integral part of every economy with research in this domain mainly aimed at not only increasing food quality and production but also reducing expenditure while maximizing profit. Climate is a major factor that affects production of vegetables and other crops there by having direct impact on pricing. Among the main challenges faced by farmers is accurate prediction of vegetable prices. Farmers therefore need to acquire a better and advanced knowledgeable model to assist them in taking proper decisions when budgeting on how much to spend on planting so that it's proportional to the expected prices of the produce, in prediction of the climatic change effects on vegetable prices, a call for variables and parameters that show the response of vegetable prices, production, consumption and storage with weather conditions is necessary. The most common approach is using the statistical models that are trained using historical yield data with simplified weather measurements, for example average temperature in the growing season, humidly and rainfall. However, such approaches do not take into consideration the weather effects on entire vegetable lifecycle inclusive of the prices for yields necessitating the need for this study. This paper presents a study on Fuzzy based vegetable price prediction considering various changes in climatic parameters including humidity, rainfall and temperature. This system model was developed based on a 4- year vegetable price data and climatic data of the same period for Kisumu (Kenya). The results show that this model can indeed be used by farmers to predict prices and hence assist them to make informed vegetable farming and investment decisions.
The importance of game technology in primary education

The importance of gamification in education is supported by the studies conducted. It is very important to teach our students to use technology effectively during the trainings. Technology is just as important in teaching one to use it in the right direction. This study aimed to determine the current situation in the research process and the opinions of teachers and students in its implementation. The main purpose of the research is to determine the importance of game technology in primary schools affiliated to the state institution. In order to achieve the purpose of this study, interviews were conducted with 32 students and 4 teachers. The universe was selected on a voluntary basis. Research questions were created by taking the expert opinions from the researcher. The findings obtained from the research were explained using the content analysis method by thematisation. It has been concluded that students and teachers have positive opinions about game technology. It has been concluded that they want to use game technology in all courses. They noted that students and teachers sometimes have problems caused by a lack of information in a negative way. In this case, it can be corrected with in-service trainings being given.&#x0D; &#x0D; Keywords: Technology, learning, elementary school, student, teacher, game technology;
Automated Recognition of <i>Plasmodium falciparum</i> Parasites from Portable Blood Levitation Imaging

Abstract In many malaria‐endemic regions, current detection tools are inadequate in diagnostic accuracy and accessibility. To meet the need for direct, phenotypic, and automated malaria parasite detection in field settings, a portable platform to process, image, and analyze whole blood to detect Plasmodium falciparum parasites, is developed. The liberated parasites from lysed red blood cells suspended in a magnetic field are accurately detected using this cellphone‐interfaced, battery‐operated imaging platform. A validation study is conducted at Ugandan clinics, processing 45 malaria‐negative and 36 malaria‐positive clinical samples without external infrastructure. Texture and morphology features are extracted from the sample images, and a random forest classifier is trained to assess infection status, achieving 100% sensitivity and 91% specificity against gold‐standard measurements (microscopy and polymerase chain reaction), and limit of detection of 31 parasites per µL. This rapid and user‐friendly platform enables portable parasite detection and can support malaria diagnostics, surveillance, and research in resource‐constrained environments.
Application of A* algorithm in intelligent vehicle path planning

Path planning is one of the important directions in the field of intelligent vehicles research. Traditional path planning algorithms generally use Dijkstra algorithm, Breadth-First-Search (BFS) algorithm and A* algorithm. Dijkstra algorithm is a search-based algorithm, which can search to an optimal path, but the disadvantage is too many expansion nodes, which leads to insufficient search efficiency. BFS algorithm is a heuristic search algorithm, which reduces the disadvantage of too many expansion nodes and improves the search efficiency by heuristic function. A* algorithm is a heuristic search algorithm that combines Dijkstra’s algorithm and BFS algorithm, which has higher search efficiency and can search to an optimal path at the same time, but it is still lacking in the search mode and smoothness of the planned route. This paper first introduces the general path planning algorithm, then introduces and analyzes the A* algorithm, and proposes improvement measures for its shortcomings; finally, the executability and effectiveness of the improved algorithm are tested using simulation, and compared with the traditional A* algorithm, and the results show that the improved A* algorithm has good effect on path planning of intelligent vehicles.
Pathfinding in stochastic environments: learning <i>vs</i> planning

Among the main challenges associated with navigating a mobile robot in complex environments are partial observability and stochasticity. This work proposes a stochastic formulation of the pathfinding problem, assuming that obstacles of arbitrary shapes may appear and disappear at random moments of time. Moreover, we consider the case when the environment is only partially observable for an agent. We study and evaluate two orthogonal approaches to tackle the problem of reaching the goal under such conditions: planning and learning. Within planning, an agent constantly re-plans and updates the path based on the history of the observations using a search-based planner. Within learning, an agent asynchronously learns to optimize a policy function using recurrent neural networks (we propose an original efficient, scalable approach). We carry on an extensive empirical evaluation of both approaches that show that the learning-based approach scales better to the increasing number of the unpredictably appearing/disappearing obstacles. At the same time, the planning-based one is preferable when the environment is close-to-the-deterministic (i.e., external disturbances are rare). Code available at https://github.com/Tviskaron/pathfinding-in-stochastic-envs.
Application of GeoGebra in the Teaching of Descriptive Geometry: Sections of Solids

Technologies came to revolutionize the teaching of technical design; they brought resources that enabled 3D and 2D graphic simulation. The objective of this research is to understand how the implementation of the dynamic software GeoGebra on the topic of cylindrical sections can improve 3D spatial visualization in students because of the challenges in understanding the 3D representations and transporting them to the 2D sheet. This is a quasi-experimental case study in a mixed qualitative and quantitative approach which was conducted in a grade 12 class on the subject of descriptive geometry. The instruments used were a pre-test, post-test and Santa Barbara Solids Test. The students of the experimental group used GeoGebra and obtained better results in the post-test than the students in the control group, which suggests an improvement in learning; however, the Santa Barbara Solids Test results showed that the difficulties in visualizing views of the projected solids in a two-dimensional plane persisted. This study is also intended to test software in a classroom with students who have never experienced a 3D technology in their educational practices and direct future research in the complex content of descriptive geometry with the help of software.
The implementation of the ARIMA-ARCH model using data mining for forecasting rainfall in Bandung city

A time series is a stochastic process which is arranged by time simultaneously. In this article, a time series model is used in accordance with Box-Jenkins' procedure. The Box-Jenkins procedure consists in identifying the model, estimating the parameters and diagnostic checking. The time series model is differentiated according to the number of variables, i.e. univariate and multivariate. The univariate method for the time series model that is often used is the Autoregressive Integrated Moving Average (ARIMA) model and the multivariate time series model is the Vector Autoregressive Integrated Moving Average (VARIMA) model. In this research, we studied the ARIMA model which is studied with a non-constant error variance. In this case, the Autoregressive Conditional Heteroscedasticity (ARCH) model is applied to outgrow the non-constant error variance. Selection of the best model by examining the minimum AIC for each model. The ARIMA-ARCH model is implemented on rainfall data in Bandung city with Knowledge Discovery in Database (KDD) in Data Mining. The methodology in the KDD process, including pre-processing, data mining process, and post-processing. Based on the results of model fitting, the best model is the ARIMA (2,1,4)-ARCH (1) model. The result of forecasting rainfall in Bandung shows a MAPE value is 11%, which has a similar pattern with actual data for short time 2-4 days. From these results, we conclude that the ARIMA-ARCH model is a good model for forecasting the rainfall in Bandung city.
Deep Space Network Scheduling Using Quantum Annealing

NASA&#x0027;s Deep Space Network (DSN) is responsible for communication and navigation for several NASA and international missions. The DSN comprises three complexes located in Goldstone (California, USA), Cambera (Australia) and Madrid (Spain). This distribution in longitude guarantees a full sky coverage. Each complex has one 70-meter and several 34-meter antennas. The network routinely serves a few dozens missions. The scheduling of the DSN is complex and involves humans interventions as well as automated solutions. In order to increase the level of automation, different computing paradigms have been explored. In this paper, we report on the quadratic unconstrained binary optimization (QUBO) formulation of the DSN scheduling, as well as a custom classical solver designed around some of the unique features of this scheduling problem. Thanks to a hybrid framework that extends the size of the problems that can be solved with a quantum annealer, we are able to generate a schedule from the QUBO formulation of the problem for one week&#x0027;s worth of user antenna requests, which represents the time period scheduled during operation. In other words, this work describes a real- world application of quantum annealing using real-world, operational data. We compare the resulting schedules&#x0027; quality to solutions obtained using a mixed-integer linear programming formulation on a commercial solver. Our custom solver, based on a quantum-inspired optimization technique called Substochatic Monte-Carlo, while much faster in generating schedules, could only treat a subset of requests and hence we report its results independently.