Reviewerly/exHarmony huggingface dataset:
Design of Online Exam Applications during the National Preparation Pandemic

This research is carried out to analyze and design a website which is a technology that is very influential on education science during the current pandemic is needed a technology that can support the course of learning and exams. One of them is about the technology of Exam Preparation Application using Web-based Random Methods at SMA Negeri 1 Pantai Cermin. The exam itself is an activity that is carried out routinely to measure the achievement of student competencies after completing one or more basic competencies. The research method used consists of several stages of research, namely data collection, system analysis and software design. The data collection method used is observation and literature study. The method of analysis and design of the system used is Rapid Application Development (RAD) using Unified Modeling Language (UML) and user interface design system design. Applications are created on two sides, namely admin applications and user applications. The programming language used for software development is PHP and for servers using MySQL as a database. The result of this study is an application based on an online exam website for national exam preparation and using random methods that can later be used by teachers and students. The application of this application can provide convenience for teachers and students in the process of working on exam practice questions in a computerized manner and can immediately see the results of the exam quickly. Keywords: Online Exam Application, Website, UML, RAD, PHP .
A novel multi‐view ordinal classification approach for software bug prediction

Abstract Software bug prediction aims to enhance software quality and testing efficiency by constructing predictive classification models using code properties. This enables the prompt detection of fault‐prone modules. There are several machine learning‐based software bug prediction studies, which mainly focus on single view data by disregarding the natural ordering relation among the class labels in the literature. Thus, these studies cause losing each view's own intrinsic structure and the inherent order of the labels that positively affect the prediction performance. To overcome this drawback, this study focuses on integrating ordering information and a multi‐view learning strategy. This paper proposes a novel approach multi‐view ordinal classification (MVOC), which learns from different views (complexity, coupling, cohesion, inheritance and scale) of the software dataset separately and predicts software bugs taking the inherent order of class labels (non‐buggy, less buggy and more buggy) into consideration. To demonstrate its prediction performance, the MVOC approach was executed on the 40 different real‐world software datasets using six different classification algorithms as base learners. In the experiments, the MVOC approach was compared with traditional classifiers and their multi‐view implementations in terms of precision, recall, f ‐measure and accuracy rate metrics. The results indicate that the MVOC approach presents better prediction performance on average than the multi‐view‐based and traditional classifiers. It is also observed from the results that the MVOC.RF model achieved the highest classification performance with an average accuracy rate of 85.65%.
Exploration of Deep Convolutional Neural Networks(Via Transfer Learning) for Handwritten Character Recognition

Recognition of handwritten characters is one of the difficult and challenging task because of the variation of characters in size, style and pattern. The complexity increased further with dictionary (alphabets, numerals, special characters), more individuals, age groups, and also with working environment. The exploration of open-source pre-trained networks for the classification of characters was minimal. This motivated us to explore the pre-trained deep convolutional networks (Alexnet, VGG-16, Resnet-50), and fine-tune them to recognize the handwritten characters using transfer learning. The experimentation results of widely used database EMNIST using pre-trained networks are in-par with the results of the state-of-art customized networks,which is specific to database and language. The classification accuracy of Resnet-50 for EMNIST (By-class: 87.24%, By-merge: 90.64%, Balanced: 89.18%, Letters: 94.90%, Digits: 99.57%).
Generator Information Enhancement Generative Adversarial Networks for Alleviating Data Imbalance Problems

In real-world datasets, numerous class distributions are imbalanced, which severely affects the classification ability of deep learning methods. Data augmentation can address such problem by creating data for the minority class to rebalance the datasets. However, it is still challenging that how to generate data from a true distribution of given finite training data. To overcome this challenge, we propose a novel and efficient synthetic oversampling approach, Generator Information Enhancement Generative Adversarial Networks (GIE-GAN). Its generative information is enhanced in two aspects. First, the generator can get common knowledge of all classes by initializing with a variational autoencoder. Second, during the training process, an independent classifier, which can provide accurate classification results for the generator, is added to the GAN for further augmenting the generative information. Experimental results on benchmark datasets show that, compared with the rival methods, our method can generate high-quality images and obtain better classifier performance of deep learning, especially for high imbalance ratios.
COMPUTER IMPLEMENTATION METHODS TECHNOLOGIES IN PROFESSIONAL TRAINING OF THE FUTURE TEACHER BY MEANS OF THE PROGRAM INTEL “LEARNING FOR THE FUTURE”

The necessity of using the latest computer technologies during teacher’s professional training is considered the effectiveness is confirmed in the article.
Impact of Land Cover Changes on the Wetland Ecosystem Water Environment Using Soft Computing Methods

Economic growth is accelerating in the entire world due to new inventions and advancements in the field of science and technology. The technology is progressing at a rapid pace and resulting in the infrastructure and technical progression of each nation. The utilization rate of land in the world has also increased substantially where the cultivated land is used for the development of industries and buildings. This land occupancy is alarming for the environment, and it is impacting the ecosystem of the earth in many adverse ways. In this study, the impact of land cover changes on the wetland ecosystem water environment has been studied in depth using soft computing techniques. The water environment data of the wetland ecosystem are obtained from real-world scenario and are mined using a particle swarm K-means clustering approach based on chaos search. The water environment assessment approach is used to analyze the influence of land cover changes on the water environment of the wetland ecosystem. The findings of this research work reveal that the cultivated land in the wetland is steadily decreasing, and the fertile forestland is steadily decreasing in the early stages and gradually increasing in the latter stages. The wetland water quality is gradually degrading over the period of time. The outcomes of the study demonstrate that a nonpoint source of pollution produced by land-use changes is the most important factor, which is affecting water quality. The analytical results’ accuracy is as high as 0.98 when compared to the real-world circumstances.
Weibo Short-text Sentiment Classification Algorithm on Serial Hybrid Network

Aiming at the problem that the word vector representation of traditional language model can not solve the representation of polysemy, and the existing emotion classification models have some shortcomings in the extraction and combination of local and global features of short text, and lack of attention to the important information in sentences, the paper proposes a Weibo short-text sentiment classification algorithm based on BERT-CNN-BiLSTM-ATT. The word vector representation fused with text context is obtained based on the pre training model BERT; Convolutional neural network (CNN) and bidirectional long-term and short-term memory network (BiLSTM) are used to extract local and global features in series, put them into the attention mechanism to obtain important parts, allocate attention weights of different sizes, and improve the classification accuracy. Finally, the obtained feature vectors are sent to softmax function for training and classification. The experimental results show that the algorithm has the highest evaluation index in the Weibo short-text test set, and the accuracy is as high as 93.78%. Compared with Word2Vec pretraining model and other hybrid neural networks, it shows that it can effectively fuse and represent multiple features, has better short-text sentiment classification effect, better evaluation criteria and great practical value.
Web-Based Performance Evaluation System Platform Using Rule-Based Algorithm

The project endeavored to design and develop a Web-based Performance Evaluation System with integration of rule-based algorithm that will analyze the performance of each reviewee based on the scores from their examination. The algorithm automatically provides suggested reading materials based on the result of the assessment. With this, the system will help the reviewees assess their own performance. The system was developed using Agile Methodology Model of Software Development. The software produced was tested using Alpha and Beta software tastings. The FURPS model was used to assess the software quality as perceived by the selected group of respondents. The system was found to be functional, meeting the designed functional requirements specifications.
XXI CENTURY STUDENT: VALUE ORIENTATIONS

The article deals with some problems of value orientations in students ’ life. The analysis of value orientations in students' life in higher educational establishments of economy and pedagogy is provided.
Training and Analysis of Hyperparameters in Neural Networks for Computer Vision Applications: A Didactic Approach

Machine Learning models are known for having millions of parameters considering different applications, such as object recognition and self-driving cars. To avoid spending more time than necessary in the training stage and more importantly, to achieve a satisfactory model, the hyperparameters should be correctly defined. The main goal of the proposed project was to identify and evaluate the influence of hyperparameters in the training of neural networks for computer vision applications. Besides that, we aim to provide a didactic approach to analyze the acquired generalization capability of a model and easy visualization of the impact of different parameters during the training phase.
Unsupervised learning for monocular dynamic motion segmentation

Dynamic object motion estimation is important for robotic and automatic driving. However, the method of object motion not only depend on manual parameter adjustment&#xFF0C;but also need large motion object label which is difficult achieved. In this paper, we propose a new dynamic rigid object motion segmentation frame, which we combine optical flow, depth map, 6-DoF pose estimation with motion segmentation. Especially, we utilize the optical flow in pose estimation and supervising the depth estimation. In the experiment, we evaluate our frame on KITTI Scenes Flow dataset. The result show that our method could accurately estimate motion object.
A Prediction of Phishing Websites by Optimal Feature Extraction using Recurrent Neural Network

Phishing is a cyber-attack threat that tries to access private information like credit card information, personal information and passwords. In the current scenario most of the online intrusions involves phishing. For digital surveillance assaults the number crosses above 78%. Yet, these sites can be related to their substance and internet browser based data can be utilized for order. The features are extracted in a comprehensive manner from different perspectives, but still there is a drawback is that training with all these extracted features tends to over-fit the model to the given training dataset. This overfitting is an effect of the small influence features that traps the neural network model. Apart from this, the training process also depends on 3rd party applications like WHOIS lookup and Page rank database. To eradicate these limitations we have proposed a website for detecting phishing sites where this approach is based on an optimal feature selection algorithm to select the essential features. The feature value index (FVI) is used to evaluate the impact and the sensitivity of the feature on the model. Then the proposed algorithm is used to select only the optimal features. The selected optimal features are then fed to the neural network to train the learning model which is the optimal phishing website prediction model. The model then predicts the probability of a website being a phishing website when its corresponding URL is fed in to the model.
Adaptive Control of Advanced G-L Fuzzy Systems with Several Uncertain Terms in Membership-Matrices

In this paper, a set of novel adaptive control strategies based on an advanced G-L (proposed by Ge-Li-Tam, called GLT) fuzzy system is proposed. Three main design points can be summarized as follows: (1) the unknown parameters in a nonlinear dynamic system are regarded as extra nonlinear terms and are further packaged into so-called nonlinear terms groups for each equation through the modeling process, which reduces the complexity of the GLT fuzzy system; (2) the error dynamics are further rearranged into two parts, adjustable membership function and uncertain membership function, to aid the design of the controllers; (3) a set of adaptive controllers change with the estimated parameters and the update laws of parameters are provided following the current form of error dynamics. Two identical nonlinear dynamic systems based on a Quantum-CNN system (Q-CNN system) with two added terms are employed for simulations to demonstrate the feasibility as well as the effectiveness of the proposed fuzzy adaptive control scheme, where the tracking error can be eliminated efficiently.
Academic Recommender System Using Engagement Advising and Backward Chaining Model

Background: The goal of academic supervision is to help students plan their academic journey and graduate on time. An intelligent support system is needed to spot potentially struggling students and identify the issues as early as possible. Objective: This study aims to develop an academic advising recommender system that improves decision-making through system utility, ease of use, and clearly visualized information. The study also aims to find the best advising relationship model to be implemented in the proposed system. Methods: The system was modeled by following the hybrid approach to obtain information and suggest recommended actions. The recommendation was modeled by backward chaining to prevent students from dropping out. Results: To validate the recommendations given by the proposed system, we used conformity level, and the result was 94.45%. To evaluate the utility of the system, we used the backbox method, resulting in satisfactory responses. Lastly, to evaluate user acceptance, we used the technology acceptance model (TAM), resulting in 85% ease of use and 91.2% perceived usefulness for the four main features, study planning, graduate timeline simulation, progress report, and visualization of academic KPIs. Conclusion: We propose an academic recommender system with KPIs visualization and academic planning information. Keywords: Academic advising model, recommender system, backward chaining, goal-driven, technology acceptance model, certainty factor
A Fast Cross-Band Spectrum Anomaly Detection Algorithm Based on Meta-Learning

Spectrum anomaly detection is an important research topic in cognitive radio. It can detect anomalies by predicting differences between the actual data. Existing deep learning-based spectral anomaly detection algorithms use a lot of training data. Due to the difference in frequency band, the detection model cannot be used directly across frequency bands. In order to solve this problem, a cross-band spectral anomaly detection method based on meta-learning is studied in this paper. Firstly, the data of different frequency bands are analyzed by using the pre-training of InceptionV3 to clarify the differences between different frequency bands. Secondly, a meta-learning data set is constructed and the optimal distribution of model parameters is found through the meta-learning training model. Finally, a small amount of target band data is used to fine-tune the model to detect anomalies in the target band. The experimental findings suggest that the proposed method is more stable than transfer learning and can detect cross-band anomalies with less target band data.
Monocular 3D Object Detection Based on Occlusion Optimization

Obtaining accurate 3D bounding boxes from monocular images is a challenging task. In autonomous driving scenarios, mutual occlusion between objects is a common situation, and that is one of the main factors affecting the accuracy of 3D object detection. Aiming at solving the occlusion problem, this paper proposes a monocular 3D object detection method that integrates occlusion differential processing. We propose an occlusion processing branch to process the filtered occlusion feature maps from global and optimize the 3D prediction results with predicted occlusion compensation. We validate our method on the KITTI 3D object detection benchmark, and experiments show that our prediction accuracy for occluded objects is significantly improved.
Classification method of Nickel plated punched steel strip based on improved Lightweight Network

High performance power battery is the core component of electric vehicle. Nickel plated punched steel strip is the key substrate for power battery manufacture. Due to the low degree of automation of key equipment in domestic production line, it can not meet the quality monitoring requirements of high-performance power battery. In order to solve the problem of false detection on punched steel strip brought by manual detection, a classification method of nickel-plated punched steel strip based on improved Lightweight Network is presented. In this method, an efficient bottleneck layer structure is used, and a location association module is presented to enhance the connection between steel strip image pixels. At the same time, the improved Focal Loss is used as the loss function to balance the difficulty and size of samples in the classification process, which improves the average accuracy and detection speed of image classification of nickel-plated punched steel strip.
Explore the Application Effect of Wireless Networks in Smart Clothing Based on Artificial Intelligence Technology

In the twenty-first century, every individual visualizes enough technological development on both the electrical and electronics sides. At the same time, they were studying artificial intelligence (AI) concepts and trying to implement them in software and hardware parts. Accuracy can be achieved by using sensors, with a combination of software and hardware. Most of the proposed concepts might respond when both clothing technology and AI are attached. While eliminating sensors from the technological concept, there would be a massive change while enhancing essential comfort and transformation. With the help of human commands, the work can be performed accurately as the machines lag in self-thinking ability. In this case, accuracy is one of the most important things to be noted. Having a shared understanding of such concepts as sensor management, cloth adjustment, and design selection will give extra time-saving moments to complete the work. In this research, a digital filter and a QRS (Quick Response System) detection algorithm were implemented to explore smart clothing through wireless networks and AI technologies. The results proved that the proposed algorithm works better than the existing algorithm by providing 2% increased accuracy.
Deblurring Reconstruction of Monitoring Video in Smart Grid Based on Depth-wise Separable Convolutional Neural Network

To improve the definition of monitoring video in smart grid, a de-blurring reconstruction model of monitoring video in smart grid based on depth-wise separable convolutional neural network is proposed. Firstly, a multi-scale feature extraction convolutional neural network is used to extract rich feature information of the input video frames. Secondly, all support frames are aligned to the target frame from the two ends of the input video sequence in chronological order, and a time and space self-attention mechanism is used to fuse the support frame features to the target frame. Thirdly, an improved depth-wise separable residual network and pixel shuffle up-sampling network are constructed to perform high-definition reconstruction of the target frame after feature fusion. Finally, the model is trained with public datasets and video datasets of smart grid and is applied to the video de-blurring reconstruction of monitoring system of smart grid. The test results indicate that the proposed model can effectively improve the definition and visual effects of monitoring video, whose average values of the peak signal-to-noise ratio and structural similarity index reach 32.18dB and 0.9132 respectively.
Motion Information Image for Action Recognition

The paper proposes an effective motion information computation method, called Motion Information Image, which can aggregate motion information of multiple frames into one frame to represent the dynamic process of compact motion in video for action recognition. Motion Information Image can adopt an average sampling strategy to represent the temporal modeling relationship of the video, which allows the clever conversion of motion information into image information. Besides, we use convolutional neural networks (CNNs) combined with Motion Information Image to build a new Motion Information Image network for end-to-end action recognition. The performance of the method is improved by 2.0%~3.0% in NTU RGB+D and NTU RGB+D 120 datasets with extensive experimental validation and exhibits significant advantages for recognizing fine-grained actions.
Proposal of a Conceptual Framework for Collaborative Design of Immersive Professional Training: Application to the Textile Industry

Abstract Immersive technologies have an increasing use in professional training. However, the usability of applications has limits due to a lack of consideration of end users in the design of these new supports. This paper reviews the literature and discovers that few approaches offer to include different collaborators' work, or the end user, in the design process. This study proposes a conceptual framework design for immersive professional training (IPT) and its application in the textile industry.