Reviewerly/exHarmony huggingface dataset:
Predicting The Monthly Average Incident Shortwave Solar Energy for Hubli, India by Using Training Functions in ANN

Solar radiation is one of the vital resources found on Earth which can be renewed and hence tested and tried to be beneficiary for humankind. Solar energy is harnessed to fulfill the basic requirements of humans i.e.; supply power to operate any kind of machine or device. The way to utilize the energy for our maximum benefit is by approximating the radiation values of Sun and this can be achieved by installing measuring equipments. The main issue arises here as the equipment's maintenance and installation cost is too high to be affordable by the general people. To overcome this inconvenience, an affordable solution was developing models and methods to calculate the radiation and find the approximate values. We focus on city, Hubli, India and estimate the monthly mean radiation received on this particular city by creating a neural network in ANN (Artificial Neural network) using MATLAB. The models are validated for 3 training functions: resilient back-propagation (RP), Scaled Conjugate Gradient (SCG) and Levenberg-Marquardt (LM). The predicted values accuracy is also tested through statistical indicators like MSE, RMSE, MBE and MAPE.
ANew Convolutional Neural Network for Recognizing Handwritten Letters of the Russian Alphabet

With the rapid development of computer technology, it is becoming increasingly important to have the ability to convert handwritten text into a digital version of printed text for copying, editing, or extracting data from it. The first stage of this process is the recognition of the letters of the proposed text. The article presents a new convolutional neural network (CNN) for image recognition of handwritten letters of the Russian alphabet. The resulting CNN algorithm transforms the image and recognizes the letter that is depicted on it. The final classification accuracy of the NN model on an independent dataset reaches up to 99%.
Time optimal trajectory planning of five degrees of freedom manipulator based on PSO algorithm

According to the kinematic constraints of the manipulator, a time-optimized 3-5-3 polynomial interpolation algorithm based on particle swarm optimization in joint space is proposed, which solves the shortcomings of polynomial interpolation trajectory planning with high order and no convex hull. Particle swarm optimization is simple in structure, easy to implement, and easy to adjust parameters. It directly selects the polynomial interpolation time as a variable, searches in the target space, and obtains the shortest interpolation time under the specified speed constraint. The simulation is carried out on the experimental platform, and compared with the traditional 3-5-3 polynomial interpolation of position, velocity and acceleration curves, it is proved that the method can achieve shorter running time and better stability of the manipulator.
Design of Experiential Teaching System for Solfeggio in Normal Universities Based on Machine Learning Algorithm

At present, with the rapid development of the Internet and its close connection with our life, more and more educators apply the Internet to teaching. However, there is little research on the application of the Internet in solfeggio teaching, and there are some problems in the teaching process, such as nonstandard use methods, inability to highlight teaching objectives, and mismatch with students’ professional level. On the premise of fully understanding the teaching objectives of solfeggio in China, this paper designs an experiential teaching system of solfeggio in normal universities based on machine learning algorithm, studies the application of digital technology in solfeggio, including Internet technology, multimedia digital technology, and the use of music software, and analyzes the auxiliary role of digital technology in solfeggio teaching by combining the specific teaching contents of online solfeggio teaching during the epidemic period. In this experiment, the average classification accuracy of WNB algorithm is 0.767, while that of BP algorithm is 0.683. Experimental results show that WNB algorithm outperforms BP algorithm in classification. At the same time, in terms of time efficiency, the average time consumption of WNB algorithm in this experiment is about 0.026 s, while that of BP algorithm is about 0.45 s. Compared with WNB algorithm, the time consumption of WNB algorithm is less. Through concrete practice, it is proved that the combination of solfeggio teaching and digital technology is of great significance to both teachers’ teaching and students’ learning.
When Should Data and Code be Made Available?

Abstract Sharing data and code as part of a research publication is crucial for ensuring the computational reproducibility of scientific work. But sharing should be done at the article submission stage, not after publication as it is now, say Rachel Heyard and Leonhard Held. Statisticians and data scientists have the skills and tools to make this change and lead by example, though there are obstacles to overcome
Study on Employee Performance Evaluation Based on Adaptive Feature Selection Fuzzy Algorithm

In our study, in terms of performance evaluation methods, the performance evaluation algorithm based on genetic algorithm and fuzzy comprehensive performance evaluation algorithm is introduced, their advantages and disadvantages are compared and analyzed, and the design idea of fuzzy performance evaluation algorithm based on compound elements is proposed. It can be divided into seven steps: first, clarify the evaluation purpose and object; second, select the optimal evaluation mode and method; third, compile the evaluation index system; fourth, it is to collect information extensively; fifth, the evaluation adopts a variety of methods, multiple angles, and multiple sides to collect materials so that the conclusion of the evaluation has sufficient factual basis; the sixth is to process the information and make a comprehensive evaluation; the seventh is to analyze the results and write an evaluation report. Based on the existing algorithms, the fuzzy performance evaluation algorithm based on compound elements is studied, and the detailed design of the algorithm is presented. Through the comparison and analysis of the performance evaluation algorithm based on compound elements and the performance evaluation algorithm of specific elements, the superiority of the performance evaluation algorithm based on compound elements is experimentally verified by comparing the operation time and classification accuracy. The performance evaluation system based on the performance evaluation algorithm designed in this study, combined with the official business of the performance evaluation system, modularizes the administrative management activities of the enterprise, digitizes the electronic office information, and conducts in-depth exploration of the unstructured natural language. A real-time performance evaluation system based on the arrangement of corporate administrative activities has been established. By designing and implementing a performance-assisted analysis system based on text content analysis, which is suitable for performance evaluation systems, it solves the imperfect problem of performance evaluation based on electronic enterprise administrative management.
Getting Started with Streamlit

With the inundation of data, and the pace at which it is created, traditional computing methods possess limited means to deliver results. On the other hand, cloud computing acts as an enabler, allowing one to overcome the limitations of the former. With increased scalability, reduced costs, and enhanced adaptability, cloud service providers, developers, and users alike stand to gain from the fruits of migrating to the cloud.
Structured light field three-dimensional measurement based on equivalent camera array model for highly reflective surfaces

Light-field cameras are gaining attention for their unique light gathering and post-capture processing capabilities. In our previous work, we combined light-field imaging with structured light technology to realize three-dimensional (3D) reconstruction for highly reflective surface. However, due to the lack of effective calibration of the light-field camera and reconstruction systems, it cannot meet the requirements of precise 3D measurement. A universal sub-aperture image extraction algorithm for the light-field camera with hexagonal microlens is presented to accurately extract the multidirectional images. Then, we explore the spatial relationship in equivalent camera array (ECA) of focused light-field camera according to the perspective variation of sub-aperture images. After that, an accurate 3D measurement system based on ECA model is proposed to achieve 3D measurement for highly reflective surface. To test the system’s practical performance, a precision analysis is conducted for a standard gauge block with ground truth. By comparing the reconstruction result of traditional method and ours, we demonstrated the validity of the proposed method to perform 3D measurement for highly reflective surface.
Text Classification Using BiGRU with Directional Self-Attention

In the field of natural language processing, text classification is a key daily task. The main goal is to obtain effective features from text information, find the correspondence between feature representations and category labels, so as to classify the text. From the perspective of data flow, it is mainly divided into five stages: text preprocessing, vector representation of text, feature extraction, classifier classification and model training to complete text classification tasks. Among them, feature extraction is a very important stage, and it is also the focus of this article. GRU can learn long-term dependencies from learned local features, and bidirectional GRU can learn hidden features in sentences. The self-attention mechanism exhibits superior performance in many fields in natural language processing. It can mine the autocorrelation of data and highlight key information by adjusting the weight of keywords. Therefore, in view of the shortcomings of existing models in text global information modeling, this paper combines bidirectional GRU and self-attention mechanism, and proposes a hybrid model BiGRU-MA for text classification, which can extract deep semantic features and solve the problem of classification performance degradation due to the lack of semantic information. This article uses text classification related technology to model, describes the modeling ideas, and introduces the technology used, and finally compares experiments with existing models to verify the effectiveness of the model.
A Systematic Study on the Application of Information Technology in Teaching Japanese Translation Courses

Technological for the purpose of interlingual communication has gone through tremendous changes because of mechanical progressions. The significance of two important technological advancements in current translation is as follows: PC helped translation apparatuses and machine translation, which is talked about in this article. These innovations have further developed translation efficiency and quality, supported overall communication, and underlined the developing requirement for state-of-the-art specialized answers for the deep rooted challenge of language boundaries. These instruments, then again, give extensive obstacles and vulnerability to the deciphering calling and industry. People have proposed new standards for several disciplines and their own lifestyles based on computer technology. Because of its significant data advantage, network information technology plays a key role in traditional language translation. Thus, it is basic to comprehend how to more readily involve network data assets in the Japanese translation process, as well as to investigate changes in network data assets and merge the commonsense headway of the Japanese translation process, in light of the significant issue of Japanese learning and exploration.
A Diverse Domain Generative Adversarial Network for Style Transfer on Face Photographs

The applications of style transfer on real time photographs are very trending now.This is used in various applications especially in social networking sites such as SnapChat and beauty cameras.A number of style transfer algorithms have been proposed but they are computationally expensive and generate artifacts in output image.Besides, most of research work only focuses on some traditional painting style transfer on real photographs.However, our work is unique as it considers diverse style domains to be transferred on real photographs by using one model.In this paper, we propose a Diverse Domain Generative Adversarial Network (DD-GAN) which performs fast diverse domain style translation on human face images.Our work is highly efficient and focused on applying different attractive and unique painting styles to human photographs while keeping the content preserved after translation.Moreover, we adopt a new loss function in our model and use PReLU activation function which improves and fastens the training procedure and helps in achieving high accuracy rates.Our loss function helps the proposed model in achieving better reconstructed images.The proposed model also occupies less memory space during training.We use various evaluation parameters to inspect the accuracy of our model.The experimental results demonstrate the effectiveness of our method as compared to state-of-the-art results.
Word-Based Bantu Language Identification using Naïve Bayes

Language identification of text has become increasingly important as large quantities of text are processed or filtered automatically. It is one of the preprocessing steps in Natural Language Processing (NLP) tasks such as information retrieval and machine translation. Few studies have worked on Bantu Languages in automatic language identification. Language identification is a challenge in Bantu languages because of lack of data and in addition to that, languages which are written similarly like Setswana and Sesotho are also challenging. In this paper, we present a word-based Na&#x00EF;ve Bayes classifier to identify words of Sesotho and Setswana language. The classifier was trained with words from both Setswana and Sesotho in a supervised manner. Adjectives, pronouns, adverbs and enumeratives are also included. The classifier shows that the two languages can be individually identified as it gives an accuracy of 71.4&#x0025;. Despite that when we increase the data to double the number of words, the model increased performance to 78&#x0025;. We also report that the classifier fails with homographs. The performance could be improved by using more data. Additionally, the syllable identification and sentence identification could be implemented along with word-based classifier.
Notes on the Product Multi-Sensor Generalized Labeled Multi-Bernoulli Filter and its Implementation

We previously presented the product multi-sensor generalized labeled multi-Bernoulli filter, which constitutes a multi-object filter for centralized and distributed multi-sensor systems with centralized estimator. It implements the Bayes parallel combination rule for generalized labeled multi-Bernoulli densities, simplifying the NP-hard multidimensional k-best assignment problem of the multi-sensor multi-object update to a polynomial-time k-shortest path problem. This way, the filter allows for efficient, parallelizable, and distributed calculation of the multi-sensor multi-object update, while showing excellent performance. However, the derivation of the filter formulas relies on a well-established approximation of the fundamental multi-sensor Gaussian identity, which was inadvertently not labeled as such in our original article. Thus, on the one hand, we clarify this mistake, discuss its consequences, and present a mathematically clean derivation of the filter yet to establish the claim of Bayes-optimality. On the other hand, we discuss implementation details and present extensive evaluations, that complete the previous publication of the filter.
Developing Linguistic Students’ Learner Autonomy through Screencasting

The aim of the study is to substantiate the effectiveness of screencasting in teaching linguistic students to develop their learner autonomy. The article discusses the concepts of learner autonomy and screencasting, reviews the Screencast-O-Matic tool and evaluates its use in the classroom. The scientific novelty of the work lies in identifying the positive aspects of using the screencasting technology in the process of linguistic students teaching, as well as in determining the limitations of this technology use. As a result, it is substantiated that the use of the screencasting technology in the process of linguistic students teaching actively contributes to the development of their learner autonomy and is an effective tool for increasing the students’ motivation to learn a foreign language, as well as work in a team.
Human-Guided Safe and Efficient Trajectory Replanning for Unmanned Aerial Vehicles

Safe and efficient local trajectory replanning is essential for the navigation of unmanned aerial vehicles (UAVs). Take the quadrotor as an example, most research works focus on the static or fully mapped environment. Flying in a dynamic environment for autonomous quadrotors is still a tricky problem. However, with the emergence of first-person-view Drone Racing in recent years, professional human pilots have shown highly-skilled techniques for navigating quadrotors to avoid collisions at high speed. Therefore, this work uses the intelligence of human users in perception and decision-making and proposes a human-guided trajectory replanning (HTP) system for the safe and efficient flight operation of quadrotors. A non-constraint optimization problem is formulated, and human guidance is designed as one term of the cost functions. The proposed approach is validated in the AirSim simulation environment. The result shows that HTP saves optimization time by 58% compared with the non-human guidance (Non-HG) baseline. In addition, the HTP can assist quadrotors to pass the specified target at a higher speed and comply better with human preferences than the Non-HG approach.
Fully automated model on breast cancer classification using deep learning classifiers

Deep learning models on the same database have varied accuracy ratings; as such, additional parameters, such as pre-processing, data augmentation and transfer learning, can influence the models’ capacity to obtain higher accuracy. In this paper, a fully automated model is designed using deep learning algorithm to capture images from patients and pre-process, segment and classify the intensity of cancer spread. In the first pre-processing step, pectoral muscles are removed from the input images, which are then downsized. The removal of pectoral muscles after identification may become crucial in classification systems. Finally, the pectoral musclesaredeleted from the picture by using an area expanding segmentation. All mammograms are downsized to reduce processing time. Each stage of the fully automated model uses an optimisation approach to obtain highaccuracy results at respective stages. Simulation is conducted to test the efficacy of the model against state-of-art models, and the proposed fully automated model is thoroughly investigated. For a more accurate comparison, we include the model in our analysis. In a nutshell, this work offers a wealth of information as well as review and discussion of the experimental conditions used by studies on classifying breast cancer images.
Phishing victimization among Malaysian young adults: cyber routine activities theory and attitude in information sharing online

Purpose The purpose of this paper is to understand the influence of exposure to motivated offenders who may alter the vulnerability levels to phishing victimization. This is particularly focused on explaining the influences of individuals’ online lifestyles and attitudes toward information sharing online on phishing susceptibility. Design/methodology/approach This conceptual paper explores the risk of phishing victimization using criminological theories. The authors draw on empirical evidence from existing cybercrime literature and revisit routine activities theory (RAT) and lifestyle RAT (LRAT) to elucidate the risk of phishing victimization. This paper proposes that cyber-RAT, which was developed from RAT and LRAT, could interpret phishing victimization. Grounded on the intervention-based theory against cybercrime phishing, this study suggests that an attitude toward precautionary behavior (information sharing online) is essential to mitigate the phishing victimization risk. Findings This paper aims to provide a clear insight into the understanding of phishing victimization risk using theoretical and empirical evidence. Originality/value The theoretical perspective outlined provides the understanding of the impacts of online routine activities on a phishing attack which in turn will increase the awareness of phishing threats. The important role of the precautionary countermeasure, that is, attitudes toward information sharing online is highlighted to reconcile the phishing victimization risk.
Statistics-Physics-Based Interpretation of the Classification Reliability of Convolutional Neural Networks in Industrial Automation Domain

Artificial intelligence-driven automation has gradually become the technical trend of the new automation era. At present, many artificial intelligence technologies have been applied to improve the intelligence level in the field of automation. Among them, convolutional neural network (CNN) technology is one of the most representative, which is used in the detection of defective products in industrial automation, robot human tracking has been widely used in the field of machine vision driven automation. However, the high dependence of the current neural network application leads to the potential failure of the defective product detection system. In this article, we model the learning and decision-making process of CNN with a statistical physical percolation model. Based on the differentiation degree and vulnerability of percolation, we propose the concept of CNN differentiation degree and summarize the empirical formula to quantify it. The relationship between the differentiation degree and vulnerability is analyzed from both adversarial attack and adversarial training perspectives to explain the decision-making mechanism of CNN and classification reliability. The physical model can approach the essence of things and finally guide the reliable CNN for industrial automation.