Reviewerly/exHarmony huggingface dataset:
Evaluation of Teaching Quality on IP Environment Driven by Multiple Values Theory Based on Big Data

Despite the fact that big data technology has been applied in education, there are no studies and cases that combine big data with ideological and political (IP) teaching quality. At the same time, the existing methods of IP teaching quality evaluation lack the consideration of multiple values, and the system is not complete and systematic. The use of big data analysis technology can improve the rigor of teaching quality assessment and make the data analysis more scientific, so as to improve the management system of universities and enhance the education quality. Therefore, this paper fully considers the background conditions of large data at this stage, on the basis of studying the methods of evaluating the quality of IP teaching in colleges. The big data about teaching quality is obtained by distributed algorithm, and multiple value indicators are drawn into the quality evaluation system as a main driver to emphasize the multiple value theory. Hierarchical analysis (AHP) method and fuzzy comprehensive evaluation (FCE) method are selected as the data analysis methods to provide evaluation basis for the proposed model. This model can further test the evaluation index system of education and further verify the rationality of the distribution of the weight of indicators at all levels. The evaluation results based on the large educational data and research data of a university show that the IP teaching quality of the university is excellent. The comprehensive evaluation model overcomes the limitations of traditional evaluation methods and provides a more comprehensive analysis about the teaching quality of IP teaching in colleges. Meanwhile, the conclusions obtained by the proposed evaluation model can be used for both the overall comprehensive evaluation of teachers’ teaching quality and a single comprehensive evaluation of the single factor affecting teaching quality. Using the evaluation results obtained by the model, we can set up advanced models and encourage backward students to have evidence. With the single-index evaluation, we can know what advantages the IP teaching or a certain teacher has and what aspects need to be strengthened. Therefore, we can put forward reasonable suggestions to progress instructing strategies and educating quality.
Data Driven Models for Contact Tracing Prediction: A Systematic Review of COVID-19

The primary objective of this research is to identify commonly used data-driven decision-making techniques for contact tracing with regards to Covid-19. The virus spread quickly at an alarming level that caused the global health community to rely on multiple methods for tracking the transmission and spread of the disease through systematic contact tracing. Predictive analytics and data-driven decision-making were critical in determining its prevalence and incidence. Articles were accessed from primarily four sources, i.e., Web of Science, Scopus, Emerald, and the Institute of Electrical and Electronics Engineers (IEEE). Retrieved articles were then analyzed in a stepwise manner by applying Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISM) that guided the authors on eligibility for inclusion. PRISM results were then evaluated and summarized for a total of 845 articles, but only 38 of them were selected as eligible. Logistic regression and SIR models ranked first (11.36%) for supervised learning. 90% of the articles indicated supervised learning methods that were useful for prediction. The most common specialty in healthcare specialties was infectious illness (36%). This was followed closely by epidemiology (35%). Tools such as Python and SPSS (Statistical Package for Social Sciences) were also popular, resulting in 25% and 16.67%, respectively. Doi: 10.28991/ESJ-2023-SPER-02 Full Text: PDF
Hybrid Grasshopper and Chameleon Swarm Optimization Algorithm for Text Feature Selection with Density Peaks Clustering

Clustering consists of various applications on machine learning, image segmentation, data mining and pattern recognition. The proper selection of clustering is significant in feature selection. Therefore, in this paper, a Text Feature Selection (FS) and Clustering using Grasshopper–Chameleon Swarm Optimization with Density Peaks Clustering algorithm (TFSC-G-CSOA-DPCA) is proposed. Initially, the input features are pre-processed for converting text into numerical form. These preprocessed text features are given to Grasshopper–Chameleon Swarm Optimization Algorithm, which selects important text features. In Grasshopper–Chameleon Swarm Optimization Algorithm, the Grasshopper Optimization Algorithm selects local feature from text document and Chameleon Swarm Optimization Algorithm selects the best global feature from local feature. These important features are tested using density peaks clustering algorithm to maximize the reliability and minimize the computational time cost. The performance of Grasshopper–Chameleon Swarm Optimization Algorithm is analyzed with 20 News groups dataset. Moreover, the performance metrics, like accuracy, precision, sensitivity, specificity, execution time and memory usage are analyzed. The simulation process shows that the proposed TFSC-G-CSOA-DPCA method provides better accuracy of 97.36%, 95.14%, 94.67% and 91.91% and maximum sensitivity of 96.25%, 87.25%, 93.96% and 92.59% compared to the existing methods such as TFSC-BBA-MCL, TFSC-MVO-K-Means C, TFSC-GWO-GOA-FCM and TFSC-WM-K-Means C, respectively.
Author Identification from Literary Articles with Visual Features: A Case Study with Bangla Documents

Author identification is an important aspect of literary analysis, studied in natural language processing (NLP). It aids identify the most probable author of articles, news texts or social media comments and tweets, for example. It can be applied to other domains such as criminal and civil cases, cybersecurity, forensics, identification of plagiarizer, and many more. An automated system in this context can thus be very beneficial for society. In this paper, we propose a convolutional neural network (CNN)-based author identification system from literary articles. This system uses visual features along with a five-layer convolutional neural network for the identification of authors. The prime motivation behind this approach was the feasibility to identify distinct writing styles through a visualization of the writing patterns. Experiments were performed on 1200 articles from 50 authors achieving a maximum accuracy of 93.58%. Furthermore, to see how the system performed on different volumes of data, the experiments were performed on partitions of the dataset. The system outperformed standard handcrafted feature-based techniques as well as established works on publicly available datasets.
Employing Generative Adversarial Network in Low-Light Animal Detection

Animal detection has been an important research topic due to its demand in surveillance and protection of endangered species. Daylight images help in detecting animals during daytime. Low-light images are used for detection in low contrast environment which is quite challenging due to image quality issues like bad contrast, high noise, reflectance, lousy illumination, etc. Some recent algorithms using deep learning have potentials to enhance low-light images for better detection performance. This work proposes to employ a preprocessing technique to enhance low-light images before the detection network. A generative adversarial network-based enhancement algorithm, called EnlightenGAN, is considered here for preprocessing. This algorithm is chosen for enhancement because it uses unpaired data. The automatic rhino detection system presented in this work uses a modified version of YOLOv3 (single-stage) object detector for better detection accuracy and faster performance. This work also introduces a modified version of our earlier one-horned rhino dataset by including low-light images. These new images are annotated manually for using with deep learning systems. The performance of the EnlightenGAN enhancement model is evaluated in terms of BRISQUE and NIQE. The performance of the overall proposed detector model is studied in terms of mAP, F-measure, GIoU loss and objectness loss. It is observed that the use of EnlightenGAN improves the detection accuracy for low-light images.
Sentiment analysis based on Chinese BERT and fused deep neural networks for sentence-level Chinese e-commerce product reviews

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Intensifying the search-based optimization of product line architectures with crossover operators

The Product Line Architecture (PLA) is a crucial artifact for the development of Software Product Lines. However, PLA is a complex artifact to be designed due to its large size and the multiple conflicting properties that need to be considered to ensure its quality, requiring a great effort for the architect. PLA designing has been formulated as an optimization problem aiming at improving some architectural properties in order to maximize both the feature modularization and the relational cohesion, and to minimize the class coupling. This kind of problem was successfully solved by multi-objective evolutionary algorithm. Nevertheless, most of existing approaches optimize PLA designs without applying the crossover operator, one of the fundamental genetic operators. To overcome these limitations, this paper aims to intensify the search-based PLA design optimization by presenting three crossover operators. These operators were empirically evaluated in quantitative and qualitative studies using three well-studied PLA designs. The experiments were conducted with eight experimental configurations of NSGA-II in comparison with a baseline that uses only mutation operators. Empirical results showed that there are significant differences among the use of only mutation and mutation with crossover. Also, we observed that the crossover operators contributed to generate solutions with better feature modularization. Finally, we could see that the proposed operators complement each other, since the experiment that combines at least two of the proposed operators achieved better results.
The English Teaching Mode under the Environment of Computer Technology

The development of Internet information technology has opened up new potential for the education sector, encouraging the development of innovative teaching and learning methods as well as the optimal use of available educational resources. In this environment, the online plus offline mixed teaching model emerged, which can not only get around time and space restrictions and link smoothly inside and outside the classroom but also enhance the form of teaching and learning and strengthen teacher-student relationships. However, to assure the quality of instruction, this research first examines the successful ways of developing an online plus offline hybrid teaching mode from the perspective of college English professors. Second, in this paper, the support vector machine and particle swarm method are used to build an evaluation model of the English teaching effect. The model has the potential to increase the accuracy and efficiency of English teaching impact evaluation while also meeting the standards of English teaching effect evaluation. The experimental findings show that, while evaluating the effects of college English instruction, the technique beats the comparison algorithm in terms of evaluation accuracy and algorithm time consumption.
The Voice Control System Implementation

This article highlights aspects of the development of speech access control systems that are part of an automated integrated system for ensuring the security of information resources and objects. The object of the study is modern technologies for the presentation, processing, and analysis of a speech signal in vocalized areas and a method for identifying the use of individual voice features in speech control systems. As part of the work done, an effective word recognition algorithm was obtained based on sets of their acoustic features. The developed methods and algorithms make it possible to improve the reliability of speech systems.
Design and Management of Comprehensive Art Student Information Database from the Perspective of Internet plus Education

With the rapid development of information technology and network technology, all kinds of data increase exponentially, and the traditional relational databases cannot handle these data well, which is also reflected in the sharp increase in the number of college students. The amount of students' information is increasing exponentially and the simple management method in the past can no longer meet the needs of the new situation. This paper adopts B/S mode and three-tier architecture and relies on network technology and ASP.NET technology to design and implement the student information management system. The system is divided into eight functional modules: comprehensive information inquiry for students, scholarship evaluation, evaluation and award, economic recognition, student loan management, student violation, communication and feedback, and system management. Different operation interfaces are designed according to four users with different permissions: students, class tutors, college administrators, and student affairs offices. This paper describes the system analysis, design, and implementation in detail. The practice results show that the initial use of the student information database management reflects well and achieves the expected goal. The system is easy to maintain and extensible and has good application value. The operation of the system has greatly improved the management efficiency of student affairs and enabled the student affairs administrators to complete various student affairs more efficiently. Campus information construction is becoming an important part of the overall construction of colleges and universities, and it is also a basic and continuous work. Accelerating the establishment of a set of comprehensive, perfect, and efficient student comprehensive management information systems is an urgent need to promote the modernization of college student management. The modernization, informatization, and standardization of college student management are a complex systematic project. Only by relying on modern information technology can we cope with the base pressure brought by the continuous enrollment expansion of college students, promote the flat and intensive utilization of college management information, and improve the efficiency and quality of service.
Issues of Commodity Market and Trade Finance in India and Its Solutions Using Blockchain Technology

A commodity market is a marketplace where different primary products and raw materials like precise metals, base metals, energy products, and agriculture products get traded. The entire trading process is managed by different governing agencies like MCX, NCDEX, NMCE, ICEX, etc. All these exchanges manage different types of commodities like future commodity trading as well as option commodity trading. For the economies of developing countries, the commodities sector is very important, and it is estimated that more than 100 developing countries rely on primary commodities for their export earnings. Currently, commodity and finance trading is facing different problems like getting new commodities, attracting small participants, fewer commodities of agriculture products, managing a huge set of documentations, inefficiency due to physical marketplace, the collaboration of all stakeholders, etc. These problems many times increase rates of commodities and surveys reported that commodity price rise can bump up inflation by 1%. This paper suggested solutions to these problems using a blockchain distributed ledger, which helps to create a network of trust and digitize every product connected to the trade and finance industry using Ethereum blockchain and smart contracts.
Emergency Project Management Decision Support Algorithm for Network Public Opinion Emergencies Based on Time Series

The present study aims at proposing a time series-based network public opinion emergency (NPOE) management decision support algorithm for the problems of low decision accuracy and long decision time in traditional similar algorithms. In this proposed algorithm, after the time series data are preprocessed, the association rules of the original indicator data of network public opinion emergencies (NPOEs) are mined, the original indicator data matrix of NPOEs will be constructed, and the improved local linear embedding approach will be employed to obtain the original indicator data of NPOEs. After carrying out the preprocessing according to the preprocessing results, the objective weight of the emergency decision index is calculated through the interval value fuzzy information entropy measurement of the emergency decision index, the emergency management decision support model is constructed, and the emergency management decision support of the NPOE is realized. The simulation experiment results show that the proposed algorithm has a better effect on the decision-making effect of the management of NPOEs, and a higher decision-making efficiency is achieved.
Taylor rate-distortion trade-off with Laplace correction-assisted coding in HEVC encoder

Video is a combination of sequenced images or frames based on spatiotemporal dimensions. The quality of video is reduced, while compressing the video. A method for compressing a video is designed to enhance video quality exclusive of dilapidation. In addition, an advanced rate-distortion (RD) trade-off, Taylor series-assisted RD trade-off, is employed for estimating the motions. In this method, the merits of the Taylor-series are introduced for improved-quality adjustment. Here, the video encoding process of the high efficiency video coding (HEVC) utilises an entropy function that is based on the probability estimation with Naive Bayes. However, the zero conditional issue is the major issue linked with probability evaluation and can lead to poor approximation. Thus, the Laplace correction is adapted to address the zero conditional problems. The designed Taylor series-based RD trade-off outperformed other methods with maximal peak signal-to-noise ratio (PSNR) of 43.890 dB and structural similarity index (SSIM) of 0.958.
Colour image encryption using an improved version of stream cipher and chaos

This paper proposes a novel image cryptosystem based on chaos theory and a modified version of Grain-128 cipher. Pixel and bit-level permutation of the plain image is performed by Hilbert curve and Hénon map to disrupt high correlation. The wellknown Grain-128 cipher is modified by replacing its Boolean function and reducing its state size. Output of modified Grain-128 cipher is bitwise XORed with randomised image data to produce cipher image. The modifications in the Grain-128 cipher is done in order to achieve higher encryption decryption efficiency. However, the proposed nonlinear function of the cipher shows superior performance compared to nonlinear functions used in several cryptographic algorithms in terms of nonlinearity value. Experimental result from statistical tests and efficiency analysis confirms the superiority of the proposed cryptosystem.
An All-Optical Reservoir Computer based on Time Stretch and Spectral Mixing

A novel all-optical Reservoir Computing (RC) based on time-stretch and spectral mixing is demonstrated. Virtual nodes in the reservoir layer are achieved in time and wavelength domains. Simulation results verify its utility in waveform classification.
Vehicle Re-identification Approach Combining Multiple Attention Mechanisms and Style Transfer

To address the impact of inter-domain style differences on cross-domain performance degradation in vehicle re-identification applications, a vehicle re-identification method combining multiple attention mechanisms and style transfer is proposed. Firstly, IBN-Net is introduced on the basis of ResNet50 for improving the generalization ability of the network, secondly, EFDMix method is used to generate more diverse feature enhancements to improve the domain adaptation ability of the model, while multiple attention mechanisms are combined to extract more discriminative vehicle features and improve the feature representation ability of the model on different datasets, and finally, cross-entropy loss with label smoothing and supervised contrastive loss for vehicle sample classification and optimising the feature distance between categories. Single-domain vehicle re-identification experiments were conducted on two datasets, VeRi-776 and VehicleID, and the results showed that our proposed method outperformed existing methods in most performance metrics. Further cross-domain comparison experiments also demonstrate the advancedness of the proposed method in terms of cross-domain.
Smoking Behavior Detection Based on TF-YOLOv5

In this paper, the determination of smoking behavior is considered as a target localization problem by building a neural network, and the advanced YOLOv5 is chosen as the baseline network. For the problem of low accuracy of YOLOv5 in detecting small targets, an improved TF-YOLOv5 is proposed by adding Transformer modules to the feature extraction part and feature fusion part, respectively, so that the network has the prediction potential of self-attention. In addition, we also integrate Coordinate Attention to find attentional regions in dense scenes, further improving the detection accuracy of our proposed TF-YOLOv5 for small targets in dense scenes and enabling us to achieve better results in the detection of smoking behavior. Experiments on our homemade smoking behavior dataset. show that our proposed TF-YOLOv5 improves 1.8% over the baseline model (YOLOv5).
Research on Multi-line Recognition Algorithm for Tibetan Document

Text recognition is an important task in computer vision, and one of the challenges is the impact of segmentation on text recognition effect. Tibetan text recognition is mostly based on complex word segmentation algorithms. For its high cost problem of manual annotation and difficulty of recognition, this paper studies the segmentation-free multi-line recognition algorithm for Tibetan text. After experiments on generating Tibetan document dataset based on Tibetan layout characteristics, good recognition results are achieved with CER reaching 0.09 %.
Backpropagation Through States: Training Neural Networks with Sequentially Semiseparable Weight Matrices

Matrix-Vector multiplications usually represent the dominant part of computational operations needed to propagate information through a neural network. This number of operations can be reduced if the weight matrices are structured. In this paper, we introduce a training algorithm for neural networks with sequentially semiseparable weight matrices based on the backpropagation algorithm. By exploiting the structures in the weight matrices, the computational complexity for computing the matrix-vector product can be reduced to the subquadratic domain. We show that this can lead to computing time reductions on a microcontroller. Furthermore, we analyze the generalization capabilities of neural networks with sequentially semiseparable matrices. Our experiments show that neural networks with structured weight matrices can outperform standard feed-forward neural networks in terms of test prediction accuracy for several real-world datasets.
Why Daubechies wavelets are so successful

In many applications, including analysis of seismic signals, Daubechies wavelets perform much better than other families of wavelets. In this paper, we provide a possible theoretical explanation for the empirical success of Daubechies wavelets. Specifically, we show that these wavelets are optimal with respect to any optimality criterion that satisfies the natural properties of scale- and shift-invariance.