Reviewerly/exHarmony huggingface dataset:
No-Reference Stereoscopic Image Quality Assessment Based on Binocular Visual Saliency Weighting

Deep learning is widely used in the field of image quality assessment. The complex binocular vision mechanism and the multidimensional characteristics make the quality assessment of stereoscopic images more challenging. In this paper, a new no-reference quality assessment method based on binocular visual saliency weighting for stereoscopic images is proposed. Firstly, considering the depth information, the 2D saliency map and the depth saliency map of the left and right views are fused in a linear weighting way to obtain the 3D saliency map. Next, in order to train the feature map, a four-channel convolutional neural network is built and the human vision characteristics are taken into account. Finally, the mappings of features are fine-tuned to quality scores, and the weights in accordance with 3D salient information are assigned to acquire the final predicted scores. The performance of the proposed metric has been evaluated on two extensively used databases. The experimental results show the effectiveness of our proposed method and the superiority over other related methods.
Adaptive Minimax-Concave Penalty based MR Image Denoising with Difference of Gaussian filter

Denoising Magnetic Resonance (MR) image as a challenging work has been widely concerned. Minimax concave penalty (MCP) is an effective image denoising method, which can effectively remove the additive Gaussian noise. However, the existing low rank matrix approximation methods can not effectively remove Rician noise in magnetic resonance imaging (MRI). To solve this problem, a MR image denoising method based on extended Differential of Gssian (DoG) filter and adaptive minimax-concave penalty is proposed. In this method, a new MCP model is used to improve the quality of MR image. In order to effectively remove Rician noise and well preserve edge details, extended DoG filter is applied to MCP model, which can effectively improve SNR and computational efficiency while retaining edge/structure features. The experimental results show that, compared with some existing methods, this method can remove noise while retaining more edges and fine structures.
Multi-intermediate Feature with Multi-stage Fusion for Domain Adaptive Person Re-ID

Unsupervised domain adaptive person re-identification (UDA Re-ID) aims at obtaining more robust and discriminative feature on unlabeled target domain by transferring knowledge from labeled source domain. Especially, approaches based on intermediate domains mixing source and target domain have achieved the state-of-the-art results for UDA Re-ID task. Specifically, we design a novel method which define multiple intermediate domain features which is a fusion of source domain and target domain and play critical bridging role with a better path from source to target domain. Furthermore, the multi-intermediate domain’s representation consists of transfer representation from the previous fusion stage and the multi-stage hidden representations captured from source domain and target domain. In order to obtain better appropriate multi-intermediate domains, we define a multi-node bridge losses on the multi-intermediate domains, source domain and target domain and it can guide to obtain a more appropriate path with multiple node defined as multi-intermediate feature from source domain to target domain. Our proposed method achieves better performance comparing with the state-of-the-art approaches in common UDA Re-ID tasks, and the mAP gain is up to on the challenging DukeMTMC → market1501 benchmark.
Categorizing Rhythmic Jumping Motion Using Motion Capture without Markers

In this study, we sought to reveal and evaluate the degree of similarity between individuals and within the same group of individuals when they perform the same movement.The methodology used was that of selecting a panel of participants, 38 normal healthy adults ranging in age from 10 to 30 years.Subsequently, we carried out the experiment by performing the movements while capturing videos using a smartphone.The two-dimensional coordinates of each joint were obtained for videos obtained using marker less motion capture software.The results obtained allowed for a cluster analysis using five time series variables: 1) vertical head movement and 2) lateral limb movement.These time series were normalized by a total time, in order to create a typology of jumping movements.136 series of movies were obtained.After checking the movies, 115 series revealed errors such as misrecognition.These series were excluded from the analysis.The remaining 21 series had data available for analysis.Cluster analysis was performed on the 21 sets of motion data, which could be classified into seven clusters based on the shape of the dendrogram.
Are Code Review Smells and Metrics Useful in Pull Request-Level Software Defect Prediction?

The process of software code review is a well-established practice in software engineering. Previous research identified quality metrics for code review. However, to our knowledge, this paper is the first that uses those review smells and metrics as predictors in software defect prediction. We used review process metrics used in other studies as well as created new ones. A machine learning model is fed with various process metrics (code review) and product metrics (software code) to be able to predict if a pull request might introduce a defect. For the GitHub repositories examined, the mean absolute errors for predictive models were equal to 0.26 (for the model built on product metrics only), 0.29 (for model built on review metrics only), and 0.25 (for model built on combined metrics). The results indicate that the quality of the code review conveys additional valuable information that can be utilized to better predict software defects. In fact, review metrics alone appeared to be almost as good predictors of software defects as investigated since a long time and widely used software product metrics.
A Case Study on Cloud Computing: Challenges, Opportunities, and Potentials

This chapter concentrates on cloud computing challenges, possibilities and potentials analyzed from customer’s point of view. The research implications of this work are focused on analyzing the implemented enterprise value of cloud computing and the obstacles and challenges towards developing its full potential. There are undoubtedly many ways businesses benefit from cloud computing services, with efficient real-time access to any information, free from traditional restrictions and limitations. Enterprises use cloud computing differently and have various intentions to integrate it. For many, the transition path was long, diverse, and with many effects and outcomes in the overall business scope. The case study presents the cloud computing journey of three enterprises that adopted and developed cloud computing at early stages, becoming outrider enterprises with valuable cloud structures.
An Efficient QMF Bank Design Using Differential Search Algorithm

To design a perfect reconstruction, quadrature mirror filter is an area of concern because of its usefulness in several digital signal processing applications. Their performance is measured in terms of ripple measure (amplitude distortion), stop-band error, and pass-band error for perfect reconstruction. In this manuscript, perfect reconstruction is obtained by optimizing the objective function, which is a weighted sum of mean-square pass-band error, mean-square stop-band error, and ripple measure. Using a population-based meta-heuristics differential search optimization algorithm, the objective function is optimized to obtain the optimal filter coefficients for the filter bank’s low pass prototype filter. The suggested algorithm results have compared to previously report evolutionary optimization algorithms. The performance attributes have been significantly improved over previously published filter bank design methodologies.
An Exploratory Case Study: Risk Filter Applicability to the Digital Strategy of the Education System in Romania. The Light at the End of the COVID-19 Tunnel

By probing into the Strategy regarding the digitalization of education in Romania updated in 2021 by the Romanian Ministry of Education and Research after the COVID-19 and aligned with the latest European Digital Agenda for 2020-2030 published by the European Commission, we are hoping to offer future researchers a set of propositions to continue the inquiry on possible opportunities derived from emerging risks associated with the digitalization. This exploratory case study aims to identify a positive application of the risk filter that is capable to assess not only the range of risks, but also their systematicity, while planning a governmental digital strategy in a critical system like education. We believe that this topic deservers immediate attention, because of the unique opportunity offered by the COVID-19 pandemic, and because a good implementation plan is one of the strongest predictors of a successful strategy. As researchers, our hope is that by understanding the systematicity of risks which could influence the implementation of digital strategy in the education system in Romania, policies and programs can be developed further based on risk analysis and correct prioritization of operational plans. This research was an exploratory case study in which a digital strategy was analysed, and the characteristics of systemic risks have been mapped to the operational plans. It was concluded that the systematic filters identified for the risks could be used for planning the projects, to identify areas of risk exposure during planning and at various stages during the preparation of projects. The final outcome of the case study is that the identification of systemic risks can help with the management of each project and could lay the foundation to a project database.
AI FOR HUMAN ASSISTANCE

A robot has been designed to assist humans in reaching their desired destination. The majority of people today are freshers and unfamiliar with new places. With very little to no awareness of the specific locations, people waste a lot of time navigating through the different available paths. This seems to be a drawback in an arena where all the focus is on getting instantaneous solutions to problems. So, our designed robots assist humans to travel the shortest distance from one place to another place. These types of robots are planned to be implemented in colleges, airports, supermarkets, and many other public places. In the present work, a path following control for assistance robots is designed. The driver is designed to help blind people reach a destination through a path. This work is on the premise that person must choose the moment when the robot must move due to its is using as support. Plus, a linear velocity is generated by an angular velocity which guides the person along the path, whenever the movement is desired by the user. A simulation is performed to verify that the operation of the controller is correct, and finally, experiments are performed on an experimental platform.
Research on Intelligent Student Management Evaluation Information System Based on Data Mining Algorithm

The evaluation of students’ comprehensive information management is of great significance in the school’s management practice, so the practical effect value of strengthening students’ comprehensive information management is significant. From the current specific analysis, to improve the specific effect of students’ comprehensive information management, on the one hand, advanced technology must be used, and on the other hand, information materials need to be mined and classified. The information will be more abundant. In short, in the practice of student information management evaluation, mining the deep connection of information is of great help to the formulation of management strategies, and to achieve the purpose of information mining, data mining technology must be applied. This paper research on the intelligent student management evaluation information system based on data mining algorithm.
Cache High Availability Intelligent Stall Management System Based on Redis Sentinel Mechanism Architecture

Stall economy refers to an economic form formed by setting up stalls to obtain sources of income. It is one of the most primitive and viable commercial activities of mankind. It prospers the economic market and makes up for a blank spot in people’s shopping. Its unique advantages, in the current context, can alleviate the employment pressure to a certain extent. However, because stall economy is a kind of marginal economy in the city. The reason why it is not elegant is that it affects the appearance and environment of the city. In view of the negative impact of the stall economy, many users, messy market and difficult management, an intelligent stall management system is designed, which is managed and supervised by government staff in the background and managed by stall households independently. The redis sentinel mechanism architecture is adopted to solve the problems of many users and strong real-time data concurrency, so as to provide urban managers, stall owners Consumers designed and developed an intelligent stall economic management system based on redis sentinel mechanism cache high availability architecture.
Application of Data Mining Technology in Microfilm Production

With the progress of society and the continuous development of computer technology, digital technology has become an indispensable part of the Internet industry. With the continuous development of digital media and the continuous popularization of micro-films, when we change how to use data mining technology for knowledge discovery, we should first have a clear goal, that is, business understanding. Otherwise, even if the mining tools are powerful and the data rich, the conclusions will be drawn. It is also meaningless; furthermore, the purpose of data mining is to dig out valuable information from massive databases, and its purpose is ultimately to be used in actual business operations. Therefore, the data we conduct data mining must be real and Effectively. This article mainly describes the various applications of digital technology in the micro-film industry. How to use digital technology to analyze and explore the production process of movies, take the weight and avoid the light, use AE special effects software, and post special effects production has become an important part of the production of film and television works. Link, it plays an important role both in the visual effect of the work and the success of the film and television works. The digital system is used to make a reasonable analysis of the current development of the micro-film industry. Intelligent and systematic construction for micro-movie audiences, through post-AE software to improve the standardization and accuracy of movies. Finally, the experimental results of digital technology show that the application of digital technology in micro-film production has increased the film transmission rate by 25%, providing technical support for the development of micro-films. The production of film has changed from the past specialization to the current popular micro-film. Moreover, the development of this new platform has lowered the threshold for the release of micro-movies. Only simple operations on common electronic devices such as mobile phones or computers, micro-movies can be uploaded and released to communicate with everyone.
Product Matching Optimization Model Based on Integrated SVM Data Stream Classification Algorithm

With the continuous innovation of products, investors are increasingly demanding higher-level products. How to reduce transaction costs has become an urgent problem to be solved at present. This paper optimizes and analyzes the matching of products based on the integrated SVM data stream classification algorithm. And the purpose is to improve the reasonable distribution of products. This article mainly applies the statistical method to the research of the product matching optimization model, and grasps the key points of product matching optimization through comparison. The survey results show that 114 out of 180 people supported the four strategies proposed in this article. These data prove that the recommendations made in this article can be supported by the public.
An Analysis of Internet Financial Risk Prevention Strategies from the Perspective of Network Security

With the rapid development of the Internet in recent years, its regulatory issues have become increasingly prominent and have attracted great attention from all walks of life. Among them, issues related to network security have become a research hotspot for many scholars. Based on the various Internet financial network trust issues, security and legal issues facing the current Internet development process, this article studies the problems, influencing factors and risk prevention of Internet financial companies from the perspective of Internet financial users in my country. Finally starts from “Improving the internal control system”, “Establishing a risk early warning system and evaluation mechanism”, “Focusing on talent cultivation”, and putting forward relevant suggestions and countermeasures.
Implementation of Intelligent Mobile Translation Software for Computer Application

With the expansion of global economic integration and exchanges and cooperation, the demand for Chinese-to-English translation in China’s domestic and foreign translation markets is also increasing, and translation software has emerged as the times require. This paper studies the intelligent mobile translation software for computer applications, understands the relevant knowledge theory of intelligent mobile translation software on the basis of literature, and then designs the intelligent mobile translation software for computer applications. The test results show that different translation models have an impact on the translation. The paraphrase corpus translated by RNN is better than the model trained by using the corpus obtained by SMT.
Development of Computer Vision Virtual Experiment Based on Virtual Simulation Technology

With the rapid development of electronic technology in today’s world, computer vision technology has also developed accordingly. In recent years, digital computer has developed rapidly and stably, and has become more and more widely and rapidly popular. This paper provides some help for the development of computer vision virtual by studying all aspects of virtual simulation technology. The main function of virtual network virtual simulation virtual technology will be to imitate and build another semi network real state system by using one of the network simulation virtual systems in the fully real virtual network state. The network virtual simulation technology will also be mainly applied to the virtual simulation and construction in the virtual network protocol system in the future. Firstly, this paper studies the significance and current situation of virtual simulation technology, and expounds the relevant theories of related technologies. Then, based on this theory, the framework of computer vision system is studied, and then the simulation is carried out. Finally, the test results show that the system module can realize the corresponding functions normally and meet the needs of users.
Prediction of Transaction Risk in Financial Market Based on Neural Network Model

In recent years, China’s economic development has grown rapidly. As an important part of China’s national economy, the financial market has become more and more important for risk avoidance and prediction in the transaction process in the financial market. China’s per capita income level has been continuously improved, and the national policies have been adjusted for high interest rate sectors such as real estate and the Internet, The prediction of transaction risk in financial market is becoming more and more important for investors in the market. With the continuous development of computer technology, neural network is widely used in the financial market. Through the neural network under computer technology, the prediction model of the financial market is built, and the data of the financial market are collected and analyzed in real time, so as to provide sufficient data for the study of real social information and people’s work. The experimental results show that the risk aversion efficiency of financial market transaction risk prediction based on neural network model is improved by 7.6%. Combined with computer intelligence, it fully displays the complexity of financial time series prediction model in wavelet clustering model and phase space reconstruction prediction model. For the limitations of manually analyzing the situation and one sidedness of the market, the risk points of historical market transactions in big data are sorted and reminded, the neural network algorithm is updated and iterated in an innovative way, and the financial risk prediction model is constructed by using the wavelet clustering prediction method.
Distributed Parallel Tall-Skinny QR Factorization: Performance Evaluation of Various Algorithms on Various Systems

In this paper, we focus on the distributed parallel computation of tall-skinny QR factorization. Among various numerical algorithms, we evaluate the performance of four typical algorithms that have different characteristics on four different supercomputer systems. Together with a brief summary of each algorithm, this paper presents the current situation of the distributed parallel tall-skinny QR factorization, which will help applications to select an appropriate numerical algorithm for tall-skinny QR factorization.
Design and Implementation of English Teaching Analysis System Based on BP Neural Network Algorithm

With the development, my country’s education level and the quality of students’ learning have been rapidly improved. However, the teaching task is heavy, but the quality is not high and the efficiency is low, which seriously restricts the development of education. Under such circumstances, it is necessary to conduct teaching analysis, and effectively analyze various teaching data, which is conducive to discovering the internal connections, thereby promoting the efficient conduct of teaching decision-making and management. At the same time, the BP neural network (BPNN) is a self-adaptive, multi-input nonlinear system, which can obtain a method of approaching the optimal solution through learning and training. This article combines the BPNN algorithm to explore its role in the design and realization of the English teaching (ET) analysis system. The system has a higher degree of fit for teaching analysis, and the obtained analysis results have smaller errors. Correspondingly, it will also improve the efficiency of ET analysis.
Analysis of Machine Translation and Computer Aided Techniques in English Translation

In order to understand the application of machine translation and computer-aided technology in English translation, this paper will carry out relevant research. This paper first discusses the current situation and development needs of English translation technology, and then puts forward the application strategies of machine translation in English translation and the main functions of computer aided technology. Through this study, the rational use of machine translation and computer-aided technology can optimize English translation results, better eliminate language differences, and output more accurate translation results.