Evaluation Of Algorithms Research Articles

ADDITION: Detecting Adversarial Examples With Image-Dependent Noise Reduction

Notwithstanding the tremendous success of deep neural networks in a range of realms, previous studies have shown that these learning models are exposed to an inherent hazard called <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">adversarial example</i> — images to which an elaborate perturbation is maliciously added could deceive a network, which entails the study of countermeasures urgently. However, existing solutions suffer from some weaknesses, e.g. parameters are usually determined empirically in some processing-based detection methods might result in a sub-optimal effect, and the directly performed processing on images might affect the classification of benign samples, leading to increment of false positive. In this paper, we propose a novel imAge-DepenDent noIse reducTION (ADDITION) model based on deep learning for adversarial detection. The ADDITION model can adaptively convert the adversarial perturbation in each image to approximate Gaussian noise by injecting image-dependent additional noise, then perform noise reduction to eliminate the adversarial perturbation, and finally detect adversarial examples by examining the classification inconsistency between the input image and its denoised version. The ADDITION model is trained end-to-end on benign samples without any prior knowledge of adversarial attacks, and thus avoid time-consuming task of generating adversarial examples in practical use. We generate more than 220,000 adversarial examples based on six attack algorithms for evaluation and present state-of-the-art comparisons on three real-word datasets. Extensive experiments demonstrate that our proposed method achieves improved performance in both detection accuracy rate and false positive rate.

Evaluation algorithm of online and offline mixed teaching quality based on multivariate statistical analysis

Evaluation algorithm of online and offline mixed teaching quality based on multivariate statistical analysis

Research on the reform and effect evaluation of three-dimensional motion animation blended teaching based on fuzzy evaluation model

Abstract This paper combines the teaching of a 3D animation course with the blended teaching mode and constructs a framework for the design of 3D animation blended teaching reform. Based on the fuzzy comprehensive evaluation algorithm, we adopt qualitative and quantitative methods to determine the evaluation index system of teaching reform, divide the course teaching objectives, teaching content, learning resources, online learning and teaching design and classroom organization into five first-level indexes, establish the weights of the evaluation index system, and complete the construction of the fuzzy comprehensive evaluation system of 3D animation blended teaching. Examine the design and teaching effects of 3D animation that blends learning from different dimensions, and examine the correlation between learners’ multidimensional recognition of teaching reform and its learning impact. By combining the weights of each index, the fuzzy comprehensive evaluation results are obtained by determining the affiliation degree of each index using the fuzzy statistical method. The operation results show that the fuzzy comprehensive evaluation scores of the five first-level indicators of teaching reform are 0.754546, 0.69943, 0.638926, 0.66888, and 0.5966. It can be seen that the comprehensive evaluation results of the five aspects have achieved good results. The evaluation of teaching reform’s effects can be targeted and feasible using a fuzzy comprehensive evaluation model.

A Non-Invasive Speech Quality Evaluation Algorithm for Hearing Aids With Multi-Head Self-Attention and Audiogram-Based Features

A Non-Invasive Speech Quality Evaluation Algorithm for Hearing Aids With Multi-Head Self-Attention and Audiogram-Based Features

Structural analysis of a rocket’s multi-engine propulsion cluster for fault detection and isolation

A multi-engine cluster is a complex thermodynamical system with many actuators, internal states, and possible faults. Therefore, structural analysis is an appealing tool to systematically analyze FDI possibilities. It can be used in the early development phase of a fault detection and isolation system to determine the possibility of fault detection and isolation. It also computes residual generator candidates. For large problems, the number of residual generator candidates is high. A novel algorithm based on the signal-to-noise ratio of the output is proposed to evaluate the candidates. Simulation is performed to analyze the utility of the structural analysis and the residual generator evaluation algorithm.

Active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities

Switchgear is the critical equipment of the distribution network, and its status evaluation is of great significance to improve the reliability of the distribution network. Addressing the issues of high subjectivity and low accuracy in current methods of status evaluation, we propose an active alarm method for switchgear status based on K-means clustering and multi-dimensional feature quantities. We configure multiple sensors at different locations inside the switchgear to monitor and obtain multi-source data based on IoT sensing technology. We establish a multi-dimensional status dataset of the switchgear and analyze its fluctuation. We acquire the relationship between the number of clusters K and the sum of squares of errors by clustering the obtained multi-dimensional dataset of switchgear through the K-means clustering status evaluation algorithm. We also calculate the sum of squares of errors under different clusters and the optimal number of clustering levels. Finally, we combine a set of switchgear operation data to obtain the switchgear health status by cluster analysis, which verifies the effectiveness of the evaluation method.

Innovation and Effect Evaluation Algorithm of Library's Refined Reading Promotion Activities in the 5G Era

Innovation and Effect Evaluation Algorithm of Library's Refined Reading Promotion Activities in the 5G Era

Online and offline hybrid teaching evaluation and monitoring system based on hierarchical analysis method and fuzzy comprehensive evaluation algorithm

Online and offline hybrid teaching evaluation and monitoring system based on hierarchical analysis method and fuzzy comprehensive evaluation algorithm

Evaluation algorithm of online and offline mixed teaching quality based on multivariate statistical analysis

Evaluation algorithm of online and offline mixed teaching quality based on multivariate statistical analysis

The Role and Path of Ideological and Political Education in Cultivating Innovative Talents

Abstract Cultivating innovative talents is an important goal of the talent cultivation strategy in the new era, and ideological and political education is an indispensable part of cultivating a correct outlook on life and the spirit of innovation. In order to fully leverage the supporting role of ideological and political education in cultivating innovative talents, this paper proposes an enhanced collaborative filtering algorithm and content recommendation algorithm. The traditional Civic and Political learning evaluation algorithm is improved by considering the forgetting factor and introducing disciplinary abilities and sequence dependence. The results of the experimental analysis point out that the average grade improvement of the seven Civics and Politics knowledge points of the students in the experimental class reaches 10.3%, the mean value of the four innovation dimensions reaches 14.95 points, and the average agreement rate between the self-assessment results of the learning effect and the results of the evaluation model is 90%. It shows that the optimized Civics personalized learning and evaluation method, based on this paper, has positive effects.

Construction of network access Layer security protection System based on zero trust architecture

In order to further solve the problem of weak attack defense existing in the traditional network architecture, improve the shortcomings of network architecture in processing capacity, hardware expansion, security protection and other aspects, and better cope with the security challenges under the new network architecture, this article aims to provide more secure computer information exchange and sharing services for enterprises. A network access layer security protection system based on zero trust architecture is constructed. At the same time, the zero-trust architecture evaluation algorithm based on fuzzy theory is used to construct the security protection model. System tests show that more than 100 hosts on the enterprise access layer network use switches that support SNMP, and the security protection system at the network access layer processes more than 1500 attacks. In addition, malicious IP address changes are effectively prevented and the security protection level is greatly improved.

Challenges and Opportunities of Civic Education in Colleges and Universities in Digital Environment

Abstract Civic education in colleges and universities plays a pivotal role in fostering students’ national identity and civic responsibilities. Enhanced by digital learning environments, it promises to cultivate exemplary citizens more effectively. This paper details the development of a MOOC interactive communication platform utilizing the open-source edX framework, which serves to enhance civic education. It delves into optimizing resource storage technologies on the platform. It introduces an innovative assignment mutual evaluation algorithm tailored for Civics and Political Science education, which accounts for rater bias and scoring patterns. The newly implemented resource storage and mutual assessment algorithms have notably improved the functionality of the civic education platform. Performance metrics from the platform indicate an encouraging trend in student achievement across three courses, with average scores of 78.32, 79.24, and 83.27, respectively, showcasing a consistent upward trajectory. Further analysis reveals a converging trend in the variance of data distribution, indicating a reduction in disparities in student learning outcomes as evidenced by a decrease in standard deviation values. Comprehensively, students’ overall test scores have shown significant improvement. Notably, mastery of subject matter knowledge saw an increase from 2.3% to 21.4%, and the pass rate escalated from 92.6% to 98.3%. Although there were initial discrepancies in the cognitive rating of civic education values between the experimental and control groups, the experimental group exhibited substantial gains, underscoring the efficacy of the platform and its methodologies.

Community-acquired Bacteremia: when should endocarditis be suspected?

This article examines the risk of infective endocarditis (IE) linked to various bacterial species causing bacteremia. While the European Society of Cardiology (ESC) and the American Heart Association (AHA) guidelines do not specify the diagnostic workup to perform based on bacterial species, certain bacteria are acknowledged to pose a higher IE risk. Community-acquired bacteremia has a higher IE risk than nosocomial bacteremia. Implantable electronic cardiovascular devices (IECD) and a history of IE or valve surgery also increase this risk. Several risk stratification systems (RSS) have been developed to guide clinicians on the necessity of echocardiography in patients with Gram-positive bacteremia, particularly S. aureus, streptococci, and enterococci. An evaluation algorithm based on these RSS is proposed to assist clinical investigations.

Study on the Path of Enhancing the Effectiveness of College Students’ Education and Management in the Context of Informatization

Abstract To cope with the problems existing in the education and Management of students in traditional colleges and universities, improve the management efficiency and realize the informatization of education management, this study proposes a set of optimization paths for the education and Management of students in colleges and universities. In this paper, an intelligent and diversified education management platform is constructed, aiming to strengthen the operation efficiency of the education management team in colleges and universities. By introducing a fuzzy evaluation algorithm to judge the educational management paths of college students and normalizing the input data, empirical evaluation results are derived. Comparative Analysis of the data in the study showed that the optimized educational management path significantly improved students’ daily behavior and mental health status. Students’ average daily online time was effectively controlled between 5.6 and 10.5 hours, the online period was more in line with the healthy work and rest, and the rates of disciplinary behaviors and Internet fraud victimization decreased respectively. At the same time, students’ mental health and learning effects were also improved. In summary, the education and management path for college students proposed in this paper significantly strengthens the management system and comprehensively cultivates students. It provides an effective informatization strategy for education management in colleges and universities.

Evaluation of mine water quality based on the PCA–PSO–BP model

Abstract To enhance the mining area's overall use of mine water in the arid area of Western China and mitigate the current water scarcity problem, this paper introduces an intelligent optimization algorithm and neural network for mine water quality evaluation and proposes a principal component analysis (PCA)–particle swarm optimization (PSO)–back propagation (BP) mine water quality evaluation model. Firstly, the model uses PCA to identify the primary factors affecting mine water quality, then enhances the optimal weights and thresholds of the BP neural network based on the PSO algorithm, and the PCA–PSO–BP evaluation model with nine input layers, nine hidden layers, and one output layer is created. In addition, using the Shicaocun Mine as an example, the results demonstrate that the PCA–PSO–BP model has accurate mine water quality evaluation results, and the prediction accuracy reached 86.8255%. This exemplifies the PSO method's superiority to the BP neural network improvement. This study not only offers a novel theoretical framework for assessing and forecasting water quality in mining regions, but it also sets the stage for the possible broad use of state-of-the-art neural networks and optimization algorithms in the coal mining industry.

Как учесть мнение населения при промышленном освоении Арктики

The article deals with the issues of taking into account the opinion of the local population when justifying and implementing projects for industrial development of the Arctic. The mechanism and procedures for assessing and taking into account the opinion of the population at all stages of the life cycle of a project for exploration and extraction of minerals from its initiation to completion are proposed by informing the population about the project of planned activities, holding public hearings, ethnological expertise, as well as ethnological audit and ethnosocial monitoring. The model of interaction of stakeholders (mining companies, local authorities and indigenous peoples of the North) is based on the concept of sharing joint benefits from the industrial development of the Arctic, which is based on the cooperation of the parties and corporate social responsibility of business in the area of planned activities. It is proposed to protect the interests of indigenous peoples by signing agreements on the socio-economic development of the territory, creating compensation funds and implementing compensation projects of socio-economic, environmental orientation and projects to preserve the culture and ethnos of indigenous peoples, which are formed on the basis of population surveys. &#x0D; The algorithm of multi-criteria evaluation, ranking and selection of compensation projects, taking into account priorities for various groups of the population, as well as economic regulators for supporting compensation projects, including direct action tools (compensation fund, etc.), and indirect action tools (tax incentives, subsidies to support traditional crafts, etc.) is proposed.

Hybrid PV and Fuel Cell Generating System for Shipboard Applications Based on Optimal Dispatch Evaluator Algorithm

The construction of an Energy Management System (EMS) for ships has been designed to improve the electrical performance of cruise lines. According to the target requirement, the specified electrical plant is an integrated power system characteristic of contemporary electric ships. Battery backup, low performance, and voltage instability are the drawbacks of the existing method. To overcome the limitations of the proposed method-based Photovoltaic (PV) renewable source using SEPIC DC–DC converters and a bidirectional fuel cell as an additional input power source. Power generation from the two different input sources is controlled through a DC bus and conversion through a centralized high-gain DC–DC converter. In this conversion, Pulse Width Modulation (PWM), which is the maximum power obtained using the optimal dispatch evaluation algorithm using an Artificial Neural Network (ANN), is obtained by adjusting the duty ratio of the Single-Ended Primary Inductance DC–DC converters (SEPIC). The electrochemical power source is a fuel cell. Hydrogen and oxygen are combined in fuel cells to create electricity, along with the consequences of heat and water being transformed into mechanical energy; in the battery management system, the battery is employed as an energy storage system to regulate any power imbalance or excess while taking the battery’s state of charge into account Electric Ships (AESs). The outcomes of the MATLAB Simulink output, fuel cell and battery storage performance analysis, efficiency, and THD analysis are summarized as follows.

Automatic non-contact grinding surface roughness measurement based on multi-focused sequence images and CNN

Microscopic images of surfaces can be used for non-contact roughness measurement by visual methods. However, the images are usually acquired manually and need to be as sharp as possible, which limits the general application of the method. This manuscript provides an automatic roughness measurement method that can apply to automatic industrial sites. This method first automatically acquires the sharpest image and then feeds the image into a convolutional neural network (CNN) model for roughness measurement. In this method, the weighted window enhanced sharpness evaluation algorithm based on the sharpness evaluation function is proposed to automatically extract the sharpest image. Then, a CNN model, CFEN, suitable for the roughness measurement task was designed and pre-trained. The results demonstrate that the measurement accuracy of the method reaches 91.25% and the time is within a few seconds. It is proved that the method has high accuracy and efficiency and is feasible in practical applications.

Retracted: Research on the Evaluation Algorithm of Social Capital Influence of Enterprise Network Marketing

Retracted: Research on the Evaluation Algorithm of Social Capital Influence of Enterprise Network Marketing

Devising an approach to the use of distance education technologies in performing control measures for technical students

The object of this study is the process of implementation of control measures with the help of distance learning technologies for students of technical specialties. The problem of the application of information and communication technologies for the effective implementation of final control during distance learning has been studied. An authentic algorithm for creating a test on the Moodle platform has been developed. The proposed algorithm is based on the use of the classical structure of the ticket, which allows implementing an approach to final control that is closest to the traditional one. This is important for assessing the acquired practical students competences of engineering specialties since for them the control measure may include a combination of textual, graphic, and computational tasks. The application of the algorithm in practice allows one to improve the quality of the final assessment by ensuring equal conditions for students, attracting the optimal set of tools for this, as well as identifying the student and thus ensuring the transparency of the control measure. Verification of the developed algorithm during the quarantine period sessions at the Faculty of Transport Construction of the National Transport University demonstrated a better adaptability of students to conduct control measures in remote form. This is confirmed by an increase in quality (from 1.16 % to 6.8 %) and success (to 7.4 %) rates among students, as well as a high level of student satisfaction in 2022 with the effectiveness of online learning, which was determined through an anonymous questionnaire. Promising steps regarding the use of the algorithm in the final evaluation are the development of measures for the implementation of automated recognition of the emotional status of students