Automatic Marking Research Articles

Design and Analysis of an Adaptive Pipeline Detection and Correction Mechanism

This paper presents a novel adaptive pipeline probe detection and correction mechanism designed to address the challenge of detection interference caused by the movement of wall-climbing robots, particularly in complex environments such as water-cooled walls. The mechanism ensures that the detection probe can accurately detect individual pipelines even when the robot deviates from its intended path. To achieve this, the system incorporates a self-adaptive deviation correction mechanism that maintains consistent detection performance without requiring adjustments to the robot's spatial position. The design includes a variable stiffness analysis of the buffer spring within the correction mechanism, which is optimized to minimize the impact of the robot's movement on the detection components. By carefully selecting the spring's size and stiffness parameters, the mechanism reduces vibration and enhances the stability and reliability of pipeline detection under offset conditions. In addition to maintaining detection accuracy, the system also supports automatic marking of pipelines that exhibit quality issues, ensuring that any detected defects are easily traceable. This adaptive mechanism not only improves detection efficiency but also enhances the overall operational stability of wall-climbing robots in industrial inspection tasks. The results demonstrate the mechanism's effectiveness in mitigating the challenges posed by uneven friction and time delays in the control system, making it a significant contribution to the field of robotic inspection systems.

Research on Automatic Scoring Method of Intelligent Translation System Based on TSO Optimized LSTM Networks

INTRODCTION: The study of automatic marking methods in the Department of Language Translation is conducive to the fairness and rationality of marking by examining the comprehensive level of the students' language, as well as sharing the objectivity and pressure of the marking teachers in marking the scripts.OBJECTIVES: Aiming at the current automatic scoring methods of translation systems, which have the problems of not considering the global nature of influence features and low precision.METHODS: This paper proposes an automatic scoring method for translation system based on intelligent optimization algorithm to improve the deep network. First, by analyzing the language translation scoring problem, selecting the key scoring influencing factors and analyzing the correlation and principal components; then, improving the long and short-term memory network through the triangle search optimization algorithm and constructing the automatic scoring model of the translation system; finally, the high efficiency of the proposed method is verified through the analysis of simulation experiments.RESULTS: The proposed method is effective and improves the accuracy of the scoring model.CONCLUSION: solves the problem of inefficient scoring in the automatic scoring method of the translation system.

Digital transformation of methods for assessing the horse exterior characteristics

Intelligent animal husbandry is becoming a priority area of the industry. On the basis of digital technologies, genomic assessment, and artificial intelligence, new opportunities are being formed to improve the organization of breeding and technological processes. For effective horse breeding, coupled with classical breeding methods, modern breeding resource management systems based on innovative approaches are needed. Accurate quantification of phenotypic information about an animal is a difficult task. Of particular importance there are the issues of objectification of animal characteristics by exterior due to the fact that the assessment of external forms is based on visual perception, is not devoid of a subjective approach and is subject to inaccuracies. One of the ways to solve this problem is to switch to a digital assessment of the phenotypes of interest. The article presents the results of the application of deep learning to solve the problem of automatic marking of characteristic points on a digital image of the studied objects. It was revealed that the created and trained neural network architecture as a whole demonstrated good accuracy.

Study of an Automatic Marking Algorithm for Subjective Questions in College English Exams Based on Deep Learning

Study of an Automatic Marking Algorithm for Subjective Questions in College English Exams Based on Deep Learning

Analysis and Implementation of an Automatic Marking System for Single-choice Question Based on OpenCV

Aiming at a common choice question answer card style, with the help of OpenCV open source library, a variety of image processing technologies were used to to realize the function of an automatic marking system for single-choice question.

Device for automatic marking of billets for large diameter pipe bends

Device for automatic marking of billets for large diameter pipe bends

Supporting the Self-Regulated Learning of Primary School Students With a Performance-Based Assessment Platform for Programming Education

This study implemented and evaluated the innovative use of a performance-based assessment platform to support the development of self-regulated learning (SRL) in senior primary students as they completed programming tasks. We embedded SRL support features into a performance-based assessment platform as scaffolding to help the students implement problem-solving strategies. A mixed-methods approach was adopted to evaluate the intervention. The students’ perceptions of their SRL skills after working through the programming tasks were measured by a survey of 45 students. The quantitative results suggested that the students benefited from the performance-based assessment platform in developing their SRL skills. A thematic analysis of interview data from 20 students further indicated that the embedded SRL scaffolding and automatic marking function helped them to solve the programming tasks. The results demonstrate that a well-designed performance-based assessment platform with embedded SRL support can be an effective tool for developing students’ SRL. The qualitative results further revealed that algorithmic thinking is an aspect of programming for which students need more SRL support.

A new method for measuring penile curvature based on digital images

Accurate and objective assessment of penile curvature is considered a critical evaluation in patients with hypospadias, as it often determines if a 1 or 2-stage procedure should be done. Due to the ease of acquisition and reusability of digital images, more research is focused on digital images; however, the current method based on digital images is not an easily accurate and objective evaluation of penile curvature amongst surgeons. In scoliosis, the Cobb method is a standard method to quantify spinal curvature. Therefore, this study introduces a new accurate, and standardized method for measuring penile curvature based on the digital image concerning the Cobb method. Twenty-two subjects were randomly selected, including 11 pediatric urologists with experience in goniometry(professional group)and 11 non-pediatric urologists without experience in goniometry (non-professional group). A total of 9 two-dimensional images of penile curvatures from 10° to 90°were obtained and stored in the research project notebook. Subjects measured 9 digital images using the new method (fixed anatomical position method) and classical method (the angle created by the interception of two ideal lines, one passing along the proximal portion of the corpora and a second passing through the tip of the penis), respectively. Measurement error was calculated as the absolute difference between the true curvature and the subject estimation. A t-test was used to evaluate the significant differences between the methods. A total of 22 subject measurement data were obtained. Mean errors using the new method ranged from 1.06° to 3.50°, compared to 3.84°to 11.83°for classical method. Mean errors were significantly lower (p<0.05) when using the new method compared to the classical method. A subgroup comparing subjects with and without prior experience with goniometry showed a statistically significant difference only for the classical method when the penis curvature is 10-40°, the mean error range of the professional group was 7.8°-9.56°, compared to 10.34°-13.02°for nonprofessional group. We emphasize the importance of penile curvature measurement and urgent need for an accurate measurement method, and then we focus on the new method compare with the previously described methods looking at mean errors and explain the reason that the new method why is accurate. Subsequently, we focus on explain the impact of experience measurement methods. Finally, the shortcomings of this paper and the prospective points are discussed:1) how to obtain more photos in practical situations; 2) using artificial intelligence methods for automatic marking of key points to achieve efficient measurement of penile curvature. In this preliminary study, we demonstrated better penile curvature estimations using the new method compared to the classical methods currently used by pediatric urologists.

Enhancing Educational Outcomes with a High-performance Online Exam System

With the development of the network, online education has entered thousands of households, and the access to online education is also very simple. Due to the continuous development of online education, a variety of online examination systems with various characteristics have been derived. Considering the simplicity and convenience of the system and the stability of performance, we have developed an online examination system using SSM framework under B/S architecture, which can detect students 'learning status through online examination and automatic marking. And automatically analyze the error rate of each problem and the weakness of each student, so that the teacher can explain such exercises. It also contains the basic information of the student, such as the mobile phone number of the student's parents. If the student is found not to complete the examination, the teacher will also call the student's parents to remind them, and can also communicate with the student's parents appropriately. For the management of the test questions can be taken excel document import. Thus, it can greatly improve the generation of test papers compared with the traditional paper examination. In terms of the types of questions in the online examination system, we only set single choice, multiple choice and judgment questions in the system, set the passing score, set the time of the examination, and also count the number of people who have passed the examination, and provide the correct answer after the examination for students to think. There is also a progress display on the right side of the answer sheet. After each question was answered, the color of the question number on the answer sheet changed. There's also a timing system on the right.

Possibilities and limitations of using machine text-processing tools in Russian radiology reports

BACKGROUND: In radiology, important information can be found not only in medical images, but also in the accompanying text descriptions created by radiologists. Identification of study protocols containing certain data and extraction of these data can be useful primarily for clinical problems; however, given the large amount of such data, the development of machine analysis algorithms is necessary.&#x0D; AIM: To estimate the possibilities and limitations of using a tool for machine processing of radiology reports to search for pathological findings.&#x0D; MATERIALS AND METHODS: To create an algorithm for automatic analysis of radiology reports, use cases were selected that participated in the experiment on the use of innovative technologies in the computer vision for the analysis of medical images in 2020. Mammography, chest X-ray, chest computed tomography (CT), and LDCT, were among the use cases performed in Moscow. A dictionary of keywords has been compiled. After the automatic marking of the reports by the developed tool, the results were assessed by a radiologist. The number of protocols analyzed by the radiologist for training and validation of the algorithms was 977 for mammography, 4,804 for all chest X-ray scans, 4,074 for chest CT, and 398 for chest LDCT. For the final testing of the developed algorithms, test datasets of 1,032 studies for mammography, 544 for chest X-ray, 5,000 for CT of the chest, and 1,082 studies for the LDCT of the chest were additionally labeled.&#x0D; RESULTS: The best results were achieved in the search for viral pneumonia in chest CT reports (accuracy 0.996, sensitivity 0.998, and specificity 0.989) and breast cancer in mammography reports (accuracy 1.0, sensitivity 1.0, and specificity 1.0). When searching for signs of lung cancer by the algorithm, the metrics were as follows: accuracy 0.895, sensitivity 0.829, and specificity 0.936, when searching for pathological changes in the chest organs in radiography and fluorography protocols (accuracy 0.912, sensitivity 1.000, and specificity 0.844).&#x0D; CONCLUSIONS: Machine methods with high accuracy can be used to automatically classify the radiology reports of mammography and chest CT with viral pneumonia. The achieved accuracy is sufficient for successful application to automatically compare the conclusions of physicians and artificial intelligence models when searching for signs of lung cancer in chest CT and LDCT, pathological findings in chest X-ray.

Channel transformer U-Net: an automatic and effective skeleton extraction network for electronic speckle pattern interferometry.

The fringe skeleton extraction method may be the most straightforward method for electronic speckle pattern interferometry (ESPI) phase extraction. Due to ESPI fringe patterns having the characteristics of high noise, low contrast, and different fringe shapes, it is very difficult to extract skeletons from ESPI fringe patterns with high accuracy. To deal with this problem, we propose a skeleton extraction method based on deep learning, called channel transformer U-Net, for directly extracting skeletons from noisy ESPI fringe patterns. In the proposed method, the advanced channel-wise cross fusion transformer module is integrated into the design of deep U-Net architecture, and a loss function by combining binary cross entropy loss and poly focal loss is proposed. In addition, a marking algorithm is proposed for phase extraction, which can realize automatic identification of a skeleton line. The effectiveness of the above proposed algorithms has been verified by computer-simulated and real-dynamic ESPI measurements. The experimental results demonstrate that the proposed channel transformer U-Net can obtain accurate, complete, and smooth skeletons in all cases. The accuracy of the skeleton extraction obtained by our proposed network can reach 0.9878, and the correlation coefficient value can reach 0.9905. The skeleton line automatic marking algorithm has strong universality.

Automatic Multiple Choice Examination Questions Marking and Grade Generator Software

This paper discusses a feasible software solution that enables automatic marking and grading of scripts. Technology keeps expanding, and more advanced innovations are being implemented with time. The marking and allocation of grades for examina- tion scripts through human efforts are gradually becoming a thing of the past. Hence, machines and software applications are introduced to make the entire marking and grading of examination scripts more efficient, fast, and less tedious. Computer vision is an artificial intelligence (AI) knowledge domain that ensures devices obtain useful information from digital images, videos, and other visual inputs. Image processing and recognition, a unique part of computer vision alongside the python program- ming language and the OpenCV library was employed for this project. These are the most used in developing most recent applications that utilize, to some extent, arti- ficial intelligence to attain specific desired results. The result of the project seeks to develop a maintainable android software application that uses image processing technology to scan patterns or images and grades results of multiple-choice question scripts based on a set marking scheme. This ensures that desired results are obtained while increasing efficiency and productivity.

Based on Haar-like feature and improved YOLOv4 navigation line detection algorithm in complex environment

In order to improve the detection accuracy of the navigation line by the unmanned automatic marking vehicle (UAMV) in the complex construction environment. Solve the problem of unqualified road markings drawn by the UAMV due to inaccurate detection during construction. A navigation line detection algorithm based on and improved YOLOv4 and improved Haar-like feature named YOLOv4-HR is proposed in this paper. Firstly, an image enhancement algorithm based on improved Haar-like features is proposed. It is used to enhance the images of the training set, make the images contain more semantic information, which improves the generalization ability of the network; Secondly, a multi-scale feature extraction network is added to the YOLOv4 network, which made model has a stronger learning ability for details and improves the accuracy of detection. Finally, a verification experiment is carried out on the self-built data set. The experimental results show that, compared with the original YOLOv4 network, the method proposed in this paper improves the AP value by 14.3% and the recall by 11.89%. The influence of factors such as the environment on the detection effect of the navigation line is reduced, and the effect of the navigation line detection in the visual navigation of the UAMV is effectively improved.

Development of an Automatic Marking System for Fabric Inspection Machine

Development of an Automatic Marking System for Fabric Inspection Machine

Structural Models of English Terms of Automated Processing of Scientific and Technical Texts Corpora

The article is devoted to the structural models of English multi-component terms from the subject area Welding types as a basis for marking the corpora of scientific and technical texts. The place of corpora of scientific and technical texts in corpus linguistics and prospects of further scientific research based on them are marked. Relevance of the research is conditioned by the necessity to create the corpus of scientific and technical texts, in general, and means of automatic marking of terms, in particular. It has been substantiated that the main problem in creating the corpus of scientific and technical texts is automatic marking of terminological word combinations. The analysis of the current state of the terminology system of the subject area Welding types has been carried out. The formal structure of elements of the Welding types terminology system is considered. The results of the analysis of two, three, four-component English terminological word combinations of the Welding types subject area and their structural models are presented. All structural models of English terminology combinations are illustrated with examples. The most productive models of English terms word combinations are highlighted. It is shown that the most productive model - the combination of a nucleus element with a noun or an adjective in the function of the prepositional definition - can be traced in two-component word combinations, but the analysis of more complex formations shows that the model of left definition attached to the term kernel is also present in them, demonstrating generic features. The necessity of enumerating all possible structural models of terminological combinations in the subject area Welding types has been substantiated. The novelty of the study is seen in the formation of a database of structural models of terminological combinations as the basis of a superstructure database on the structure of terms to improve the quality of automatic marking of the bodies of scientific and technical texts and processing of terms-candidates in the conduct of body studies.

An Automatic Marking Method Based on Object Detection Algorithm in Aerial Images

Aerial images of brassica napus in field were taken by UAV equipment but often contained unnecessary areas, which caused interferences and inconveniences to the subsequent study of identification of flowering stage by brassica napus images. To solve above problems, YOLO-V4 object detection algorithm based on Convolutional Neural Network (CNN) is adopted to build a model to detect brassica napus distribution area in aerial images, and model is trained iteratively by training data sets and setting initial parameters. As a result, the mean average precision (mAP) of the object detection model reached 96.24%, and the average loss is only 0.5843. In addition, the generalization and robustness ability of model have been evaluated by testing data sets. Conclusion shows the model proposed in this paper can indeed achieve an application of object detection and automatic labeling in aerial brassica napus images with a good performance.

Enhancing coding skills with CloudStor SWAN

CloudStor SWAN (AARNet, 2022) is a research-focused web service for running analyses that is available to staff and students at many research institutes and Universities across Australia and New Zealand. In 2021, we used SWAN as a teaching tool in the master-level subject, Computational Genomics (COMP90016) at The University of Melbourne. This subject aims to teach students how to analyse large genomic datasets using best practices software tools, pipelines and student-written, custom code. &#x0D; &#x0D; Although CloudStor SWAN was not conceived as a teaching tool, we worked with their technical staff to tailor the service to our use case. This innovative use of existing research infrastructure allowed us to effectively transition the subject to remote learning. Students and staff could log in to the service using their existing University credentials, from anywhere in the world, without the use of a VPN. The ability to access the platform from a web browser allowed for a consistent computing environment for all students regardless of operating system, and without having to worry about software installations on local machines. This presented a significantly improved experience from the custom servers that had been used in the past.&#x0D; &#x0D; We used SWAN for weekly workshops during semester and for assessment in the form of assignments and an exam. It allowed us to format subject material in Jupyter notebooks where we could seamlessly integrate text, graphics and code. Additionally, assessed code questions can incorporate automatic marking and written submissions can be checked for plagiarism. SWAN also allowed us to introduce students to the UNIX command line, an important skillset that was not previously taught in the University of Melbourne Master of Science (Bioinformatics) program.&#x0D; &#x0D; From a student perspective, SWAN allowed for a practical skillset to be developed alongside theoretical knowledge from other aspects of the course. The platform was simple to learn and allowed students to focus on the subject content and the tasks asked of them, rather than on the interface. From a teacher’s perspective, having a unified platform allowed for a single set of clear instructions, improved troubleshooting and clearer management of tool versions and software dependencies. The use of Jupyter notebooks simplified lesson plans and assessments by integrating multiple elements into single documents. This element also made the lessons more easily sharable between colleagues and collaborators.&#x0D; &#x0D; Our integration of this technology into our tertiary teaching has served as a model for a similar use at a different Australian university. We hope to share the lessons learned from this subject, the advantages of using CloudStor SWAN in a teaching environment for both staff and students and provide some advice for others who may want to adapt it to fit their own teaching needs.&#x0D; Presentation link: https://youtu.be/8tutCO1hd9c&#x0D; References&#x0D; &#x0D; AARNet. (2022). CloudStor: Access, store, share and work with your data in one place. https://www.aarnet.edu.au/network-and-services/cloud-services/cloudstor

Assessment of biomedical engineering knowledge using true-false questions.

The COVID-19 pandemic has caused a shift from on-campus to remote online examinations, which are usually difficult to invigilate. Meanwhile, closed-ended question formats, such as true–false (TF), are particularly suited to these examination conditions, as they allow automatic marking by computer software. While previous studies have reported the score characteristics in TF questions in conventional supervised examinations, this study investigates the efficacy of using TF questions in online, unsupervised examinations at the undergraduate level of Biomedical Engineering. We examine the TF and other question-type scores of 57 students across three examinations held in 2020 under online, unsupervised conditions. Our analysis shows significantly larger coefficient of variance (CV) in scores in TF questions (42.7%) than other question types (22.3%). The high CV in TF questions may be explained by different answering strategies among students, with 13.3 ± 17.2% of TF questions left unanswered (zero marks) and 16.4 ± 11.5% of TF questions guessed incorrectly (negative marks awarded). In unsupervised, open-book examination where sharing of answers among students is a potential risk; questions that induce a larger variation in responses may be desirable to differentiate among students. We also observed a significant relationship (r = 0.64, p < 0.05) between TF scores and the overall subject scores, indicating that TF questions are an effective predictor of overall student performance. Our results from this initial analysis suggests that TF questions are useful for assessing biomedical-theme content in online, unsupervised examinations, and are encouraging for their ongoing use in future assessments.Supplementary InformationThe online version contains supplementary material available at 10.1007/s13246-021-01088-x.

Особенности разметки научно-технических текстов в аспекте создания специализированного корпуса

The article deals with the peculiarities of the markup of scientific and technical texts in developing a corpus of highly specialized texts. The scientific and technical texts as sources of filling the corpus are listed. The scientific and technical texts are analyzed from the position of markup of textual elements of different levels. The necessity of introducing interlevel types of markup of scientific and technical texts is substantiated. The significance of introducing structural markup when creating a corpus of scientific and technical texts is emphasized. The structural elements of scientific and technical texts for filling the corpus are listed. The current state of the problem of automatic extraction of terms from scientific and technical texts is analyzed. It is shown that the greatest difficulty is the marking of multicomponent terminological units in the corpus of scientific and technical texts. We identify literary terms as objects that require the development of additional tools for their processing, which may include various letters, symbols, numbers or their combinations. References as a factor influencing the classification and rubrication of scientific and technical texts are analyzed. The necessity of studying the types of references, as well as the ways of their automatic marking in the corpus of scientific and technical texts is substantiated. The necessity of introducing a separate marking of examples in scientific and technical texts is substantiated.

АВТОМАТИЧЕСКОЕ МАРКИРОВАНИЕ СЕТЕВОГО ТРАФИКА БРАУЗЕРА ДЛЯ АНАЛИЗА И КЛАССИФИКАЦИИ НА ПРИМЕРЕ ПЛАТФОРМЫ "REMOTETOPOLOGY"

Purpose: Network traffic analysis is a security standard in most remote work systems. This task is becoming more complex every day due to the use of encryption systems on an increasing number of devices. The traditional method of traffic research in the OSI model does not provide the proper level of reliability. Method: Machine learning is one of the promising methods for solving the problem of network traffic analysis. In the first place is the identification of unwanted traffic, which can be interpreted as a tool for bypassing the system. In order for the system to be used in the educational and production process, it is necessary to take into account the issue of ergonomics of the solution. The influence of the monitoring and analysis system on the response rate. The purpose of the work: The purpose of the work is: the formation of tagged HTTP/HTTPS traffic data, to intercept encrypted tunnel traffic. It is proposed to develop a browser plugin whose tasks are to collect data from the user's browser using the Chromium API. The collected data is combined with network streams to form labeled data used later for ML model training datasets. Novelty: The novelty elements of the presented solution are the use of Machine learning to classify network traffic based on labeled data. Result: using the presented solution for automatic marking of browser network traffic for analysis and classification, we decided to consider the operation of each system in an independent encrypted channel. The launch of the RemoteTopology project has been switched to HTTP, while each connection is performed in its own VPN session and guarantees the security of the connection. Within the connection, the marking of streams is performed, which allows you to quickly and efficiently process data for subsequent ML analysis. Practical relevance: the presented solution is proposed to be implemented within the framework of the RemoteTopology platform to identify in real time deviations between the reference system that already identifies the user and "undesirable systems" that the user can use to bypass the protection system.