Automatic Tool Research Articles

Explainable Automatic Detection of Fiber–Cement Roofs in Aerial RGB Images

Following European directives, asbestos–cement corrugated roofing tiles must be eliminated by 2025. Therefore, identifying asbestos–cement rooftops is the first necessary step to proceed with their removal. Unfortunately, asbestos detection is a challenging task. Current procedures for identifying asbestos require human exploration, which is costly and slow. This has motivated the interest of governments and companies in developing automatic tools that can help to detect and classify these types of materials that are dangerous to the population. This paper explores multiple computer vision techniques based on Deep Learning to advance the automatic detection of asbestos in aerial images. On the one hand, we trained and tested two classification architectures, obtaining high accuracy levels. On the other, we implemented an explainable AI method to discern what information in an RGB image is relevant for a successful classification, ensuring that our classifiers’ learning process is guided by the right variables—color, surface patterns, texture, etc.—observable on asbestos rooftops.

Design and dynamic characteristics of automatic vertical drilling tool

Higher performance is needed in deeper and longer horizontal wellbore drilling. In vertical drilling conditions, wellbore trajectory often be led to deviation due to objective conditions. Based on the engineering issues, this paper presents a new automatic vertical drilling tool, including the innovative design and working mechanism research. This tool working mechanism are carried out, such as the mechanics model of eccentric tube and the relationships between eccentric torque and deflection angle. The eccentric tube torque increases with the wellbore deviation angle. When wellbore deviation angle is constant, the eccentric tube torque reaches maximum value when its deflection angle is 90°. The internal flow parameters are studied by fluid field simulation, such as drilling fluid flow rate, pressure drop, and pressure distribution inside this tool during downhole drilling process. The larger the diameter of switch hole, the higher is the pressure drop around, which leads to the force on push block increasing. The drilling fluid pressure increases with higher input flow rate inside this tool. The comparison of results, the data of simulation and theoretical model calculations, verified the reliability and accuracy of research. This study has provided innovative ideas and reference for drilling tool design and wellbore trajectory control methods.

Automatic measurement of lower limb alignment in portable devices based on deep learning for knee osteoarthritis

BackgroundFor knee osteoarthritis patients, analyzing alignment of lower limbs is essential for therapy, which is currently measured from standing long-leg radiographs of anteroposterior X-ray (LLR) manually. To address the time wasting, poor reproducibility and inconvenience of use caused by existing methods, we present an automated measurement model in portable devices for assessing knee alignment from LLRs.MethodWe created a model and trained it with 837 conforming LLRs, and tested it using 204 LLRs without duplicates in a portable device. Both manual and model measurements were conducted independently, then we recorded knee alignment parameters such as Hip knee ankle angle (HKA), Joint line convergence angle (JCLA), Anatomical mechanical angle (AMA), mechanical Lateral distal femoral angle (mLDFA), mechanical Medial proximal tibial angle (mMPTA), and the time required. We evaluated the model’s performance compared with manual results in various metrics.ResultIn both the validation and test sets, the average mean radial errors were 2.778 and 2.447 (P<0.05). The test results for native knee joints showed that 92.22%, 79.38%, 87.94%, 79.82%, and 80.16% of the joints reached angle deviation<1° for HKA, JCLA, AMA, mLDFA, and mMPTA. Additionally, for joints with prostheses, 90.14%, 93.66%, 86.62%, 83.80%, and 85.92% of the joints reached that. The Chi-square test did not reveal any significant differences between the manual and model measurements in subgroups (P>0.05). Furthermore, the Bland-Altman 95% limits of agreement were less than ± 2° for HKA, JCLA, AMA, and mLDFA, and slightly more than ± 2 degrees for mMPTA.ConclusionThe automatic measurement tool can assess the alignment of lower limbs in portable devices for knee osteoarthritis patients. The results are reliable, reproducible, and time-saving.

Delayed Heating Calculations Using the MCNP-ORIGEN Activation Automation Tool

An accurate assessment of the deposited energy across a reactor geometry allows for a better determination of the heat removal requirements and ensures effective cooling after shutdown. This work discusses several methods in detail that target the heat deposition in specific reactor regions, leading to a choice of a prevalent method for the calculation workflow. This paper introduces a delayed heating calculation workflow based on the formal three-step process. The workflow relies on the MCNP-ORIGEN Activation Automation tool for performing the first two steps of the process, while the third step is conducted via MCNP photon transport simulations. This paper showcases two applications to demonstrate the workflow and simulation outputs. These include an Advanced Test Reactor (ATR) experiment and the RA-6 reactor structures.

Differential-Linear Cryptanalysis of GIFT family and GIFT-based Ciphers

At CHES 2017, Banik et al. proposed a lightweight block cipher GIFT consisting of two versions GIFT-64 and GIFT-128. Recently, there are lots of authenticated encryption schemes that adopt GIFT-128 as their underlying primitive, such as GIFT-COFB and HyENA. To promote a comprehensive perception of the soundness of the designs, we evaluate their security against differential-linear cryptanalysis. For this, automatic tools have been developed to search differential-linear approximation for the ciphers based on S-boxes. With the assistance of the automatic tools, we find 13-round differential-linear approximations for GIFT-COFB and HyENA. Based on the distinguishers, 18-round key-recovery attacks are given for the message processing phase and initialization phase of both ciphers. Moreover, the resistance of GIFT-64/128 against differential-linear cryptanalysis is also evaluated. The 12-round and 17-round differential-linear approximations are found for GIFT-64 and GIFT-128 respectively, which lead to 18-round and 19-round key-recovery attacks respectively. Here, we stress that our attacks do not threaten the security of these ciphers.

Detection and segmentation of lettuce seedlings from seedling-growing tray imagery using an improved mask R-CNN method

Real-time monitoring and management of lettuce plants, especially during the seedling growth stage, is important in order to protect them from various environmental stresses and to prevent yield loss. Various and similar symptoms may occur as a result of different unfavorable environmental stresses. Visual inspection alone may result in an incorrect and late diagnosis, obstructing subsequent corrective action for the damaged seedlings in an early stage. The purposes of this study were to detect and segment lettuce seedlings from the background of seedling-growing trays using an improved mask R-CNN and to estimate seedling size based on the output of the proposed technique. Lettuce seedling images were taken from a plant factory using an automatic image acquisition tool. The seedlings in the images were 1 to 3 weeks old. An annotation of 1,000 lettuce seedling image datasets was prepared using an online annotation tool. An improved mask R-CNN was implemented using ResNet-101 with CB-Net, which provided enhanced image feature extraction. Transfer learning was used to train the model network with a smaller dataset and reduce the processing time. Bounding boxes and annotations were set as inputs for the training model. Training and test datasets were prepared for the model evaluation. The sizes of the seedlings were determined by applying a binary mask to the output-masked seedling images. The total number of pixels was used to determine the seedling size. The training loss of the selected method was less than 0.20 %. The identification of lettuce seedlings from 150 randomly chosen test photos revealed that the best-fit image had a performance F1 score of 93 %, corresponding to 92 % precision and 95 % recall. A strong linear relationship (R2>0.99) between manual and model-estimated leaf area confirmed the proposed model accuracy throughout the lettuce growth stages. The improved mask R-CNN could detect lettuce seedlings in the tray image background and also extract the leaf area of identified lettuce seedlings.

Enhancing Foundation Shade Suggestions through Skin Tone Identification

As we concentrate on addressing the challenges in responsible beauty product recommendation, particularly when it involves comparing the product’s color with a person’s skin tone, similar as for foundation and robe p conditions. The features uprooted using the prints from illuminated terrain can be largely deceiving or indeed be inharmonious to be compared with the product attributes. Hence bad illumination condition can oppressively degrade quality of the recommendation. We introduce a machine learning frame for illumination assessment which classifies images into having moreover good or bad illumination condition. We also make an automatic stoner guidance tool which informs a stoner holding their camera if their illumination condition is good or bad. This way, the stoner is handed with rapid-fire feedback and can interactively control how the print is taken for their recommendation. Only a many studies are devoted to this problem, substantially due to the lack of dataset that's large, labeled, and different both in terms of skin tones and light patterns. Lack of similar dataset leads to neglecting skin tone diversity. Thus, we begin by constructing a different synthetic dataset that simulates colorful skin tones and light patterns in addition to a being facial image dataset. Next, we train a Convolutional Neural Network (CNN) for illumination assessment that outperforms the being results using the synthetic dataset. Eventually, we dissect how the work improves the shade recommendation for colorful foundation products

Multiscale modelling of particulate composites with spherical inclusions

AbstractThis paper presents a novel and effective strategy for modelling three-dimensional periodic representative volume elements (RVE) of particulate composites. The proposed method aims to generate an RVE that can represent the microstructure of particulate composites with hollow spherical inclusions for homogenization (e.g., deriving the full-field effective elastic properties). The RVE features periodic and randomised geometry suitable for the application of periodic boundary conditions in finite element analysis. A robust algorithm is introduced following the combined theories of Monte Carlo and collision driven molecular dynamics to pack spherical particles in random spatial positions within the RVE. This novel technique can achieve a high particle-matrix volume ratio of up to 50% while still maintaining geometric periodicity across the domain and random distribution of inclusions within the RVE. Another algorithm is established to apply periodic boundary conditions (PBC) to precisely generate full field elastic properties of such microstructures. Furthermore, a user-friendly automatic ABAQUS CAE plug-in tool ‘Gen_PRVE’ is developed to generate three-dimensional RVE of any spherical particulate composite or porous material. Gen_PRVE provides users with a great deal of flexibility to generate Representative Volume Elements (RVEs) with varying side dimensions, sphere sizes, and periodic mesh resolutions. In addition, this tool can be effectively utilized to conduct a rapid mesh convergence study, an RVE size sensitivity study, and investigate the impact of inclusion/matrix volume fraction on the solution. Lastly, examples of these applications are presented.

Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists

ObjectivesTo assess urologist attitudes toward clinical decision support embedded into the electronic health record and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence, enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited. MethodsA sequential explanatory mixed methods study from 2019-2020 was performed. First, survey responses from the 2019 American Urological Association annual census evaluated attitudes toward an automatic clinical decision support tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on clinical decision support impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. ResultsAmong a weighted sample of 12,366 practicing urologists, the majority agreed clinical decision support would help decision-making (70.9%, 95% CI 68.7–73.2%), aid patient counseling (78.5%, 95% CI 76.5–80.5%), save time (58.1%, 95% CI 55.7–60.5%), and improve patient outcomes (42.9%, 95% CI 40.5–45.4%). More years in practice was negatively associated with agreement (p<0.001). Urologists described how clinical decision support could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. ConclusionsUrologists have favorable attitudes toward the potential for clinical decision support in the electronic health record. Smart design will be critical to ensure effective implementation and impact.

Dosimetric Impact of Delineation and Motion Uncertainties on the Heart and Substructures in Lung Cancer Radiotherapy

AimsDelineation variations and organ motion produce difficult-to-quantify uncertainties in planned radiation doses to targets and organs at risk. Similar to manual contouring, most automatic segmentation tools generate single delineations per structure; however, this does not indicate the range of clinically acceptable delineations. This study develops a method to generate a range of automatic cardiac structure segmentations, incorporating motion and delineation uncertainty, and evaluates the dosimetric impact in lung cancer. Materials and methodsEighteen cardiac structures were delineated using a locally developed auto-segmentation tool. It was applied to lung cancer planning CTs for 27 curative (planned dose ≥50 Gy) cases, and delineation variations were estimated by using ten mapping-atlases to provide separate substructure segmentations. Motion-related cardiac segmentation variations were estimated by auto-contouring structures on ten respiratory phases for 9/27 cases that had 4D-planning CTs. Dose volume histograms (DVHs) incorporating these variations were generated for comparison. ResultsVariations in mean doses (Dmean), defined as the range in values across ten feasible auto-segmentations, were calculated for each cardiac substructure. Over the study cohort the median variations for delineation uncertainty and motion were 2.20–11.09 Gy and 0.72–4.06 Gy, respectively. As relative values, variations in Dmean were between 18.7%–65.3% and 7.8%–32.5% for delineation uncertainty and motion, respectively. Doses vary depending on the individual planned dose distribution, not simply on segmentation differences, with larger dose variations to cardiac structures lying within areas of steep dose gradient. ConclusionRadiotherapy dose uncertainties from delineation variations and respiratory-related heart motion were quantified using a cardiac substructure automatic segmentation tool. This predicts the ‘dose range’ where doses to structures are most likely to fall, rather than single DVH curves. This enables consideration of these uncertainties in cardiotoxicity research and for future plan optimisation. The tool was designed for cardiac structures, but similar methods are potentially applicable to other OARs.

FEATURES OF THE AUTOMATIC MACHINE LEARNING TOOLS IN THE TASKS OF FRACTOGRAPHIC ANALYSIS

The article is devoted to the development of an information system for analyzing the mechanisms of metal destruction based on automatic machine learning (AutoML). The theoretical foundations of AutoML are investigated, a number of tasks and AutoML tools suitable for them are presented, and the characteristics of their application in the analysis of fractographic images in the framework of quantitative metallography are given. The principles of creating an information system for analyzing the mechanisms of metal destruction based on AutoML, using modern software development tools and tools, are described. Attention is paid to the collection and preparation of fractographic images for further analysis, as well as the development of machine learning algorithms for automatic analysis and classification of damage characteristics and causes of destruction of metal materials. Various AutoML tools are compared through the prism of the tasks they solve, and the choice of the tool is justified Liner.io as an optimal option in solving problems of automation of fractographic analysis. The practical results of using the most suitable automatic machine learning tool for the analysis of fractographic images Liner.ai are presented, and also an assessment of the prospects of using this tool to solve problems both in metallographic research and in other industries is given. The examples are provided with visualization of the learning results of the neural network model and comparative analysis of the marked-up fractographic image and the prediction result of the neural network model.

TCMeta: a multilingual dataset of COVID tweets for relation-level metaphor analysis

AbstractThe COVID pandemic spurred the use of various metaphors, some very common and universal, others depending on the language, country and culture. The use of metaphors by the general public, especially in languages other than English, has not yet been sufficiently investigated, one of the reasons being the lack of resources and automatic tools for metaphor analysis. To fill this gap, we introduce TCMeta, a dataset of tweets annotated for metaphors around COVID-19, in two languages from ten different countries. The dataset contains metaphoric phrases covering four source domains. Furthermore, we introduce a semi-automatic methodology to annotate more than 2000 tweets in English and Slovene. To the best of our knowledge, this is the first multilingual semi-automatically compiled dataset of user-generated texts aimed at investigating metaphorical language about the pandemic. It is also the first Slovene dataset of tweets annotated for metaphors.

Automating OneNote Desktop Application using Test Complete Tool

The subsequent research paper presents an outline regarding the Test Complete automation tool. The main purpose of this research paper is to understand automating desktop applications namely OneNote using Test Complete by writing code using JavaScript programming language.

The effect of real-time EF automatic tool on cardiac ultrasound performance among medical students.

Point-of-care ultrasound (POCUS) is a sensitive, safe, and efficient tool used in many clinical settings and is an essential part of medical education in the United States. Numerous studies present improved diagnostic performances and positive clinical outcomes among POCUS users. However, others stress the degree to which the modality is user-dependent, rendering high-quality POCUS training necessary in medical education. In this study, the authors aimed to investigate the potential of an artificial intelligence (AI) based quality indicator tool as a teaching device for cardiac POCUS performance. The authors integrated the quality indicator tool into the pre-clinical cardiac ultrasound course for 4th-year medical students and analyzed their performances. The analysis included 60 students who were assigned to one of two groups as follows: the intervention group using the AI-based quality indicator tool and the control group. Quality indicator users utilized the tool during both the course and the final test. At the end of the course, the authors tested the standard echocardiographic views, and an experienced clinician blindly graded the recorded clips. Results were analyzed and compared between the groups. The results showed an advantage in quality indictor users' median overall scores (P = 0.002) with a relative risk of 2.3 (95% CI: 1.10, 4.93, P = 0.03) for obtaining correct cardiac views. In addition, quality indicator users also had a statistically significant advantage in the overall image quality in various cardiac views. The AI-based quality indicator improved cardiac ultrasound performances among medical students who were trained with it compared to the control group, even in cardiac views in which the indicator was inactive. Performance scores, as well as image quality, were better in the AI-based group. Such tools can potentially enhance ultrasound training, warranting the expansion of the application to more views and prompting further studies on long-term learning effects.

Artificial intelligence in pharmacovigilance - Opportunities and challenges.

Pharmacovigilance (PV) is a data-driven process to identify medicine safety issues at the earliest by processing suspected adverse event (AE) reports and extraction of health data. The PV case processing cycle starts with data collection, data entry, initial checking completeness and validity, coding, medical assessment for causality, expectedness, severity, and seriousness, subsequently submitting report, quality checking followed by data storage and maintenance. This requires a workforce and technical expertise and therefore, is expensive and time-consuming. There has been exponential growth in the number of suspected AE reports in the PV database due to smart collection and reporting of individual case safety reports, widening the base by increased awareness and participation by health-care professionals and patients. Processing of the enormous volume and variety of data, making its sensible use and separating "needles from haystack," is a challenge for key stakeholders such as pharmaceutical firms, regulatory authorities, medical and PV experts, and National Pharmacovigilance Program managers. Artificial intelligence (AI) in health care has been very impressive in specialties that rely heavily on the interpretation of medical images. Similarly, there has been a growing interest to adopt AI tools to complement and automate the PV process. The advanced technology can certainly complement the routine, repetitive, manual task of case processing, and boost efficiency; however, its implementation across the PV lifecycle and practical impact raises several questions and challenges. Full automation of PV system is a double-edged sword and needs to consider two aspects - people and processes. The focus should be a collaborative approach of technical expertise (people) combined with intelligent technology (processes) to augment human talent that meets the objective of the PV system and benefit all stakeholders. AI technology should enhance human intelligence rather than substitute human experts. What is important is to emphasize and ensure that AI brings more benefits to PV rather than challenges. This review describes the benefits and the outstanding scientific, technological, and policy issues, and the maturity of AI tools for full automation in the context to the Indian health-care system.

Utilization of marketing automation tools for delivery of a faculty development curriculum

Background Physician clinical educators play important roles in teaching, providing feedback, and evaluating trainees, but they often have variable preparation and competing demands on their time that make universal participation in workshops, seminars, or short courses designed to foster these skillsets inefficient or impossible. Methods We designed and implemented a 52-week synchronous curriculum designed to address faculty opportunities to improve teaching skills, feedback for residents and medical students, and evaluation skills, which were delivered using marketing automation tools, including text messaging and email. We evaluated the programmatic impact and feasibility of using the implementation science framework. Results Over a 104-week evaluation period, there were at least 10,499 total content impressions and 4558 unique recipients, indicating the significant reach of this program to approximately 120 faculty members. Faculty engagement with continuing education materials remained stable or increased over the 2-year evaluation period, indicating that programs like ours can have sustainable impacts. Resident evaluations of faculty across the six key domains also improved after the implementation of the program. Conclusions Our experience with digital marketing tools reflects that they can be used to deliver impactful curricular content to faculty for continuing educational purposes and that faculty can use these resources in a sustainable way. However, because of the incomplete reach with any single communication, this type of content delivery is not appropriate for isolation as a material of critical importance. More research is needed to identify the best practices and additional education-related uses of this technology.

Evolution of Mechanization and Automation in Dairy Farming

The paper shows the development of technologies and tools of mechanization and automation in livestock farming for the period from 1930 to the present. (Research purpose) Given the cyclical nature of processes and the nuanced interpretations of scientific heritage, it is necessary to investigate the historical experience and contributions of scientists to the development and practical implementation of mechanization and automation tools in livestock farming. (Methods and materials) The paper examines the primary stages of mechanization and automation in cow milking, starting with the development of the first Soviet three-stroke milking machine DA-3. It considers the contributions made by specialized institutes of the All-Russian Academy of Agricultural Sciences and individual scientists in formulating testing methodologies for milking machines. Additionally, it explores the development and adoption of novel electromechanical tools for dairy farms. (Results and discussion) Considered in this paper are the pioneering efforts in the creation of the first milking parlors. These include the stationary herringbone type machines with a parallel-pass design, spearheaded by V.S. Krasnov, V.F. Korolev, V.P. Larin, V.P. Pokhvalensky, and A.N. Dormidontov, and the development of mobile carousel parlors of the «rotating herringbone» type, engineered by the design bureau of the Siberian Research Institute of Agriculture under the supervision of I.I. Teslenko, N.V. Krasnoshchekov, K.S. Shapovalov, N.K. Vazenmiller, A.V. Goldenfang. (Conclusions) The paper notes the significance of adopting a systematic approach to the developing of machinery for livestock farming mechanization. This approach was actively pursued by N.M. Morozov, the member of the Russian Academy of Sciences. A significant milestone in the advancement of the theory and practice of animal husbandry processes was the pioneering work in developing and implementing technology for flow-conveyor animal service, headed by Member of the Russian Academy of Sciences L.P. Kormanovsky. The paper reveals the necessity of conducting a historical practices from previous generations. This approach helps to develop modern models of machinery and equipment and avoid mere replication of foreign designs. It also facilitates independent research and development efforts, incorporating insights from the accumulated experience of utilizing equipment and technologies in Russia.

LAMPAT: Low-Rank Adaption for Multilingual Paraphrasing Using Adversarial Training

Paraphrases are texts that convey the same meaning while using different words or sentence structures. It can be used as an automatic data augmentation tool for many Natural Language Processing tasks, especially when dealing with low-resource languages, where data shortage is a significant problem. To generate a paraphrase in multilingual settings, previous studies have leveraged the knowledge from the machine translation field, i.e., forming a paraphrase through zero-shot machine translation in the same language. Despite good performance on human evaluation, those methods still require parallel translation datasets, thus making them inapplicable to languages that do not have parallel corpora. To mitigate that problem, we proposed the first unsupervised multilingual paraphrasing model, LAMPAT (Low-rank Adaptation for Multilingual Paraphrasing using Adversarial Training), by which monolingual dataset is sufficient enough to generate a human-like and diverse sentence. Throughout the experiments, we found out that our method not only works well for English but can generalize on unseen languages as well. Data and code are available at https://github.com/phkhanhtrinh23/LAMPAT.

Towards Automated RISC-V Microarchitecture Design with Reinforcement Learning

Microarchitecture determines the implementation of a microprocessor. Designing a microarchitecture to achieve better performance, power, and area (PPA) trade-off has been increasingly difficult. Previous data-driven methodologies hold inappropriate assumptions and lack more tightly coupling with expert knowledge. This paper proposes a novel reinforcement learning-based (RL) solution that addresses these limitations. With the integration of microarchitecture scaling graph, PPA preference space embedding, and proposed lightweight environment in RL, experiments using commercial electronic design automation (EDA) tools show that our method achieves an average PPA trade-off improvement of 16.03% than previous state-of-the-art approaches with 4.07× higher efficiency. The solution qualities outperform human implementations by at most 2.03× in the PPA trade-off.

Optimizing IT FinOps and Sustainability through Unsupervised Workload Characterization

The widespread adoption of public and hybrid clouds, along with elastic resources and various automation tools for dynamic deployment, has accelerated the rapid provisioning of compute resources as needed. Despite these advancements, numerous resources persist unnecessarily due to factors such as poor digital hygiene, risk aversion, or the absence of effective tools, resulting in substantial costs and energy consumption. Existing threshold-based techniques prove inadequate in effectively addressing this challenge. To address this issue, we propose an unsupervised machine learning framework to automatically identify resources that can be de-provisioned completely or summoned on a schedule. Application of this approach to enterprise data has yielded promising initial results, facilitating the segregation of productive workloads with recurring demands from non-productive ones.