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Freedom of design and the cost-effective production of structural parts have led to much research interest in Wire Arc Additive Manufacturing (WAAM). Nevertheless, WAAM is subject to design constraints and fundamentally differs from other additive manufacturing processes. Consequently, design guidelines and supporting design evaluation tools adapted to WAAM are needed. One geometric approach to design evaluation is the use of a three-dimensional medial axis transformation (3D-MAT) to derive local geometry indicators. Previous works define the thickness and radius indicators. In this work, the angle between opposing faces and a mass gradient indicator are added. To apply the literature design rules regarding wall thickness, clearance, bead angle, and edge radius to specific geometry regions, features are classified by the indicators. Following a literature suggestion, wall and corner regions are differentiated by the angle indicator. An angle of 65 ° is identified as an effective separation limit. Additionally, the analogy of Heuvers' spheres to the MAT helps estimate a limit of k H − 1 k H + 1 for the mass gradient ( k H : Heuvers' factor). Finally, tests on example parts demonstrate the method's effectiveness in verifying compliance to the specified rules. With a numerical complexity of O (n 2) , this method is more efficient than finite element analyses, providing early feedback in the design process. [ABSTRACT FROM AUTHOR]

A frequently encountered security issue in writing tests is nonauthentic text submission: Test takers submit texts that are not their own but rather are copies of texts prepared by someone else. In this report, we propose AutoESD, a human‐in‐the‐loop and automated system to detect nonauthentic texts for a large‐scale writing tests, and report its performance on an operational data set. The AutoESD system utilizes multiple automated text similarity measures to identify suspect texts and provides an analytics‐enhanced web application to help human experts review the identified texts. To evaluate the performance of AutoESD, we obtained its similarity measures on TOEFL iBT® test writing responses collected from multiple remote administrations and examined their distributions. The results were highly encouraging in that the distributional characteristics of AutoESD similarity measures were effective in identifying suspect texts and the measures could be computed quickly without affecting the operational score turnaround timeline. Report Number: RR‐24‐08 [ABSTRACT FROM AUTHOR]

In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates the potential for automated evaluation of C-ITS message content against relevant standards. It leverages our novel tools, Karlos and C-ITS SIM, to facilitate this process. Through detailed laboratory testing and data analysis, the study showcases the effectiveness of these automated solutions in enhancing the accuracy and reliability of message content validation. [ABSTRACT FROM AUTHOR]

In the context of formative assessment in classrooms, the incorporation of automated evaluation (AE) systems and teachers' interactions with them hold significant importance. This study aimed to investigate the cognitive processes of pre-service teachers as they engaged with an AE system. We developed an unsupervised learning-based AE system, the Scoring Assistant using Artificial Intelligence (SAAI). SAAI calculates scores without relying on predefined labels and generates scientific keywords from student responses to provide constructive feedback. We collected a substantial number of constructed responses from students, and four pre-service teachers evaluated these responses initially without any external assistance and then re-evaluated them using SAAI scores as a reference point. Employing a mixed-methods approach, this study demonstrated a strong level of consistency between human raters and SAAI scores. Pre-service teachers also reflectively recalibrated their assessments and adjusted their rubrics to identify students' learning more accurately. This study highlights the practical application of AE in real classroom settings and demonstrates how AE can enhance efficiency and accuracy in K-12 science assessments, thus supporting teachers. [ABSTRACT FROM AUTHOR]

Freedom of design and the cost-effective production of structural parts have led to much research interest in Wire Arc Additive Manufacturing (WAAM). Nevertheless, WAAM is subject to design constraints and fundamentally differs from other additive manufacturing processes. Consequently, design guidelines and supporting design evaluation tools adapted to WAAM are needed. One geometric approach to design evaluation is the use of a three-dimensional medial axis transformation (3D-MAT) to derive local geometry indicators. Previous works define the thickness and radius indicators. In this work, the angle between opposing faces and a mass gradient indicator are added. To apply the literature design rules regarding wall thickness, clearance, bead angle, and edge radius to specific geometry regions, features are classified by the indicators. Following a literature suggestion, wall and corner regions are differentiated by the angle indicator. An angle of 65° is identified as an effective separation limit. Additionally, the analogy of Heuvers’ spheres to the MAT helps estimate a limit of kH−1kH+1 for the mass gradient (kH: Heuvers’ factor). Finally, tests on example parts demonstrate the method’s effectiveness in verifying compliance to the specified rules. With a numerical complexity of O(n2), this method is more efficient than finite element analyses, providing early feedback in the design process.

This research paper addresses the challenge of manually grading subjective papers. It examines existing methods and suggests a new approach using computers and advanced tools to enhance the speed and accuracy of grading. Through experimentation, the study assesses the performance of this novel method. The results contribute to automated grading systems for subjective papers. Additionally, this paper proposes a novel approach utilizing natural language processing (NLP) techniques, including WordNet, cosine similarity, and TF-IDF, to automatically assess descriptive answers. [ABSTRACT FROM AUTHOR]

The objective of the research is to enhance the functionality of the Evaluator software agent within the Distributed eLearning Center (DeLC), a platform providing extensive support for e-learning activities. This paper is focused on presenting the latest step in the evolution of one software agent (Evaluator Agent), which helps teachers during evaluating students' exams, being responsible for evaluation of short free text answers, provided by the students. To accomplish its job, the agent has its own dictionary, created from a set of words and expressions, provided by test creators for each question of that type, and from the words of each student's answer, marked as successful by the teacher. Now this agent's effectiveness could be increased by extending its dictionary with much larger knowledge base or so-called Large Language Models, made accessible through third party AI, as ChatGPT. Experiments and results are provided to compare the change in agent's behavior after the integration with the AI system. The results presented in this work are from first experiments and are deliberately limited in number to enable manual verification. [ABSTRACT FROM AUTHOR]

The current education landscape is marked by the growing integration of artificial intelligence technologies, which seek to improve interaction and efficiency in educational environments. However, many of these tools operate on separate platforms, complicating their use and reducing their potential effectiveness. This study introduces InteractiveClass, an innovative tool that fully integrates with Microsoft Teams, facilitating automated assessment and student interaction without requiring multiple interfaces. InteractiveClass encourages active student participation through real-time questions, quizzes, discussion forums, and other activities. In addition, it uses AI capabilities to offer immediate and personalized feedback and perform automatic evaluations. This integration improves usability and promotes student engagement in hybrid or fully online environments. Study results reveal significant improvements in assessment efficiency and student satisfaction. Students who used the tool showed a 30% increase in class participation and improved their grades by 25% compared to those who did not use it. Additionally, the tool demonstrated high consistency in assessments, with a precision of 95% compared to manual assessments. These findings underline the potential of InteractiveClass to transform education through AI technology, offering a practical and effective solution to the challenges of modern education.

In the field of Cooperative Intelligent Transport Systems (C-ITSs), the traditional approach to testing often emphasizes technological parameters, leaving the validation of message content insufficiently addressed. Since the content of these messages is crucial for the correct functioning of C-ITS, this article demonstrates the potential for automated evaluation of C-ITS message content against relevant standards. It leverages our novel tools, Karlos and C-ITS SIM, to facilitate this process. Through detailed laboratory testing and data analysis, the study showcases the effectiveness of these automated solutions in enhancing the accuracy and reliability of message content validation.

In the realm of material characterization, the mechanical properties of polymer foams play a pivotal role in shaping their applicability across diverse industries. In this pursuit, we present a novel approach to standardize and automate the assessment of key mechanical parameters for Expanded Polypropylene (EPP) foams using a self‐developed Python script, made freely available for the scientific community. We precisely determine the compression modulus, plateau onset, and onset of densification strain for EPP foams across various densities. The script's effectiveness is demonstrated through comparisons with manual evaluations and established standards, highlighting its superiority, consistency, and suitability for a wide range of materials and conditions. Moreover, the script enables the analysis of energy absorption, shedding light on the intricate relationship between density and energy dissipation. Finally, our approach was extended to other foams to provide insight on their mechanical properties. The automated methodology ensures accuracy, reproducibility, and efficiency, thereby advancing the understanding of foam materials and facilitating informed design decisions. This research contributes to laying a foundation for the standardized assessment of foam mechanical properties, which could potentially facilitate their effective use in other applications. [ABSTRACT FROM AUTHOR]

IntelliHire introduces an advanced automated system to revolutionize candidate evaluation, addressing the limitations of traditional techniques such as biased interviews and manual resume screening. By leveraging cutting-edge technology, IntelliHire provides an impartial assessment of candidates' knowledge, positive mindset, resume content, facial expressions, ethical benchmarks, and language proficiency. This comprehensive method employs diverse components, including contextual resume parsing, facial expressions and personality evaluation, and robust assessments using deep learning models and Machine Learning algorithms. The benefits include reduced bias, enhanced efficiency, cost savings, and refined candidate selection. This research contributes to the evolution of human resources and recruitment strategies, with potential for further development and enhancement of IntelliHire's capabilities. [ABSTRACT FROM AUTHOR]

Architectural spatial layout configurations are the result of a complex design process aiming at managing the most advantageous location of all spaces with certain objectives and constraints. In the presence of broadening the scope of understanding spatial phenomenon, its role, and its composition in scope of various programmatic, ideological, formal, and engineering aspects, embedded in a complex theoretical background. The aspect of evaluation is the least apparent and there are significant limitations to what can be achieved there. This paper is aiming at evaluating functionality (such as quality of space, use, Access, adjacency, non-adjacency, proximity etc.), build quality (such as performance, efficiency, cost, etc.), impact (such as user performance, internal quality, urban, social, and cultural integration, etc.), indication of the quality of the architectural space design and the quality of the layout configuration. To achieve this research goal, we devised all possible spatial relation, affecting these qualities, as inputs for a given architectural space using descriptive rule blocks. We define this fuzzy logic system for residential spaces that's been blended into a layout to evaluate the layout configuration. We define all input variables, output variables, and fuzzy sets, and present space-space relations using membership functions. The paper proposes a framework based on fuzzy logic approach for automated evaluation of architectural spatial layout configurations.

The globally accepted evaluation method for facial palsy is the House–Brackmann facial grading system; however, it does not reflect minute changes. Several methods have been attempted, but there is no universally accepted evaluation method that is non-time-consuming and quantitative. Recently, Emotrics, a two-dimensional analysis that incorporates machine-learning techniques, has been used in various clinical fields. However, its reliability and validity have not yet been determined. Therefore, this study aimed to examine and establish the reliability and validity of Emotrics. All patients had previously received speech therapy for facial palsy at our hospital between January and November 2022. In speech therapy at our hospital, Emotrics was routinely used to measure the state of the patient's facial palsy. A frame was created to standardize and overcome the limitation of the two-dimensional analysis. Interrater, intrarater, and intrasubject reliability were evaluated with intraclass correlation coefficients (ICC) by measuring the indicators that reflect eye and mouth functions. Validity was evaluated using Spearman's correlation for each Emotrics parameter and the House–Brackmann facial grading system. A total of 23 patients were included in this study. For all parameters, there was significant interrater and intrarater reliability (ICC, 0.61 to 0.99). Intrasubject reliability showed significant reliability in most parameters (ICC, 0.68 to 0.88). Validity showed a significant correlation in two parameters (p-value < 0.001). This single-center study suggests that Emotrics could be a quantitative and efficient facial-palsy evaluation method with good reliability. Therefore, Emotrics is expected to play a key role in assessing facial palsy and in monitoring treatment effects more accurately and precisely. [ABSTRACT FROM AUTHOR]

The design of efficient representations is well established as a fruitful way to explore and analyze complex or large data. In these representations, data are encoded with various visual attributes depending on the needs of the representation itself. To make coherent design choices about visual attributes, the visual search field proposes guidelines based on the human brain’s perception of features. However, information visualization representations frequently need to depict more data than the amount these guidelines have been validated on. Since, the information visualization community has extended these guidelines to a wider parameter space.This paper contributes to this theme by extending visual search theories to an information visualization context. We consider a visual search task where subjects are asked to find an unknown outlier in a grid of randomly laid out distractors. Stimuli are defined by color and shape features for the purpose of visually encoding categorical data. The experimental protocol is made of a parameters space reduction step (i.e., sub-sampling) based on a machine learning model, and a user evaluation to validate hypotheses and measure capacity limits. The results show that the major difficulty factor is the number of visual attributes that are used to encode the outlier. When redundantly encoded, the display heterogeneity has no effect on the task. When encoded with one attribute, the difficulty depends on that attribute heterogeneity until its capacity limit (7 for color, 5 for shape) is reached. Finally, when encoded with two attributes simultaneously, performances drop drastically even with minor heterogeneity.

Copyright of Journal of Engineering Sciences is the property of Faculty of Engineering - Assiut University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The use of online tools is a common practice for evaluating the accessibility of a website, identifying problems, and providing useful feedback on how to fix detected issues. For ease of accessibility validation, many tools have been developed and implemented successfully over the years. Yet, the results of these tools show differences in terms of coverage, correctness, and reporting-related issues. In this study, we compared online accessibility evaluation tools to understand to what extent they differ in detecting accessibility problems in websites. A total of 41 government websites of different countries were tested for violations of accessibility guidelines using six evaluation tools. We observed that each tool generated different evaluation data for the same websites. As some of the tools are complementary to each other, meaning the highest coverage and completeness can be possible with the right combination of evaluation tools. Therefore, we suggest different tools should be utilized to provide consistency and obtain reliable data from online evaluation tools, thereby improving tool effectiveness.

Electrospun nonwovens are widely applied in biomedicine and various other fields. For control of the manufacturing process and quality assurance Scanning electron microscopy (SEM) imaging is one standard practice. In this study, statistical datasets of 60 SEM images of three nonwoven samples were evaluated using Gaussian fit to obtain numerical results of their fiber diameter distributions. The question of how much effort is required for acceptable imaging and processing is being discussed. As determined here, for reliable statistics, a minimum surface area of the nonwoven has to be evaluated. The fiber diameter should be in a range of approximately 2 - 3% of the edge length of the square equivalent of the evaluated image area, using sufficiently magnified SEM images, in which the fiber diameter is imaged over at least 30 pixels.

Introduction. Being an essential part of teaching and learning, feedback in close connection with evaluation is the focus of many researchers. Their interest lies mainly in automated systems, learners’ and teachers’ perceptions of writing feedback and feedback on feedback, new forms of feedback and their efficacy for motivation and writing performance. The review aims to identify the prevailing directions of research in the field. Methods. The review is based on 194 documents extracted from the Scopus database. The ultimate results of the search for “writing feedback” were limited to a field filter (social sciences, arts & humanities), a language filter (English), a document type (article, review, book chapter, conference paper) as well to manual screening in accordance with the inclusion criteria and relevance to the theme. Results and Discussion. Seven directions of research were identified: automated and non-automated evaluation; feedback on writing: general issues; automated feedback; peer review and teacher feedback on writing; perceptions and emotions relating to writing feedback; feedback on scholarly writing; evaluation and improvement in Chinese calligraphy. The reviewed documents proved the prominence of the topic and greater interest in new computer-mediated forms of feedback on writing. Conclusion. The results of the review may serve as a guidance for researchers at large and potential JLE authors focused on teaching and learning writing. The limitations of the review are linked to the scope and methods applied.

INTRODUCTION: Tuberculosis (TB) is a chronic, progressive infection that often has a latent period after the initial infection period. Early awareness from those period to have better prevention steps becomes an indispensable part for patients who want to lengthen their lives. Hence, applying cutting-edge technologies to support the medical business domain plays a key role in improving speed and accuracy in methods of diagnosis. Deep Neural Network-based technique (DNN) is one of such methods which offers positive results by leveraging the advantages of analyzing deeply the data, especially image data format via tons of deep layers of the neural networks. Our study was wrapped up by objectively assessing the performance of modern Deep Neural Network approaches and suggesting a model offering good results in terms of the selected metrics as defined later. In order to achieve optimized results, the chosen model must adapt well to the datasets but require less hardware and computational resources. OBJECTIVES: Our objective is to pick up and train a Deep Neural Network architecture which is highly trusted and flexibly fitted and applied to various datasets with minimum configurations. This will be used to produce good predictions based on the input data which are Chest X-ray images retrieved from the published datasets. METHODS: We have been approaching this problem by using the recognized datasets which have already been published before, then resizing them to the consistent input data for training purposes. In terms of Deep Neural Networks, we picked up VGG16 as the baseline network architecture, then use other ones which are state-of-the-art networks for comparison purposes. After all, we recommend the neural network architecture offering the most positive results based on accuracy and recall measurements. So that, this network architecture will show flexibility when fitting into diverse datasets representing different areas in the world that suffered from Tuberculosis before. RESULTS: After conducting the experiments, we observed that the Mobilenet model produced great results based on the predefined metrics for most of the proposed datasets. It shows the versatility which is applicable to all CXR datasets, especially for the Tuberculosis ones. CONCLUSION: Tuberculosis is still one of the most dangerous illnesses in the world that needs vital methods to prevent and detect soon so that patients are able to keep their lives longer. After this research, we are constantly improving the current accuracy of the models and applying the current results of this research for later problems such as detecting the Tuberculosis areas in real-time and supporting doctors to make decisions based on the current status of patients.

Peripheral facial palsy (PFP) is an alteration in the functioning of some facial muscles following an injury to the facial nerve. This pathology has functional and aesthetic consequences that impact the quality of life of patients. Their care is essential and begins with an accurate assessment. Currently, scoring scales such as Sunnybrook Facial Grading System (SFGS) or House-Brackmann Grading System (HBGS) are used, based on clinician judgment. However, these evaluation methods can be subject to a certain degree of subjectivity. Recent advances in technology have led to increased interest in artificial intelligence (AI). AI could make it possible to develop an objective, automated and quantitative assessment tool, applicable in a clinical setting. This approach aims to reduce the subjectivity induced by current evaluation. We conducted a retrospective study of 38 patients with moderate-severe to total PFPs. The objective of the study is to identify the benefits and limitations of Emotrics+, a facial metrics tool based on AI, in order to determine whether the tool is applicable in the clinic. This protocol took place at two different time periods (14days and 1year post-PFP) using the SFGS scale and the Emotrics+ software. We evaluated the inter-rater and intra-rater reliability in order to determine the reliability and the reproducibility of the two tools. Then, we established a correlation between the two tools to determine if Emotrics+ followed SFGS's trend. Our currents results do not support the immediate applicability of this software. However, with appropriates adjustments, Emotrics+ has a certain potential., (Copyright © 2024 The Author(s). Published by Elsevier Masson SAS.. All rights reserved.)

In Centrifugal Partition Chromatography, two immiscible liquids are used as mobile and stationary phases. During operation, bleeding of the stationary phase cannot be eliminated completely. For optimal separation performance, however, it is crucial to maintain sufficient amounts of stationary phase in the system, which is quantitatively measured by the retention value. With an online measurement of that retention value, it is possible to make predictions about the separation performance of the system. Therefore, an image processing algorithm was developed in this study, allowing quick and effortless online evaluation of retention by image analysis. Finally, the results were compared with proven analysis methods to evaluate the measurements’ validity. With the help of the new algorithm, it was possible to improve the number of pictures analyzed per time and the precision compared to the previously used technique.

There are several studies dealing with the phenotypic variance of the vertebral number in the spinal column of rabbits. According to the literature the number of thoracic and lumbar vertebrae varies between 11-13 and 6-8, respectively. The length of the m. longissimus dorsi (MLD) - a valuable meat part of rabbits - is determined by the length of the vertebral column therefore the number of vertebrae may have economic importance in breeding. The aim of this study was to create an automatic counter using computed tomography (CT) images. In the first step, a skeleton binary mask was created using the radiodensity range between 120 and 3071 HU, then the lumbar and thoracic regions were processed by two different methods. The lumbar part was evaluated based on the frequency of the bone voxels along the axial plane. The number of thoracic vertebrae was determined from the number of ribs. The left and right ribs were processed separately. The developed method was tested on CT examination of 40 Hycole rabbits compared to manual evaluation. The results of the automatic algorithm had few errors: in one case in the lumbar region (2.5%) and in 3 cases in the thoracic region (5%). The automated evaluation process takes a few seconds per individual and then the program visualizes the results on a graph. The incorrectly evaluated rabbits are recognizable on graphs and they can be easily corrected with a minimal time investment. [ABSTRACT FROM AUTHOR]

Purpose: Use of automated perfusion software has gained importance for imaging of stroke patients for mechanical thrombectomy (MT). We aim to compare four perfusion software packages: 1) with respect to their association with 3‑month functional outcome after successful reperfusion with MT in comparison to visual Cerebral Blood Volume - Alberta Stroke Program Early CT Score (CBV-ASPECTS) and collateral scoring and 2) with respect to their agreement in estimation of core and penumbra volume. Methods: This retrospective, multicenter cohort study (2015–2019) analyzed data from 8 centers. We included patients who were functionally independent before and underwent successful MT of the middle cerebral artery. Primary outcome measurements were the relationship of core and penumbra volume calculated by each software, qualitative assessment of collaterals and CBV-APECTS with 3‑month functional outcome and disability (modified Rankin scale >2). Quantitative differences between perfusion software measurements were also assessed. Results: A total of 215 patients (57% women, median age 77 years) from 8 centers fulfilled the inclusion criteria. Multivariable analyses showed a significant association of RAPID core (common odds ratio, cOR 1.02; p = 0.015), CBV-ASPECTS (cOR 0.78; p = 0.007) and collaterals (cOR 0.78; p = 0.001) with 3‑month functional outcome (shift analysis), while RAPID core (OR 1.02; p = 0.018), CBV-ASPECTS (OR 0.77; p = 0.024), collaterals (OR 0.78; p = 0.007) and OLEA core (OR 1.02; p = 0.029) were significantly associated with 3‑month functional disability. Mean differences on core estimates between VEOcore and RAPID were 13.4 ml, between syngo.via and RAPID 30.0 ml and between OLEA and RAPID −3.2 ml. Conclusion: Collateral scoring, CBV-ASPECTS and RAPID were independently associated with functional outcome at 90 days. Core and Penumbra estimates using automated software packages varied significantly and should therefore be used with caution. [ABSTRACT FROM AUTHOR]

Purpose: Size and density measurements of the ablation zones on contrast-enhanced computed tomography (CT) after radiofrequency ablation (RFA) of hepatic metastases of primary breast or colorectal cancer were acquired over time. Materials and Methods: Twenty-five liver metastases [colorectal cancer (CRC): n = 16; mean size: 19.6 ± 8.5 mm; breast cancer (BC): n = 9; 27.9 ± 13.6 mm] in 15 patients (CRC: n = 11; age: 65.4 ± 6.5 years; BC: n = 4; 62.0 ± 13.8 years) treated by RFA were included in this retrospective study. All patients had undergone postinterventional serial follow-up using multidetector CT (MDCT) (1 day/1/4/7/10/14/18/23/>24 months) without evidence of local tumor recurrence during the follow-up. The ablation zones were evaluated using a commercial software tool (Syngo CT Oncology) in order to determine volumetric, RECIST-, WHO- and density changes over the course of time. Results were compared by applying repeated measures analysis of variance and displayed graphically. Results: The RF ablation zones demonstrated significant shrinkage (P ≤ 0.05) over the first 7 months (volume, RECIST, WHO) of the follow-up. Follow-up after 7 months did not show any significant changes in size (P > 0.05) (mean volume (ml): 55.2/34.7/26.3/16.5/12.7/10.0/8.9/8.1/7.5; RECIST (mm): 58.7/49.3/43.7/37.8/34.2/31.3/29.1/27.3/24.8; WHO (mm2): 2458.3/1769.3/1341.8/1027.1/870.1/720.2/649.0/570.4/511.3). Mean density values decreased significantly between 1 day (58.9 HU) and 1 month (47.5 HU) after the procedure. Conclusion: Typical changes in size and density values of RF-induced, recurrence-free ablation zones after RFA of hepatic metastases of colorectal and breast cancer were acquired, showing a significant decrease in density of the ablation zone within the first month and significant shrinkage within the first 7 months after RFA.

In the earliest product design stages, it can be challenging to decide which product concepts make the cut and are worth working out in more detail. Especially with the advent of technologies like concept generators that can generate a vast number of candidates, often a lot of assumptions are made when choosing the most promising candidates. While performance attributes can be simulated or otherwise estimated under different loads, it can be difficult to balance many desirable and undesirable performance attribute values. Here, active preference learning offers a solution methodology that leverages easy-to-give designer feedback to rank a large set of product concepts. Experimental evaluation of pertinent active preference learning algorithms demonstrates that accurate concept rankings can be learned given only minimal user effort, even when there is noise in the designer's feedback. Moreover, this paper proposes and evaluates the use of a graph kernel to learn geometric preferences affecting the ranking, in addition to simulated performance attributes. [ABSTRACT FROM AUTHOR]

Grammar-checking is a concept of interest in many natural language environments. It typically involves the evaluation of the grammaticality of transmitted messages, be it written or verbal. Automated grammaticality evaluation is the examination of natural language text for grammatical accuracy using computer software. The current study examined the different approaches adopted in computational linguistics for evaluating the grammaticality of texts. The categories of approaches examined are those within the shallow parsing induced grammar approach. The study tried to show the trend so far and the possible direction for the future. [ABSTRACT FROM AUTHOR]

Copyright of Der Pneumologe is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Experimental Technology & Management is the property of Experimental Technology & Management Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)