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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]

• We conducted probability of detection and false positive analyses to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. • We compared three image-processing-based segmentation and three sequential-signal-based deep learning algorithms for automatic data analysis. • We demonstrated the potential of using advanced deep-learning techniques to solve complex non-destructive examination data interpretation tasks. Nowadays, the reliability of data analysis and decision-making based on deep learning (DL) remains a primary concern in promoting DL technology for industrial non-destructive testing and evaluation (NDT&E). This study focuses on the quantitative assessment of the reliability of various automated data analysis techniques in NDT&E. To achieve this, optical pulsed thermography was employed to inspect three non-planar carbon-fiber-reinforced polymer (CFRP) samples, each containing embedded Teflon to simulate debonding defects. After applying thermographic signal reconstruction and first-order derivation processing to the raw thermal data, automated analysis was performed using three image-processing-based segmentation methods and three sequential-signal-based DL algorithms. Additionally, an experienced inspector manually analyzed the data for comparison purposes. Subsequently, the probability of detection and false positive analyses were conducted to quantitatively evaluate and compare the reliability of the automated and human-based evaluations. The comparison results demonstrated that optimal DL classification and advanced image-processing-based segmentation techniques could achieve performance levels close to that of human inspectors in defect detection, even for challenging non-planar CFRP samples. [ABSTRACT FROM AUTHOR]

The compaction quality of a highway subgrade is influenced by the compaction parameters and soil properties. Current practice lacks reliable models to assess this multivariate situation. This paper describes a monitoring and evaluation method based on intelligent compaction (IC) and artificial neural networks (ANNs) to assess the compaction quality of a highway subgrade. Field compaction tests were conducted at various roller speeds and vibratory modes. The compactness was predicted using a particle-swarm-optimization optimized backpropagation neural network (PSO-BP-NN) model. The model was integrated into the IC system to automatically evaluate compaction quality. It also demonstrated the advantage of high prediction accuracy with multiple input variables of operation parameters and soil gradation. By efficiently controlling subgrade compaction, the model can be employed for engineering practice and facilitate IC applications. • An advanced ANN model is proposed to evaluate the compaction quality. • Four compaction modes are identified to characterize different compaction parameters. • The particle gradation is an influential factor and is integrated into the correlation model. • The proposed evaluation model demonstrates a good generalization for different scenarios. [ABSTRACT FROM AUTHOR]

5-Bromo-2-deoxyuridine (BrdU) staining is often used to evaluate cortical layer formation during mammalian brain development. This method allows the quantification of newly generated cells and therefore the study of the effects of xenobiotics or genetic factors on proliferation, cell death and migration behavior in a quantitative manner. However, these endpoints are generally assessed by time-consuming manual evaluation. In the present work, we introduce a novel procedure to identify and quantify BrdU+ cells within cortical layers, using the commercially available vHCS-Scan V.6.3.1 software to identify BrdU+ cell coordinates and the novel program 'BrdeLuxe' to define cortical layers and quantitatively assign BrdU+ cells to them. This procedure is compared to BrdU+ cell counting with the freeware 'ImageJ' in respect to the manual evaluation, all by two different researchers. BrdeLuxe shows high accuracy and precision for the determination of total number of BrdU+ cells compared to the manual counting, while ImageJ does not reach such results. Accuracy and precision are also higher for employing the BrdeLuxe program to evaluate the percentage of BrdU+ cells per brain layer compared to ImageJ. In terms of running time, BrdeLuxe is the fastest method of the three making it more suitable for multiple brain slices analyses. [ABSTRACT FROM AUTHOR]

Abstract: The Web has been growing in size and complexity and is used for the most diverse activities in our everyday life, becoming almost indispensable. Besides, Web applications are becoming more popular, and consequently used by a wide range of people. Thus, it is important to evaluate the accessibility of those applications to guarantee that everyone can access the information. Currently, there are some tools to evaluate the accessibility of classical Web pages, which use WCAG guidelines. However, Web applications impose different challenges, so it is mandatory to find a way to automatically obtain the dynamically introduced HTML code, in order to evaluate what users really experience. This paper details a new process of accessibility evaluation of Web applications, which evaluates the content by triggering possible events that partially change the Web page. It also presents an experimental study with several Web applications, demonstrating the potential of this framework in evaluating Web applications. [Copyright &y& Elsevier]

Abstract: Although breast density is considered a strong predictor of breast cancer risk, its quantitative assessment is difficult. The aim of this study is to demonstrate that breast density assessment with a fully automated software is feasible and correlates with the semi-automated evaluation and the quantitative BI-RADS standards. A data set of 160 mammograms was evaluated by three blinded radiologists. Intra-observer (reader 1: k =0.71; reader 2: k =0.76; reader 3: k =0.62) and inter-observer (reader 1 vs reader 2: k =0.72; reader 2 vs reader 3: k =0.80; reader 3 vs reader 1: k =0.72) variability for the semi-automated software were good on a four-grade scale (D1/D2/D3/D4) and correlated with BI-RADS evaluation made by other two blinded radiologists (r =0.65, p <0.01). Inter-observer (reader 1 vs reader 2: k =0.85; reader 2 vs reader 3: k =0.91; reader 3 vs reader 1: k =0.85) variability for the semi-automated software was very good on a two-grade scale (D1–D2/D3–D4). The use of the fully automated software eliminated intra- and inter-observer differences, correlated with BI-RADS categories (r =0.62, p <0.01) and can replace the semi-automated one (Bland–Altman statistics). Our study demonstrates that automated estimation of breast density is feasible and eliminates subjectivity. Furthermore both the semi-automated and the fully automated density estimation are more accurate than BI-RADS quantitative evaluation and could also be used in the daily clinical practice. [Copyright &y& Elsevier]

Abstract: Within the field of incident management split second decisions have to be made, usually on the basis of incomplete and partially incorrect information. As a result of these conditions, errors occur in such decision processes. In order to avoid repetition of such errors, historic cases, disaster plans, and training logs need to be thoroughly analysed. This paper presents a formal approach for such an analysis that pays special attention to spatial and temporal aspects, to information exchange, and to organisational structure. The formal nature of the approach enables automation of analysis, which is illustrated by case studies of two disasters. [Copyright &y& Elsevier]

• A support to reduce effort of pedagogues in large-scale to evaluate novelty in mass examination. • To identify features for evaluating novelty in mass examination of design education. • To design an expert system to evaluate novelty from image-based solutions. • To evaluate performance of the model to optimize trust in it. An inherent criterion of evaluation in Design education is novelty. Novelty is a measure of newness in solutions which is evaluated based on relative comparison with its frame of reference. Evaluating novelty is subjective and generally depends on expert's referential metrics based on their knowledge and persuasion. Pedagogues compare and contrast solution for cohort of students in mass examination aspiring admission to Design schools. Large number of students participate in mass examinations, and in situations like this, examiners are confronted with multiple challenges in subjective evaluation such as- 1) Errors encountered in evaluation due to stipulated timeline, 2) Errors encountered due to prolonged working hours, 3) Errors encountered due to stress in performing repeated task on a large-scale. Pedagogues remain ever-inquisitive and vigilant about the evaluation process being consistent and accurate due to monotony of repeated task. To mitigate these challenges, a computational model is proposed for automating evaluation of novelty in image-based solutions. This model is developed by mixed-method research, where features for evaluating novelty are investigated by conducting a survey study. Further, these features were utilized to evaluate novelty and generate score for image-based solutions using Computer Vision (CV) and Deep Learning (DL) techniques. The performance metric of the model when measured reveals a negligible difference between scores of experts and scores of proposed model. These comparative analysis of the proposed model with human experts' confirm the competence of the devised model and would go a long way to establish trust of pedagogues by ensuring reduced error and stress during the evaluation process. [ABSTRACT FROM AUTHOR]

• A novel multi-class factorization machine approach with deep neural network is developed. • It performs post-disaster building damage assessment in an automatic and data-driven manner. • A total of 39,352 buildings in 2015 Napa earthquake are taken as a case study for demonstration. • It improves the classification performance over many other popular machine learning methods. • The well-trained model can significantly reduce the burden in earthquake field investigations. This paper develops a hybrid neural network architecture named multi-class factorization machine with deep neural network (multi-FMDNN) to fuse multi-source information for the automatic post-earthquake building damage evaluation. The novel algorithm is a combination of the factorization machine (FM) and the deep neural network (DNN), which adopts the one-vs-all strategy to fuse results from multiple base classifiers. 39,352 buildings affected by the 2015 Nepal earthquake are taken as a case study to validate the effectiveness of the proposed multi-FMDNN. Experimental results confirm that the proposed model outperforms over many other popular machine learning methods due to the powerful feature learning ability, ultimately reaching an overall accuracy, macro F1-score, and weighted F1-score in the value of 0.703, 0.737, and 0.702, respectively. Features associated with building structural characteristics are found to contribute more to classifying damage grades precisely. Besides, data preprocessing for data cleaning, encoding, and transformation is a necessary step to bring additional performance enhancement. For significance in the knowledge aspect, a novel multi-FMDNN algorithm is developed, which is superior in extracting both low- and high-order feature representation automatically from large volumes of destroyed buildings-related data and learning the optimal feature interactions simultaneously to pursue more accurate classification. For significance in the application aspect, the predicted results provide deep insights into a better understanding of the building vulnerability in seismic areas and inform data-driven decisions in disaster relief efforts. A promising future scope is to make full use of the available pre-event data along with some post-event data, which is possible to return fairly promising predictions and reduce the burden in earthquake field investigations for rapid responses. In future work, advanced techniques associated with data augment, hyperparameter optimization, and others will be implemented to constantly improve the overall accuracy and generalizability of the prediction model. [ABSTRACT FROM AUTHOR]

Abstract: The CYber DEfenSe Trainer (CYDEST) is a virtualized training platform for network defense and computer forensics. It uses virtual machines to provide tactical level exercises for personnel such as network administrators, first responders, and digital forensics investigators. CYDEST incorporates a number of features to reduce instructor workload and to improve training realism, including: (1) automated assessment of trainee performance, (2) automated attacks that respond dynamically to the student''s actions, (3) a full fidelity training environment, (4) an unrestricted user interface incorporating real tools, and (5) continuous, remote accessibility via the Web. [Copyright &y& Elsevier]