Search

Due to remarkable development of advanced data, it has gotten drawn-out to recover applicable query items from the web crawler. The customary web search tool furnishes pertinent site pages however with the blend of unessential pages with less coverage rate and high slithering time. To overcome the issues, this paper proposes an Intelligent Multi-Agent design that utilizes Differential Evolution Algorithm (DEA) to extricate more pertinent site pages with high coverage rate and less slithering time. Three intelligent agents, specifically, Trajectory pattern mining agent, Query clustering agent and Web page classification agent are employed to track the relevancy of the pages, hidden web links and classify the searchable and non-searchable forms respectively. The extrication of relevant pages is optimized by the DEA algorithm. DEA calculation like selection, crossover and mutation are utilized for optimizing the extraction process. In any case, in Genetic Algorithm (GA) determination of chromosome depends on the wellness esteem, where the low quality chromosomes don't get by in the future. The Differential Evolutionary activity help the helpless chromosome to get by in the opposition and it will create the upgraded results. The learnt by experience of the intelligent agents help to work on the presentation of the framework. The exploration result shows that the proposed architecture gives the high precision and recall ratesover the current web crawlers. The creeping time for recovering the website pages is likewise diminished utilizing the proposed DEAIMA architecture. • Due to remarkable development of data, it has gotten drawn-out to recover applicable query items from the web crawler. • The customary web search tool furnishes pertinent site pages with less coverage rate and high slithering time. • The exploration result shows that the proposed engineering gives the preferred accuracy and review over the current web crawlers. [ABSTRACT FROM AUTHOR]

• A tailor-made steel reinforcing method was found effective in shear strength improvement. • Fibre adoption enhanced crack-opening resistance, ultimate shear strength and shear deformation. • It is essential to provide sufficient web reinforcement above and below web openings. • The simplified analytical model yielded safe predictions. Application of fibre-reinforced concrete or novel web reinforcement detailing could be an effective way to enhance structural behaviour of continuous deep beams with web openings. However, there are very few experimental works studying the synergistic effects of these two methods. Therefore, an experimental programme is introduced in this paper to study synergistic effects of shear reinforcement and hybrid fibres on shear strength of nine double-span deep beams with unsymmetrical web openings (denoted as pierced span). The test specimens were designed to consist of a pierced beam span with a web opening and an adjacent solid beam span. The spans with openings were designed with different shear reinforcement configurations. It was experimentally shown that provision of sufficient web steels above and below web openings has the most important and beneficial effect on ultimate shear strength of the pierced spans. Besides, a tailored steel reinforcing method was also proposed that is capable of improving strut capacity near the web opening. In addition, adoption of hybrid fibre reinforcement led to enhanced crack-opening resistance, ultimate shear strength and shear deformation. Based on the research outcomes, it was recommended to adopt the proposed detailing method or fibre reinforcement dosage for shear strength enhancement of continuous deep beams with web openings. Furthermore, an analytical model was adopted to the shear strength of continuous deep beams accounting for web openings, steel stirrups, and hybrid fibres, which yielded safe predictions. [ABSTRACT FROM AUTHOR]

• A total of sixteen deep beam specimens were tested by considering the beam depth (i.e., 300 mm ∼ 1200 mm) and the shear-span ratio (i.e., 0.8, 1.3 and 1.8) as test variables. • Twelve beams were reinforced with BFRP bars while the other four beams were reinforced with steel bars to act as controls. • The corresponding size effect of BFRP-RC deep beams was quantitatively studied, and the applicability of current design codes and scientific calculation methods were discussed. • The size effect of FRP-RC deep beams is not reasonably considered in current design codes, while, Jin's theoretical model can scientifically reflect the effect of beam depth and shear-span ratio on the nominal shear strength. Basalt Fiber Reinforced Polymer (BFRP) bars appear effective in reducing corrosion-related damage and are gradually used as substitute for Steel bars in engineering practices. However, most of available efforts are focused on the size effect of BFRP-reinforced concrete (BFRP-RC) deep beams without web reinforcement, while less efforts have been studied on BFRP-RC deep beams with web reinforcement. Moreover, the quantitative influence of the shear-span ratio on the shear capacity of BFRP-RC deep beams is always in dispute. The purpose of the work is to investigate the quantitative influence of beam depth and shear-span ratio on the shear behavior of BFRP-RC deep beams with web reinforcement. A total of sixteen specimens were tested by considering the beam depth (i.e., 300 mm ∼ 1200 mm) and the shear-span ratio (i.e., 0.8, 1.3 and 1.8) as test variables. Twelve beams were reinforced with BFRP bars while the other four beams were reinforced with steel bars to act as controls. The corresponding size effect of BFRP-RC deep beams with web reinforcement was quantitatively studied, and the applicability of current design codes and scientific calculation methods were discussed. The results indicate that: 1) the increase of shear-span ratio reduces the nominal ultimate shear strength but has an ignorable influence on the size effect; 2) the nominal ultimate shear strength of BFRP-RC deep beams decline by about 46% as the beam depth increases from 300 mm to 1200 mm (both horizontal and vertical web reinforcement ratio are 0.51%); 3) the size effect of FRP-RC deep beams is not reasonably considered in current design codes, while, Jin's theoretical model can scientifically reflect the effect of beam depth and shear-span ratio on the nominal shear strength. [ABSTRACT FROM AUTHOR]

Tor (The Onion Router) is one of the most famous anonymous networks in the Deep Web. It provides a wide range of legal and illegal hidden services to the user. Recognizing such illicit domains is a challenging task for Cyber Security and Law Enforcement Agencies. However, doing it manually, based only on the officers' experience, is slow and prone to errors. Therefore, in this paper, we propose an automatic method based on perceptual hashing to recognize services on the Tor network only by means of their snapshots. Firstly, we introduce and make publicly available DUSI-2K (Darknet Usage Service Images-2K), an image dataset which contains snapshots from active Tor service domains. We also present a new, efficient, robust and discriminative image hashing method, named F-DNS, built by incorporating the Dominant Neighborhood Structure (DNS) map and the Global Neighborhood Structure (GNS) texture energy map extracted from the discrete cosine transform of the image. In order to evaluate the efficiency of our hashing method, we carry out intra- and inter- tests using images from some state-of-the-art datasets subject to various content-preserving operations. The high correlation coefficient values that our method obtains, indicates that F-DNS performs better than other state-of-the-art methods, especially in the case of rotation. Additionally, we assess F-DNS for recognizing the category of Tor domains based on their snapshots using the DUSI-2K dataset. We compare its performance with three typical image classification methods, i.e. Bag of Visual Words (BoVW) and features obtained from ResNet50 and Inception-ResNet-v2. F-DNS outperforms all of them, with an accuracy of 98.75%, against 31.39%, 82.70% and 85.19%, respectively. Fine-tuning ResNet50 and Inception-Resnet-v2 for DUSI-2K does not improve the result, attaining 37.12% and 79.15%, respectively. [ABSTRACT FROM AUTHOR]

Discovering new anticancer drugs has been widely concerned and remains an open challenge. Target- and phenotypic-based experimental screening represent two mainstream anticancer drug discovery methods, which suffer from time-consuming, labor-intensive, and high experimental costs. In this study, we collected 485,900 compounds involving in 3,919,974 bioactivity records against 426 anticancer targets and 346 cancer cell lines from academic literature, as well as 60 tumor cell lines from NCI-60 panel. A total of 832 classification models (426 target- and 406 cell-based predictive models) were then constructed to predict the inhibitory activity of compounds against targets and tumor cell lines using FP-GNN deep learning method. Compared to the classical machine learning and deep learning methods, the FP-GNN models achieve considerable overall predictive performance, with the highest AUC values of 0.91, 0.88, 0.91 for the test sets of targets, academia-sourced and NCI-60 cancer cell lines, respectively. A user-friendly webserver called DeepCancerMap and its local version were developed based on these high-quality models, enabling users to perform anticancer drug discovery-related tasks including large-scale virtual screening, profiling prediction of anticancer agents, target fishing, and drug repositioning. We anticipate this platform to accelerate the discovery of anticancer drugs in the field. DeepCancerMap is freely available at https://deepcancermap.idruglab.cn. A precise and user-friendly deep learning-based web server called DeepCancer was built for the discovery of anticancer drug. [Display omitted] • A comprehensive anticancer dataset was constructed including 25,631,674 records. • Advanced DL algorithm FP-GNN was employed and outperformed classical methods. • Time-split external test set has proved the satisfactory reliability and precision. • A platform with functions of Selectivity Predict, VS and Similarity Search was built. [ABSTRACT FROM AUTHOR]

• Predicting ultimate shear strength of reinforced concrete deep beams is considered complex for their non-linear behavior. • Traditional prediction methods incapacitate to solve this issue precisely, especially if there are openings. • Artificial intelligence algorithms provide the facilities to establish robust predicting model for these structural members. • Support vector regression, multilayer perceptron regressor, gradient boosting regressor, and Ensemble algorithms were tested. • Stacking Ensemble model was the most fitting model among the examined models. The prediction of nominal shear capacity (V n) of reinforced concrete (RC) deep beams with web openings is quite complex due to their highly nonlinear behavior. In this paper, Artificial intelligence (AI) methods have been applied to overcome that complexity by providing a reliable predicting model for RC deep beams having web openings. Support vector regression (SVR), multi-layer perceptron regressor (MLP), gradient boosting regressor (GBR), and Ensemble algorithms have been examined on the ten most influential input parameters. Input data includes both experimental results of 179 specimens with various opening shapes and simulated results of 5032 specimens. Among the explored AI algorithms, it was found that Stacking Ensemble showed the best results with a determination coefficient (R2) of 0.998. Finally, it can be concluded that AI algorithms are considered a sufficient and powerful tool for predicting the nominal shear strength (V n) of RC deep beams with web openings. [ABSTRACT FROM AUTHOR]

• Investigating the behavior of RC deep beams with web openings based on the results of numerical simulations using the concrete damage plasticity (CDP) model. • Six influential parameters were tested on the ultimate strength, load versus mid-span deflection response, and cracks trajectory. • The behavior of RC deep beams with web openings mainly depends on the openings' size and location. • Openings located through the shear zone significantly reduced the ultimate load, especially openings located on the loading path and near the bearing plates. This study investigates the behavior of RC deep beams with web openings based on the results of numerical simulations using the Abaqus/standard software. The numerical results of the validated specimens showed good agreement with the experimental ones. Then, the proposed concrete damage plasticity (CDP) model was used to examine the effect of various influential parameters, such as the shear span-to-height ratio (a/H), opening size, opening location, concrete compressive strength (f c '), main reinforcement ratio (ρ s), and web reinforcement ratio (ρ web). It was found that the presence of openings impairs the bearing capacity of RC deep beams, especially openings with sizes of 0.3 and 0.4 of the overall height of the beam. Moreover, openings located through the shear zone significantly reduce the ultimate load, especially on the loading path and near the bearing plates. This is due to the high concentration of shear cracks at the corners of the openings through the loading path line. The behavior of RC deep beams with web openings mainly depends on the openings' size and location. In addition, decreasing the shear span-to-height ratio increases the ultimate load. Meanwhile, increasing the concrete compressive strength (f c ') increases the ultimate load. Similarly, increasing the main reinforcement ratio leads to a higher ultimate load due to adequate cracking control. The same finding can be revealed for the influence of the web reinforcement ratio on the bearing capacity of RC deep beams with web openings. Increasing the main reinforcement ratio from 0.45% to 0.57% and increasing the web reinforcement ratio from 0.29% to 0.33% can enhance the bearing capacity of RC deep beams by 11.36% and 3.26%, respectively. Finally, the numerical investigation reveals that the proposed simulated model can be used to effectively investigate the behavior of RC deep beams with openings overcoming their complexity. [ABSTRACT FROM AUTHOR]

Data integration systems on the Deep Web offer a transparent means to query multiple data sources at once. Result merging– the generation of an overall ranked list of results from different sources in response to a query– is a key component of a data integration system. In this work we present a result merging model, called Active Relevance Weight Estimation model. Different from the existing techniques for result merging, we estimate the relevance of a data source in answering a query at query time. The relevances for a set of data sources are expressed with a (normalized) weighting scheme: the larger the weight for a data source the more relevant the source is in answering a query. We estimate the weights of a data source in each subset of the data sources involved in a training query. Because an online query may not exactly match any training query, we devise methods to obtain a subset of training queries that are related to the online query. We estimate the relevance weights of the online query from the weights of this subset of training queries. Our experiments show that our method outperforms the leading merging algorithms with comparable response time. [ABSTRACT FROM AUTHOR]

Background: The popularization of anonymous markets such as Silk Road is challenging current drug policy and may provide a new context for old issues, such as adulteration of heroin with fentanyl derivatives. The aims of this paper are to report the presence of ocfentanil, a novel, potent, non-controlled fentanyl analog, in samples sold as heroin in the hidden web, and to summarize the effects reported by users.Methods: In 2015, four samples allegedly bought as heroin in cryptomarkets of the hidden web were sent to Energy Control for analysis. Energy Control is a Spanish harm reduction NGO that offers anonymous drug checking with the purpose of adapting counselling to the specific substances present in the drug and monitor the drug market. Identification was performed by GC/MS and LC/MS/MS. We contacted the submitters of the samples and performed an Internet search to retrieve additional information.Results: One sample contained ocfentanil, caffeine and heroin. Three samples contained the aforementioned substances plus paracetamol. Two out of the four contacted users reported distinct short acting, opioid-like effects. No fora discussion could be found about the effects of ocfentanil, neither web pages nor individuals advertising the substance.Conclusion: We report the presence of a new substance detected in the hidden web as an adulterant of heroin, ocfentanil. It has short acting opioid-like effects, roughly the same potency as fentanyl, and can be injected, snorted or smoked. Severe side effects have been associated with its use, including one death. No discussion about this substance could be found in the Internet, which suggests this substance has not been sold as such. Available data about purities of drugs purchased in cryptomarkets suggest that adulteration is not a severe problem and this agrees with users' perceptions. However, this study suggests that adulteration is a real threat not only at the street level, but also for users that buy substances in cryptomarkets, and suggest the need for harm reduction initiatives in this setting. [ABSTRACT FROM AUTHOR]

Blueberries have become an important fruit crop around the world. Most blueberries are hand-harvested to maintain high quality before the packing process for fresh market distribution. However, in the last 10 years increasing acreage has been machine harvested to reduce labor costs although machine harvesting causes bruising in more than 20% of blueberries. Non-bruised blueberries remain firmer and can be cold stored longer than bruised blueberries, while the bruised fruit cannot be sold, leading to a substantial economic loss. Current packing line sorting technology can sort soft berries but is unable to detect and sort out bruised fruit. Blueberry bruise assessment is necessary for providing the means to improve the harvester efficiency and fruit sorting process in the packing house. The goal of this study was to develop a web browser-based application (Web App) that users can access easily and determine the blueberry bruises accurately and quickly. We annotated 1725 blueberries to train MobileNet SSD and MobileNet-UNet, two deep learning models, generating a berry detection model, a berry segmentation model, and a bruise segmentation model. The average precision (AP) for the berry detection model was 0.977. The mean intersection over union (IoU) for berry segmentation and bruise segmentation was 0.979 and 0.773, respectively. Bruises were assessed for 56 images with 50 sliced blueberries in each image using the trained models that implemented in the Web App we developed. The bruise ratio data obtained from three different hardware devices were compared with the results that were manually annotated. Linear regression analyses showed a high correlation between the results from the deep learning models and the ground truth. The bruise ratio prediction using three hardware devices achieved an accuracy of 78.7%, 79.0%, and 78.9%, respectively, indicating that the model performance was satisfactory regardless of the hardware configuration. The average processing time for each image under three hardware configurations revealed that our Web App was superior to the manual method. The Web App reduced the time needed for assessing and tabulating bruise damage for 50 fruit samples from about 15 min needed for manual visual method to less than 30 s with comparable accuracy. This Web App is a robust tool for blueberry breeders, farmers and packers for evaluating berry bruises. • We developed a convenient and robust Web App to quantify blueberry bruise damage. • The Web App reduced manual evaluation time from 15 min to 30 s. • The bruise ratio prediction achieved over 78% accuracy on three hardware devices. • The Web App is a reliable tool for blueberry breeders, packers, and shippers. [ABSTRACT FROM AUTHOR]

Nematodes are ubiquitous in soil representing different trophic levels and occupying a central position in the detritus food web. Nematodes have been widely used for biomonitoring of soil quality and health. However, their application in bio-indicator is limited due to the taxonomic identification is laborious, and largely relying on morphological characters which require substantial prior nematology experiences. In present study, we developed the web application NemaRec using the I-Nema dataset. NemaRec adopts the deep convolutional neural networks approach for nematode image identification, incorporate I-Nema dataset in a user friendly interface, and further extends it to the calculation of feeding types, c-p values, MI and PPI indices for environment evaluation. Within 19 studied genera, the model can properly identify up to 60% genera, 76% of c-p values and 76% feeding types in specimen-based dataset (images taken from microscopy), and 94%–97% in augmented dataset (image treated with random flip and Gaussian noise). The pipeline was further incorporated into a user-friendly web application NemaRec. NemaRec offers high-throughput online identification while simultaneously collect images uploaded by users for future modeling training. To our knowledge this is the first soil nematode image identification system using deep learning approach. NemaRec is available http://168.138.167.251:8080/). • CNN-based deep learning approach was used in image-based nematode identification. • Nematode species identification and ecological indices calculation were incorporated into a single pipeline. • User uploaded images will be used as training database for consistently model improvment. [ABSTRACT FROM AUTHOR]

Abstract: Deep web or hidden web refers to the hidden part of the Web (usually residing in structured databases) that remains unavailable for standard Web crawlers. Obtaining content of the deep web is challenging and has been acknowledged as a significant gap in the coverage of search engines. The paper proposes a novel deep web crawling framework based on reinforcement learning, in which the crawler is regarded as an agent and deep web database as the environment. The agent perceives its current state and selects an action (query) to submit to the environment (the deep web database) according to Q-value. While the existing methods rely on an assumption that all deep web databases possess full-text search interfaces and solely utilize the statistics (TF or DF) of acquired data records to generate the next query, the reinforcement learning framework not only enables crawlers to learn a promising crawling strategy from its own experience, but also allows for utilizing diverse features of query keywords. Experimental results show that the method outperforms the state of art methods in terms of crawling capability and relaxes the assumption of full-text search implied by existing methods. [Copyright &y& Elsevier]

Abstract: In order to query and retrieve the rich and useful information hidden in the Deep Web efficiently, extensive research on domain-specific Deep Web Data Integration Systems (DWDIS) has been carried out in recent years. In DWDIS, large-scale automatic integration of query interfaces of domain-specific Web Databases (WDBs) remains a serious challenge due to the scale of the problem and the great diversity of the WDBs' query interfaces. To address this challenge, in this paper, we first give a definition of the constraint matrix which can accurately describe three types of constraints (hierarchical constraints, group constraints and precedence constraints) and the strengths of attributes of a query interface, and then prove that the schema tree of the query interface corresponds to only one constraint matrix, and vice versa. Furthermore, we transform the problem of integrating domain-specific query interfaces into a problem of integrating the constraint matrices and set up a multi-objective optimization problem model. To effectively solve the optimization model, some strategies to extend and merge the constraint matrices are designed. A method for automatically detecting and filtering abnormal data (noises) in the query interfaces is also proposed. More importantly, a novel and efficient algorithm applicable to large-scale automatic integration of domain-specific query interfaces is developed. Finally, the proposed algorithm is evaluated by experiments on the real query interface data set. Our theoretical analysis and experimental results show that the proposed algorithm outperforms existing state-of-the-art integration algorithms of domain-specific query interfaces. [Copyright &y& Elsevier]

Abstract: Integrating Deep Web data sources require highly accurate matches between the attributes of the query interfaces. While interface matching has received more attentions recently, current approaches are still not sufficiently perfect: (a) they all suppose that every interface attribute type has been predefined; (b) most of them combine multiple matchers taking into account different aspects of information about schema, but the weights of individual matchers are usually manually generated, and there may exist a high degree of inconsistency among different matchers; and (c) most of them only consider one-to-one matches of attributes over the interfaces and lack effective mathematical modeling. Therefore, a novel deep web query interface matching approach called ETTA-IM is proposed based on evidence theory and task assignment. Varied kinds of type recognizers are defined to identify the types of interface attributes which are used to divide the schema space into several schema subspaces. A modified D-S evidence theory is used to automatically combine multiple matchers and to solve high conflicts among different matchers. One-to-one match decision is converted to extended task assignment problem and some tree structure heuristic rules are used to perform one-to-many match decision. Experiments show that ETTA-IM approach yields high precision and recall measures. [Copyright &y& Elsevier]

Abstract: Many deep web data sources are ranked data sources, i.e., they rank the matched documents and return at most the top k number of results even though there are more than k documents matching the query. While estimating the size of such ranked deep web data source, it is well known that there is a ranking bias—the traditional methods tend to underestimate the size when queries overflow (match more documents than the return limit). Numerous estimation methods have been proposed to overcome the ranking bias, such as by avoiding overflowing queries during the sampling process, or by adjusting the initial estimation using a fixed function. We observe that the overflow rate has a direct impact on the accuracy of the estimation. Under certain conditions, the actual size is close to the estimation obtained by unranked model multiplied by the overflow rate. Based on this result, this paper proposes a method that allows overflowing queries in the sampling process. [Copyright &y& Elsevier]

Abstract: Matching query interfaces is a crucial step in data integration across multiple Web databases. The problem is closely related to schema matching that typically exploits different features of schemas. Relying on a particular feature of schemas is not sufficient. We propose an evidential approach to combining multiple matchers using Dempster–Shafer theory of evidence. First, our approach views the match results of an individual matcher as a source of evidence that provides a level of confidence on the validity of each candidate attribute correspondence. Second, it combines multiple sources of evidence to get a combined mass function that represents the overall level of confidence, taking into account the match results of different matchers. Our combination mechanism does not require the use of weighing parameters, hence no setting and tuning of them is needed. Third, it selects the top k attribute correspondences of each source attribute from the target schema based on the combined mass function. Finally it uses some heuristics to resolve any conflicts between the attribute correspondences of different source attributes. Our experimental results show that our approach is highly accurate and effective. [Copyright &y& Elsevier]

• A new Flickr images dataset is collected to learn what is of interest to users. • An improved triplet deep ranking model is proposed to generate video summaries. • A novel efficient and accurate entropy-based video segmentation method is proposed. Video summarization facilitates rapid browsing and efficient video indexing in many video browsing website applications, such as sport video highlights, dynamic video cover. In these applications, it is most important to generate user video summaries that capture interesting video content that users prefer. While many existing methods generate video summaries based on low-level features, this paper first proposes to mine large-scale Flickr images and find "interest" and "non-interest" images from Flickr for the same query to learn what is of interest to users. Unlike existing pairwise ranking-based methods for video summarization, we then propose an improved triplet deep ranking model that is easier to converge to learn the relationship between "interest" and "non-interest" Flickr images, and exploit what visual content of the original video is indeed preferred by users. In the training process, triplets (interest image p + , interest image p ′ + , non-interest image p - ) are selected as input to train a model with three parallel deep convolutional networks. In the video summarization process, an efficient entropy-based video segmentation method is proposed for dividing the original video into segments and the visual interest scores of the segments are estimated using the trained ranking network for summarization (SumNet). Then, an optimal subset of the segments is selected to create a summary capturing interesting visual content. We evaluate and compare our method with several state-of-the-art methods, experimental results show that our method achieves an improvement over the best baseline method by 9.6 % in terms of mean Average Precision (mAP) accuracy. [ABSTRACT FROM AUTHOR]

A growing number of linked data sources are published on the Web. They form a single huge data space referred to as the Web of data. These data sources contain both the data and the schema describing them, but the data is not constrained by this schema. Indeed, two instances of the same class may be described by different properties. This flexibility for describing the data eases their evolution, but it comes at the cost of losing the description of the data, which can be useful in many contexts. The different structures of a class represent its versions. These versions provide useful information on property co-occurrence for a class, but their discovery can be very costly, and even impossible because the data sources are remote. Furthermore, they may have some access limitations, either on the query execution time, or on the number of queries, or on the size of the results. In this paper, we present S c h e m a D e c r y p t + + , a novel approach for the parallel discovery of a versioned schema for a remote data source. Our approach discovers the versions on-line, without uploading or browsing the data source. Broadly speaking, S c h e m a D e c r y p t + + allows to discover co-occurrences between properties from any set of properties: (i) specified by the user; (ii) describing the instances of a class or (iii) specified in the schema. S c h e m a D e c r y p t + + relies on our previous approach for schema discovery, S c h e m a D e c r y p t ; in the present work we introduce a new strategy of parallelization of class version exploration, based on the discovery of a set of occurrence rules between the properties of the class. This strategy enables to overcome the source querying restrictions, the combinatorial explosion of the candidate versions and it improves the performances. We present some experimental evaluations on DBpedia to demonstrate the effectiveness of our approach. • A novel approach for parallel discovery of schema versions in a remote source. • Overcoming source querying restrictions and combinatorial explosion of versions. • SchemaDecrypt++, the implementation of our approach, is publicly available. • A powerful tool for discovering the hidden structure of Web data. • Experiments on real remote sources with access restrictions. [ABSTRACT FROM AUTHOR]

Data preparation, whether for populating enterprise data warehouses or as a precursor to more exploratory analyses, is recognised as being laborious, and as a result is a barrier to cost-effective data analysis. Several steps that recur within data preparation pipelines are amenable to automation, but it seems important that automated decisions can be refined in the light of user feedback on data products. There has been significant work on how individual data preparation steps can be refined in the light of feedback. This paper goes further, by proposing an approach in which feedback on the correctness of values in a data product can be used to revise the results of diverse data preparation components. The approach uses statistical techniques, both in determining which actions should be applied to refine the data preparation process and to identify the values on which it would be most useful to obtain further feedback. The approach has been implemented to refine the results of matching, mapping and data repair components in the VADA data preparation system, and is evaluated using deep web and open government data sets from the real estate domain. The experiments have shown how the approach enables feedback to be assimilated effectively for use with individual data preparation components, and furthermore that synergies result from applying the feedback to several data preparation components. • Feedback on results can inform changes to a complete data preparation pipeline. • The pipeline can include matching, mapping generation and data repair. • A statistical approach can establish which actions to take based on feedback. • The same statistical approach can be used to target results for feedback. [ABSTRACT FROM AUTHOR]

• A deep learning-based hybrid training strategy is designed to perform the task of Ischemic Stroke lesion segmentation. • The architecture combines U-Net and Fractal-Net into one training scheme and employs the concept of a cascaded architecture. • The accuracy of the model is enhanced by removing the redundant parts from the segmenter's input. • A voting mechanism has been made use of to further improve the segmentation accuracy. • The experimental results demonstrate the superiority of the presented method on the ISLES benchmark dataset. Background and objectives: Acute stroke lesion segmentation is of paramount importance as it can aid medical personnel to render a quicker diagnosis and administer consequent treatment. Automation of this task is technically exacting due to the variegated appearance of lesions and their dynamic development, medical discrepancies, unavailability of datasets, and the requirement of several MRI modalities for imaging. In this paper, we propose a composite deep learning model primarily based on the self-similar fractal networks and the U-Net model for performing acute stroke diagnosis tasks automatically to assist as well as expedite the decision-making process of medical practitioners. Methods: We put forth a new deep learning architecture, the Classifier-Segmenter network (CSNet), involving a hybrid training strategy with a self-similar (fractal) U-Net model, explicitly designed to perform the task of segmentation. In fractal networks, the underlying design strategy is based on the repetitive generation of self-similar fractals in place of residual connections. The U-Net model exploits both spatial as well as semantic information along with parameter sharing for a faster and efficient training process. In this new architecture, we exploit the benefits of both by combining them into one hybrid training scheme and developing the concept of a cascaded architecture, which further enhances the model's accuracy by removing redundant parts from the Segmenter's input. Lastly, a voting mechanism has been employed to further enhance the overall segmentation accuracy. Results: The performance of the proposed architecture has been scrutinized against the existing state-of-the-art deep learning architectures applied to various biomedical image processing tasks by submission on the publicly accessible web platform provided by the MICCAI Ischemic Stroke Lesion Segmentation (ISLES) challenge. The experimental results demonstrate the superiority of the proposed method when compared to similar submitted strategies, both qualitatively and quantitatively in terms of some of the well known evaluation metrics, such as Accuracy, Dice-Coefficient, Recall, and Precision. Conclusions: We believe that our method may find use as a handy tool for doctors to identify the location and extent of irreversibly damaged brain tissue, which is said to be a critical part of the decision-making process in case of an acute stroke. [ABSTRACT FROM AUTHOR]

Background: The United States is facing a "triple wave" epidemic fueled by novel synthetic opioids. Cryptomarkets, anonymous marketplaces located on the deep web, play an increasingly important role in the distribution of illicit substances. This article presents the data collected and processed by the eDarkTrends platform concerning the availability trends of novel synthetic opioids listed on one cryptomarket.Methods: Listings from the DreamMarket cryptomarket "Opioids" and "Research Chemicals" sections were collected between March 2018 and January 2019. Collected data were processed using eDarkTrends Named Entity Recognition algorithm to identify opioid drugs, and to analyze their availability trends in terms of frequency of listings, available average weights, average prices, and geographic indicators of shipment origin and destination information.Results: 95,011 opioid-related listings were collected through 26 crawling sessions. 33 novel synthetic opioids were identified in 3.3 % of the collected listings. 44.7 % of these listings advertised fentanyl (pharmaceutical and non-pharmaceutical) or fentanyl analogs for an average of 2.8 kgs per crawl. "Synthetic heroin" accounted for 33.2 % of novel synthetic opioid listings for an average 1.1 kgs per crawl with 97.7 % of listings advertised as shipped from Canada. Other novel synthetic opioids (e.g., U-47,700, AP-237) represented 22 % of these listings for an average of 6.1 kgs per crawl with 97.2 % of listings advertised as shipped from China.Conclusions: Our data indicate consistent availability of a wide variety of novel synthetic opioids both in retail and wholesale-level amounts. Identification of new substances highlights the value of cryptomarket data for early warning systems of emerging substance use trends. [ABSTRACT FROM AUTHOR]

Ulnar-mammary syndrome (UMS) is a rare syndromic limb malformation caused by heterozygous mutations in TBX3. The name highlights the two commonly involved body parts i.e. mammary gland and ulnar ray of the upper limbs, although a more extensive systemic involvement is also known to occur. Here, we report the surprising finding of a patient with a de novo mutation in TBX3 whose clinical presentation is limited to dorsalization of both little fingers and slightly deep 4th web spaces. We review the literature to confirm that this should be considered as a forme fruste phenotype of UMS. [ABSTRACT FROM AUTHOR]