Query: Neural Attentional Rating Regression with Review-level Explanations

Relevant passages:
A Convolutional Neural Network for Modelling Sentences <=SEP=> The ability to accurately represent sentences is central to language understanding. We describe a convolutional architecture dubbed the Dynamic Convolutional Neural Network (DCNN) that we adopt for the semantic modelling of sentences. The network uses Dynamic k-Max Pooling, a global pooling operation over linear sequences. The network handles input sentences of varying length and induces a feature graph over the sentence that is capable of explicitly capturing short and long-range relations. The network does not rely on a parse tree and is easily applicable to any language. We test the DCNN in four experiments: small scale binary and multi-class sentiment prediction, six-way question classification and Twitter sentiment prediction by distant supervision. The network achieves excellent performance in the first three tasks and a greater than 25% error reduction in the last task with respect to the strongest baseline.

Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering <=SEP=> Building a successful recommender system depends on understanding both the dimensions of people’s preferences as well as their dynamics. In certain domains, such as fashion, modeling such preferences can be incredibly difficult, due to the need to simultaneously model the visual appearance of products as well as their evolution over time. The subtle semantics and non-linear dynamics of fashion evolution raise unique challenges especially considering the sparsity and large scale of the underlying datasets. In this paper we build novel models for the One-Class Collaborative Filtering setting, where our goal is to estimate users’ fashion-aware personalized ranking functions based on their past feedback. To uncover the complex and evolving visual factors that people consider when evaluating products, our method combines high-level visual features extracted from a deep convolutional neural network, users’ past feedback, as well as evolving trends within the community. Experimentally we evaluate our method on two large real-world datasets from Amazon.com, where we show it to outperform stateof-the-art personalized ranking measures, and also use it to visualize the high-level fashion trends across the 11-year span of our dataset.

Joint Deep Modeling of Users and Items Using Reviews for Recommendation <=SEP=> A large amount of information exists in reviews written by users. This source of information has been ignored by most of the current recommender systems while it can potentially alleviate the sparsity problem and improve the quality of recommendations. In this paper, we present a deep model to learn item properties and user behaviors jointly from review text. The proposed model, named Deep Cooperative Neural Networks (DeepCoNN), consists of two parallel neural networks coupled in the last layers. One of the networks focuses on learning user behaviors exploiting reviews written by the user, and the other one learns item properties from the reviews written for the item. A shared layer is introduced on the top to couple these two networks together. The shared layer enables latent factors learned for users and items to interact with each other in a manner similar to factorization machine techniques. Experimental results demonstrate that DeepCoNN significantly outperforms all baseline recommender systems on a variety of datasets.

Convolutional Neural Networks for Sentence Classification <=SEP=> We report on a series of experiments with convolutional neural networks (CNN) trained on top of pre-trained word vectors for sentence-level classification tasks. We show that a simple CNN with little hyperparameter tuning and static vectors achieves excellent results on multiple benchmarks. Learning task-specific vectors through fine-tuning offers further gains in performance. We additionally propose a simple modification to the architecture to allow for the use of both task-specific and static vectors. The CNN models discussed herein improve upon the state of the art on 4 out of 7 tasks, which include sentiment analysis and question classification.

Ratings meet reviews, a combined approach to recommend <=SEP=> Most existing recommender systems focus on modeling the ratings while ignoring the abundant information embedded in the review text. In this paper, we propose a unified model that combines content-based filtering with collaborative filtering, harnessing the information of both ratings and reviews. We apply topic modeling techniques on the review text and align the topics with rating dimensions to improve prediction accuracy. With the information embedded in the review text, we can alleviate the cold-start problem. Furthermore, our model is able to learn latent topics that are interpretable. With these interpretable topics, we can explore the prior knowledge on items or users and recommend completely "cold"' items. Empirical study on 27 classes of real-life datasets show that our proposed model lead to significant improvement compared with strong baseline methods, especially for datasets which are extremely sparse where rating-only methods cannot make accurate predictions.

Nonlinear model predictive control for aerial manipulation <=SEP=> This paper presents a nonlinear model predictive controller to follow desired 3D trajectories with the end effector of an unmanned aerial manipulator (i.e., a multirotor with a serial arm attached). To the knowledge of the authors, this is the first time that such controller runs online and on board a limited computational unit to drive a kinematically augmented aerial vehicle. Besides the trajectory following target, we explore the possibility of accomplishing other tasks during flight by taking advantage of the system redundancy. We define several tasks designed for aerial manipulators and show in simulation case studies how they can be achieved by either a weighting strategy, within a main optimization process, or a hierarchical approach consisting on nested optimizations. Moreover, experiments are presented to demonstrate the performance of such controller in a real robot.

The generic model query language GMQL - Conceptual specification, implementation, and runtime evaluation <=SEP=> The generic model query language GMQL is designed to query collections of conceptual models created in arbitrary graph-based modelling languages. Querying conceptual models means searching for particular model subgraphs that comply with a predefined pattern query. Such a query specifies the structural and semantic properties of the model language from the literature and formally specify the language’s syntax and semantics. We conduct an analysis of GMQL's theoretical and practical runtime performance concluding that it returns query results within satisfactory time. Given its generic nature, GMQL contributes to a broad range of different model analysis scenarios ranging from business process compliance management to model translation and business process weakness detection. As GMQL returns results with acceptable runtime performance, it can be used to query large collections of hundreds or thousands of conceptual models containing not only process models, but also data models or organizational charts. In this paper, we furthermore evaluate GMQL against the backdrop of existing query approaches thereby carving out its advantages and limitations as well as pointing toward future research. & 2014 Elsevier Ltd. All rights reserved.

Proving Theorems about LISP Functions <=SEP=> We d e s c r i b e some s i m p l e h e u r i s t i c s comb in ing e v a l u a t i o n and ma themat i ca l i n d u c t i o n wh ich we have implemented in a program t h a t a u t o m a t i c a l l y p roves a wide v a r i e t y o f theorems about r e c u r s i v e LISP f u n c t i o n s . The method the program uses t o gene ra te i n d u c t i o n f o r m u l a s i s d e s c r i b e d a t l e n g t h . The theorems proved by t h e p r o ­ gram i n c l u d e t h a t REVERSE i s i t s own i n v e r s e and t h a t a p a r t i c u l a r SORT program is c o r r e c t . Append ix B c o n t a i n s a l i s t o f the theorems p roved by t h e p r o g r a m .

Ferrite-magnet spoke-type IPMSM with W-shaped magnet placement <=SEP=> Rare earth magnets that have a large energy product are generally used in high-efficiency interior permanent-magnet synchronous motors (IPMSMs). However, efficient rare-earth-free motors are needed because of problems such as the high prices of and export restrictions on rare earth materials. This paper introduces a ferrite-magnet spoke-type IPMSM that employs a W-shaped magnet placement in order to achieve high efficiency. The effectiveness of the W-shaped magnet placement is verified from the results of two-dimensional finite element analysis and experiments on a prototype.

Deep Joint Rain Detection and Removal from a Single Image <=SEP=> In this paper, we address a rain removal problem from a single image, even in the presence of heavy rain and rain streak accumulation. Our core ideas lie in our new rain image model and new deep learning architecture. We add a binary map that provides rain streak locations to an existing model, which comprises a rain streak layer and a background layer. We create a model consisting of a component representing rain streak accumulation (where individual streaks cannot be seen, and thus visually similar to mist or fog), and another component representing various shapes and directions of overlapping rain streaks, which usually happen in heavy rain. Based on the model, we develop a multi-task deep learning architecture that learns the binary rain streak map, the appearance of rain streaks, and the clean background, which is our ultimate output. The additional binary map is critically beneficial, since its loss function can provide additional strong information to the network. To handle rain streak accumulation (again, a phenomenon visually similar to mist or fog) and various shapes and directions of overlapping rain streaks, we propose a recurrent rain detection and removal network that removes rain streaks and clears up the rain accumulation iteratively and progressively. In each recurrence of our method, a new contextualized dilated network is developed to exploit regional contextual information and to produce better representations for rain detection. The evaluation on real images, particularly on heavy rain, shows the effectiveness of our models and architecture.

5G Spectrum: is china ready? <=SEP=> With a considerable ratio of the world's mobile users, China has been actively promoting research on 5G, in which the spectrum issue is of great interest. New 5G characteristics put forward a lot of requirements for spectrum in terms of total amount, candidate bands, as well as new challenges for spectrum usage methods and management. Based on China's current situation, this article first discusses the 5G vision, spectrum demands, and potential candidate bands. Furthermore, it is indicated that spectrum sharing will bring many benefits for 5G systems, and different sharing scenarios are summarized. Finally, based on the current framework of spectrum management in China, potential services classification and spectrum assessment are proposed to accommodate new 5G requirements.

Scalable Semantic Web Data Management Using Vertical Partitioning <=SEP=> Efficient management of RDF data is an important factor in realizing the Semantic Web vision. Performance and scalability issues are becoming increasingly pressing as Semantic Web technology is applied to real-world applications. In this paper, we examine the reasons why current data management solutions for RDF data scale poorly, and explore the fundamental scalability limitations of these approaches. We review the state of the art for improving performance for RDF databases and consider a recent suggestion, “property tables.” We then discuss practically and empirically why this solution has undesirable features. As an improvement, we propose an alternative solution: vertically partitioning the RDF data. We compare the performance of vertical partitioning with prior art on queries generated by a Web-based RDF browser over a large-scale (more than 50 million triples) catalog of library data. Our results show that a vertical partitioned schema achieves similar performance to the property table technique while being much simpler to design. Further, if a column-oriented DBMS (a database architected specially for the vertically partitioned case) is used instead of a row-oriented DBMS, another order of magnitude performance improvement is observed, with query times dropping from minutes to several seconds.

A robust fault modeling and prediction technique for heterogeneous network <=SEP=> This paper proposes a modelling technique for fault identification in a heterogeneous network. Heterogeneous network is the integration of wired network, cellular network, wireless local area network (WLAN), and Adhoc network, etc. There may be several numbers of causes and sub-causes of fault in each type of network. These causes of fault must be mitigated in order to have a secure and reliable system. The paper introduces a model based approach that starts with the enumeration of possible causes of each fault over the heterogeneous network. A framework is then created by such causes which diagrammatically describe the causes and sub-causes responsible for the occurrence of a fault. This paper suggests a conceptual fault cause tree model based on the probabilistic framework for ranking the faults and their possible causes. An effective mitigation strategy is required to mitigate the causes and sub-causes. Once mitigation of the cause creating a fault is achieved, the system is analyzed and tested for accuracy and reliability. The Proposed technique assures that all sub-causes even at the lowest level of abstraction is taken into consideration in making the system more robust against the particular fault. The proposed model is also used to analyze the faulty probability of a heterogeneous network.

One-against-all multi-class SVM classification using reliability measures <=SEP=> Support vector machines (SVM) is originally designed for binary classification. To extend it to multi-class scenario, a typical conventional way is to decompose an M-class problem into a series of two-class problems, for which one-against-all is the earliest and one of the most widely used implementations. However, certain theoretical analysis reveals a drawback, i.e., the competence of each classifier is totally neglected when the results of classification from the multiple classifiers are combined for the final decision. To overcome this limitation, this paper introduces reliability measures into the multi-class framework. Two measures are designed: static reliability measure (SRM) and dynamic reliability measure (DRM). SRM works on a collective basis and yields a constant value regardless of the location of the test sample. DRM, on the other hand, accounts for the spatial variation of the classifier's performance. Based on these two reliability measures, a new decision strategy for the one-against-all method is proposed, which is tested on benchmark data sets and demonstrates its effectiveness.

Modeling a DC Power System in Hardware-inthe-Loop Environment <=SEP=> ii Abstract (in Finnish) iiiin Finnish) iii

Motion Capture and Retargeting of Fish by Monocular Camera <=SEP=> Accurate motion capture and flexible retargeting of underwater creatures such as fish remain to be difficult due to the long-lasting challenges of marker attachment and feature description for soft bodies in the underwater environment. Despite limited new research progresses appeared in recent years, the fish motion retargeting with a desirable motion pattern in real-time remains elusive. Strongly motivated by our ambitious goal of achieving high-quality data-driven fish animation with a light-weight, mobile device, this paper develops a novel framework of motion capturing and retargeting for a fish. We capture the motion of actual fish by a monocular camera without the utility of any marker. The elliptical Fourier coefficients are then integrated into the contour-based feature extraction process to analyze the fish swimming patterns. This novel approach can obtain the motion information in a robust way, with smooth medial axis as the descriptor for a soft fish body. For motion retargeting, we propose a two-level scheme to properly transfer the captured motion into new models, such as 2D meshes (with texture) generated from pictures or 3D models designed by artists, regardless of different body geometry and fin proportions among various species. Both motion capture and retargeting processes are functioning in real time. Hence, the system can simultaneously create fish animation with variation, while obtaining video sequences of real fish by a monocular camera.

Joint shot boundary detection and key frame extraction <=SEP=> Representing a video by a set of key frames is useful for efficient video browsing and retrieving. But key frame extraction keeps a challenge in the computer vision field. In this paper, we propose a joint framework to integrate both shot boundary detection and key frame extraction, wherein three probabilistic components are taken into account, i.e. the prior of the key frames, the conditional probability of shot boundaries and the conditional probability of each video frame. Thus the key frame extraction is treated as a Maximum A Posteriori which can be solved by adopting alternate strategy. Experimental results show that the proposed method preserves the scene level structure and extracts key frames that are representative and discriminative.

Design and analysis of cross-fed rectangular array antenna; an X-band microstrip array antenna, operating at 11 GHz <=SEP=> In this paper, an X-Band Microstrip Patch antenna termed as the Cross fed rectangular array antenna has been presented with an enhanced radiation efficiency. The proposed antenna is designed and simulated using FEKO 5.5 suite. It is then fabricated on a 40 × 40mm2 Rogers RT-Duroid 5880LZ dielectric material board. This antenna is composed of a rectangular patch array in a cross fashion with four patches, each with a dimension of 12.3 mm × 9.85 mm excited using a wire-port. The antenna operates at a frequency of 11.458 GHz in the X-Band (8-12 GHz). It has achieved stable radiation efficiency of 80.71% with a gain of 7.4 dB at the operating frequency. It is thus inferred that this antenna can be used for X-Band applications such as in maritime navigation and airborne radars.

Deep Cross-Modal Projection Learning for Image-Text Matching <=SEP=> The key point of image-text matching is how to accurately measure the similarity between visual and textual inputs. Despite the great progress of associating the deep cross-modal embeddings with the bi-directional ranking loss, developing the strategies for mining useful triplets and selecting appropriate margins remains a challenge in real applications. In this paper, we propose a cross-modal projection matching (CMPM) loss and a cross-modal projection classification (CMPC) loss for learning discriminative image-text embeddings. The CMPM loss minimizes the KL divergence between the projection compatibility distributions and the normalized matching distributions defined with all the positive and negative samples in a mini-batch. The CMPC loss attempts to categorize the vector projection of representations from one modality onto another with the improved norm-softmax loss, for further enhancing the feature compactness of each class. Extensive analysis and experiments on multiple datasets demonstrate the superiority of the proposed approach.

Efficient Ordered Combinatorial Semi-Bandits for Whole-Page Recommendation <=SEP=> Multi-Armed Bandit (MAB) framework has been successfully applied in many web applications. However, many complex real-world applications that involve multiple content recommendations cannot fit into the traditional MAB setting. To address this issue, we consider an ordered combinatorial semi-bandit problem where the learner recommends S actions from a base set of K actions, and displays the results in S (out of M ) different positions. The aim is to maximize the cumulative reward with respect to the best possible subset and positions in hindsight. By the adaptation of a minimumcost maximum-flow network, a practical algorithm based on Thompson sampling is derived for the (contextual) combinatorial problem, thus resolving the problem of computational intractability. With its potential to work with whole-page recommendation and any probabilistic models, to illustrate the effectiveness of our method, we focus on Gaussian process optimization and a contextual setting where click-throughrate is predicted using logistic regression. We demonstrate the algorithms’ performance on synthetic Gaussian process problems and on large-scale news article recommendation datasets from Yahoo! Front Page Today Module.

Overweight among primary school-age children in Malaysia. <=SEP=> This study is a secondary data analysis from the National Health Morbidity Survey III, a population-based study conducted in 2006. A total of 7,749 children between 7 and 12 years old were recruited into the study. This study seeks to report the prevalence of overweight (including obesity) children in Malaysia using international cut-off point and identify its associated key social determinants. The results show that the overall prevalence of overweight children in Malaysia was 19.9%. The urban residents, males, Chinese, those who are wealthy, have overweight or educated guardians showed higher prevalence of overweight. In multivariable analysis, higher likelihood of being overweight was observed among those with advancing age (OR=1.15), urban residents (OR=1.16, 95% CI: 1.01-1.36), the Chinese (OR=1.45, 95% CI: 1.19-1.77), boys (OR=1.23, 95% CI: 1.08-1.41), and those who came from higher income family. In conclusion, one out of five of 7-12 year-old-children in Malaysia were overweight. Locality of residence, ethnicity, gender, guardian education, and overweight guardian were likely to be the predictors of this alarming issue. Societal and public health efforts are needed in order to reduce the burden of disease associated with obesity.

Disconnected aging: Cerebral white matter integrity and age-related differences in cognition <=SEP=> Cognition arises as a result of coordinated processing among distributed brain regions and disruptions to communication within these neural networks can result in cognitive dysfunction. Cortical disconnection may thus contribute to the declines in some aspects of cognitive functioning observed in healthy aging. Diffusion tensor imaging (DTI) is ideally suited for the study of cortical disconnection as it provides indices of structural integrity within interconnected neural networks. The current review summarizes results of previous DTI aging research with the aim of identifying consistent patterns of age-related differences in white matter integrity, and of relationships between measures of white matter integrity and behavioral performance as a function of adult age. We outline a number of future directions that will broaden our current understanding of these brain-behavior relationships in aging. Specifically, future research should aim to (1) investigate multiple models of age-brain-behavior relationships; (2) determine the tract-specificity versus global effect of aging on white matter integrity; (3) assess the relative contribution of normal variation in white matter integrity versus white matter lesions to age-related differences in cognition; (4) improve the definition of specific aspects of cognitive functioning related to age-related differences in white matter integrity using information processing tasks; and (5) combine multiple imaging modalities (e.g., resting-state and task-related functional magnetic resonance imaging; fMRI) with DTI to clarify the role of cerebral white matter integrity in cognitive aging.

Enzyme-Enhanced Microbial Fuel Cells <=SEP=> 

Mesh Location in Open Ventral Hernia Repair: A Systematic Review and Network Meta-analysis <=SEP=> There is no consensus on the ideal location for mesh placement in open ventral hernia repair (OVHR). We aim to identify the mesh location associated with the lowest rate of recurrence following OVHR using a systematic review and meta-analysis. A search was performed for studies comparing at least two of four locations for mesh placement during OVHR (onlay, inlay, sublay, and underlay). Outcomes assessed were hernia recurrence and surgical site infection (SSI). Pairwise meta-analysis was performed to compare all direct treatment of mesh locations. A multiple treatment meta-analysis was performed to compare all mesh locations in the Bayesian framework. Sensitivity analyses were planned for the following: studies with a low risk of bias, incisional hernias, by hernia size, and by mesh type (synthetic or biologic). Twenty-one studies were identified (n = 5,891). Sublay placement of mesh was associated with the lowest risk for recurrence [OR 0.218 (95 % CI 0.06–0.47)] and was the best of the four treatment modalities assessed [Prob (best) = 94.2 %]. Sublay was also associated with the lowest risk for SSI [OR 0.449 (95 % CI 0.12–1.16)] and was the best of the 4 treatment modalities assessed [Prob (best) = 77.3 %]. When only assessing studies at low risk of bias, of incisional hernias, and using synthetic mesh, the probability that sublay had the lowest rate of recurrence and SSI was high. Sublay mesh location has lower complication rates than other mesh locations. While additional randomized controlled trials are needed to validate these findings, this network meta-analysis suggests the probability of sublay being the best location for mesh placement is high.

Learned Step Size Quantization <=SEP=> We present here Learned Step Size Quantization, a method for training deep networks such that they can run at inference time using low precision integer matrix multipliers, which offer power and space advantages over high precision alternatives. The essence of our approach is to learn the step size parameter of a uniform quantizer by backpropagation of the training loss, applying a scaling factor to its learning rate, and computing its associated loss gradient by ignoring the discontinuity present in the quantizer. This quantization approach can be applied to activations or weights, using different levels of precision as needed for a given system, and requiring only a simple modification of existing training code. As demonstrated on the ImageNet dataset, our approach achieves better accuracy than all previous published methods for creating quantized networks on several ResNet network architectures at 2-, 3and 4-bits of precision.

Design of EMG Acquisition Circuit to Control an Antagonistic Mechanism Actuated by Pneumatic Artificial Muscles PAMs <=SEP=> -A pneumatically actuated antagonistic pair of muscles with joint mechanism (APMM) is supported and developed to be essential for bionic and biomimetic applications to emulate the biological muscles by realizing various kinds of locomotion based on normal electrical activity of biological muscles. This Paper aims to compare the response of antagonistic pairs of muscles mechanism (APMM) based on the pneumatic artificial muscles (PAMs) to an EMG signal that was acquired throw a designed circuit and an EMG Laboratory acquisition kit. The response is represented as a joint rotary displacement generated by the contraction and extension of the pneumatic artificial muscles. A statistical study was done to prove the efficiency of the designed circuit the response of antagonistic pairs of muscles mechanism. The statistical result showed that there is no significant difference of voltage data in both EMG acquired signal between reference kit and designed circuit. An excellent correlation behavior between the EMG control signal and the response of APMM as an angular displacement has been discussed and statistically analyzed. Index Term-Pneumatic Artificial Muscles, Biomechatronics, Electromyogram EMG, Pneumatic Proportional Directional Control Valve, Signal Processing, Bionic.

Dependency Parsing and Domain Adaptation with LR Models and Parser Ensembles <=SEP=> We present a data-driven variant of the LR algorithm for dependency parsing, and extend it with a best-first search for probabilistic generalized LR dependency parsing. Parser actions are determined by a classifier, based on features that represent the current state of the parser. We apply this parsing framework to both tracks of the CoNLL 2007 shared task, in each case taking advantage of multiple models trained with different learners. In the multilingual track, we train three LR models for each of the ten languages, and combine the analyses obtained with each individual model with a maximum spanning tree voting scheme. In the domain adaptation track, we use two models to parse unlabeled data in the target domain to supplement the labeled out-ofdomain training set, in a scheme similar to one iteration of co-training.

Analysis of Blockage Effects on Urban Cellular Networks <=SEP=> Large-scale blockages such as buildings affect the performance of urban cellular networks, especially at higher frequencies. Unfortunately, such blockage effects are either neglected or characterized by oversimplified models in the analysis of cellular networks. Leveraging concepts from random shape theory, this paper proposes a mathematical framework to model random blockages and analyze their impact on cellular network performance. Random buildings are modeled as a process of rectangles with random sizes and orientations whose centers form a Poisson point process on the plane. The distribution of the number of blockages in a link is proven to be a Poisson random variable with parameter dependent on the length of the link. Our analysis shows that the probability that a link is not intersected by any blockages decays exponentially with the link length. A path loss model that incorporates the blockage effects is also proposed, which matches experimental trends observed in prior work. The model is applied to analyze the performance of cellular networks in urban areas with the presence of buildings, in terms of connectivity, coverage probability, and average rate. Our results show that the base station density should scale superlinearly with the blockage density to maintain the network connectivity. Our analyses also show that while buildings may block the desired signal, they may still have a positive impact on the SIR coverage probability and achievable rate since they can block significantly more interference.

Depth enhanced visual-inertial odometry based on Multi-State Constraint Kalman Filter <=SEP=> There have been increasing demands for developing robotic system combining camera and inertial measurement unit in navigation task, due to their low-cost, lightweight and complementary properties. In this paper, we present a Visual Inertial Odometry (VIO) system which can utilize sparse depth to estimate 6D pose in GPS-denied and unstructured environments. The system is based on Multi-State Constraint Kalman Filter (MSCKF), which benefits from low computation load when compared to optimization-based method, especially on resource-constrained platform. Features are enhanced with depth information forming 3D landmark position measurements in space, which reduces uncertainty of position estimate. And we derivate measurement model to access compatibility with both 2D and 3D measurements. In experiments, we evaluate the performance of the system in different in-flight scenarios, both cluttered room and industry environment. The results suggest that the estimator is consistent, substantially improves the accuracy compared with original monocular-based MSKCF and achieves competitive accuracy with other research.

Web scraping technologies in an API world <=SEP=> Web services are the de facto standard in biomedical data integration. However, there are data integration scenarios that cannot be fully covered by Web services. A number of Web databases and tools do not support Web services, and existing Web services do not cover for all possible user data demands. As a consequence, Web data scraping, one of the oldest techniques for extracting Web contents, is still in position to offer a valid and valuable service to a wide range of bioinformatics applications, ranging from simple extraction robots to online meta-servers. This article reviews existing scraping frameworks and tools, identifying their strengths and limitations in terms of extraction capabilities. The main focus is set on showing how straightforward it is today to set up a data scraping pipeline, with minimal programming effort, and answer a number of practical needs. For exemplification purposes, we introduce a biomedical data extraction scenario where the desired data sources, well-known in clinical microbiology and similar domains, do not offer programmatic interfaces yet. Moreover, we describe the operation of WhichGenes and PathJam, two bioinformatics meta-servers that use scraping as means to cope with gene set enrichment analysis.