Query: A Dual Attentive Neural Network Framework with Community Metadata for Answer Selection

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Convolutional Neural Network Architectures for Matching Natural Language Sentences <=SEP=> Semantic matching is of central importance to many natural language tasks [2, 28]. A successful matching algorithm needs to adequately model the internal structures of language objects and the interaction between them. As a step toward this goal, we propose convolutional neural network models for matching two sentences, by adapting the convolutional strategy in vision and speech. The proposed models not only nicely represent the hierarchical structures of sentences with their layerby-layer composition and pooling, but also capture the rich matching patterns at different levels. Our models are rather generic, requiring no prior knowledge on language, and can hence be applied to matching tasks of different nature and in different languages. The empirical study on a variety of matching tasks demonstrates the efficacy of the proposed model on a variety of matching tasks and its superiority to competitor models.

ABCNN: Attention-Based Convolutional Neural Network for Modeling Sentence Pairs <=SEP=> How to model a pair of sentences is a critical issue in many NLP tasks such as answer selection (AS), paraphrase identification (PI) and textual entailment (TE). Most prior work (i) deals with one individual task by fine-tuning a specific system; (ii) models each sentence’s representation separately, rarely considering the impact of the other sentence; or (iii) relies fully on manually designed, task-specific linguistic features. This work presents a general Attention Based Convolutional Neural Network (ABCNN) for modeling a pair of sentences. We make three contributions. (i) The ABCNN can be applied to a wide variety of tasks that require modeling of sentence pairs. (ii) We propose three attention schemes that integrate mutual influence between sentences into CNNs; thus, the representation of each sentence takes into consideration its counterpart. These interdependent sentence pair representations are more powerful than isolated sentence representations. (iii) ABCNNs achieve state-of-the-art performance on AS, PI and TE tasks. We release code at: https://github.com/yinwenpeng/Answer_Selection.

node2vec: Scalable Feature Learning for Networks <=SEP=> Prediction tasks over nodes and edges in networks require careful effort in engineering features used by learning algorithms. Recent research in the broader field of representation learning has led to significant progress in automating prediction by learning the features themselves. However, present feature learning approaches are not expressive enough to capture the diversity of connectivity patterns observed in networks. Here we propose node2vec, an algorithmic framework for learning continuous feature representations for nodes in networks. In node2vec, we learn a mapping of nodes to a low-dimensional space of features that maximizes the likelihood of preserving network neighborhoods of nodes. We define a flexible notion of a node's network neighborhood and design a biased random walk procedure, which efficiently explores diverse neighborhoods. Our algorithm generalizes prior work which is based on rigid notions of network neighborhoods, and we argue that the added flexibility in exploring neighborhoods is the key to learning richer representations.
 We demonstrate the efficacy of node2vec over existing state-of-the-art techniques on multi-label classification and link prediction in several real-world networks from diverse domains. Taken together, our work represents a new way for efficiently learning state-of-the-art task-independent representations in complex networks.

A Neural Attention Model for Abstractive Sentence Summarization <=SEP=> Summarization based on text extraction is inherently limited, but generation-style abstractive methods have proven challenging to build. In this work, we propose a fully data-driven approach to abstractive sentence summarization. Our method utilizes a local attention-based model that generates each word of the summary conditioned on the input sentence. While the model is structurally simple, it can easily be trained end-to-end and scales to a large amount of training data. The model shows significant performance gains on the DUC-2004 shared task compared with several strong baselines.

Question/Answer Matching for CQA System via Combining Lexical and Sequential Information <=SEP=> Community-based Question Answering (CQA) has become popular in knowledge sharing sites since it allows users to get answers to complex, detailed, and personal questions directly from other users. Large archives of historical questions and associated answers have been accumulated. Retrieving relevant historical answers that best match a question is an essential component of a CQA service. Most state of the art approaches are based on bag-of-words models, which have been proven successful in a range of text matching tasks, but are insufficient for capturing the important word sequence information in short text matching. In this paper, a new architecture is proposed to more effectively model the complicated matching relations between questions and answers. It utilises a similarity matrix which contains both lexical and sequential information. Afterwards the information is put into a deep architecture to find potentially suitable answers. The experimental study shows its potential in improving matching accuracy of question and answer.

Development of attentional networks in childhood <=SEP=> Recent research in attention has involved three networks of anatomical areas that carry out the functions of orienting, alerting and executive control (including conflict monitoring). There have been extensive cognitive and neuroimaging studies of these networks in adults. We developed an integrated Attention Network Test (ANT) to measure the efficiency of the three networks with adults. We have now adapted this test to study the development of these networks during childhood. The test is a child-friendly version of the flanker task with alerting and orienting cues. We studied the development of the attentional networks in a cross-sectional experiment with four age groups ranging from 6 through 9 (Experiment 1). In a second experiment, we compared children (age 10 years) and adult performance in both child and adults versions of the ANT. Reaction time and accuracy improved at each age interval and positive values were found for the average efficiency of each of the networks. Alertness showed evidence of change up to and beyond age 10, while conflict scores appear stable after age seven and orienting scores do not change in the age range studied. A final experiment with forty 7-year-old children suggested that children like adults showed independence between the three networks under some conditions.

Mathematical modelling of infectious diseases. <=SEP=> INTRODUCTION
Mathematical models allow us to extrapolate from current information about the state and progress of an outbreak, to predict the future and, most importantly, to quantify the uncertainty in these predictions. Here, we illustrate these principles in relation to the current H1N1 epidemic.


SOURCES OF DATA
Many sources of data are used in mathematical modelling, with some forms of model requiring vastly more data than others. However, a good estimation of the number of cases is vitally important.


AREAS OF AGREEMENT
Mathematical models, and the statistical tools that underpin them, are now a fundamental element in planning control and mitigation measures against any future epidemic of an infectious disease. Well-parameterized mathematical models allow us to test a variety of possible control strategies in computer simulations before applying them in reality.


AREAS OF CONTROVERSY
The interaction between modellers and public-health practitioners and the level of detail needed for models to be of use.


GROWING POINTS
The need for stronger statistical links between models and data.


AREAS TIMELY FOR DEVELOPING RESEARCH
Greater appreciation by the medical community of the uses and limitations of models and a greater appreciation by modellers of the constraints on public-health resources.

Predicting sports events from past results Towards effective betting on football matches <=SEP=> A system for predicting the results of football matches that beats the bookmakers’ odds is presented. The predictions for the matches are based on previous results of the teams involved.

Modern Intrusion Detection, Data Mining, and Degrees of Attack Guilt <=SEP=> This chapter examines the state of modern intrusion detection, with a particular emphasis on the emerging approach of data mining. The discussion paralleIs two important aspects of intrusion detection: general detection strategy (misuse detection versus anomaly detection) and data source (individual hosts versus network trafik). Misuse detection attempts to match known patterns of intrusion , while anomaly detection searches for deviations from normal behavior . Between the two approaches, only anomaly detection has the ability to detect unknown attacks. A particularly promising approach to anomaly detection combines association mining with other forms of machine learning such as classification. Moreover, the data source that an intrusion detection system employs significantly impacts the types of attacks it can detect. There is a tradeoff in the level of detailed information available verD. Barbará et al. (ed .), Applications of Data Mining in Computer Security © Kluwer Academic Publishers 2002 s

AGA: Attribute-Guided Augmentation <=SEP=> We consider the problem of data augmentation, i.e., generating artificial samples to extend a given corpus of training data. Specifically, we propose attributed-guided augmentation (AGA) which learns a mapping that allows to synthesize data such that an attribute of a synthesized sample is at a desired value or strength. This is particularly interesting in situations where little data with no attribute annotation is available for learning, but we have access to a large external corpus of heavily annotated samples. While prior works primarily augment in the space of images, we propose to perform augmentation in feature space instead. We implement our approach as a deep encoder-decoder architecture that learns the synthesis function in an end-to-end manner. We demonstrate the utility of our approach on the problems of (1) one-shot object recognition in a transfer-learning setting where we have no prior knowledge of the new classes, as well as (2) object-based one-shot scene recognition. As external data, we leverage 3D depth and pose information from the SUN RGB-D dataset. Our experiments show that attribute-guided augmentation of high-level CNN features considerably improves one-shot recognition performance on both problems.

A Case Study on Barriers and Enhancements of the PET Bottle-to-Bottle Recycling Systems in Germany and Sweden <=SEP=> Problem: The demand of beverages in PET bottles is constantly increasing. In this context, environmental, technological and regulatory aspects set a stronger focus on recycling. Generally, the reuse of recycled material from post-consumer PET bottles in bottle-to-bottle applications is seen as least environmentally harmful. However, closedloop systems are not widely implemented in Europe. Previous research mainly focuses on open-loop recycling systems and generally lacks discussion about the current German and Swedish systems and their challenges. Furthermore, previous studies lack theoretical and practical enhancements for bottle-to-bottle recycling from a managerial perspective. Purpose: The purpose of this study is to compare the PET bottle recycling systems in Germany and Sweden, analyse the main barriers and develop enhancements for closedloop systems. Method: This qualitative study employs a case study strategy about the two cases of Germany and Sweden. In total, 14 semi-structured interviews are conducted with respondents from different industry sectors within the PET bottle recycling systems. The empirical data is categorised and then analysed by pattern matching with the developed theoretical framework. Conclusion: Due to the theoretical and practical commitment to closed-loop recycling, the Swedish PET bottle recycling system outperforms the Germany system. In Germany, bottle-to-bottle recycling is currently performed on a smaller scale without a unified system. The main barriers for bottle-to-bottle recycling are distinguished into (1) quality and material factors, (2) regulatory and legal factors, (3) economic and market factors and (4) factors influenced by consumers. The enhancements for the systems are (1) quality and material factors, (2) regulatory and legal factors, (3) recollection factors and (4) expanding factors. Lastly, the authors provide further recommendations, which are (1) a recycling content symbol on bottle labels, (2) a council for bottle quality in Germany, (3) a quality seal for the holistic systems, (4) a reduction of transportation in Sweden and (5) an increase of consumer awareness on PET bottle consumption.

Managing Portfolios of Development Projects in a Complex Environment How the UN assign priorities to Programs at the Country <=SEP=> 

Learning Image Embeddings using Convolutional Neural Networks for Improved Multi-Modal Semantics <=SEP=> We construct multi-modal concept representations by concatenating a skip-gram linguistic representation vector with a visual concept representation vector computed using the feature extraction layers of a deep convolutional neural network (CNN) trained on a large labeled object recognition dataset. This transfer learning approach brings a clear performance gain over features based on the traditional bag-of-visual-word approach. Experimental results are reported on the WordSim353 and MEN semantic relatedness evaluation tasks. We use visual features computed using either ImageNet or ESP Game images.

Molecular and Morphological Characterization of Aphelenchoides fuchsi sp. n. (Nematoda: Aphelenchoididae) Isolated from Pinus eldarica in Western Iran. <=SEP=> Aphelenchoides fuchsi sp. n. is described and illustrated from bark and wood samples of a weakened Mondell pine in Kermanshah Province, western Iran. The new species has body length of 332 to 400 µm (females) and 365 to 395 µm (males). Lip region set off from body contour. The cuticle is weakly annulated, and there are four lines in the lateral field. The stylet is 8 to 10 μm long and has small basal swellings. The excretory pore is located ca one body diam. posterior to metacorpus valve or 51 to 62 μm from the head. The postuterine sac well developed (60-90 µm). Spicules are relatively short (15-16 μm in dorsal limb) with apex and rostrum rounded, well developed, and the end of the dorsal limb clearly curved ventrad like a hook. The male tail has usual three pairs of caudal papillae (2+2+2) and a well-developed mucro. The female tail is conical, terminating in a complicated step-like projection, usually with many tiny nodular protuberances. The new species belongs to the Group 2 sensu Shahina, category of Aphelenchoides species. Phylogenetic analysis based on small subunit (SSU) and partial large subunit (LSU) sequences of rRNA supported the morphological results.

The future of seawater desalination: energy, technology, and the environment. <=SEP=> In recent years, numerous large-scale seawater desalination plants have been built in water-stressed countries to augment available water resources, and construction of new desalination plants is expected to increase in the near future. Despite major advancements in desalination technologies, seawater desalination is still more energy intensive compared to conventional technologies for the treatment of fresh water. There are also concerns about the potential environmental impacts of large-scale seawater desalination plants. Here, we review the possible reductions in energy demand by state-of-the-art seawater desalination technologies, the potential role of advanced materials and innovative technologies in improving performance, and the sustainability of desalination as a technological solution to global water shortages.

A bidirectional soft pneumatic fabric-based actuator for grasping applications <=SEP=> THIS paper presents the development of a bidirectional fabric-based soft pneumatic actuator requiring low fluid pressurization for actuation, which is incorporated into a soft robotic gripper to demonstrate its utility. The bidirectional soft fabric-based actuator is able to provide both flexion and extension. Fabrication of the fabric actuators is simple as compared to the steps involved in traditional silicone-based approach. In addition, the fabric actuators are able to generate comparably larger vertical grip resistive force at lower operating pressure than elastomeric actuators and 3D-printed actuators, being able to generate resistive grip force up to 20N at 120 kPa. Five of the bidirectional soft fabric-based actuators are deployed within a five-fingered soft robotic gripper, complete with five casings and a base. It is capable of grasping a variety of objects with maximum width or diameter closer to its bending curvature. A cutting task involved bimanual manipulation was demonstrated successfully with the gripper. To incorporate intelligent control for such a task, a soft force made completely of compliant material was attached to the gripper, which allows determination of whether the cutting task is completed. To the authors' knowledge, this work is the first study which incorporates two soft robotic grippers for bimanual manipulation with one of the grippers sensorized to provide closed loop control.

Principles of GNSS, inertial, and multisensor integrated navigation systems, 2nd edition [Book review] <=SEP=> This second edition of Dr. Grove's book (the original was published in 2008) could arguably be considered a new work. At just under 1,000 pages (including the 11 appendices on the DVD), the second edition is 80% longer than the original. Frankly, the word "book" hardly seems adequate, considering the wide range of topics covered. "Mini-encyclopedia" seems more appropriate. The hardcover portion of the book comprises 18 chapters, and the DVD includes the aforementioned appendices plus 20 fully worked examples, 125 problems or exercises (with answers), and MATLAB routines for the simulation of many of the algorithms discussed in the main text. Here is a brief overview of the contents: ▸ Chapters 1–3: an overview of the diversity of positioning techniques and navigation systems; fundamentals of coordinate frames, kinematics and earth models; introduction to Kaiman filtering ▸ Chapters 4–6: inertial sensors, inertial navigation, and lower-cost dead reckoning systems ▸ Chapters 7–12: principles of radio positioning, short-, medium-, and long-range radio navigation, as well as extensive coverage of global navigation satellite systems (GNSS) ▸ Chapter 13: environmental feature matching. ▸ Chapters 14–16: various integration topics, including inertial navigation system (INS)/GNSS integration, alignment, zero-velocity updates, and multisensor integration ▸ Chapter 17: fault detection. ▸ Chapter 18: applications and trends. In summary, this book is an excellent reference (with numerous nuggets of wisdom) that should be readily handy on the shelf of every practicing navigation engineer. In the hands of an experienced instructor, the book will also serve students as a great textbook. However, the lack of examples integrated in the main text makes it difficult for the book to serve as a self-study guide for those that are new to the field.

True self-configuration for the IoT <=SEP=> For the Internet of Things to finally become a reality, obstacles on different levels need to be overcome. This is especially true for the upcoming challenge of leaving the domain of technical experts and scientists. Devices need to connect to the Internet and be able to offer services. They have to announce and describe these services in machine-understandable ways so that user-facing systems are able to find and utilize them. They have to learn about their physical surroundings, so that they can serve sensing or acting purposes without explicit configuration or programming. Finally, it must be possible to include IoT devices in complex systems that combine local and remote data, from different sources, in novel and surprising ways. We show how all of that is possible today. Our solution uses open standards and state-of-the art protocols to achieve this. It is based on 6LowPAN and CoAP for the communications part, semantic web technologies for meaningful data exchange, autonomous sensor correlation to learn about the environment, and software built around the Linked Data principles to be open for novel and unforeseen applications.

From turtles to Tangible Programming Bricks: explorations in physical language design <=SEP=> This article provides a historical overview of educational computing research at MIT from the mid-1960s to the present day, focusing on physical interfaces. It discusses some of the results of this research: electronic toys that help children develop advanced modes of thinking through free-form play. In this historical context, the article then describes and discusses the author’s own research into tangible programming, culminating in the development of the Tangible Programming Bricks system—a platform for creating microworlds for children to explore computation and scientific thinking.

Ray: A Distributed Framework for Emerging AI Applications <=SEP=> The next generation of AI applications will continuously interact with the environment and learn from these interactions. These applications impose new and demanding systems requirements, both in terms of performance and flexibility. In this paper, we consider these requirements and present Ray—a distributed system to address them. Ray implements a unified interface that can express both task-parallel and actor-based computations, supported by a single dynamic execution engine. To meet the performance requirements, Ray employs a distributed scheduler and a distributed and fault-tolerant store to manage the system’s control state. In our experiments, we demonstrate scaling beyond 1.8 million tasks per second and better performance than existing specialized systems for several challenging reinforcement learning applications.

Wave-front Transformation with Gradient Metasurfaces <=SEP=> Relying on abrupt phase discontinuities, metasurfaces characterized by a transversely inhomogeneous surface impedance profile have been recently explored as an ultrathin platform to generate arbitrary wave fronts over subwavelength thicknesses. Here, we outline fundamental limitations of passive gradient metasurfaces in molding the impinging wave and show that local phase compensation is essentially insufficient to realize arbitrary wave manipulation, but full-wave designs should be considered. These findings represent a critical step towards realistic and highly efficient conformal wave manipulation beyond the scope of ray optics, enabling unprecedented nanoscale light molding.

Adaptation impact and environment models for architecture-based self-adaptive systems <=SEP=> Self-adaptive systems have the ability to adapt their behavior to dynamic operating conditions. In reaction to changes in the environment, these systems determine the appropriate corrective actions based in part on information about which action will have the best impact on the system. Existing models used to describe the impact of adaptations are either unable to capture the underlying uncertainty and variability of such dynamic environments, or are not compositional and described at a level of abstraction too low to scale in terms of specification effort required for non-trivial systems. In this paper, we address these shortcomings by describing an approach to the specification of impact models based on architectural system descriptions, which at the same time allows us to represent both variability and uncertainty in the outcome of adaptations, hence improving the selection of the best corrective action. The core of our approach is a language equipped with a formal semantics defined in terms of Discrete Time Markov Chains that enables us to describe both the impact of adaptation tactics, as well as the assumptions about the environment. To validate our approach, we show how employing our language can improve the accuracy of predictions used for decision-making in the Rainbow framework for architecture-based self-adaptation.

Induction of Root and Pattern Lexicon for Unsupervised Morphological Analysis of Arabic <=SEP=> We propose an unsupervised approach to learning non-concatenative morphology, which we apply to induce a lexicon of Arabic roots and pattern templates. The approach is based on the idea that roots and patterns may be revealed through mutually recursive scoring based on hypothesized pattern and root frequencies. After a further iterative refinement stage, morphological analysis with the induced lexicon achieves a root identification accuracy of over 94%. Our approach differs from previous work on unsupervised learning of Arabic morphology in that it is applicable to naturally-written, unvowelled text.

An Unequal Wilkinson Power Divider for a Frequency and Its First Harmonic <=SEP=> This letter presents a Wilkinson power divider operating at a frequency and its first harmonic with unequal power divider ratio. To obtain the unequal property, four groups of 1/6 wavelength transmission lines with different characteristic impedances are needed to match all ports. Theoretically, closed-form equations for the design are derived based on transmission line theory. Experimental results have indicated that all the features of this novel power divider can be fulfilled at f 0 and 2f 0 simultaneously.

Adaptive Haptic Feedback Steering Wheel for Driving Simulators <=SEP=> Controlling a virtual vehicle is a sensory-motor activity with a specific rendering methodology that depends on the hardware technology and the software in use. We propose a method that computes haptic feedback for the steering wheel. It is best suited for low-cost, fixed-base driving simulators but can be ported to any driving simulator platform. The goal of our method is twofold. 1) It provides an efficient yet simple algorithm to model the steering mechanism using a quadri-polar representation. 2) This model is used to compute the haptic feedback on top of which a tunable haptic augmentation is adjusted to overcome the lack of presence and the unavoidable simulation loop latencies. This algorithm helps the driver to laterally control the virtual vehicle. We also discuss the experimental results that demonstrate the usefulness of our haptic feedback method.

Competing Values in the Era of Digitalization <=SEP=> This case study examines three different digital innovation projects within Auto Inc -- a large European automaker. By using the competing values framework as a theoretical lens we explore how dynamic capabilities occur in a firm trying to meet increasing demands in originating and innovating from digitalization. In this digitalization process, our study indicates that established socio-technical congruences are being challenged. More so, we pinpoint the need for organizations to find ways to embrace new experimental learning processes in the era of digitalization. While such a change requires long-term commitment and vision, this study presents three informal enablers for such experimental processes these enablers are timing, persistence, and contacts.

Why Your Encrypted Database Is Not Secure <=SEP=> Encrypted databases, a popular approach to protecting data from compromised database management systems (DBMS's), use abstract threat models that capture neither realistic databases, nor realistic attack scenarios. In particular, the "snapshot attacker" model used to support the security claims for many encrypted databases does not reflect the information about past queries available in any snapshot attack on an actual DBMS.
 We demonstrate how this gap between theory and reality causes encrypted databases to fail to achieve their "provable security" guarantees.

A high performance parallel algorithm for 1-D FFT <=SEP=> In this paper we propose a parallel high performance FFT algorithm based on a multi-dimensional formulation. We use this to solve a commonly encountered FFT based kernel on a distributed memory parallel machine, the IBM scalable parallel system, SP1. The kernel requires a forward FFT computation of an input sequence, multiplication of the transformed data by a coefficient array, and finally an inverse FFT computation of the resultant data. We show that the multidimensional formulation helps in reducing the communication costs and also improves the single node performance by effectively utilizing the memory system of the node. We implemented this kernel on the IBM SP1 and observed a performance of 1.25 GFLOPS on a 64-node machine.

Dual-Band Textile MIMO Antenna Based on Substrate-Integrated Waveguide (SIW) Technology <=SEP=> A dual-band textile antenna for multiple-input-multiple-output (MIMO) applications, based on substrate-integrated waveguide (SIW) technology, is designed. The fundamental SIW cavity mode is designed to resonate at 2.4 GHz. Meanwhile, the second and third modes are modified and combined by careful placement of a via within the cavity to enable wideband coverage in the 5-GHz WLAN band. The simple antenna topology can be fabricated fully using textiles in a planar form, ensuring reliability and comfort. Numerical and experimental results indicate satisfactory antenna performance when worn on body in terms of impedance bandwidth, radiation efficiency, and specific absorption ratio (SAR). In order to validate its potential for MIMO applications, two elements of the proposed SIW antenna are arranged in six configurations to study the performance in terms of mutual coupling and envelope correlation. It is observed that the placement of the shorted edges of the two elements adjacent to each other produces the lowest mutual coupling and consequently the best envelope correlation.

Low self-esteem during adolescence predicts poor health, criminal behavior, and limited economic prospects during adulthood. <=SEP=> Using prospective data from the Dunedin Multidisciplinary Health and Development Study birth cohort, the authors found that adolescents with low self-esteem had poorer mental and physical health, worse economic prospects, and higher levels of criminal behavior during adulthood, compared with adolescents with high self-esteem. The long-term consequences of self-esteem could not be explained by adolescent depression, gender, or socioeconomic status. Moreover, the findings held when the outcome variables were assessed using objective measures and informant reports; therefore, the findings cannot be explained by shared method variance in self-report data. The findings suggest that low self-esteem during adolescence predicts negative real-world consequences during adulthood.