title: Securing Internet of Things with Lightweight IPsec

abstract: Real-world deployments of wireless sensor networks (WSNs) require ::: secure communication. It is important that a receiver is able to verify that sensor ::: data was generated by trusted nodes. In some cases it may also be necessary ::: to encrypt sensor data in transit. Recently, WSNs and traditional IP networks ::: are more tightly integrated using IPv6 and 6LoWPAN. Available IPv6 protocol ::: stacks can use IPsec to secure data exchange. Thus, it is desirable to extend ::: 6LoWPAN such that IPsec communication with IPv6 nodes is possible. It is ::: beneficial to use IPsec because the existing end-points on the Internet do not ::: need to be modified to communicate securely with the WSN. Moreover, using ::: IPsec, true end-to-end security is implemented and the need for a trustworthy ::: gateway is removed. ::: In this paper we provide End-to-End (E2E) secure communication between ::: an IP enabled sensor nodes and a device on traditional Internet. This is the ::: first compressed lightweight design, implementation, and evaluation of 6LoWPAN ::: extension for IPsec on Contiki. Our extension supports both IPsec's Authentication ::: Header (AH) and Encapsulation Security Payload (ESP). Thus, ::: communication endpoints are able to authenticate, encrypt and check the integrity ::: of messages using standardized and established IPv6 mechanisms.


title: Analysis of electromagnetic wave scattering from building walls with periodic and random surface

abstract: In this paper, the authors numerically analyze the scattering of electromagnetic waves from building walls by using the FVTD method. They consider three different types of rough surfaces such as periodic, random, and composite structures. They calculate the bistatic normalized radar cross section (NRCS) for horizontal and vertical polarization, and take account of the conventional optical reflection which corresponds to the n-th Bragg reflection for periodic structures. In addition, they investigate what conditions are needed in order to be able to ignore the higher order Bragg reflection for the periodic structures.


title: Mapping with an Autonomous Car

abstract: In this paper, we present an approach towards mapping and safe navigation in real, large-scale environments with an autonomous car. The goal is to enable the car to autonomously navigate on roads while avoiding obstacles and while simultaneously learning an accurate three-dimensional model of the environment. To achieve these goals, we apply probabilistic state estimation techniques, network-based pose optimization, and a sensor-based traversability analysis approach. In order to achieve fast map learning, our system compresses the sensor data using multi-level surface maps. The overall systems runs on a modified Smart car equipped with different types of sensors. We present several results obtained from extensive experiments which illustrate the capabilities of our vehicle.


title: A Global/Local Affinity Graph for Image Segmentation

abstract: Construction of a reliable graph capturing perceptual grouping cues of an image is fundamental for graph-cut based image segmentation methods. In this paper, we propose a novel sparse global/local affinity graph over superpixels of an input image to capture both short- and long-range grouping cues, and thereby enabling perceptual grouping laws, including proximity, similarity, continuity, and to enter in action through a suitable graph-cut algorithm. Moreover, we also evaluate three major visual features, namely, color, texture, and shape, for their effectiveness in perceptual segmentation and propose a simple graph fusion scheme to implement some recent findings from psychophysics, which suggest combining these visual features with different emphases for perceptual grouping. In particular, an input image is first oversegmented into superpixels at different scales. We postulate a gravitation law based on empirical observations and divide superpixels adaptively into small-, medium-, and large-sized sets. Global grouping is achieved using medium-sized superpixels through a sparse representation of superpixels’ features by solving a $\ell _{0}$ -minimization problem, and thereby enabling continuity or propagation of local smoothness over long-range connections. Small- and large-sized superpixels are then used to achieve local smoothness through an adjacent graph in a given feature space, and thus implementing perceptual laws, for example, similarity and proximity. Finally, a bipartite graph is also introduced to enable propagation of grouping cues between superpixels of different scales. Extensive experiments are carried out on the Berkeley segmentation database in comparison with several state-of-the-art graph constructions. The results show the effectiveness of the proposed approach, which outperforms state-of-the-art graphs using four different objective criteria, namely, the probabilistic rand index, the variation of information, the global consistency error, and the boundary displacement error.


title: Ad Click Prediction in Sequence with Long Short-Term Memory Networks: an Externality-aware Model

abstract: Ad click prediction is a task to estimate the click-through rate (CTR) in sponsored ads, the accuracy of which impacts user search experience and businesses' revenue. State-of-the-art sponsored search systems typically model it as a classification problem and employ machine learning approaches to predict the CTR per ad. In this paper, we propose a new approach to predict ad CTR in sequence which considers user browsing behavior and the impact of top ads quality to the current one. To the best of our knowledge, this is the first attempt in the literature to predict ad CTR by using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) cells. The proposed model is evaluated on a real dataset and we show that LSTM-RNN outperforms DNN model on both AUC and RIG. Since the RNN inference is time consuming, a simplified version is also proposed, which can achieve more than half of the gain with the overall serving cost almost unchanged.


title: Attentive Moment Retrieval in Videos

abstract: In the past few years, language-based video retrieval has attracted a lot of attention. However, as a natural extension, localizing the specific video moments within a video given a description query is seldom explored. Although these two tasks look similar, the latter is more challenging due to two main reasons: 1) The former task only needs to judge whether the query occurs in a video and returns an entire video, but the latter is expected to judge which moment within a video matches the query and accurately returns the start and end points of the moment. Due to the fact that different moments in a video have varying durations and diverse spatial-temporal characteristics, uncovering the underlying moments is highly challenging. 2) As for the key component of relevance estimation, the former usually embeds a video and the query into a common space to compute the relevance score. However, the later task concerns moment localization where not only the features of a specific moment matter, but the context information of the moment also contributes a lot. For example, the query may contain temporal constraint words, such as "first'', therefore need temporal context to properly comprehend them. To address these issues, we develop an Attentive Cross-Modal Retrieval Network. In particular, we design a memory attention mechanism to emphasize the visual features mentioned in the query and simultaneously incorporate their context. In the light of this, we obtain the augmented moment representation. Meanwhile, a cross-modal fusion sub-network learns both the intra-modality and inter-modality dynamics, which can enhance the learning of moment-query representation. We evaluate our method on two datasets: DiDeMo and TACoS. Extensive experiments show the effectiveness of our model as compared to the state-of-the-art methods.


title: ChaCha20 and Poly1305 for IETF Protocols

abstract: This document defines the ChaCha20 stream cipher as well as the use of ::: the Poly1305 authenticator, both as stand-alone algorithms and as a ::: "combined mode", or Authenticated Encryption with Associated Data ::: (AEAD) algorithm. This document does not introduce any new crypto, ::: but is meant to serve as a stable reference and an implementation ::: guide. It is a product of the Crypto Forum Research Group (CFRG).


title: A Font Setting Based Bayesian Model to Extract Mathematical Expression in PDF Files

abstract: This paper proposes a Font Setting based Bayesian (FSB) model to extract mathematical expressions (MEs) in the portable document format (PDF) files. The FSB model is a self-adaptive unsupervised algorithm which first uses rules to identify ME and non-ME (NME) and then extracts the remaining ME using the Bayesian inference based on the observation that MEs tend to repeatedly represented in a particular style. PDF files are first processed using a PDF parser and document layout is analyzed using projection profiling cutting based algorithm to detect columns and lines. Heuristic rules derived from the knowledge of math usage and writing practices are employed to reason about the posterior probability of a char being ME vs. NME, conditional upon the font and value information. Based on the char level posterior probability, Bayesian inference is used to infer a none-separable character set (NSCS) being ME or not. Consecutive (fragmented) ME NSCS are merged to produce final results. Experimental results show that our approach achieves 0.006 (0.135) false rate and 0.111/0.093 miss rate for IME (EME) extraction. As for NSCS classification, our approach achieves 93.1% precision, 90.5% recall rate, and F1 score of 0.918. The processing time is markedly shorter than supervised machine learning techniques, and the extracted information and analytics products can be used for high level applications.


title: A psychobiological theory of attachment

abstract: This article describes a neurobiological basis for the “first attachment” of the primate infant to its caretaker. The infant normally internalizes a neurobiological “image” of the behavioral and emotional characteristics of its caregiver that later regulates important features of its brain function. Current models of sensorimotor analysis and its relation to emotion suggest that sensorimotor systems are also habit and memory systems, their functional status and lability regulated in part by biogenic amine systems. The intertwined development of neural and social functions can sometimes go awry. If the attachment process fails or the caregiver is incompetent, the infant may become socially dysfunctional. This helps explain the developmental psychopathology and later vulnerability to adult psychopathology that result from disruptions of social attachment.


title: Landrush: Rethinking In-Situ Analysis for GPGPU Workflows

abstract: In-situ analysis on the output data of scientific simulations has been made necessary by ever-growing output data volumes and increasing costs of data movement as supercomputing is moving towards exascale. With hardware accelerators like GPUs becoming increasingly common in high end machines, new opportunities arise to co-locate scientific simulations and online analysis performed on the scientific data generated by the simulations. However, the asynchronous nature of GPGPU programming models and the limited context-switching capabilities on the GPU pose challenges to co-locating the scientific simulation and analysis on the same GPU. This paper dives deeper into these challenges to understand how best to co-locate analysis with scientific simulations on the GPUs in HPC clusters. Specifically, our 'Landrush' approach to GPU sharing proposes a solution that utilizes idle cycles on the GPU to provide an improved time-to-answer, that is, the total time to run the scientific simulation and analysis of the generated data. Landrush is demonstrated with experimental results obtained from leadership high-end applications on ORNL's Titan supercomputer, which show that (i) GPU-based scientific simulations have varying degrees of idle cycles to afford useful analysis task co-location, and (ii) the inability to context switch on the GPU at instruction granularity can be overcome by careful control of the analysis kernel launches and software-controlled early completion of analysis kernel executions. Results show that Landrush is superior in terms of time-to-answer compared to serially running simulations followed by analysis or by relying on the GPU driver and hardwired thread dispatcher to run analysis concurrently on a single GPU.


Deliverable 5 Measuring Presence: A Guide to Current Measurement Approaches


title: Collaborative visualization: Definition, challenges, and research agenda

abstract: The conflux of two growing areas of technology - collaboration and visualization - into a new research direction, coLLaborative visualization, provides new research chaLLenges. TechnoLogy now aLLows us to easily connect and collaborate with one another - in settings as diverse as over networked computers, across mobile devices, or using shared displays such as interactive waLLs and tabletop surfaces. Digital information is now reguLarLy accessed by muLtipLe people in order to share information, to view it together, to analyze it, orto form decisions. VisuaLizations are used to deal more effectively with Large amounts of information white interactive visuaLizations aLLow users to expLore the underlying data. White researchers face many chaLLenges in coLLaboration and in visualization, the emergence of coLLaborative visualization poses additional chaLLenges, but it is also an exciting opportunity to reach new audiences and applications for visualization tooLs and techniques.] to provide a definition, clear scope, and overview of the evolving field of coLLaborative visualization, (2) to help pinpoint the unique focus of coLLaborative visualization with its specific aspects, chaLLenges, and requirements within the intersection of generaL computer-supported cooperative work and visualization research, and (3) to draw attention to important future research questions to be addressed by the community. We conclude by discussing a research agenda for future work on coLLaborative visualization and urge for a new generation of visuaLization toots that are designed with coLLaboration in mind from their very inception.


title: A point cluster method for in vivo motion analysis: applied to a study of knee kinematics.

abstract: A new method for deriving limb segment motion from markers placed on the skin is described. The method provides a basis for determining the artifact associated with nonrigid body movement of points placed on the skin. The method is based on a cluster of points uniformly distributed on the limb segment. Each point is assigned an arbitrary mass. The center of mass and the inertia tensor of this cluster of points are calculated. The eigenvalues and eigenvectors of the inertia tensor are used to define a coordinate system in the cluster as well as to provide a basis for evaluating non-rigid body movement. The eigenvalues of the inertia tensor remain invariant if the segment is behaving as a rigid body, thereby providing a basis for determining variations for nonrigid body movement. The method was tested in a simulation model where systematic and random errors were introduced into a fixed cluster of points. The simulation demonstrated that the error due to nonrigid body movement could be substantially reduced. The method was also evaluated in a group of ten normal subjects during walking. The results for knee rotation and translation obtained from the point cluster method compared favorably to results previously obtained from normal subjects with intra-cortical pins placed into the femur and tibia. The resulting methodology described in this paper provides a unique approach to the measurement of in vivo motion using skin-based marker systems.


title: VoCo: text-based insertion and replacement in audio narration

abstract: Editing audio narration using conventional software typically involves many painstaking low-level manipulations. Some state of the art systems allow the editor to work in a text transcript of the narration, and perform select, cut, copy and paste operations directly in the transcript; these operations are then automatically applied to the waveform in a straightforward manner. However, an obvious gap in the text-based interface is the ability to type new words not appearing in the transcript, for example inserting a new word for emphasis or replacing a misspoken word. While high-quality voice synthesizers exist today, the challenge is to synthesize the new word in a voice that matches the rest of the narration. This paper presents a system that can synthesize a new word or short phrase such that it blends seamlessly in the context of the existing narration. Our approach is to use a text to speech synthesizer to say the word in a generic voice, and then use voice conversion to convert it into a voice that matches the narration. Offering a range of degrees of control to the editor, our interface supports fully automatic synthesis, selection among a candidate set of alternative pronunciations, fine control over edit placements and pitch profiles, and even guidance by the editors own voice. The paper presents studies showing that the output of our method is preferred over baseline methods and often indistinguishable from the original voice.


title: ThermoCoach: Reducing Home Energy Consumption with Personalized Thermostat Recommendations

abstract: Thermostats have the potential for tremendous impact on global energy consumption, but unfortunately they are often not used effectively. In this paper, we present a new system called ThermoCoach that improves thermostat usability by giving personalized and actionable recommendations for thermostat use. The system senses human occupancy patterns in a home and emails the household suggested setpoint schedules that can be modified or activated with the click of a button. We performed a randomized controlled trial by deploying over 600 devices in 40 homes from 12 weeks to compare ThermoCoach with a manually programmable thermostat and the Nest learning thermostat. Results indicate that ThermoCoach saves 4.7% more energy than a manually programmable thermostat and 12.4% more energy than the Nest learning thermostat while significantly improving comfort over both approaches.


title: Manipulation task simulation using ROS and Gazebo

abstract: This paper intends to create a simulation of manipulator and illustrates the methods of how to implement robot control in a short time. Here we complete the grasp and place mission using Gazebo virtual world and Robot Operating System (ROS). ROS is a distributed framework that is widely used in robotics. Considering the advantages of its easier hardware abstraction and code reuse, ROS was chosen to rapidly organize task architecture and, due to its compatibility with ROS, Gazebo was chosen as the main platform to simulate the designated motion of virtual manipulator.


title: 6LoWPAN fragmentation attacks and mitigation mechanisms

abstract: 6LoWPAN is an IPv6 adaptation layer that defines mechanisms to make IP connectivity viable for tightly resource-constrained devices that communicate over low power, lossy links such as IEEE 802.15.4. It is expected to be used in a variety of scenarios ranging from home automation to industrial control systems. To support the transmission of IPv6 packets exceeding the maximum frame size of the link layer, 6LoWPAN defines a packet fragmentation mechanism. However, the best effort semantics for fragment transmissions, the lack of authentication at the 6LoWPAN layer, and the scarce memory resources of the networked devices render the design of the fragmentation mechanism vulnerable. In this paper, we provide a detailed security analysis of the 6LoWPAN fragmentation mechanism. We identify two attacks at the 6LoWPAN design-level that enable an attacker to (selectively) prevent correct packet reassembly on a target node at considerably low cost. Specifically, an attacker can mount our identified attacks by only sending a single protocol-compliant 6LoWPAN fragment. To counter these attacks, we propose two complementary, lightweight defense mechanisms, the content chaining scheme and the split buffer approach. Our evaluation shows the practicality of the identified attacks as well as the effectiveness of our proposed defense mechanisms at modest trade-offs.


title: Echo: An Edge-Centric Code Offloading System With Quality of Service Guarantee

abstract: Code offloading is a promising way to accelerate mobile applications and reduce the energy consumption of mobile devices by shifting some computation to the cloud. However, existing code offloading systems suffer from a long communication delay between mobile devices and the cloud. To address this challenge, in this paper, we consider to deploy edge nodes in close proximity to mobile devices and study how they benefit code offloading. We design an edge-centric code offloading system, called Echo, over a three-layer computing hierarchy consisting of mobile devices, the edge, and the cloud. A critical problem needs to be addressed by Echo is to decide which methods should be offloaded to which computing platform (the edge or the cloud). Different from existing offloading systems that let mobile devices individually make offloading decisions, Echo implements a centralized decision engine at the edge. This edge-centric design can fully exploit limited hardware resources at the edge to provide offloading services with the quality-of-service guarantee. Furthermore, we propose some novel mechanisms, e.g., lazy object transmission and differential object update, to further improve system performance. The results of a small-scale real deployment and trace-driven simulations show that Echo significantly outperforms existing code offloading systems at both execution time and energy consumption.


title: Bayesian Deep Learning on a Quantum Computer

abstract: Bayesian methods in machine learning, such as Gaussian processes, have great advantages compared to other techniques. In particular, they provide estimates of the uncertainty associated with a prediction. Extending the Bayesian approach to deep architectures has remained a major challenge. Recent results connected deep feedforward neural networks with Gaussian processes, allowing training without backpropagation. This connection enables us to leverage a quantum algorithm designed for Gaussian processes and develop a new algorithm for Bayesian deep learning on quantum computers. The properties of the kernel matrix in the Gaussian process ensure the efficient execution of the core component of the protocol, quantum matrix inversion, providing an at least polynomial speedup over the classical algorithm. Furthermore, we demonstrate the execution of the algorithm on contemporary quantum computers and analyze its robustness with respect to realistic noise models.


title: Online dating system design and relational decision making: Choice, algorithms, and control

abstract: One of the most popular ways to initiate romantic relationships today is through online dating. Typical dating systems follow one of three formats, see-and-screen (e.g., Match.com), algorithm (e.g., eHarmony.com), and blended (e.g., OkCupid.com), which differ in the amount of individual control and algorithmic involvement they offer users. Do different features affect daters' decisions and expectations regarding relational development with selected partners? Study 1 indicated that although daters appreciated the personal control over mate selection afforded by see-and-screen systems, they also enjoyed using algorithmic systems. Study 2 found that blended systems provided “the best of both worlds” by offering dual benefits of control and algorithmic validation during mate selection. Findings shed light on self-determination theory, decision making, and relationship formation more broadly.


title: Heritage Image Classification by Convolution Neural Networks

abstract: In this project, we have explored the use of Convolution Neural Networks for semantic classification of heritage images. Recently four architectures, AlexNet, GoogLeNet, ResNet, and SENet have been proposed. We have tested them with two different learning modalities. Besides conventional training from scratch, we have resorted to pre-trained networks that have been fine-tuned on a huge amount of data in order to avoid overfitting problems and to reduce design/computing time. Experiments on a dataset we have built by ourselves from Heritage repository show that with the help of this step of fine-tuning, one of the simplest (and oldest) networks AlexNet is able to produce similar results than more sophisticated ones as ResNet which is pretty 3 times more consuming in computing time, mainly due to the number of the layer it uses. With this model, we have been able to show that our model can classify in the right way more than 98% of the images belonging to the dataset test. From now, this model can be used to classify the full dataset of the Heritage repository.


On Passive Wireless Device Fingerprinting using Infinite Hidden Markov Random Field


title: An Intention-Topic Model Based on Verbs Clustering and Short Texts Topic Mining

abstract: Microblog, Twitter, status messages, the classified information website and so on are experiencing explosive growth with the development of web2.0, people prefer to use short texts to express their intentions and activities. Yet, when people submit some requirements through short texts, they hope to get a feedback which can help them to solve their problems rather than relevant content. Sometimes people need corresponding intention rather than similar content. However, current researches cannot solve the problem well. In this paper, we propose an intentiontopic model: Verb-Biterm Topic Model(V-BTM), which aims at corresponding intention matching. Intention is expressed by verbs and topic is expressed by BTM. Intention is the action of people want to express and topic is the goal of the intention. The key of the model is that people tend to express their intention with verbs and tend to express the topic with non-verb. In this model, firstly, we distinguish intentions with the verb clustering with the help of word2vec which is a deep learning tool. Secondly, we mine the topic using Biterm Topic Model(BTM) on the data without verbs. We carry out experiments on real-world short text collections. The results demonstrate that our approach can get better verb clustering and mine more coherent topics. Furthermore, the new model can be the base of our future researches.


title: Closed-form Solution for IMU based LSD-SLAM Point Cloud Conversion into the Scaled 3D World Environment

abstract: SLAM is a very popular research stream in computer vision and robotics nowadays. For more effective SLAM implementation it is necessary to have reliable informa- tion about the environment, also the data should be aligned and scaled according to the real world coordinate system. Monocular SLAM research is an attractive sub-stream, because of the low equipment cost, size and weight. In this paper we present a way to build a conversion from LSD-SLAM coordinate space to the real world coordinates using a true metric scale with IMU sensor data implementation. The causes of differences between the real and calculated spaces are explained and the possibility of conversions between the spaces is proved. Additionally, a closed-form solution for inter space trans- formation calculation is presented. The synthetic method of generating high level accurate and well controlled input data for the LSD-SLAM algorithm is presented. Finally, the reconstructed 3D environment representation is delivered as an output of the implemented conversion.


Implementation and Optimization of Differentiable Neural Computers


title: A New Extended Rough Set Model Based on the Dynamic Tolerance Relation and Improved Tolerance Degree Calculating Method

abstract: A dynamic tolerance relation of extended rough set model is presented on the basis of analyzing the existing models. Firstly, an improved method to calculate the tolerance degree of two objects more objective is given. On this basis, a dynamic tolerance degree parameter is selected in this paper. It can adjust the extended model from equivalence relation to tolerance relation, besides it can adjust its value dynamically according to the dynamic adjustment of data set, so it can enlarge the scale of data which can be disposed by this model and enhance the model’s disposal ability. The UCI database is selected to demonstrate the validity of this new extended model.


title: Optimal Big Data Sharing Approach for Tele-Health in Cloud Computing

abstract: The rapid development of tele-health systems have received driving engagements from various emerging techniques, such as big data and cloud computing. Sharing data among multiple tele-health systems is an adaptive approach for improving service quality via the network-based technologies. However, current implementations of data sharing in cloud computing is still facing the restrictions caused by the networking capacities and virtual machine switches. In this paper, we focus on the problem of data sharing obstacles in cloud computing and propose an approach that uses dynamic programming to produce optimal solutions to data sharing mechanisms. The proposed approach is called Optimal Telehealth Data Sharing Model (OTDSM), which considers transmission probabilities, maximizing network capacities, and timing constraints. Our experimental results have proved the flexibility and adoptability of the proposed method.


title: An Energy-Aware Ant Colony Algorithm for Network-Aware Virtual Machine Placement in Cloud Computing

abstract: The energy cost is one of the major concerns for the cloud providers. Virtual machine placement has been demonstrated as an effective method for energy saving. In addition to constraints caused by the physical machine resources such as CPU and memory (PM-constraints), the constraints caused by the network resource such as bandwidth (Net-constraints) are also crucial, since virtual machines are not isolated and require communication with each other to exchange data. However, most current research on data center power optimization only focuses on server resource. As a result, the optimization results are often inferior, because server consolidation without considering the network may cause traffic congestion and thus degraded network performance. We take the traffic demands between virtual machines into consideration and formulate the virtual machine placement problem under both PM-constraints and Net-constraints to minimize the energy cost, and propose an approach based on ant colony optimization to solve the problem. We evaluate the expected performance of our proposed algorithm through a simulation study, providing strong indications to the superiority of our proposed solution.


An Overview of Data Mining Techniques Applied for Heart Disease Diagnosis and Prediction


title: Functional Neuroanatomy of Different Olfactory Judgments

abstract: Abstract Humans routinely make judgments about olfactory stimuli. However, few studies have examined the functional neuroanatomy underlying the cognitive operations involved in such judgments. In order to delineate this functional anatomy, we asked 12 normal subjects to perform different judgments about olfactory stimuli while regional cerebral blood flow (rCBF) was measured with PET. In separate conditions, subjects made judgments about the presence (odor detection), intensity, hedonicity, familiarity, or edibility of different odorants. An auditory task served as a control condition. All five olfactory tasks induced rCBF increases in the right orbitofrontal cortex (OFC), but right OFC activity was highest during familiarity judgments and lowest during the detection task. Left OFC activity increased significantly during hedonic and familiarity judgments, but not during other odor judgments. Left OFC activity was significantly higher during hedonicity judgments than during familiarity or other olfactory judgments. These data demonstrate that aspects of odor processing in the OFC are lateralized depending on the type of olfactory task. They support a model of parallel processing in the left and right OFC in which the relative level of activation depends on whether the judgment involves odor recognition or emotion. Primary visual areas also demonstrated a differential involvement in olfactory processing depending on the type of olfactory task: significant rCBF increases were observed in hedonic and edibility judgments, whereas no significant rCBF increases were found in the other three judgments. These data indicate that judgments of hedonicity and edibility engage circuits involved in visual processing, but detection, intensity, and familiarity judgments do not.


title: TextTiling: Segmenting Text Into Multi-Paragraph Subtopic Passages

abstract: TextTiling is a technique for subdividing texts into multi-paragraph units that represent passages, or subtopics. The discourse cues for identifying major subtopic shifts are patterns of lexical co-occurrence and distribution. The algorithm is fully implemented and is shown to produce segmentation that corresponds well to human judgments of the subtopic boundaries of 12 texts. Multi-paragraph subtopic segmentation should be useful for many text analysis tasks, including information retrieval and summarization.


title: Wearable Sensing for Solid Biomechanics: A Review

abstract: Understanding the solid biomechanics of the human body is important to the study of structure and function of the body, which can have a range of applications in health care, sport, well-being, and workflow analysis. Conventional laboratory-based biomechanical analysis systems and observation-based tests are designed only to capture brief snapshots of the mechanics of movement. With recent developments in wearable sensing technologies, biomechanical analysis can be conducted in less-constrained environments, thus allowing continuous monitoring and analysis beyond laboratory settings. In this paper, we review the current research in wearable sensing technologies for biomechanical analysis, focusing on sensing and analytics that enable continuous, long-term monitoring of kinematics and kinetics in a free-living environment. The main technical challenges, including measurement drift, external interferences, nonlinear sensor properties, sensor placement, and muscle variations, that can affect the accuracy and robustness of existing methods and different methods for reducing the impact of these sources of errors are described in this paper. Recent developments in motion estimation in kinematics, mobile force sensing in kinematics, sensor reduction for electromyography, and the future direction of sensing for biomechanics are also discussed.


title: Social Media and the Elections

abstract: In the United States, social media sites—such as Facebook, Twitter, and YouTube—are currently being used by two out of three people ( 1 ), and search engines are used daily ( 2 ). Monitoring what users share or search for in social media and on the Web has led to greater insights into what people care about or pay attention to at any moment in time. Furthermore, it is also helping segments of the world population to be informed, to organize, and to react rapidly. However, social media and search results can be readily manipulated, which is something that has been underappreciated by the press and the general public.


title: Inverse dynamics control of floating base systems using orthogonal decomposition

abstract: Model-based control methods can be used to enable fast, dexterous, and compliant motion of robots without sacrificing control accuracy. However, implementing such techniques on floating base robots, e.g., humanoids and legged systems, is non-trivial due to under-actuation, dynamically changing constraints from the environment, and potentially closed loop kinematics. In this paper, we show how to compute the analytically correct inverse dynamics torques for model-based control of sufficiently constrained floating base rigid-body systems, such as humanoid robots with one or two feet in contact with the environment. While our previous inverse dynamics approach relied on an estimation of contact forces to compute an approximate inverse dynamics solution, here we present an analytically correct solution by using an orthogonal decomposition to project the robot dynamics onto a reduced dimensional space, independent of contact forces. We demonstrate the feasibility and robustness of our approach on a simulated floating base bipedal humanoid robot and an actual robot dog locomoting over rough terrain.


title: A four-component decomposition of POLSAR images based on the coherency matrix

abstract: A four-component decomposition scheme of the coherency matrix is presented here for the analysis of polarimetric synthetic aperture radar (SAR) images. The coherency matrix is used to deal with nonreflection symmetric scattering case, which is an extension of covariance matrix approach. The same decomposition results have been obtained. The advantage of this approach is explicit expressions of four scattering powers in terms of scattering matrix elements, which serve the interpretation of polarimetric SAR data quantitatively


APA CENTENNIAL FEATURE Classrooms : Goals , Structures , and Student Motivation


title: A Robust Panel Extraction Method for Manga

abstract: Automatically extracting frames/panels from digital comic pages is crucial for techniques that facilitate comic reading on mobile devices with limited display areas. However, automatic panel extraction for manga, i.e., Japanese comics, can be especially challenging, largely because of its complex panel layout design mixed with various visual symbols throughout the page. In this paper, we propose a robust method for automatically extracting panels from digital manga pages. Our method first extracts the panel block by closing open panels and identifying a page background mask. It then performs a recursive binary splitting to partition the panel block into a set of sub-blocks, where an optimal splitting line at each recursive level is determined adaptively.


title: MAGIX: Model Agnostic Globally Interpretable Explanations

abstract: Explaining the behavior of a black box machine learning model at the instance level is useful for building trust. However, it is also important to understand how the model behaves globally. Such an understanding provides insight into both the data on which the model was trained and the patterns that it learned. We present here an approach that learns if-then rules to globally explain the behavior of black box machine learning models that have been used to solve classification problems. The approach works by first extracting conditions that were important at the instance level and then evolving rules through a genetic algorithm with an appropriate fitness function. Collectively, these rules represent the patterns followed by the model for decisioning and are useful for understanding its behavior. We demonstrate the validity and usefulness of the approach by interpreting black box models created using publicly available data sets as well as a private digital marketing data set.


title: Cognitum Ontorion: Knowledge Representation and Reasoning System

abstract: At any point of human activity, knowledge and expertise are a key factors in understanding and solving any given problem. In present days, computer systems have the ability to support their users in an efficient and reliable way in gathering and processing knowledge. In this chapter we show how to use Cognitum Ontorion system in this areas. In first section, we identify emerging issues focused on how to represent and inference knowledge. Next, we briefly discuss models and methodology of agent-oriented system analysis and design. In the third section, the semantic knowledge management framework of the system is reviewed. Finally, we recapitulate by discussing the usability of Ontorion based on a case study, in which an instance of software process simulation modelling environment is executed and further discussed. In the last section, we provide future work directions and put forward final conclusions.


title: Ontology Engineering Methodologies

abstract: In this chapter we present a methodology for introducing and maintaining ontology based knowledge management applications into enterprises with a focus on Knowledge Processes and Knowledge Meta Processes. While the former process circles around the usage of ontologies, the latter process guides their initial set up.We illustrate our methodology by an example from a case study on skills management. The methodology serves as a scaffold for Part B “Ontology Engineering” of the handbook. It shows where more specific concerns of ontology engineering find their place and how they are related in the overall process.


title: Planecell: Representing the 3D Space with Planes

abstract: Reconstruction based on the stereo camera has received considerable attention recently, but two particular challenges still remain. The first concerns the need to aggregate similar pixels in an effective approach, and the second is to maintain as much of the available information as possible while ensuring sufficient accuracy. To overcome these issues, we propose a new 3D representation method, namely, planecell, that extracts planarity from the depth-assisted image segmentation and then projects these depth planes into the 3D world. An energy function formulated from Conditional Random Field that generalizes the planar relationships is maximized to merge coplanar segments. We evaluate our method with a variety of reconstruction baselines on both KITTI and Middlebury datasets, and the results indicate the superiorities compared to other 3D space representation methods in accuracy, memory requirements and further applications.


title: Super class AB transconductor with slew-rate enhancement using QFG MOS techniques

abstract: A CMOS Super class-AB transconductor using Quasi Floating Gates (QFG) techniques to improve the speed and slew rate is presented. The QFG technique is applied to a static DC current source in the classic class-AB OTA, boosting the bias current for large input voltages. The new proposed OTA consumes the same static power as the traditional OTA, however the chip area is increased by 8.5% due to the extra capacitor required. The class-AB transconductor has been fabricated using a standard 0.5 μm CMOS technology. The circuit consumes 112 μW using a supply voltage of ±0.9 V and a bias current of 5 μA. The fabricated cell requires a die area of 180 μm × 162 μm.


title: Dual Band Notched UWB Monopole Antenna Using Embedded Omega Slot and Fractal Shaped Ground Plane

abstract: This paper presents the development of an Ultra Wide Band (UWB) monopole antenna with dual band notch characteristics. Modifled crown-square shaped fractal slots in the ground-plane are implemented to enhance the impedance bandwidth to around 58% as compared to conventional square monopole antenna without slots. Impedance bandwidth of the proposed antenna is approximately 114% with Voltage standing wave ratio (VSWR) < 2. In addition to this, two omega-shaped (›) slots have been incorporated in the radiating patch to render band-notch characteristics centered at 5.5GHz band assigned to IEEE802.11a and HIPERLAN/2 as well as X-band for satellite communication centered at 7.5GHz band. Measured antenna gain is stable over the entire UWB region except at the notch bands. Radiation pattern of the antenna show that the proposed antenna exhibits nearly monopole like E plane radiation patterns and omni-directional H plane radiation patterns throughout the band. A fabricated prototype is developed with close agreement between simulated and measured results.


title: Autonomous hovering of a fixed-wing micro air vehicle

abstract: Recently, there is a need to acquire intelligence in hostile or dangerous environments such as caves, forests, or urban areas. Rather than risking human life, backpackable, bird-sized aircraft, equipped with a wireless camera, can be rapidly deployed to gather reconnaissance in such environments. However, they first must be designed to fly in tight, cluttered terrain. This paper discusses an additional flight modality for a fixed-wing aircraft, enabling it to supplement existing endurance superiority with hovering capabilities. An inertial measurement sensor and an onboard processing and control unit, used to achieve autonomous hovering, are also described. This is, to the best of our knowledge, the first documented success of hovering a fixed-wing micro air vehicle autonomously


title: Fabrication of microneedles precisely imitating mosquito's proboscis by nanoscale tree dimensional laser lithography and its characterization

abstract: Aiming at painless medical applications, a hollow microneedle imitating mosquito's proboscis is developed using three-dimensional laser lithography. Unlike other reported needles, presented microneedle is divided into two half parts, each is shaped like eaves-through and dependently moved. The tip of each part is three-dimensionally sharpened, and several jagged edges project to the outside. The combined halves act as one hollow microneedle, which can draw up human blood by capillary action. The microneedle can puncture an artificial skin without buckling. The resistive force during insertion can be reduced when the two halves are alternately advanced, which mimics the motion of mosquito's proboscis.


title: An Optimal Radial Basis Function Neural Network Enhanced Adaptive Robust Kalman Filter for GNSS/INS Integrated Systems in Complex Urban Areas

abstract: Inertial Navigation System (INS) is often combined with Global Navigation Satellite System (GNSS) to increase the positioning accuracy and continuity. In complex urban environments, GNSS/INS integrated systems suffer not only from dynamical model errors but also GNSS observation gross errors. However, it is hard to distinguish dynamical model errors from observation gross errors because the observation residuals are affected by both of them in a loosely-coupled integrated navigation system. In this research, an optimal Radial Basis Function (RBF) neural network-enhanced adaptive robust Kalman filter (KF) method is proposed to isolate and mitigate the influence of the two types of errors. In the proposed method, firstly a test statistic based on Mahalanobis distance is treated as judging index to achieve fault detection. Then, an optimal RBF neural network strategy is trained on-line by the optimality principle. The network's output will bring benefits in recognizing the above two kinds of filtering fault and the system is able to choose a robust or adaptive Kalman filtering method autonomously. A field vehicle test in urban areas with a low-cost GNSS/INS integrated system indicates that two types of errors simulated in complex urban areas have been detected, distinguished and eliminated with the proposed scheme, success rate reached up to 92%. In particular, we also find that the novel neural network strategy can improve the overall position accuracy during GNSS signal short-term outages.


USE OF GROUND PENETRATING RADAR TO DELINEATE BRIDGE DECK REPAIR AREAS


title: Learning in virtual worlds: Using communities of practice to explain how people learn from play

abstract: Although there is interest in the educational potential of online multiplayer games and virtual worlds, there is still little evidence to explain specifically whatandhowpeoplelearnfromtheseenvironments.Thispaperaddressesthis issue by exploring the experiences of couples that play World of Warcraft together. Learning outcomes were identified (involving the management of ludic, social and material resources) along with learning processes, which followed Wenger’s model of participation in Communities of Practice. Comparing this with existing literature suggests that productive comparisons can be drawn with the experiences of distance education students and the social pressures that affect their participation.


title: Asymptotically optimal sampling-based kinodynamic planning

abstract: Sampling-based algorithms are viewed as practical solutions for high-dimensional motion planning. Recent progress has taken advantage of random geometric graph theory to show how asymptotic optimality can also be achieved with these methods. Achieving this desirable property for systems with dynamics requires solving a two-point boundary value problem BVP in the state space of the underlying dynamical system. It is difficult, however, if not impractical, to generate a BVP solver for a variety of important dynamical models of robots or physically simulated ones. Thus, an open challenge was whether it was even possible to achieve optimality guarantees when planning for systems without access to a BVP solver. This work resolves the above question and describes how to achieve asymptotic optimality for kinodynamic planning using incremental sampling-based planners by introducing a new rigorous framework. Two new methods, STABLE_SPARSE_RRT SST and SST*, result from this analysis, which are asymptotically near-optimal and optimal, respectively. The techniques are shown to converge fast to high-quality paths, while they maintain only a sparse set of samples, which makes them computationally efficient. The good performance of the planners is confirmed by experimental results using dynamical systems benchmarks, as well as physically simulated robots.


Long-Term On-Board Prediction of Pedestrians in Traffic Scenes


title: Multiple Complex Extreme Learning Machine using Holomorphic Mapping for Prediction of Wind Power Generation System

abstract: In this paper, a wind prediction system for the wind power generation using ensemble of multiple complex extreme learning machines (C-ELM) is proposed. The extreme learning machines is a single layer feed forward neural network having a fast learning and better generalization ability than the gradient-based learning methods. C-ELM is chosen as base classifier because it is very suitable for processing of non-linear data. For using the wind data in complex domain the wind speed and direction are represented as a complex number. This paper uses the elegant theory of conformal mapping to find better transformations in the complex domain for enhancing its prediction capability. Finally, to improve the generalization ability of the prediction system and to reduce the error encountered in single model predictors, an ensemble of multiple C-ELMS is used. The individual CELM model in the ensemble has different activation functions of the hidden layer neuron. Performance analysis proves that the predictions generated through our method are effective when compared to other complex valued neural network prediction systems.


title: Attribute And-Or Grammar for Joint Parsing of Human Pose, Parts and Attributes

abstract: This paper presents an attribute and-or grammar (A-AOG) model for jointly inferring human body pose and human attributes in a parse graph with attributes augmented to nodes in the hierarchical representation. In contrast to other popular methods in the current literature that train separate classifiers for poses and individual attributes, our method explicitly represents the decomposition and articulation of body parts, and account for the correlations between poses and attributes. The A-AOG model is an amalgamation of three traditional grammar formulations: (i) Phrase structure grammar representing the hierarchical decomposition of the human body from whole to parts; (ii) Dependency grammar modeling the geometric articulation by a kinematic graph of the body pose; and (iii) Attribute grammar accounting for the compatibility relations between different parts in the hierarchy so that their appearances follow a consistent style. The parse graph outputs human detection, pose estimation, and attribute prediction simultaneously, which are intuitive and interpretable. We conduct experiments on two tasks on two datasets, and experimental results demonstrate the advantage of joint modeling in comparison with computing poses and attributes independently. Furthermore, our model obtains better performance over existing methods for both pose estimation and attribute prediction tasks.


Summarization of Corporate Risk Factor Disclosure through Topic Modeling


title: An Efficient Approach to Scale up k-medoid based Algorithms in Large Databases

abstract: Scalable data mining algorithms have become crucial to efficiently support KDD processes on large databases. In this paper, we address the task of scaling up k-medoids based algorithms through the utilization of metric access methods, allowing clustering algorithms to be executed by database management systems in a fraction of the time usually required by the traditional approaches. Experimental results based on several datasets, including synthetic and real data, show that the proposed algorithm may reduce the number of distance calculations by a factor of more than a thousand times when compared to existing algorithms while producing clusters of comparable quality.


title: A Strong Lexical Matching Method for the Machine Comprehension Test

abstract: Machine comprehension of text is the overarching goal of a great deal of research in natural language processing. The Machine Comprehension Test (Richardson et al., 2013) was recently proposed to assess methods on an open-domain, extensible, and easy-to-evaluate task consisting of two datasets. In this paper we develop a lexical matching method that takes into account multiple context windows, question types and coreference resolution. We show that the proposed method outperforms the baseline of Richardson et al. (2013), and despite its relative simplicity, is comparable to recent work using machine learning. We hope that our approach will inform future work on this task. Furthermore, we argue that MC500 is harder than MC160 due to the way question answer pairs were created.


title: Social Media for Opioid Addiction Epidemiology: Automatic Detection of Opioid Addicts from Twitter and Case Studies

abstract: Opioid (e.g., heroin and morphine) addiction has become one of the largest and deadliest epidemics in the United States. To combat such deadly epidemic, there is an urgent need for novel tools and methodologies to gain new insights into the behavioral processes of opioid abuse and addiction. The role of social media in biomedical knowledge mining has turned into increasingly significant in recent years. In this paper, we propose a novel framework named AutoDOA to automatically detect the opioid addicts from Twitter, which can potentially assist in sharpening our understanding toward the behavioral process of opioid abuse and addiction. In AutoDOA, to model the users and posted tweets as well as their rich relationships, a structured heterogeneous information network (HIN) is first constructed. Then meta-path based approach is used to formulate similarity measures over users and different similarities are aggregated using Laplacian scores. Based on HIN and the combined meta-path, to reduce the cost of acquiring labeled examples for supervised learning, a transductive classification model is built for automatic opioid addict detection. To the best of our knowledge, this is the first work to apply transductive classification in HIN into drug-addiction domain. Comprehensive experiments on real sample collections from Twitter are conducted to validate the effectiveness of our developed system AutoDOA in opioid addict detection by comparisons with other alternate methods. The results and case studies also demonstrate that knowledge from daily-life social media data mining could support a better practice of opioid addiction prevention and treatment.


title: Wheelchair control using an EOG- and EMG-based gesture interface

abstract: In our previous study, we presented a nonverbal interface that used biopotential signals, such as electrooculargraphic (EOG) and electromyographic (EMG), captured by a simple brain-computer interface. In this paper, we apply the nonverbal interface to hands-free control of an electric wheelchair. Based on the biopotential signals, the interface recognizes the operator's gestures, such as closing the jaw, wrinkling the forehead, and looking towards left and right. By combining these gestures, the operator controls linear and turning motions, velocity, and the steering angle of the wheelchair. Experimental results for navigating the wheelchair in a hallway environment confirmed the feasibility of the proposed method.


title: Unsupervised metric fusion by cross diffusion

abstract: Metric learning is a fundamental problem in computer vision. Different features and algorithms may tackle a problem from different angles, and thus often provide complementary information. In this paper, we propose a fusion algorithm which outputs enhanced metrics by combining multiple given metrics (similarity measures). Unlike traditional co-training style algorithms where multi-view features or multiple data subsets are used for classification or regression, we focus on fusing multiple given metrics through diffusion process in an unsupervised way. Our algorithm has its particular advantage when the input similarity matrices are the outputs from diverse algorithms. We provide both theoretical and empirical explanations to our method. Significant improvements over the state-of-the-art results have been observed on various benchmark datasets. For example, we have achieved 100% accuracy (no longer the bull's eye measure) on the MPEG-7 shape dataset. Our method has a wide range of applications in machine learning and computer vision.


title: Training feedforward neural networks using Sine-Cosine algorithm to improve the prediction of liver enzymes on fish farmed on nano-selenite

abstract: Analytical prediction of oxidative stress biomarkers in ecosystem provides an expressive result for many stressors. These oxidative stress biomarkers including superoxide dismutase, glutathione peroxidase and catalase activity in fish liver tissue were analyzed within feeding different levels of selenium nanoparticles. Se-nanoparticles represent a salient defense mechanism in oxidative stress within certain limits; however, stress can be engendered from toxic levels of these nanoparticles. For instance, prediction of the level of pollution and/or stressors was elucidated to be improved with different levels of selenium nanoparticles using the bio-inspired Sine-Cosine algorithm (SCA). In this paper, we improved the prediction accuracy of liver enzymes of fish fed by nano-selenite by developing a neural network model based on SCA, that can train and update the weights and the biases of the network until reaching the optimum value. The performance of the proposed model is better and achieved more efficient than other models.


- 13086 MICROSURGICAL DEVICES BY POP-UP BOOK MEMS


Advertising Spending and Market Capitalization


title: Biometric Recognition Using 3D Ear Shape

abstract: Previous works have shown that the ear is a promising candidate for biometric identification. However, in prior work, the preprocessing of ear images has had manual steps and algorithms have not necessarily handled problems caused by hair and earrings. We present a complete system for ear biometrics, including automated segmentation of the ear in a profile view image and 3D shape matching for recognition. We evaluated this system with the largest experimental study to date in ear biometrics, achieving a rank-one recognition rate of 97.8 percent for an identification scenario and an equal error rate of 1.2 percent for a verification scenario on a database of 415 subjects and 1,386 total probes.


title: Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization

abstract: Global covariance pooling in Convolutional neural neworks has achieved impressive improvement over the classical first-order pooling. Recent works have shown matrix square root normalization plays a central role in achieving state-of-the-art performance. However, existing methods depending heavily on eigenvalue decomposition (EIG) or singular value decomposition (SVD), suffering from inefficient training due to limited support of EIG and SVD on GPU. Towards addressing this problem, we propose an iterative matrix square root normalization method for end-to-end training of covariance pooling networks. At the core of our method is a meta-layer designed with loop-embedded directed graph structure. The meta-layer consists of three consecutive nonlinear structured layers, which perform pre-normalization, coupled matrix iteration and post-compensation, respectively. Our method is much faster than EIG or SVD based ones, since it involves only matrix multiplications, suitable for parallel implementation on GPU. Moreover, the proposed network with ResNet architecture can converge in much less epochs, further accelerating network training. On large-scale ImageNet, we achieve competitive performance superior to existing covariance pooling networks. By finetuning our models pretrained on ImageNet, we establish state-of-the-art results on three challenging fine-grained benchmarks.


title: Big Data and the Internet of Things

abstract: Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this results in an ecosystem of highly interconnected devices referred to as the Internet of Things—IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amount of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to “improve” their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.


title: "Sleepiness" is serious in adolescence: Two surveys of 3235 Canadian students

abstract: BackgroundEvidence is growing that sleep problems in adolescents are significant impediments to learning and negatively affect behaviour, attainment of social competence and quality of life. The objectives of the study were to determine the level of sleepiness among students in high school, to identify factors to explain it, and to determine the association between sleepiness and performance in both academic and extracurricular activitiesMethodsA cross-sectional survey of 2201 high school students in the Hamilton Wentworth District School Board and the Near North District School Board in Ontario was conducted in 1998/9. A similar survey was done three years later involving 1034 students in the Grand Erie District School Board in the same Province. The Epworth Sleepiness Scale (ESS) was used to measure sleepiness and we also assessed the reliability of this tool for this population. Descriptive analysis of the cohort and information on various measures of performance and demographic data were included. Regression analysis, using the generalised estimating equation (GEE), was utilized to investigate factors associated with risk of sleepiness (ESS>10).ResultsSeventy per cent of the students had less than 8.5 hours weeknight sleep. Bedtime habits such as a consistent bedtime routine, staying up late or drinking caffeinated beverages before bed were statistically significantly associated with ESS, as were weeknight sleep quantity and gender. As ESS increased there was an increase in the proportion of students who felt their grades had dropped because of sleepiness, were late for school, were often extremely sleepy at school, and were involved in fewer extracurricular activities. These performance measures were statistically significantly associated with ESS. Twenty-three percent of the students felt their grades had dropped because of sleepiness. Most students (58–68%) reported that they were "really sleepy" between 8 and 10 A.M.ConclusionSleep deprivation and excessive daytime sleepiness were common in two samples of Ontario high school students and were associated with a decrease in academic achievement and extracurricular activity. There is a need to increase awareness of this problem in the education and health communities and to translate knowledge already available to strategies to address it.


title: Emulating U2F authenticator devices

abstract: Password authentication has been made more secure by adding additional factors. Unfortunately, popular two-factor authentication methods are vulnerable to attacks themselves. Therefore, the FIDO alliance proposed Universal Second Factor (U2F), an open standard aiming for unified interfaces, secure protocols, and hardware-backed tokens for authentication. However, U2F requires the user to purchase and constantly carry yet another distinct device. We propose an enhancement to U2F making it more user friendly. First, we enable the use of existing cryptographic devices a user already carries around (e. g. a bank card or her smartphone). Second, we provide increased token mobility by enabling remote authenticator tokens. Our showcases demonstrate logging into a web application as well as logging into Microsoft's Windows 10 with a cryptographic smart card via card reader or NFC, Austria's eID, a smartphone, a cloud key service, and a remote U2F token. Our evaluations indicate that our approach is more usable and easier to deploy than stock U2F while it keeps its security features intact. Only a few additional risks are induced by the underlying cryptographic devices. All in all, we believe that our enhancements can push acceptance of U2F even further and thus, make everyone benefit from state-of-the-art authentication.


title: Substrate-Integrated Waveguide Filters Based on Dual-Mode Air-Filled Resonant Cavities

abstract: This paper presents the design and implementation of a novel class of substrate-integrated waveguide (SIW) filters, based on partially air-filled cavity resonators. Removing the dielectric in a portion of the SIW cavity allows obtaining dual-mode cavities: such cavities represent a doublet, used as the building blocks in the design of filters. A thorough modal analysis of the air-filled resonant cavity based on transmission lines’ representation and a detailed study of the doublet permit to demonstrate a full control of the operation frequency, pass bandwidth, spurious-free region, and position of the transmission zeros of the filter. This paper presents the design, fabrication, and measurement of several two-pole filters based on this concept, with the aim to show the capabilities of the proposed structure and of the design technique. A five-pole filter is also presented, to describe the extension of the proposed approach to higher order filters.


title: Consumer use intention of online financial products: the Yuebao example

abstract: Abstract Drawing on a valence framework and innovation diffusion theory, this study examines Yuebao deployment to model consumers’ intention to use financial products offered online. We collect data via an online survey among a young age bracket and use a VisualPLS graphic interface to test our model. The results indicate that first, compatibility and advantages in relative utility significantly enhance consumers’ use intentions and that ease of use has an indirect influence. Second, the negative utility of perceived risk no longer significantly affects intention to use. This mainly lies in consumers’ perceived risk focused on the security of online financial products, which has become a part of all online transactions. Third, trust indirectly affects intention to use by influencing utility.


title: Robust Tracking via Convolutional Networks without Learning

abstract: Deep networks have been successfully applied to visual tracking by learning a generic representation offline from numerous training images. However the offline training is time-consuming and the learned generic representation may be less discriminative for tracking specific objects. In this paper we present that, even without learning, simple convolutional networks can be powerful enough to develop a robust representation for visual tracking. In the first frame, we randomly extract a set of normalized patches from the target region as filters, which define a set of feature maps in the subsequent frames. These maps measure similarities between each filter and the useful local intensity patterns across the target, thereby encoding its local structural information. Furthermore, all the maps form together a global representation, which maintains the relative geometric positions of the local intensity patterns, and hence the inner geometric layout of the target is also well preserved. A simple and effective online strategy is adopted to update the representation, allowing it to robustly adapt to target appearance variations. Our convolution networks have surprisingly lightweight structure, yet perform favorably against several state-of-the-art methods on a large benchmark dataset with 50 challenging videos.


title: Blockchain technology as infrastructure in public sector

abstract: The blockchain technology has evolved beyond traditional payment solutions in the finance sector and offers a potential for transforming many sectors including the public sector. The novel integration of technology and economy that open public block-chains have brought represents both challenges to and opportunities for enhancing digital public services. So far, the public sector has lagged behind other sectors in both research and exploration of this technology, but pilot cases show that there is a great potential for reforming and even transforming public service delivery. We argue that the open blockchain technology is best understood as a possible information infrastructure, given its universal, evolving, open and transparent nature. A comparison with Internet is meaningful despite obvious differences between the two. Based on some case studies, we have developed an analytical framework for better understanding the potential benefits as well as the existing challenges when introducing blockchain technology in the public sector.


title: Collaborative and Structural Recommendation of Friends using Weblog-based Social Network Analysis

abstract: In this paper, we address the problem of link recommendation in weblogs and similar social networks. First, we present an approach based on collaborative recommendation using the link structure of a social network and content-based recommendation using mutual declared interests. Next, we describe the application of this approach to a small representative subset of a large real-world social network: the user/community network of the blog service LiveJournal. We then discuss the ground features available in LiveJournal’s public user information pages and describe some graph algorithms for analysis of the social network. These are used to identify candidates, provide ground truth for recommendations, and construct features for learning the concept of a recommended link. Finally, we compare the performance of this machine learning approach to that of the rudimentary recommender system provided by LiveJournal.


title: Cost-aware travel tour recommendation

abstract: Advances in tourism economics have enabled us to collect massive amounts of travel tour data. If properly analyzed, this data can be a source of rich intelligence for providing real-time decision making and for the provision of travel tour recommendations. However, tour recommendation is quite different from traditional recommendations, because the tourist's choice is directly affected by the travel cost, which includes the financial cost and the time. To that end, in this paper, we provide a focused study of cost-aware tour recommendation. Along this line, we develop two cost-aware latent factor models to recommend travel packages by considering both the travel cost and the tourist's interests. Specifically, we first design a cPMF model, which models the tourist's cost with a 2-dimensional vector. Also, in this cPMF model, the tourist's interests and the travel cost are learnt by exploring travel tour data. Furthermore, in order to model the uncertainty in the travel cost, we further introduce a Gaussian prior into the cPMF model and develop the GcPMF model, where the Gaussian prior is used to express the uncertainty of the travel cost. Finally, experiments on real-world travel tour data show that the cost-aware recommendation models outperform state-of-the-art latent factor models with a significant margin. Also, the GcPMF model with the Gaussian prior can better capture the impact of the uncertainty of the travel cost, and thus performs better than the cPMF model.


Display Infrastructure for Virtual Environments ( DIVE )


title: Gait energy volumes and frontal gait recognition using depth images

abstract: Gait energy images (GEIs) and its variants form the basis of many recent appearance-based gait recognition systems. The GEI combines good recognition performance with a simple implementation, though it suffers problems inherent to appearance-based approaches, such as being highly view dependent. In this paper, we extend the concept of the GEI to 3D, to create what we call the gait energy volume, or GEV. A basic GEV implementation is tested on the CMU MoBo database, showing improvements over both the GEI baseline and a fused multi-view GEI approach. We also demonstrate the efficacy of this approach on partial volume reconstructions created from frontal depth images, which can be more practically acquired, for example, in biometric portals implemented with stereo cameras, or other depth acquisition systems. Experiments on frontal depth images are evaluated on an in-house developed database captured using the Microsoft Kinect, and demonstrate the validity of the proposed approach.


title: A first look into a Convolutional Neural Network for speech emotion detection

abstract: We propose a real-time Convolutional Neural Network model for speech emotion detection. Our model is trained from raw audio on a small dataset of TED talks speech data, manually annotated into three emotion classes: “Angry”, “Happy” and “Sad”. It achieves an average accuracy of 66.1%, 5% higher than a feature-based SVM baseline, with an evaluation time of few hundred milliseconds. We also provide an in-depth model visualization and analysis. We show how our neural network effectively activates during the speech sections of the waveform regardless of the emotion, ignoring the silence parts which do not contain information. On the frequency domain the CNN filters distribute throughout all the spectrum range, with higher concentration around the average pitch range related to that emotion. Each filter also activates at multiple frequency intervals, presumably due to the additional contribution of amplitude-related feature learning. Our work will allow faster and more accurate emotion detection modules for human-machine empathetic dialog systems and other related applications.


title: A High-Dimensional Set Top Box Ad Targeting Algorithm Including Experimental Comparisons to Traditional TV Algorithms

abstract: We present a method for targeting ads on television that works on today's TV systems. The method works by mining vast amounts of Set Top Box data, as well as advertiser customer data. From both sources the system builds demographic profiles, and then looks for media that have the highest match per dollar to the customer profile. The method was tested in four live television campaigns, comprising over 22,000 airings, and we present experimental results.


title: Lightweight Encryption Algorithm Based on Modified XTEA for Low-Resource Embedded Devices

abstract: The number of resource-limited wireless devices utilized in many areas of Internet of Things is growing rapidly; there is a concern about privacy and security. Various lightweight block ciphers are proposed; this work presents a modified lightweight block cipher algorithm. A Linear Feedback Shift Register is used to replace the key generation function in the XTEA1 Algorithm. Using the same evaluation conditions, we analyzed the software implementation of the modified XTEA using FELICS (Fair Evaluation of Lightweight Cryptographic Systems) a benchmarking framework which calculates RAM footprint, ROM occupation and execution time on three largely used embedded devices: 8-bit AVR microcontroller, 16-bit MSP microcontroller and 32-bit ARM microcontroller. Implementation results show that it provides less software requirements compared to original XTEA. We enhanced the security level and the software performance.


Partial Elements and Recursion via Dominances in Univalent Type Theory


title: A Simple Word Embedding Model for Lexical Substitution

abstract: The lexical substitution task requires identifying meaning-preserving substitutes for a target word instance in a given sentential context. Since its introduction in SemEval-2007, various models addressed this challenge, mostly in an unsupervised setting. In this work we propose a simple model for lexical substitution, which is based on the popular skip-gram word embedding model. The novelty of our approach is in leveraging explicitly the context embeddings generated within the skip-gram model, which were so far considered only as an internal component of the learning process. Our model is efficient, very simple to implement, and at the same time achieves state-ofthe-art results on lexical substitution tasks in an unsupervised setting.


title: High Extinction Ratio Hybrid Graphene-Silicon Photonic Crystal Switch

abstract: In this letter, we demonstrate a compact optical switch realized by integrating a graphene layer with a silicon photonic crystal cavity fabricated using deep UV immersion lithography and a novel transfer printing approach. A 17-dB extinction ratio and 0.75-nm shift in the cavity resonance are measured for a swing voltage of only 1.2 V. The graphene layer is limited to $\mu $ m in size. The experimental results are linked to a theoretical model and used to predict possible improvements to the design.


title: P2Cast: peer-to-peer patching scheme for VoD service

abstract: Providing video on demand (VoD) service over the Internet in a scalable way is a challenging problem. In this paper, we propose P2Cast - an architecture that uses a peer-to-peer approach to cooperatively stream video using patching techniques, while only relying on unicast connections among peers. We address the following two key technical issues in P2Cast: (1) constructing an application overlay appropriate for streaming; and (2) providing continuous stream playback (without glitches) in the face of disruption from an early departing client. Our simulation experiments show that P2Cast can serve many more clients than traditional client-server unicast service, and that it generally out-performs multicast-based patching if clients can cache more than of a stream's initial portion. We handle disruptions by delaying the start of playback and applying the shifted forwarding technique. A threshold on the length of time during which arriving clients are served in a single session in P2Cast serves as a knob to adjust the balance between the scalability and the clients' viewing quality in P2Cast.


title: A Child's Garden of Curses: A Gender, Historical, and Age-Related Evaluation of the Taboo Lexicon

abstract: Child swearing is a largely unexplored topic among language researchers, although assumptions about what children know about taboo language form the basis for language standards in many settings. The purpose of the studies presented here is to provide descriptive data about the emergence of adultlike swearing in children; specifically, we aim to document what words children of different ages know and use. Study 1 presents observational data from adults and children (ages 1–12). Study 2 compares perceptions of the inappropriateness of taboo words between adults and older (ages 9–12) and younger (ages 6–8) children. Collectively these data indicate that by the time children enter school they have the rudiments of adult swearing, although children and adults differ in their assessments of the inappropriateness of mild taboo words. Comparisons of these data with estimates obtained in the 1980s allow us to comment on whether swearing habits are changing over the years. Child swearing data can be applied to contemporary social problems and academic issues.


title: E-GOVERNANCE IN INDIA – PROBLEMS AND ACCEPTABILITY

abstract: Governments and public sector organizations around the world are facing to reform their public administration organizations and deliver more efficient and cost effective services, as well as better information and knowledge to their stakeholders. E-governance is the effective use of Information & Communication Technology (ICT) to improve the system of governance that is in place, and thus provide better services to the Citizens. e-Governance is considered as a high priority agenda in India, as it is considered to be the only means of taking IT to the “Common Public”. Developments in e-Governance provide opportunities to harness the power of Information and Communication Technology (ICT) to make the business of governance inexpensive, qualitatively responsive, and truly encompassing. In this paper we discuss about the basic problems and acceptability of e-Governance in India.


title: A 16b 6GS/S nyquist DAC with IMD <-90dBc up to 1.9GHz in 16nm CMOS

abstract: Advanced communication systems require DACs with high linearity over a wide bandwidth while consuming low power and small area [1]-[6]. In this work, a 16b 6GS/s Nyquist current-steering DAC in 16nm CMOS is presented. Utilizing bounded INL calibration and thermometer DEM to tackle voltage and timing errors, this DAC achieves an INL 80dBc up to 900MHz while occupying an area of 0.52mm2 and dissipating 350mW from 1.0V and 3.0V supplies.


title: Adversarial Attacks on Graph Neural Networks via Meta Learning

abstract: Deep learning models for graphs have advanced the state of the art on many tasks. Despite their recent success, little is known about their robustness. We investigate training time attacks on graph neural networks for node classification that perturb the discrete graph structure. Our core principle is to use meta-gradients to solve the bilevel problem underlying training-time attacks, essentially treating the graph as a hyperparameter to optimize. Our experiments show that small graph perturbations consistently lead to a strong decrease in performance for graph convolutional networks, and even transfer to unsupervised embeddings. Remarkably, the perturbations created by our algorithm can misguide the graph neural networks such that they perform worse than a simple baseline that ignores all relational information. Our attacks do not assume any knowledge about or access to the target classifiers.


title: Enhanced Attacks on Defensively Distilled Deep Neural Networks

abstract: Deep neural networks (DNNs) have achieved tremendous success in many tasks of machine learning, such as the image classification. Unfortunately, researchers have shown that DNNs are easily attacked by adversarial examples, slightly perturbed images which can mislead DNNs to give incorrect classification results. Such attack has seriously hampered the deployment of DNN systems in areas where security or safety requirements are strict, such as autonomous cars, face recognition, malware detection. Defensive distillation is a mechanism aimed at training a robust DNN which significantly reduces the effectiveness of adversarial examples generation. However, the state-of-the-art attack can be successful on distilled networks with 100% probability. But it is a white-box attack which needs to know the inner information of DNN. Whereas, the black-box scenario is more general. In this paper, we first propose the epsilon-neighborhood attack, which can fool the defensively distilled networks with 100% success rate in the white-box setting, and it is fast to generate adversarial examples with good visual quality. On the basis of this attack, we further propose the region-based attack against defensively distilled DNNs in the black-box setting. And we also perform the bypass attack to indirectly break the distillation defense as a complementary method. The experimental results show that our black-box attacks have a considerable success rate on defensively distilled networks.


CAMOU: LEARNING A VEHICLE CAMOUFLAGE


title: Exploring Lexical and Syntactic Features for Language Variety Identification

abstract: We present a method to discriminate between texts written in either the Netherlandic or the Flemish variant of the Dutch language. The method draws on a feature bundle representing text statistics, syntactic features, and word n-grams. Text statistics include average word length and sentence length, while syntactic features include ratios of function words and part-of-speech n-grams. The effectiveness of the classifier was measured by classifying Dutch subtitles developed for either Dutch or Flemish television. Several machine learning algorithms were compared as well as feature combination methods in order to find the optimal generalization performance. A machine-learning meta classifier based on AdaBoost attained the best F-score of 0.92.


title: An Efficient Hardware Architecture for a Neural Network Activation Function Generator

abstract: This paper proposes an efficient hardware architecture for a function generator suitable for an artificial neural network (ANN). A spline-based approximation function is designed that provides a good trade-off between accuracy and silicon area, whilst also being inherently scalable and adaptable for numerous activation functions. This has been achieved by using a minimax polynomial and through optimal placement of the approximating polynomials based on the results of a genetic algorithm. The approximation error of the proposed method compares favourably to all related research in this field. Efficient hardware multiplication circuitry is used in the implementation, which reduces the area overhead and increases the throughput.


title: Creating an Infrastructure for Training in the Responsible Conduct of Research: The University of Pittsburgh??s Experience

abstract: In response to public concerns about the consequences of research misconduct, academic institutions have become increasingly cognizant of the need to implement comprehensive, effective training in the responsible conduct of research (RCR) for faculty, staff, students, and external collaborators. The ability to meet this imperative is challenging as universities confront declining financial resources and increasing complexity of the research enterprise. The authors describe the University of Pittsburgh's design, implementation, and evaluation of a Web-based, institution-wide RCR training program called Research and Practice Fundamentals (RPF). This project, established in 2000, was embedded in the philosophy, organizational structure, and technology developed through the Integrated Advanced Information Management Systems grant from the National Library of Medicine. Utilizing a centralized, comprehensive approach, the RPF system provides an efficient mechanism for deploying content to a large, diverse cohort of learners and supports the needs of research administrators by providing access to information about who has successfully completed the training. During its first 3 years of operation, the RPF served over 17,000 users and issued more than 38,000 training certificates. The 18 modules that are currently available address issues required by regulatory mandates and other content areas important to the research community. RPF users report high levels of satisfaction with content and ease of using the system. Future efforts must explore methods to integrate non-RCR education and training into a centralized, cohesive structure. The University of Pittsburgh's experience with the RPF demonstrates the importance of developing an infrastructure for training that is comprehensive, scalable, reliable, centralized, affordable, and sustainable.


Twister2: Design of a big data toolkit


title: The Public Value Creation of eGovernment: An Empirical Study from Citizen Perspective

abstract: Due to its inherent benefits, governments all over the world have invested huge capital on eGovernment with a view to improving internal efficiency and providing better and quality services to the citizenry. However, the success of eGovernment is best measured by the perceptions of citizens who use the services. This paper, the second of a two-part study seeks to investigate the types of public value that are desired as outcomes of eGovernment as well as the salient factors that are significant in predicting value creation from the perspective of citizens. To fulfil its objectives, the study adopts a theoretical model that integrates two streams of research: (i) the updated DeLone and McLean IS Success Model and (ii) Public Value. Using survey data collected from Nigeria, the model is empirically tested and validated through structural equation modelling analysis. Findings identify democracy, reflexivity and productivity in order of importance, as the three distinct value types desired as outcomes by citizens. Furthermore, results show service quality as the most salient factor significant in predicting citizen satisfaction and citizen trust in eGovernment, while both service quality and citizen satisfaction are significant in predicting net benefits. This research has both theoretical and practical implications. First, it contributes to the advancement of knowledge in the public value creation of eGovernment. Second, it provides a practical reference point for improving service delivery in the public sector.


title: A Deep Network Architecture for Super-Resolution-Aided Hyperspectral Image Classification With Classwise Loss

abstract: The supervised deep networks have shown great potential in improving the classification performance. However, training these supervised deep networks is very challenging for hyperspectral image given the fact that usually only a small amount of labeled samples are available. In order to overcome this problem and enhance the discriminative ability of the network, in this paper, we propose a deep network architecture for a super-resolution (SR)-aided hyperspectral image classification with classwise loss (SRCL). First, a three-layer SR convolutional neural network (SRCNN) is employed to reconstruct a high-resolution image from a low-resolution image. Second, an unsupervised triplet-pipeline CNN (TCNN) with an improved classwise loss is built to encourage intraclass similarity and interclass dissimilarity. Finally, SRCNN, TCNN, and a classification module are integrated to define the SRCL, which can be fine-tuned in an end-to-end manner with a small amount of training data. Experimental results on real hyperspectral images demonstrate that the proposed SRCL approach outperforms other state-of-the-art classification methods, especially for the task in which only a small amount of training data are available.


title: Identifying Languages at the Word Level in Code-Mixed Indian Social Media Text.

abstract: Language identification at the document level has been considered an almost solved problem in some application areas, but language detectors fail in the social media context due to phenomena such as utterance internal code-switching, lexical borrowings, and phonetic typing; all implying that language identification in social media has to be carried out at the word level. The paper reports a study to detect language boundaries at the word level in chat message corpora in mixed EnglishBengali and English-Hindi. We introduce a code-mixing index to evaluate the level of blending in the corpora and describe the performance of a system developed to separate multiple languages.


title: WebSAIL wikifier at ERD 2014

abstract: In this paper, we report on our participation in Entity Recognition and Disambiguation Challenge 2014. We present WebSAIL Wikifier, an entity recognition and disambiguation system that identifies and links textual mentions to their referent entities in Wikipedia and later maps to the ERD set of target entities. WebSAIL Wikifier can handle both short text and long text queries. The system uses hybrid rule-based NER to discover mentions and a supervised machine learning approach to disambiguate their entities. The system achieves F1 of 0.641 and 0.687 on short track and long track respectively.


title: A Memristor-Based Optimization Framework for AI Applications

abstract: Memristors have recently received significant attention as ubiquitous device-level components for building a novel generation of computing systems. These devices have many promising features, such as non-volatility, low power consumption, high density, and excellent scalability. The ability to control and modify biasing voltages at the two terminals of memristors make them promising candidates to perform matrix-vector multiplications and solve systems of linear equations. In this article, we discuss how networks of memristors arranged in crossbar arrays can be used for efficiently solving optimization and machine learning problems. We introduce a new memristor-based optimization framework that combines the computational merit of memristor crossbars with the advantages of an operator splitting method, alternating direction method of multipliers (ADMM). Here, ADMM helps in splitting a complex optimization problem into subproblems that involve the solution of systems of linear equations. The capability of this framework is shown by applying it to linear programming, quadratic programming, and sparse optimization. In addition to ADMM, implementation of a customized power iteration (PI) method for eigenvalue/eigenvector computation using memristor crossbars is discussed. The memristor-based PI method can further be applied to principal component analysis (PCA). The use of memristor crossbars yields a significant speed-up in computation, and thus, we believe, has the potential to advance optimization and machine learning research in artificial intelligence (AI).


title: Threshold selection using Renyi's entropy

abstract: Image segmentation is an important and fundamental task in many digital image processing systems. Image segmentation by thresholding is the simplest technique and involves the basic assumption that objects and background in the digital image have distinct gray-level distributions. In this paper, we present a general technique for thresholding of digital images based on Renyi's entropy. Our method includes two of the previously proposed well known global thresholding methods. The effectiveness of the proposed method is demonstrated by using examples from the real-world and synthetic images.


General Systems Theory : Applications for Organization and Management


title: Differential long-term outcomes of zotarolimus-eluting stents compared with sirolimus-eluting and paclitaxel-eluting stents in diabetic and nondiabetic patients: Two-year subgroup analysis of the ZEST randomized trial

abstract: OBJECTIVES ::: To evaluate the differential treatment effects of zotarolimus-eluting stents (ZES), sirolimus-eluting stents (SES), and paclitaxel-eluting stents (PES) according to diabetic status. ::: ::: ::: BACKGROUND ::: Diabetic patients have a higher risk of ischemic complications after stenting than nondiabetic patients. ::: ::: ::: METHODS ::: Using data from the ZEST randomized trial, comparing ZES with SES and PES, we evaluated relative outcomes among stents in diabetic and nondiabetic patients. The primary outcome was a major adverse cardiac event (MACE), defined as a composite of death, myocardial infarction, or ischemia-driven target-vessel revascularization. ::: ::: ::: RESULTS ::: Of the 2,645 patients enrolled in the ZEST trial, 760 (29%) had diabetes mellitus. Baseline clinical and angiographic characteristics were similar in the three stent groups, regardless of diabetic status. In diabetic patients, ZES showed similar rates of MACE as compared to PES (13.8% vs. 15.3%, P = 0.58), but higher rates of MACE than SES (13.8% vs. 7.7%, P = 0.05). In nondiabetic patients, ZES showed similar rates of MACE as compared to SES (10.3% vs. 10.8%, P = 0.72), whereas significantly lower rates of MACE compared to PES (10.3% vs. 15.3%, P = 0.01). In comparing the ZES and SES groups, there was a substantial interaction between diabetic status and stent types on MACE occurrence (Interaction P = 0.07). However, in comparison of ZES and PES, there were no significant interactions between diabetes and stent type on MACE (Interaction P = 0.25). ::: ::: ::: CONCLUSIONS ::: In diabetic patients, SES showed the lowest rate of MACE compared with ZES and PES. But, in nondiabetic patients, SES and ZES showed significantly lower rates of MACE than PES. ZES shows a diabetes-related interaction on MACE compared with SES, but not with PES.


title: Match-Tensor: a Deep Relevance Model for Search

abstract: The application of Deep Neural Networks for ranking in search engines may obviate the need for the extensive feature engineering common to current learning-to-rank methods. However, we show that combining simple relevance matching features like BM25 with existing Deep Neural Net models often substantially improves the accuracy of these models, indicating that they do not capture essential local relevance matching signals. We describe a novel deep Recurrent Neural Net-based model that we call Match-Tensor. The architecture of the Match-Tensor model simultaneously accounts for both local relevance matching and global topicality signals allowing for a rich interplay between them when computing the relevance of a document to a query. On a large held-out test set consisting of social media documents, we demonstrate not only that Match-Tensor outperforms BM25 and other classes of DNNs but also that it largely subsumes signals present in these models.