title: Rejection Sensitivity and Depressive Symptoms in Women

abstract: It is proposed that interpersonal loss that communicates rejection increases the risk for depression specifically in individuals who not only expect rejection but are also concerned about preventing it. Accordingly, the role of rejection sensitivity (RS)—the disposition to anxiously expect, readily perceive, and overreact to rejection—in women’s depressive reactions to rejection by a romantic partner was examined. A 6-month longitudinal study of college women revealed that women high in RS compared with those who are low became more depressed when they experienced a partner-initiated breakup but not when they experienced a self-initiated or mutually initiated breakup. By contrast, RS was not associated with increased depression in response to failing to achieve an academic goal. These results support the view that depression in high-RS women is a reaction to a loss in a valued goal domain, that is, failure to prevent rejection in an important relationship.


title: Advanced Material Rendering in Blender

abstract: Physically correct and realistic visual appearance rendering or analysis of material surface visual properties require complex descriptive models capable of modelling material dependence on variable illumination and viewing conditions. The recent most advanced representation of visual properties of surface materials is a Bidirectional texture function (BTF). BTF is 7D function of planar coordinates, spectral coordinate, and viewing and illumination angles, respectively. Unlike smooth textures, it specifies their altering appearance due to varying illumination and viewing conditions. This BTF visual appearance dependency on viewing and illumination conditions significantly complicates not only its acquisition, representation, and modelling but also makes its rendering noticeably more demanding. BTF textures are acquired by costly measurements of real materials and their subsequent nontrivial processing. While several techniques for measurement or processing of BTF textures have been described already, there is no environment allowing BTF texture rendering. This contribution describes novel Blender texture plugin for the purpose of BTF texture mapping and rendering. The plugin benefits from our previously developed BTF Roller texture enlargement method which is the integral part of its implementation. The presented plugin allows creating realistic computer animations with additional BTF textures of any required size mapped onto an object surfaces while the other functionality of Blender retains.


title: Ontology-based Modeling of Clinical Practice Guidelines: A Clinical Decision Support System for Breast Cancer Follow-up Interventions at Primary Care Settings Abstract

abstract: Breast cancer follow-up care can be provided by family physicians after specialists complete the primary treatment. Cancer Care Nova Scotia has developed a breast cancer follow-up Clinical Practice Guideline (CPG) targeting family physicians. In this paper we present a project to computerize and deploy the said CPG in a Breast Cancer Follow-up Decision Support System (BCF-DSS) for use by family physicians in a primary care setting. We present a semantic web approach to model the CPG knowledge and employ a logic-based proof engine to execute the CPG in order to infer patient-specific recommendations. We present the three stages of the development of BCF-DSS--i.e., (a) Computerization of the paper-based CPG for Breast Cancer follow-up; (b) Development of three ontologies--i.e., the Breast Cancer Ontology, the CPG ontology based on the Guideline Element Model (GEM) and a Patient Ontology; and (c) Execution of the Breast Cancer follow-up CPG through a logic-based CPG execution engine.


Congenital Thyrotoxicosis, Hepatosplenomegaly and Jaundice in Two Infants of Exophthalmic Mothers


title: Learning a variational network for reconstruction of accelerated MRI data

abstract: Purpose: To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning. Theory and Methods: Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data. Results: The variational network approach is evaluated on a clinical knee imaging protocol. The variational network reconstructions outperform standard reconstruction algorithms in terms of image quality and residual artifacts for all tested acceleration factors and sampling patterns. Conclusion: Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, i.e., reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow.


title: Learning class-specific descriptors for deformable shapes using localized spectral convolutional networks

abstract: In this paper, we propose a generalization of convolutional neural networks (CNN) to non-Euclidean domains for the analysis of deformable shapes. Our construction is based on localized frequency analysis (a generalization of the windowed Fourier transform to manifolds) that is used to extract the local behavior of some dense intrinsic descriptor, roughly acting as an analogy to patches in images. The resulting local frequency representations are then passed through a bank of filters whose coefficient are determined by a learning procedure minimizing a task-specific cost. Our approach generalizes several previous methods such as HKS, WKS, spectral CNN, and GPS embeddings. Experimental results show that the proposed approach allows learning class-specific shape descriptors significantly outperforming recent state-of-the-art methods on standard benchmarks.


title: Global Illumination using Photon Maps

abstract: This paper presents a two pass global illumination method based on the concept of photon maps. It represents a significant improvement of a previously described approach both with respect to speed, accuracy and versatility. In the first pass two photon maps are created by emitting packets of energy (photons) from the light sources and storing these as they hit surfaces within the scene. We use one high resolution caustics photon map to render caustics that are visualized directly and one low resolution photon map that is used during the rendering step. The scene is rendered using a distribution ray tracing algorithm optimized by using the information in the photon maps. Shadow photons are used to render shadows more efficiently and the directional information in the photon map is used to generate optimized sampling directions and to limit the recursion in the distribution ray tracer by providing an estimate of the radiance on all surfaces with the exception of specular and highly glossy surfaces.


title: A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects

abstract: Delay tolerant networks (DTNs) may lack continuous network connectivity. Routing in DTNs is thus challenging since it must handle network partitioning, long delays, and dynamic topology in such networks. In recent years, social-based approaches, which attempt to exploit social behaviors of DTN nodes to make better routing decision, have drawn tremendous interests in DTN routing design. In this article, we summarize the social properties in DTNs, and provide a survey of recent social-based DTN routing approaches. To improve routing performance, these methods either take advantages of positive social characteristics such as community and friendship to assist packet forwarding or consider negative social characteristics such as selfishness. We conclude by discussing some open issues and challenges in social-based approaches regarding the design of DTN routing protocols.


title: HMOG: A New Biometric Modality for Continuous Authentication of Smartphone Users

abstract: In this paper, we introduce Hand Movement, Orientation, and Grasp (HMOG), a behavioral biometric to continuously authenticate smartphone users. HMOG unobtrusively captures subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated the authentication and biometric key generation (BKG) performance of HMOG features on typing data collected from 100 subjects. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 6.92% (walking) and 10.20% (sitting) when we combined HMOG, tap, and keystroke features. We performed additional experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved EERs of 13.7% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features led to EERs between 31.3% and 36.7%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at 16Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy.


title: ScreenerNet: Learning Curriculum for Neural Networks

abstract: We propose to learn a curriculum or a syllabus for supervised learning with deep neural networks. Specifically, we learn weights for each sample in training by an attached neural network, called ScreenerNet, to the original network and jointly train them in an end-to-end fashion. We show the networks augmented with our ScreenerNet achieve early convergence with better accuracy than the state-of-the-art rule-based curricular learning methods in extensive experiments using three popular vision datasets including MNIST, CIFAR10 and Pascal VOC2012, and a Cartpole task using Deep Q-learning.


( DEPSCOR FY 09 ) Obfuscation and Deobfuscation of Intent of Computer Programs


Robot motion planning with task specifications via regular languages


title: Depth Video Enhancement Based on Weighted Mode Filtering

abstract: This paper presents a novel approach for depth video enhancement. Given a high-resolution color video and its corresponding low-quality depth video, we improve the quality of the depth video by increasing its resolution and suppressing noise. For that, a weighted mode filtering method is proposed based on a joint histogram. When the histogram is generated, the weight based on color similarity between reference and neighboring pixels on the color image is computed and then used for counting each bin on the joint histogram of the depth map. A final solution is determined by seeking a global mode on the histogram. We show that the proposed method provides the optimal solution with respect to L1 norm minimization. For temporally consistent estimate on depth video, we extend this method into temporally neighboring frames. Simple optical flow estimation and patch similarity measure are used for obtaining the high-quality depth video in an efficient manner. Experimental results show that the proposed method has outstanding performance and is very efficient, compared with existing methods. We also show that the temporally consistent enhancement of depth video addresses a flickering problem and improves the accuracy of depth video.


title: Probabilistic group recommendation via information matching

abstract: Increasingly, web recommender systems face scenarios where they need to serve suggestions to groups of users; for example, when families share e-commerce or movie rental web accounts. Research to date in this domain has proposed two approaches: computing recommendations for the group by merging any members' ratings into a single profile, or computing ranked recommendations for each individual that are then merged via a range of heuristics. In doing so, none of the past approaches reason on the preferences that arise in individuals when they are members of a group. In this work, we present a probabilistic framework, based on the notion of information matching, for group recommendation. This model defines group relevance as a combination of the item's relevance to each user as an individual and as a member of the group; it can then seamlessly incorporate any group recommendation strategy in order to rank items for a set of individuals. We evaluate the model's efficacy at generating recommendations for both single individuals and groups using the MovieLens and MoviePilot data sets. In both cases, we compare our results with baselines and state-of-the-art collaborative filtering algorithms, and show that the model outperforms all others over a variety of ranking metrics.


title: The Hamiltonian Cycle Problem is Linear-Time Solvable for 4-Connected Planar Graphs

abstract: Abstract An algorithm is presented for finding a Hamiltonian cycle in 4-connected planar graphs. The algorithm uses linear time and storage space, while the previously best one given by Gouyou-Beauchamps uses O ( n 3 ) time and space, where n is the number of vertices in a graph.


Deep Learning using Support Vector Machines


title: Fast and deep deformation approximations

abstract: Character rigs are procedural systems that compute the shape of an animated character for a given pose. They can be highly complex and must account for bulges, wrinkles, and other aspects of a character's appearance. When comparing film-quality character rigs with those designed for real-time applications, there is typically a substantial and readily apparent difference in the quality of the mesh deformations. Real-time rigs are limited by a computational budget and often trade realism for performance. Rigs for film do not have this same limitation, and character riggers can make the rig as complicated as necessary to achieve realistic deformations. However, increasing the rig complexity slows rig evaluation, and the animators working with it can become less efficient and may experience frustration. In this paper, we present a method to reduce the time required to compute mesh deformations for film-quality rigs, allowing better interactivity during animation authoring and use in real-time games and applications. Our approach learns the deformations from an existing rig by splitting the mesh deformation into linear and nonlinear portions. The linear deformations are computed directly from the transformations of the rig's underlying skeleton. We use deep learning methods to approximate the remaining nonlinear portion. In the examples we show from production rigs used to animate lead characters, our approach reduces the computational time spent on evaluating deformations by a factor of 5X-10X. This significant savings allows us to run the complex, film-quality rigs in real-time even when using a CPU-only implementation on a mobile device.


title: On the design and development of attitude stabilization, vision-based navigation, and aerial gripping for a low-cost quadrotor

abstract: This paper presents the design and development of autonomous attitude stabilization, navigation in unstructured, GPS-denied environments, aggressive landing on inclined surfaces, and aerial gripping using onboard sensors on a low-cost, custom-built quadrotor. The development of a multi-functional micro air vehicle (MAV) that utilizes inexpensive off-the-shelf components presents multiple challenges due to noise and sensor accuracy, and there are control challenges involved with achieving various capabilities beyond navigation. This paper addresses these issues by developing a complete system from the ground up, addressing the attitude stabilization problem using extensive filtering and an attitude estimation filter recently developed in the literature. Navigation in both indoor and outdoor environments is achieved using a visual Simultaneous Localization and Mapping (SLAM) algorithm that relies on an onboard monocular camera. The system utilizes nested controllers for attitude stabilization, vision-based navigation, and guidance, with the navigation controller implemented using a nonlinear controller based on the sigmoid function. The efficacy of the approach is demonstrated by maintaining a stable hover even in the presence of wind gusts and when manually hitting and pulling on the quadrotor. Precision landing on inclined surfaces is demonstrated as an example of an aggressive maneuver, and is performed using only onboard sensing. Aerial gripping is accomplished with the addition of a secondary camera, capable of detecting infrared light sources, which is used to estimate the 3D location of an object, while an under-actuated and passively compliant manipulator is designed for effective gripping under uncertainty. ::: ::: The quadrotor is therefore able to autonomously navigate inside and outside, in the presence of disturbances, and perform tasks such as aggressively landing on inclined surfaces and locating and grasping an object, using only inexpensive, onboard sensors.


title: Rewarding creativity: when does it really matter?

abstract: This study examined the possibility that the relation between extrinsic rewards (e.g., pay and recognition) and employee creativity varied as a function of two conditions: employee job complexity and employee cognitive style. Our results showed a positive relation between extrinsic rewards and creativity for employees with an adaptive cognitive style who worked on relatively simple jobs. We found a weak relation between rewards and creativity for employees with an innovative cognitive style who worked on complex jobs and a negative relation for those in the adaptive style/complex job and innovative style/simple job conditions. We discussed theoretical and practical implications of these findings.


title: PLS path modeling

abstract: A presentation of the Partial Least Squares approach to Structural Equation Modeling (or PLS Path Modeling) is given together with a discussion of its extensions. This approach is compared with the estimation of Structural Equation Modeling by means of maximum likelihood (SEM-ML). Notwithstanding, this approach still shows some weaknesses. In this respect, some new improvements are proposed. Furthermore, PLS path modeling can be used for analyzing multiple tables so as to be related to more classical data analysis methods used in this field. Finally, a complete treatment of a real example is shown through the available software.


title: Understanding consumers' continuance intention towards mobile purchase: A theoretical framework and empirical study - A case of China

abstract: We examined factors that influence mobile shoppers' continued purchase intention.System quality has a significant effect on trust and satisfaction.Information quality and service quality significantly affect trust and flow.Privacy and security concerns significantly affect trust, flow and satisfaction.Trust, flow and satisfaction conjointly affect continued purchase intention. Although mobile purchase is convenient in the age of mobile commerce, many consumers still do not utilize mobile purchase to its full potential. From the mobile vendor's perspective, retaining current customers and facilitating their continued purchase are crucial to create profitability and achieve a sustainable development. An understanding of the continuance intention towards mobile purchase can provide insights into mobile vendors' marketing campaigns. Therefore, it is important to examine the determinants that impact continuance intentions of consumers for mobile purchase. Drawing upon information success model, flow theory and trust, this study proposed and empirically tested an integrated model to better understand the determinants of consumers' continued intention to purchase on mobile sites. Empirical data from 462 users who had experience with mobile purchase were tested against the proposed research model by using structural equation modelling (SEM). The results indicated that information quality, and privacy and security concerns are the main factors affecting trust, whereas service quality is the main factor affecting flow. System quality, and privacy and security concerns affect satisfaction. Trust affects flow, which in turn affects satisfaction. These three factors together affect continued intention towards mobile purchase. The findings of this study provide several important implications for mobile commerce research and practice.


title: Natural image denoising: Optimality and inherent bounds

abstract: The goal of natural image denoising is to estimate a clean version of a given noisy image, utilizing prior knowledge on the statistics of natural images. The problem has been studied intensively with considerable progress made in recent years. However, it seems that image denoising algorithms are starting to converge and recent algorithms improve over previous ones by only fractional dB values. It is thus important to understand how much more can we still improve natural image denoising algorithms and what are the inherent limits imposed by the actual statistics of the data. The challenge in evaluating such limits is that constructing proper models of natural image statistics is a long standing and yet unsolved problem. To overcome the absence of accurate image priors, this paper takes a non parametric approach and represents the distribution of natural images using a huge set of 1010 patches. We then derive a simple statistical measure which provides a lower bound on the optimal Bayesian minimum mean square error (MMSE). This imposes a limit on the best possible results of denoising algorithms which utilize a fixed support around a denoised pixel and a generic natural image prior. Our findings suggest that for small windows, state of the art denoising algorithms are approaching optimality and cannot be further improved beyond ∼ 0.1dB values.


title: Design of Smart Home Energy Management System.

abstract: A Home Energy Management (HEM) system plays a crucial role in realizing residential Demand Response programs in the smart grid environment. It provides a homeowner the ability to automatically perform smart load controls based on utility signals, customers preference and load priority. The HEMs communication time delay to perform load control is analyzed, along with its residual energy consumption. The main aim is to design how each load performs when being controlled by the HEM unit and measure electrical measurements for the different loads. Demand response (DR) is defined as changes in electricity use by demand-side resources from their normal consumption patterns in response to changes in the price of electricity. HEM system comprises an HEM unit that provides monitoring and control functionalities for a homeowner, and load controllers that gather electrical consumption data from selected appliances and perform local control based on command signals from the HEM system. A gateway, such as a smart meter, can be used to provide an interface between a utility and the data base for the electrical consumption is maintained.


title: Automatic testing of embedded software based on state‐transition requirement specifications

abstract: This paper proposes an automatic testing method in which the testing data is constructed on the basis of state-transition specifications and the testing of real machines with embedded software is automatically performed. In automatic testing based on specifications, in general a test scenario with the same extent of abstraction as the specification is difficult to convert to testing data that can be used by the automatic tester. In the proposed testing method, the event action in the state-transition specification is related to the setting and referral of the memory value in the embedded debugger, which makes it possible to convert the test scenario to testing data and to perform the testing automatically. A CASE tool to support the proposed testing method is realized and its performance is evaluated. The effectiveness and the problems of the proposed testing method are noted. © 2003 Wiley Periodicals, Inc. Electron Comm Jpn Pt 2, 86(9): 64–75, 2003; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/ecjb.10182


title: Apps of Steel: Are Exercise Apps Providing Consumers With Realistic Expectations?

abstract: Objective. To quantify the presence of health behavior theory constructs in iPhone apps targeting physical activity. Methods. This study used a content analysis of 127 apps from Apple’s (App Store) Health & Fitness category. Coders downloaded the apps and then used an established theory-based instrument to rate each app’s inclusion of theoretical constructs from prominent behavior change theories. Five common items were used to measure 20 theoretical constructs, for a total of 100 items. A theory score was calculated for each app. Multiple regression analysis was used to identify factors associated with higher theory scores. Results. Apps were generally observed to be lacking in theoretical content. Theory scores ranged from 1 to 28 on a 100-point scale. The health belief model was the most prevalent theory, accounting for 32% of all constructs. Regression analyses indicated that higher priced apps and apps that addressed a broader activity spectrum were associated with higher total theory scores. Conclus...


title: Learning to Estimate the Travel Time

abstract: Vehicle travel time estimation or estimated time of arrival (ETA) is one of the most important location-based services (LBS). It is becoming increasingly important and has been widely used as a basic service in navigation systems and intelligent transportation systems. This paper presents a novel machine learning solution to predict the vehicle travel time based on floating-car data. First, we formulate ETA as a pure spatial-temporal regression problem based on a large set of effective features. Second, we adapt different existing machine learning models to solve the regression problem. Furthermore, we propose a Wide-Deep-Recurrent (WDR) learning model to accurately predict the travel time along a given route at a given departure time. We then jointly train wide linear models, deep neural networks and recurrent neural networks together to take full advantages of all three models. We evaluate our solution offline with millions of historical vehicle travel data. We also deploy the proposed solution on Didi Chuxing's platform, which services billions of ETA requests and benefits millions of customers per day. Our extensive evaluations show that our proposed deep learning algorithm significantly outperforms the state-of-the-art learning algorithms, as well as the solutions provided by leading industry LBS providers.


title: Qualitative decision making with correlation coefficients of hesitant fuzzy linguistic term sets

abstract: The hesitant fuzzy linguistic term set (HFLTS) is a new and flexible tool in representing hesitant qualitative information in decision making. Correlation measures and correlation coefficients have been applied widely in many research domains and practical fields. This paper focuses on the correlation measures and correlation coefficients of HFLTSs. To start the investigation, the definition of HFLTS is improved and the concept of hesitant fuzzy linguistic element (HFLE) is introduced. Motivated by the idea of traditional correlation coefficients of fuzzy sets, intuitionistic fuzzy sets and hesitant fuzzy sets, several different types of correlation coefficients for HFLTSs are proposed. The prominent properties of these correlation coefficients are then investigated. In addition, considering that different HFLEs may have different weights, the weighted correlation coefficients and ordered weighted correlation coefficients are further investigated. Finally, an application example concerning the traditional Chinese medical diagnosis is given to illustrate the applicability and validation of the proposed correlation coefficients of HFLTSs in the process of qualitative decision making.


abstract: This paper presents the results of ICDAR2017 Competition on Page Object Detection (POD). POD is to detect page objects (tables, mathematical equations, graphics, figures, etc.) from document images. This competition makes use of a dataset consists of 2,000 document page images This dataset contains abundant page objects with various types and layouts. During the competition, we received 13 different teams' registrations and finally 8 of them submitted their results. All teams used deep learning as the basic method, then combined different traditional features or methods to improve the detection performance. The team NLPR-PAL achieved the averaged F1 of 0.898 and mAPs of 0.805 in the detection of all page objects under the IOU threshold 0.8. In this overview paper, we summarize the task design, dataset, results, and the approaches used by those teams of this competitions.


title: Foreground Clustering for Joint Segmentation and Localization in Videos and Images

abstract: This paper presents a novel framework in which video/image segmentation and localization are cast into a single optimization problem that integrates information from low level appearance cues with that of high level localization cues in a very weakly supervised manner. The proposed framework leverages two representations at different levels, exploits the spatial relationship between bounding boxes and superpixels as linear constraints and simultaneously discriminates between foreground and background at bounding box and superpixel level. Different from previous approaches that mainly rely on discriminative clustering, we incorporate a foreground model that minimizes the histogram difference of an object across all image frames. Exploiting the geometric relation between the superpixels and bounding boxes enables the transfer of segmentation cues to improve localization output and vice-versa. Inclusion of the foreground model generalizes our discriminative framework to video data where the background tends to be similar and thus, not discriminative. We demonstrate the effectiveness of our unified framework on the YouTube Object video dataset, Internet Object Discovery dataset and Pascal VOC 2007.


title: A 1-MHz High-Efficiency 12-V Buck Voltage Regulator With a New Current-Source Gate Driver

abstract: This paper proposes a new current-source gate drive circuit for a synchronous buck converter. The proposed driver can drive two MOSFETs independently with different drive currents for optimal design. For the control MOSFET, the optimal design involves a tradeoff between switching loss reduction and drive circuit loss; while for the synchronous-rectifier MOSFET, the optimal design involves a tradeoff between body diode conduction loss and drive circuit loss. Furthermore, the new drive circuit can achieve: 1) significant switching loss reduction; 2) gate energy recovery and high gate drive voltage to reduce R DS(ON) conduction losses; 3) reduced conduction loss and reverse recovery loss of the body diode; and 4) zero-voltage switching of all the drive switches. The improved driver using integrated inductors is presented with multiphase buck voltage regulators (VRs) to reduce the number of magnetic cores and the core loss. The experimental results prove that a significant efficiency improvement has been achieved. At 1.5-V output, the new driver improves the efficiency from 84% using a conventional driver to 87.3% at 20 A, and at 30 A, from 79.4% to 82.8%. Overall, the new driver approach is attractive from the standpoints of both performance and cost-effectiveness.


title: Dynamic model and balancing control for two-wheeled self-balancing mobile robot on the slopes

abstract: This paper studies the balance control of two-wheeled self-balancing robot on the slope. The dynamic model was first established using Lagrange method, which was proved correct by the simulation results of its zero-state response and zero-input response. A set of balance equations were then obtained from the kinetic model, and the Lyapunov method was carried out to estimate its stability and controllability. Finally, the robot's linear model in the equilibrium position was obtained. The linear quadratic optimal regulator was designed to control its attitude and speed. The simulation results show that the two-wheeled self-balancing robot in the slope situations can keep balance without displacement.


title: The (R) Evolution of social media in software engineering

abstract: Software developers rely on media to communicate, learn, collaborate, and coordinate with others. Recently, social media has dramatically changed the landscape of software engineering, challenging some old assumptions about how developers learn and work with one another. We see the rise of the social programmer who actively participates in online communities and openly contributes to the creation of a large body of crowdsourced socio-technical content. In this paper, we examine the past, present, and future roles of social media in software engineering. We provide a review of research that examines the use of different media channels in software engineering from 1968 to the present day. We also provide preliminary results from a large survey with developers that actively use social media to understand how they communicate and collaborate, and to gain insights into the challenges they face. We find that while this particular population values social media, traditional channels, such as face-to-face communication, are still considered crucial. We synthesize findings from our historical review and survey to propose a roadmap for future research on this topic. Finally, we discuss implications for research methods as we argue that social media is poised to bring about a paradigm shift in software engineering research.


title: Non-rigid puzzles

abstract: Shape correspondence is a fundamental problem in computer graphics and vision, with applications in various problems including animation, texture mapping, robotic vision, medical imaging, archaeology and many more. In settings where the shapes are allowed to undergo non-rigid deformations and only partial views are available, the problem becomes very challenging. To this end, we present a non-rigid multi-part shape matching algorithm. We assume to be given a reference shape and its multiple parts undergoing a non-rigid deformation. Each of these query parts can be additionally contaminated by clutter, may overlap with other parts, and there might be missing parts or redundant ones. Our method simultaneously solves for the segmentation of the reference model, and for a dense correspondence to (subsets of) the parts. Experimental results on synthetic as well as real scans demonstrate the effectiveness of our method in dealing with this challenging matching scenario.


title: Fine-Grained Visual Comparisons with Local Learning

abstract: Given two images, we want to predict which exhibits a particular visual attribute more than the other -- even when the two images are quite similar. Existing relative attribute methods rely on global ranking functions, yet rarely will the visual cues relevant to a comparison be constant for all data, nor will humans' perception of the attribute necessarily permit a global ordering. To address these issues, we propose a local learning approach for fine-grained visual comparisons. Given a novel pair of images, we learn a local ranking model on the fly, using only analogous training comparisons. We show how to identify these analogous pairs using learned metrics. With results on three challenging datasets -- including a large newly curated dataset for fine-grained comparisons -- our method outperforms state-of-the-art methods for relative attribute prediction.


title: Dirichlet process mixture model for correcting technical variation in single-cell gene expression data

abstract: We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this approach is superior to global normalization followed by clustering. We show identifiability and weak convergence guarantees of our method and present a scalable Gibbs inference algorithm. This method improves cluster inference in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.


title: Zero-Shot Learning with Structured Embeddings

abstract: Despite significant recent advances in image classification, fine-grained classification remains a challenge. In the present paper, we address the zero-shot and few-shot learning scenarios as obtaining labeled data is especially difficult for fine-grained classification tasks. First, we embed state-of-the-art image descriptors in a label embedding space using side information such as attributes. We argue that learning a joint embedding space, that maximizes the compatibility between the input and output embeddings, is highly effective for zero/few-shot learning. We show empirically that such embeddings significantly outperforms the current state-of-the-art methods on two challenging datasets (Caltech-UCSD Birds and Animals with Attributes). Second, to reduce the amount of costly manual attribute annotations, we use alternate output embeddings based on the word-vector representations, obtained from large text-corpora without any supervision. We report that such unsupervised embeddings achieve encouraging results, and lead to further improvements when combined with the supervised ones.


title: A UML-based pattern specification technique

abstract: Informally described design patterns are useful for communicating proven solutions for recurring design problems to developers, but they cannot be used as compliance points against which solutions that claim to conform to the patterns are checked. Pattern specification languages that utilize mathematical notation provide the needed formality, but often at the expense of usability. We present a rigorous and practical technique for specifying pattern solutions expressed in the unified modeling language (UML). The specification technique paves the way for the development of tools that support rigorous application of design patterns to UML design models. The technique has been used to create specifications of solutions for several popular design patterns. We illustrate the use of the technique by specifying observer and visitor pattern solutions.


Spatio-Temporal Analysis of Reverted Wikipedia Edits.


title: PNP-eSCR ESD protection device with tunable trigger and holding voltage for high voltage applications

abstract: A novel ESD device (PNP with embedded SCR), with tunable VT1 and VH, is proposed for high voltage applications. Tuning is achieved through design and process options. The trigger mechanism is determined by the series connection of PNP(s) and diode. The holding voltage is determined by the eSCR and additional PNP(s).


title: Quantum speed-up of Markov chain based algorithms

abstract: We develop a generic method for quantizing classical algorithms based on random walks. We show that under certain conditions, the quantum version gives rise to a quadratic speed-up. This is the case, in particular, when the Markov chain is ergodic and its transition matrix is symmetric. This generalizes the celebrated result of L. K. Grover (1996)and a number of more recent results, including the element distinctness result of Ambainis and the result of Ambainis, Kempe and Rivosh that computes properties of quantum walks on the d-dimensional torus. Among the consequences is a faster search for multiple marked items. We show that the quantum escape time, just like its classical version, depends on the spectral properties of the transition matrix with the marked rows and columns deleted.


title: Modern Concepts of Frontal Sinus Surgery

abstract: Objectives/Hypothesis To validate the endonasal surgical approach to frontal sinus in inflammatory sinus disease, trauma, and selective tumor surgery, and to define the role of external approaches to the frontal sinus. Endonasal frontal sinusotomy can range from endoscopic removal of obstructing frontal recess cells or uncinate process to the more complex unilateral or bilateral removal of the frontal sinus floor as described in the Draf II–III drainage procedures. In contrast, the osteoplastic frontal sinusotomy remains the “gold standard” for external approaches to frontal sinus disease. ::: ::: ::: ::: Methods A retrospective review of 1286 patients undergoing either endonasal or external frontal sinusotomy by the authors at four university teaching programs from 1977. Prior author reports were updated and previously unreported patient series were combined. ::: ::: ::: ::: Results Six hundred thirty-five patients underwent type I frontal sinusotomy, 312 type II sinusotomy, and 156 type III sinusotomy. A successful result was seen in these groups, 85.2% to 99.3%, 79% to 93.3%, and 91.5% to 95%, respectively. External frontal sinusotomy or osteoplastic frontal sinusotomy was successfully performed in 187 of 194 patients. Clinical symptoms, endoscopic findings, computed tomography, and magnetic resonance image scanning, and reoperation rate measured postoperative success. ::: ::: ::: ::: Conclusions A stepwise approach to the surgical treatment of frontal sinusitis, trauma, and selective benign tumors yields successful results as defined by specific criteria which vary from 79% to 97.8%. The details of specific techniques are discussed, essential points emphasized, and author variations noted.


title: LTEV2Vsim: An LTE-V2V simulator for the investigation of resource allocation for cooperative awareness

abstract: With the increasing needs of the automotive industry, 3GPP is developing new functionalities in the long term evolution (LTE) cellular technology to specifically address vehicle-to-everything (V2X) connectivity, including in particular vehicle-to-vehicle (V2V) direct communications. At the basis of most enabled applications, either dealing with safety, traffic management or infotainment, it is of particular importance the cooperative awareness service, corresponding to messages sent periodically in broadcast by all vehicles to inform about their status and intentions. To optimize the usage of radio resources, allocations of packets are required to be smartly performed, following algorithms that currently represent a challenging open issue. Having in mind to face the design and validation of resource allocation in LTE-V2V networks when cooperative awareness is addressed, we implemented a simulator, called LTEV2Vsim, which will be shared with all the interested researchers. LTEV2Vsim is written in MATLAB and manages vehicles mobility either following simple models or more complex and realistic input files, and performs allocations following different implemented algorithms, both network controlled and autonomous. Finally, to demonstrate the possibilities of LTEV2Vsim, some example results are provided.


title: Automatically identifying wild animals in camera trap images with deep learning

abstract: Having accurate, detailed, and up-to-date information about wildlife location and behavior across broad geographic areas would revolutionize our ability to study, conserve, and manage species and ecosystems. Currently such data are mostly gathered manually at great expense, and thus are sparsely and infrequently collected. Here we investigate the ability to automatically, accurately, and inexpensively collect such data from motion sensor cameras. These camera traps enable pictures of wildlife to be collected inexpensively, unobtrusively, and at high-volume. However, identifying the animals, animal attributes, and behaviors in these pictures remains an expensive, time-consuming, manual task often performed by researchers, hired technicians, or crowdsourced teams of human volunteers. In this paper, we demonstrate that such data can be automatically extracted by deep neural networks (aka deep learning), which is a cutting-edge type of artificial intelligence. In particular, we use the existing human-labeled images from the Snapshot Serengeti dataset to train deep convolutional neural networks for identifying 48 species in 3.2 million images taken from Tanzania's Serengeti National Park. We train neural networks that automatically identify animals with over 92% accuracy. More importantly, we can choose to have our system classify only the images it is highly confident about, allowing valuable human time to be focused only on challenging images. In this case, our automatic animal identification system saves approximately ~8.2 years (at 40 hours per week) of human labeling effort (i.e. over 17,000 hours) while operating on a 3.2-million-image dataset at the same 96.6% accuracy level of crowdsourced teams of human volunteers. Those efficiency gains immediately highlight the importance of using deep neural networks to automate data extraction from camera trap images.


title: Novel High Step-Up DC–DC Converter With Coupled-Inductor and Switched-Capacitor Techniques

abstract: A novel high step-up dc-dc converter with coupled-inductor and switched-capacitor techniques is proposed in this paper. The capacitors are charged in parallel and are discharged in series by the coupled inductor, stacking on the output capacitor. Thus, the proposed converter can achieve high step-up voltage gain with appropriate duty ratio. Besides, the voltage spike on the main switch can be clamped. Therefore, low on-state resistance RDS(ON) of the main switch can be adopted to reduce the conduction loss. The efficiency can be improved. The operating principle and steady-state analyses are discussed in detail. Finally, a prototype circuit with 24-V input voltage, 400-V output voltage, and 200-W output power is implemented in the laboratory. Experiment results confirm the analysis and advantages of the proposed converter.


title: Wideband Microstrip Comb-Line Linear Array Antenna Using Stubbed-Element Technique for High Sidelobe Suppression

abstract: This paper presents a novel wideband microstrip comb-line linear array antenna using end feeding developed in the millimeter-wave. The proposed antenna is designed for 28 GHz and has 12 elements. The reflection-canceling slit structure technique is adopted to develop a broadside beam with low reflections at the input. The proposed array is designed using stub-studded radiating elements that create more resonances to broaden the impedance bandwidth. The structure is a novel comb-line linear array antenna with stub-studded radiating elements and was developed by applying stubs to eight of the radiating elements of the array to increase the input impedance bandwidth. This helps to attain a wideband linear array antenna on a single dielectric substrate, which conventionally has a narrow bandwidth. The input impedance bandwidth is from 23.5 to 33.11 GHz, which is equivalent to a bandwidth of 33.95%. The radiation pattern bandwidth is from 27.5 to 29.25 GHz for sidelobe levels of below −20 dB equivalent to a bandwidth of 6.167%. The proposed antenna array presents features such as a simple structure with low fabrication cost and an enhanced impedance bandwidth with high sidelobe suppression. The feasibility of the proposed antenna array was evaluated by simulation and measurement in the millimeter-wave band.


title: Racial microaggressions and perceptions of Internet memes

abstract: Although more blatant forms of discrimination have declined, racial prejudice continues to manifest itself in subtle ways. For example, People of Color experience racial microaggressions (i.e., subtle slights or 'put downs') in their face-to-face interactions (Nadal, 2011) and in online contexts (Clark etźal., 2011). This study investigates whether experiencing subtle racial discrimination offline can influence perceptions of online content, specifically racial themed Internet memes. Results indicate that although both People of Color and Whites viewed racial themed memes to be more offensive than non-racial themed memes (control images), for People of Color the ratings of racial themed memes were predicted by previous discrimination; those who reported experiencing more racial microaggressions in everyday settings rated racial themed memes as more offensive. The same pattern of results did not emerge for ratings of non-racial themed memes or for White participants. These results provide initial evidence that experiencing racial microaggressions in offline interactions may lead individuals from racial minority groups to be more likely to perceive racial discrimination in online settings. Non-Whites experienced more racial microaggressions than Whites.Regardless of race, racist memes were rated as more offensive than non-racist memes.For non-Whites, experiencing microaggressions predicted ratings of racist memes.This same relationship did not emerge for Whites.


title: Innovation and Creativity in Organizations

abstract: Creativity and innovation in any organization are vital to its successful performance. The authors review the rapidly growing body of research in this area with particular attention to the period 2002 to 2013, inclusive. Conceiving of both creativity and innovation as being integral parts of essentially the same process, we propose a new, integrative definition. We note that research into creativity has typically examined the stage of idea generation, whereas innovation studies have commonly also included the latter phase of idea implementation. The authors discuss several seminal theories of creativity and innovation and then apply a comprehensive levels-of-analysis framework to review extant research into individual, team, organizational, and multilevel innovation. Key measurement characteristics of the reviewed studies are then noted. In conclusion, we propose a guiding framework for future research comprising 11 major themes and 60 specific questions for future studies.


title: Notice of Retraction Risk management in construction industry

abstract: Construction industry is highly risk prone, with complex and dynamic project environments creating an atmosphere of high uncertainty and risk. The industry is vulnerable to various technical, sociopolitical and business risks. The track record to cope with these risks has not been very good in construction industry. As a result, the people working in the industry bear various failures, such as, failure of abiding by quality and operational requirements, cost overruns and uncertain delays in project completion. In light of this, it can be said that an effective systems of risk assessment and management for construction industry remains a challenging task for the industry practitioners. The aim of the this research is to identify and evaluate current risks and uncertainties in the construction industry through extensive literature survey and aims to make a basis for future studies for development of a risk management framework to be adopted by prospective investors, developers and contractors in Pakistan.


title: Advances in optimizing recurrent networks

abstract: After a more than decade-long period of relatively little research activity in the area of recurrent neural networks, several new developments will be reviewed here that have allowed substantial progress both in understanding and in technical solutions towards more efficient training of recurrent networks. These advances have been motivated by and related to the optimization issues surrounding deep learning. Although recurrent networks are extremely powerful in what they can in principle represent in terms of modeling sequences, their training is plagued by two aspects of the same issue regarding the learning of long-term dependencies. Experiments reported here evaluate the use of clipping gradients, spanning longer time ranges with leaky integration, advanced momentum techniques, using more powerful output probability models, and encouraging sparser gradients to help symmetry breaking and credit assignment. The experiments are performed on text and music data and show off the combined effects of these techniques in generally improving both training and test error.


title: A preliminary study of the sensory distribution of the penile dorsal and ventral nerves: implications for effective penile block for circumcision

abstract: OBJECTIVE ::: To determine the sensory innervation of the penis, as regional anaesthesia is often used either for postoperative analgesia or as the sole anaesthetic technique for circumcision. Since first described in 1978 the dorsal penile nerve block has become the standard technique, but some blocks are ineffective; a better understanding of the sensory innervation of the penis might improve the efficacy of the dorsal penile block technique. ::: ::: ::: PATIENTS AND METHODS ::: In 13 men undergoing circumcision with local anaesthetic, cutaneous sensation was tested before and after infiltration of the dorsal aspect of the penis, and then again after infiltration of the ventral aspect. The area of anaesthesia was mapped using pin-prick sensation. ::: ::: ::: RESULTS ::: Ten of the 13 patients showed a similar pattern of sensory distribution. After the dorsal block, the dorsal aspect of the shaft of the penis and glans penis became insensate. The ventral aspect of the shaft remained sensate up to and including the frenulum. After successful ventral infiltration all sensate areas became insensate and circumcision proceeded. In one case the frenulum and distal ventral foreskin was anaesthetized after the dorsal block and ventral infiltration was not required. No patient experienced pain during circumcision. ::: ::: ::: CONCLUSION ::: For consistently successful regional anaesthesia of the foreskin in circumcision, a dorsal block must be used. This should be combined with ventral infiltration at the site of incision. This method will avoid inconsistencies and allow pain-free circumcision using local anaesthesia in most men.


www.nrc.ca/irc/ircpubs Psychological Processes Influencing Lighting Quality


title: A hybrid neural network-first principles approach to process modeling

abstract: A hybrid neural network-first principles modeling scheme is developed and used to model a fedbatch bioreactor. The hybrid model combines a partial first principles model, which incorporates the available prior knowledge about the process being modeled, with a neural network which serves as an estimator of unmeasuredprocess parameters that are difficult to model from first principles. This hybrid model has better properties than standard “black-box” neural network models in that it is able to interpolate and extrapolate much more accurately, is easier to analyze and interpret, and requires significantly fewer training examples. Two alternative state and parameter estimation strategies, extended Kalman filtering and NLP optimization, are also considered. When no a priori known model of the unobserved process parameters is available, the hybrid network model gives better estimates of the parameters, when compared to these methods. By providing a model of these unmeasured parameters, the hybrid network can also make predictions and hence can be used for process optimization. These results apply both when full and partial state measurements are available, but in the latter case a state reconstruction method must be used for the first principles component of the hybrid model.


title: VeCure: A practical security framework to protect the CAN bus of vehicles

abstract: Vehicles are being revolutionized by integrating modern computing and communication technologies in order to improve both user experience and driving safety. As a result, vehicular systems that used to be closed systems are opening up various interfaces, such as Bluetooth, 3G/4G, GPS, etc., to the outside world, thus introducing new opportunities for cyber attacks. It has been recently demonstrated that modern vehicles are vulnerable to several remote attacks launched through Bluetooth and cellular interfaces, allowing the attacker to take full control of the vehicle. The common root cause of these attacks is the lack of message authentication for the vehicle's internal bus system, called Controller Area Network (CAN). In this work, we propose VeCure - a practical security framework for vehicular systems, which can fundamentally solve the message authentication issue of the CAN bus. VeCure is designed to be compatible with existing vehicle system architectures, and employs a trust group structure and a novel message authentication scheme with offline computation capability to minimize online message processing delay and deployment cost. We built a proof-of-concept prototype on a testbed using Freescale's automotive development boards. The experimental results show that VeCure only introduces 50us additional delay to process a message, which is at least 20-fold faster than any existing solution.


title: A Causal Approach to Hierarchical Decomposition in Reinforcement Learning

abstract: Reinforcement learning provides a means for autonomous agents to improve their action selection strategies without the need for explicit training information provided by an informed instructor. Theoretical and empirical results indicate that reinforcement learning algorithms can efficiently determine optimal or approximately optimal policies in tasks of limited size. However, as the size of a task grows, reinforcement learning algorithms become less consistent and less efficient at determining a useful policy. A key challenge in reinforcement learning is to develop methods that facilitate scaling reinforcement learning algorithms up to larger, more realistic tasks. ::: We present a series of algorithms that take advantage of task structure to make reinforcement learning more efficient in realistic tasks that display such structure. In each algorithm, we assume that the state space of a task is factored; i.e., states are collections of values of a set of state variables. Our work combines hierarchical decomposition and state abstraction to reduce the size of a task prior to applying reinforcement learning. Hierarchical decomposition breaks a task into several subtasks that can be solved separately. For hierarchical decomposition to simplify learning, it is critical that each subtask is easier to solve than the overall task. To achieve the goal of simplifying the subtasks, we perform state abstraction separately for each subtask. ::: We begin by presenting an algorithm that uses experience from the environment to dynamically perform state abstraction for each subtask in an existing hierarchy of subtasks. Since our goal is to automate hierarchical decomposition as well as state abstraction, a second algorithm uses a dynamic Bayesian network action representation to automatically decompose a task into a hierarchy of subtasks. In addition, the algorithm provides an efficient way to perform state abstraction for each resulting subtask. A third algorithm constructs compact representations of activities that represent solutions to the subtasks. These compact representations enable the use of planning to efficiently approximate solutions to higher-level subtasks without interacting with the environment. Our fourth and final algorithm provides a means to learn a dynamic Bayesian network representation of actions from experience in tasks for which the representation is not available prior to learning. ::: The dissertation provides a detailed description of each algorithm as well as some theoretical results. We also present empirical results of each algorithm in a series of experiments. In tasks that display certain types of structure, the simplifications introduced by our algorithms significantly improve the performance of reinforcement learning. The results indicate that our algorithms provide a promising approach to make reinforcement learning better suited to solve realistic tasks in which these types of structure are present.


title: Mining Twitter Data with Resource Constraints

abstract: Social media analysis constitutes a scientific field that is rapidly gaining ground due to its numerous research challenges and practical applications, as well as the unprecedented availability of data in real time. Several of these applications have significant social and economical impact, such as journalism, crisis management, advertising, etc. However, two issues regarding these applications have to be confronted. The first one is the financial cost. Despite the abundance of information, it typically comes at a premium price, and only a fraction is provided free of charge. For example, Twitter, a predominant social media online service, grants researchers and practitioners free access to only a small proportion (1%) of its publicly available stream. The second issue is the computational cost. Even when the full stream is available, off the shelf approaches are unable to operate in such settings due to the real-time computational demands. Consequently, real world applications as well as research efforts that exploit such information are limited to utilizing only a subset of the available data. In this paper, we are interested in evaluating the extent to which analytical processes are affected by the aforementioned limitation. In particular, we apply a plethora of analysis processes on two subsets of Twitter public data, obtained through the service's sampling API's. The first one is the default 1% sample, whereas the second is the Garden hose sample that our research group has access to, returning 10% of all public data. We extensively evaluate their relative performance in numerous scenarios.


title: A Highly Parallel Multi-class Pattern Classification on GPU

abstract: Multi-class pattern classification has a variety of applications and could be achieved using artificial neural networks (ANN). There are two major system architectures for using ANNs in multi-class pattern classification: using a single ANN and using multiple ANNs. Independent of what architecture is used, one of the main concerns of using ANNs is that with increasing number of pattern classes and training datasets, the training time will increase dramatically which renders the ANN unfeasible. In this paper, the vast computational power of Graphics Processing Units (GPUs) is utilized to mitigate this problem. Different architectures and different methods of feeding pattern classes are implemented in a GPU platform. Different methods have been proposed to achieve maximum parallelism and subsequently maximize throughput. Our implementation exceeds the state-of-the-art in literature in terms of speed and the accurate use of GPU resources. As a result, the proposed approach's run time is about 75% shorter than the previous approaches. In multi-ANN architecture, due to the inherent parallelism in the proposed implementation, the execution time of a system for a digit recognition application is reduced from seven hours in CPU to about 4 seconds in GPU.


title: Adapting Tree Structures for Processing with SIMD Instructions

abstract: In this paper, we accelerate the processing of tree-based index structures by using SIMD instructions. We adapt the B + -Tree and prefix B-Tree (trie) by changing the search algorithm on inner nodes from binary search to k-ary search. The k-ary search enables the use of SIMD instructions, which are commonly available on most modern processors today. The main challenge for using SIMD instructions on CPUs is their inherent requirement for consecutive memory loads. The data for one SIMD load instruction must be located in consecutive memory locations and cannot be scattered over the entire memory. The original layout of tree-based index structures does not satisfy this constraint and must be adapted to enable SIMD usage. Thus, we introduce two tree adaptations that satisfy the specific constraints of SIMD instructions. We present two di↵erent algorithms for transforming the original tree layout into a SIMD-friendly layout. Additionally, we introduce two SIMD-friendly search algorithms designed for the new layout. Our adapted B + -Tree speeds up search processes by a factor of up to eight for small data types compared to the original B + -Tree using binary search. Furthermore, our adapted prefix B-Tree enables a high search performance even for larger data types. We report a constant 14 fold speedup and an 8 fold reduction in memory consumption compared to the original B + -Tree.


title: Machine Reading with Background Knowledge

abstract: Intelligent systems capable of automatically understanding natural language text are important for many artificial intelligence applications including mobile phone voice assistants, computer vision, and robotics. Understanding language often constitutes fitting new information into a previously acquired view of the world. However, many machine reading systems rely on the text alone to infer its meaning. In this paper, we pursue a different approach; machine reading methods that make use of background knowledge to facilitate language understanding. To this end, we have developed two methods: The first method addresses prepositional phrase attachment ambiguity. It uses background knowledge within a semi-supervised machine learning algorithm that learns from both labeled and unlabeled data. This approach yields state-of-the-art results on two datasets against strong baselines; The second method extracts relationships from compound nouns. Our knowledge-aware method for compound noun analysis accurately extracts relationships and significantly outperforms a baseline that does not make use of background knowledge.


title: An Exploratory Study on Estimating the Ability of High Skilled Crowd Workers

abstract: For both clients and workers, crowdsourcing has been recognized as an attractive way of utilizing human resources online. However, because of the variety and overwhelming size and scale of the workforce available, it is often difficult for clients to identify the most appropriate candidates. To ease this difficulty, Crowdsourcing Service Providers (CSPs) provide information, such as worker's skills, the number of tasks completed, and other indicators to help clarify their ability. However, it is hard to tell how useful this information really is in determining whether or not a candidate is the right one for a task. In this paper, in addition to looking at information provided by CSPs, Individual Work Performance (IWP) as an indicator of overall job performance and the Big Five traits as indicator of personality are obtained from crowd workers. Whether these indicators help in predicting the quality of their performance is investigated. A task of translating a Japanese article into English was posted at a Japanese CSP. This resulted in submissions from 36 crowd workers who also filled out a survey related to IWP and the Big Five traits. Based on the data collected, the correlation between these indicators and the quality of output was examined. The results showed that CSP-provided indicators actually have limited correlation with the quality of output. On the other hand, there was a moderate correlation between the quality and one of the subsets of IWP (CWB: Counterproductive Work Behavior), as well as one of the subsets of the Big Five traits (conscientiousness). In particular, there was a strong correlation between low performers and CWB and conscientiousness.


title: Comparison of fractional-slot concentrated winding and PM-assisted synchronous reluctance motors for class IV electric vehicles

abstract: The design and comparative analysis of three 100 kW motors, a fractional-slot concentrated winding machine (FSCW) surface mounted permanent magnet and two permanent magnet-assisted synchronous reluctance machines (PMa SynRMs), were considered for a Class IV step van electric vehicle application. The electromagnetic, thermal, demagnetization, and acoustic performances of each machine have been presented as well as vehicle dynamic simulations for various urban drive cycles. The results show that all three motors are suitable for a Class IV electric vehicle. The advantages and the disadvantages of the machines with respect to their application for Class IV vehicles have been highlighted in this work.


title: Quantifying political leaning from tweets and retweets

abstract: Media outlets and pundits have been quick to embrace online social networks to disseminate their own opinions. But pundits’ opinions and news coverage are often marked by a clear political bias, as widely evidenced during the fiercely contested 2012 U.S. presidential elections. Given the wide availability of such data from sites like Twitter, a natural question is whether we can quantify the political leanings of media outlets using OSN data. In this work, by drawing a correspondence between tweeting and retweeting behavior, we formulate political leaning estimation as an ill-posed linear inverse problem. The result is a simple and scalable approach that does not require explicit knowledge of the network topology. We evaluate our method with a dataset of 119 million election-related tweets collected from April to November, and use it to study the political leaning of prominent tweeters and media sources.


title: Exosomes in tumor microenvironment: novel transporters and biomarkers

abstract: Tumor microenvironment (TME) plays an integral part in the biology of cancer, participating in tumor initiation, progression, and response to therapy. Exosome is an important part of TME. Exosomes are small vesicles formed in vesicular bodies with a diameter of 30-100 nm and a classic "cup" or "dish" morphology. They can contain microRNAs, mRNAs, DNA fragments and proteins, which are shuttled from a donor cell to recipient cells. Exosomes secreted from tumor cells are called tumor-derived (TD) exosomes. There is emerging evidence that TD exosomes can construct a fertile environment to support tumor proliferation, angiogenesis, invasion and premetastatic niche preparation. TD exosomes also may facilitate tumor growth and metastasis by inhibiting immune surveillance and by increasing chemoresistance via removal of chemotherapeutic drugs. Therefore, TD-exosomes might be potential targets for therapeutic interventions via their modification or removal. For example, exosomes can serve as specific delivery vehicles to tumors of drugs, small molecules, or agents of prevention and gene therapy. Furthermore, the biomarkers detected in exosomes of biological fluids imply a potential for exosomes in the early detection and diagnosis, prediction of therapeutic efficacy, and determining prognosis of cancer. Although exosomes may serve as cancer biomarkers and aid in the treatment of cancer, we have a long way to go before we can further enhance the anti-tumor therapy of exosomes and develop exosome-based cancer diagnostic and therapeutic strategies.


title: A Corpus-Based Investigation Of Definite Description Use

abstract: We present the results of a study of the use of definite descriptions in written texts aimed at assessing the feasibility of annotating corpora with information about definite description interpretation. We ran two experiments, in which subjects were asked to classify the uses of definite descriptions in a corpus of 33 newspaper articles, containing a total of 1,412 definite descriptions. We measured the agreement among annotators about the classes assigned to definite descriptions, as well as the agreement about the antecedent assigned to those definites that the annotators classified as being related to an antecedent in the text. The most interesting result of this study from a corpus annotation perspective was the rather low agreement (K = 0.63) that we obtained using versions of Hawkins's and Prince's classification schemes; better results (K = 0.76) were obtained using the simplified scheme proposed by Fraurud that includes only two classes, first-mention and subsequent-mention. The agreement about antecedents was also not complete. These findings raise questions concerning the starategy of evaluating systems for definite description interpretation by comparing their results with a standardized annotation. From a linguistic point of view, the most interesting observations were the great number of discourse-new definites in our corpus (in one of our experiments, about 50% of the definites in the collection were classified as discourse-new, 30% as anaphoric, and 18% as associative/bridging) and the presence of definites that did not seem to require a complete disambiguation.


title: Smartphone security evaluation The malware attack case

abstract: The adoption of smartphones, devices transforming from simple communication devices to ‘smart’ and multipurpose devices, is constantly increasing. Amongst the main reasons are their small size, their enhanced functionality and their ability to host many useful and attractive applications. However, this vast use of mobile platforms makes them an attractive target for conducting privacy and security attacks. This scenario increases the risk introduced by these attacks for personal mobile devices, given that the use of smartphones as business tools may extend the perimeter of an organization's IT infrastructure. Furthermore, smartphone platforms provide application developers with rich capabilities, which can be used to compromise the security and privacy of the device holder and her environment (private and/or organizational). This paper examines the feasibility of malware development in smartphone platforms by average programmers that have access to the official tools and programming libraries provided by smartphone platforms. Towards this direction in this paper we initially propose specific evaluation criteria assessing the security level of the well-known smartphone platforms (i.e. Android, BlackBerry, Apple iOS, Symbian, Windows Mobile), in terms of the development of malware. In the sequel, we provide a comparative analysis, based on a proof of concept study, in which the implementation and distribution of a location tracking malware is attempted. Our study has proven that, under circumstances, all smartphone platforms could be used by average developers as privacy attack vectors, harvesting data from the device without the users knowledge and consent.


title: Color schemes for a sectorless ATM controller working position

abstract: In sectorless air traffic management (ATM) concept, air traffic controllers are no longer in charge of a certain sector. Instead, the sectorless airspace is considered as a single unit and controllers are assigned certain aircraft, which might be located anywhere in the sectorless airspace. The air traffic controllers are responsible for these geographically independent aircraft all the way from their entry into the airspace to the exit.


title: A 3-arm randomized controlled trial on the effects of dance movement intervention and exercises on elderly with early dementia

abstract: Dementia is characterized by a progressive decline and deterioration of brain regions such as memory, spatial navigation and language, along with disturbances in daily functioning. Non-pharmacological interventions that offer a holistic approach by targeting cognitive functioning, prognosis and the psychological and social effects of dementia require rigorous investigation. The well-established benefits of physical activity for cognitive functioning and psychological support in dementia have been observed with dance-movement intervention. There is substantial evidence that dance-movement interventions provide emotional and social advantages. Thus, a randomized controlled trial (RCT) is planned to investigate the positive effects of a dance movement intervention, compared with mild physical exercise, on the physical and psychological well-being of elderly Chinese individuals with early dementia. A 3-arm RCT with waitlist control design will be used in this study. Two hundred and one elderly participants with very mild to mild dementia will be screened and randomized into the following groups: (i) dance movement based intervention, (ii) stretching and exercise intervention and (iii) no intervention waitlist-control group. The two intervention groups will receive a 1-h intervention, twice a week, for 12 weeks. The participants will be assessed four times over the course of 12 months: baseline before randomization, post-intervention (3 months), 6 months from baseline and 12 months from baseline. The primary outcomes will be compared between assessment points and between groups on neuropsychiatric symptoms, psychosocial well-being and cognitive and daily functioning. Secondary outcomes will assess the changes in salivary cortisol levels and their relationships with the primary outcome measures. This study will provide substantial evidence of the efficacy of a dance-movement-based intervention in slowing down dementia progression, due to its ability to act as a buffer against decline and improve areas affected by dementia. We also anticipate an association between cortisol levels and the outcome measures. The further development of this intervention into a structural program may be warranted for early psychosocial support among elderly populations. The trial has been registered in the Chinese Clinical Trial Registry (ChiCTR-IOR-15006541).


title: Architectural Options for Cloud Migration

abstract: Identifying and examining potential cloud migration constraints and adopting a suitable architectural solution are important steps to facilitate successful application migration to the cloud.


title: Impact of weight loss on plasminogen activator inhibitor (PAI-1), factor VII, and other hemostatic factors in moderately overweight adults

abstract: Based on previous cross-sectional findings, we hypothesized that weight loss could improve several hemostatic factors associated with cardiovascular disease. In a randomized controlled trial, moderately overweight men and women were assigned to one of four weight loss treatment groups or to a control group. Measurements of plasminogen activator inhibitor-1 (PAI-1) antigen, tissue-type plasminogen activator (t-PA) antigen, D-dimer antigen, factor VII activity, fibrinogen, and protein C antigens were made at baseline and after 6 months in 90 men and 88 women. Net treatment weight loss was 9.4 kg in men and 7.4 kg in women. There was no net change (p > 0.05) in D-dimer, fibrinogen, or protein C with weight loss. Significant (p < 0.05) decreases were observed in the combined treatment groups compared with the control group for mean PAI-1 (31% decline), t-PA antigen (24% decline), and factor VII (11% decline). Decreases in these hemostatic variables were correlated with the amount of weight lost and the degree that plasma triglycerides declined; these correlations were stronger in men than women. These findings suggest that weight loss can improve abnormalities in hemostatic factors associated with obesity.


title: Dynamic Malware Detection Using API Similarity

abstract: Hackers create different types of Malware such as Trojans which they use to steal user-confidential information (e.g. credit card details) with a few simple commands, recent malware however has been created intelligently and in an uncontrolled size, which puts malware analysis as one of the top important subjects of information security. This paper proposes an efficient dynamic malware-detection method based on API similarity. This proposed method outperform the traditional signature-based detection method. The experiment evaluated 197 malware samples and the proposed method showed promising results of correctly identified malware.


title: Machine Learning and Deep Learning Techniques to Predict Overall Survival of Brain Tumor Patients using MRI Images

abstract: This paper presents a method to automatically predict the survival rate of patients with a glioma brain tumor by classifying the patients MRI image using machine learning (ML) methods. The dataset used in this study is BraTS 2017, which provides 163 samples; each sample has four sequences of MRI brain images, the overall survival time in days, and the patients age. The dataset is labeled into three classes of survivors: short-term, mid-term, and long-term. To improve the prediction results, various types of features were extracted and trained by various ML methods. Features considered included volumetric, statistical and intensity texture, histograms and deep features; ML techniques employed included support vector machine (SVM), k-nearest neighbors (KNN), linear discriminant, tree, ensemble and logistic regression. The best prediction accuracy based on classification is achieved by using deep learning features extracted by a pre-trained convolutional neural network (CNN) and was trained by a linear discriminant.


title: Four central questions about prediction in language processing

abstract: The notion that prediction is a fundamental principle of human information processing has been en vogue over recent years. The investigation of language processing may be particularly illuminating for testing this claim. Linguists traditionally have argued prediction plays only a minor role during language understanding because of the vast possibilities available to the language user as each word is encountered. In the present review I consider four central questions of anticipatory language processing: Why (i.e. what is the function of prediction in language processing)? What (i.e. what are the cues used to predict up-coming linguistic information and what type of representations are predicted)? How (what mechanisms are involved in predictive language processing and what is the role of possible mediating factors such as working memory)? When (i.e. do individuals always predict up-coming input during language processing)? I propose that prediction occurs via a set of diverse PACS (production-, association-, combinatorial-, and simulation-based prediction) mechanisms which are minimally required for a comprehensive account of predictive language processing. Models of anticipatory language processing must be revi sed to take multiple mechanisms, mediating factors, and situational context into account. Finally, I conjecture that the evidence considered here is consistent with the notion that prediction is an important aspect but not a fundamental principle of language processing. This article is part of a Special Issue entitled SI: Prediction and Attention.


title: Customer knowledge management - improving performance of customer relationship management with knowledge management

abstract: In this paper, we illustrate the use of modern information technology to provide knowledge support to CRM processes. This knowledge support allows for performance enhancement in customer oriented business processes. We base our reasoning on an integrated customer knowledge management process model, which identifies six CRM sub-processes and four aspects of knowledge management. This integrated model aims at achieving knowledge transparency, knowledge dissemination, knowledge development and knowledge efficiency. The application of the four aspects content, competence, community and composition in specific CRM sub-processes are demonstrated in several action research cases. These cases were implemented in European companies in the financial services sector and concern the support of customer-facing processes across all channels. They validate the proposed architecture while indicating critical success factors for a successful implementation of customer knowledge management.


title: The clinicopathological significance and potential drug target of E-cadherin in NSCLC

abstract: Human epithelial cadherin (E-cadherin), a member of transmembrane glycoprotein family, encoded by the E-cadherin gene, plays a key role in cell-cell adhesion, adherent junction in normal epithelial tissues, contributing to tissue differentiation and homeostasis. Although previous studies indicated that inactivation of the E-cadherin is mainly induced by hypermethylation of E-cadherin gene, evidence concerning E-cadherin hypermethylation in the carcinogenesis and development of non-small cell lung carcinoma (NSCLC) remains controversial. In this study, we conducted a meta-analysis to quantitatively evaluate the effects of E-cadherin hypermethylation on the incidence and clinicopathological characteristics of NSCLC. A comprehensive search of PubMed and Embase databases was performed up to October 2014. Analyses of pooled data were performed. Odds ratios (ORs) were calculated and summarized. Our meta-analysis combining 18 published articles demonstrated that the hypermethylation frequencies in NSCLC were significantly higher than those in normal control tissues, OR = 3.55, 95 % confidence interval (CI) = 1.98–6.36, p < 0.0001. Further analysis showed that E-cadherin hypermethylation was not strongly associated with the sex or smoking status in NSCLC patients. In addition, E-cadherin hypermethylation was also not strongly associated with pathological types, differentiated status, clinical stages, or metastatic status in NSCLC patients. The results from the current study indicate that the hypermethylation frequency of E-cadherin in NSCLC is strongly associated with NSCLC incidence and it may be an early event in carcinogenesis of NSCLC. We also discussed the potential value of E-cadherin as a drug target that may bring new direction and hope for cancer treatment through gene-targeted therapy.


title: Automatic Music Transcription Leveraging Generalized Cepstral Features and Deep Learning

abstract: Spectral features are limited in modeling musical signals with multiple concurrent pitches due to the challenge to suppress the interference of the harmonic peaks from one pitch to another. In this paper, we show that using multiple features represented in both the frequency and time domains with deep learning modeling can reduce such interference. These features are derived systematically from conventional pitch detection functions that relate to one another through the discrete Fourier transform and a nonlinear scaling function. Neural networks modeled with these features outperform state-of-the-art methods while using less training data.


title: Deep Layer Prior Optimization for Single Image Rain Streaks Removal

abstract: Visible distortions caused by rain streaks have significant negative effects on the performance of many vision and learning algorithms. Most of the existing deraining approaches propose to build complex prior models to formulate the appearance of rain streaks. Unfortunately, these human-designed priors tend to over-smooth the background and leave too many rain streaks since the distribution of rain streaks is complex and disordered. In this work, we exploit a deep layer prior under the maximum a posterior framework to recover the intrinsic rain structure. The optimization of the resulted variational energy can be understood as simultaneously performing rain and image propagations based on data-dependent residual networks and task cues (e.g., total variation regularization), respectively. Experimental results on both synthetic and real test images demonstrate the effectiveness of our approach against both designed priors and fully data-dependent convolutional neural networks.


title: The Myth of the Shareholder Franchise

abstract: The power of shareholders to replace the board is a central element in the accepted theory of the modern public corporation with dispersed ownership. This power, however, is largely a myth. I document in this paper that the incidence of electoral challenges during the 1996-2005 decade was very low. After presenting this evidence, the paper analyzes why electoral challenges to directors are so rare, and then makes the case for arrangements that would provide shareholders with a viable power to remove directors. Under the proposed default arrangements, companies will have, at least every two years, elections with shareholder access to the corporate ballot, reimbursement of campaign expenses for candidates who receive a sufficiently significant number of votes (for example, one-third of the votes cast), and the opportunity to replace all the directors; companies will also have secret ballot and majority voting in all directors elections. Furthermore, opting out of default election arrangements through shareholder-approved bylaws should be facilitated, but boards should be constrained from adopting without shareholder approval bylaws that make director removal more difficult. Finally, I examine a wide range of possible objections to the proposed reform of corporate elections, and I conclude that they do not undermine the case for such a reform. The paper, which is based on the Raben Lecture in Corporate Law at Yale and the Uri and Caroline Bauer Lecture at Cardozo, is scheduled to appear in May 2007 in the Virginia Law Review together with responses to it by Martin Lipton and William Savitt, Jonathan Macey, John Olson, Lynn Stout, and E. Norman Veasey.


title: Generating Big Data repositories for research in medical imaging

abstract: The production of medical imaging data has grown tremendously in the last decades. The proliferation of digital acquisition equipment has enabled even small institutions to produce considerable amounts of studies. Furthermore, the general trend for new imaging modalities is to produce more data per examinations. As a result, the design and implementation of tomorrow's storage and communication systems must deal with Big Data issues. Moreover, new realities such as the outsourcing of medical imaging infrastructures to the Cloud impose additional pressure on these systems. The research on technologies for coping with Big Data issues on large scale medical imaging environments is still in its early stages. This is mostly due to the difficulty of implementing and validating new technological approaches in real environments, without interfering with clinical practice. Therefore, it is crucial to create test bed environments for research purposes. This article proposes a methodology for creating simulated Big Data repositories. The system is able to use a real world repository to collect data from a representative time window and expand it according to research needs. In addition, the solution provides anonymization tools and allows exporting two types of simulated data: a structured file repository containing DICOM objects and a statistical summary of the archive content based on its characteristics, such as the number of produced studies per day, their modality and their associated data volumes.


title: Classification of Cervical Cancer Dataset

abstract: Cervical cancer is the leading gynecological malignancy worldwide. This paper presents diverse classification techniques and shows the advantage of feature selection approaches to the best predicting of cervical cancer disease. There are thirty-two attributes with eight hundred and fifty-eight samples. Besides, this data suffers from missing values and imbalance data. Therefore, over-sampling, under-sampling and embedded over and under sampling have been used. Furthermore, dimensionality reduction techniques are required for improving the accuracy of the classifier. Therefore, feature selection methods have been studied as they divided into two distinct categories, filters and wrappers. The results show that age, first sexual intercourse, number of pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the main predictive features with high accuracy with 97.5%. Decision Tree classifier is shown to be advantageous in handling classification assignment with excellent performance.


title: MR-NTD: Manifold Regularization Nonnegative Tucker Decomposition for Tensor Data Dimension Reduction and Representation

abstract: With the advancement of data acquisition techniques, tensor (multidimensional data) objects are increasingly accumulated and generated, for example, multichannel electroencephalographies, multiview images, and videos. In these applications, the tensor objects are usually nonnegative, since the physical signals are recorded. As the dimensionality of tensor objects is often very high, a dimension reduction technique becomes an important research topic of tensor data. From the perspective of geometry, high-dimensional objects often reside in a low-dimensional submanifold of the ambient space. In this paper, we propose a new approach to perform the dimension reduction for nonnegative tensor objects. Our idea is to use nonnegative Tucker decomposition (NTD) to obtain a set of core tensors of smaller sizes by finding a common set of projection matrices for tensor objects. To preserve geometric information in tensor data, we employ a manifold regularization term for the core tensors constructed in the Tucker decomposition. An algorithm called manifold regularization NTD (MR-NTD) is developed to solve the common projection matrices and core tensors in an alternating least squares manner. The convergence of the proposed algorithm is shown, and the computational complexity of the proposed method scales linearly with respect to the number of tensor objects and the size of the tensor objects, respectively. These theoretical results show that the proposed algorithm can be efficient. Extensive experimental results have been provided to further demonstrate the effectiveness and efficiency of the proposed MR-NTD algorithm.


title: Social Network Sites: Antecedents of User Adoption and Usage.

abstract: The use of social networking websites has become a current international phenomenon. Popular websites include MySpace, Facebook, and Friendster. Their rapid widespread use warrants a better understanding. However, there has been little empirical research studying the factors that determine the use of this hedonic computer-mediated communication technology This study contributes to our understanding of the antecedents that influence adoption and use of social networking websites by examining the effect of the perceptions of playfulness, critical mass, trust, and normative pressure on the use of social networking sites.. Structural equation modeling was used to examine the patterns of inter-correlations among the constructs and to empirically test the hypotheses. Each of the antecedents has a significant direct effect on intent to use social networking websites, with playfulness and critical mass the strongest indicators. Intent to use and playfulness had a significant direct effect on actual usage.


title: Exploring Phase Change Memory and 3D Die-Stacking for Power/Thermal Friendly, Fast and Durable Memory Architectures

abstract: Emerging three-dimensional (3D) integration technology allows for the direct placement of DRAM on top of a microprocessor, significantly reducing the wire-delay between the two and thereby alleviating memory latency and bandwidth constraints. However, the increase in power density of 3D technology leads to elevated on-chip temperature, which results in an exponential rise in charge leakage of DRAM. Consequently, the refresh frequency of 3D die-stacked DRAM needs to be doubled (or more) to retain data at the expense of additional power overhead. In this work, we investigate using Phase-change Random Access Memory (PRAM) as a promising candidate to achieve scalable, low power and thermal friendly memory system architecture in the upcoming 3D-stacking technology era. Using analytical model, circuit- and architectural- level simulations that capture both physical and electrical characteristics of PRAM, we show that the higher temperature of 3D chips is beneficial to PRAM power savings due to its unique, heat-driven programming mechanisms. Moreover, we show that the Through Silicon Vias (TSVs) ubiquitously used in 3D implementations contribute further PRAM power savings due to their substantially lower resistance to the high PRAM programming current. To effectively integrate PRAM into a conventional memory hierarchy, we propose architecture and OS support to address its write latency and reliability disadvantages. We present a hybrid PRAM/DRAM memory architecture and exploit an OS- level paging scheme to improve PRAM write performance and lifetime. Moreover, we leverage the error-correcting capability of strong ECC codes to expand PRAM lifespan and use wear-out aware OS page allocation to minimize ECC performance overhead. Our experimental results show that compared to die-stacked planar DRAM, our design reduces the overall power consumption of the memory system by 54% with 6% performance degradation, consequently alleviating the thermal constraint of 3D chips by up to 4.25°C and achieving a speedup of up to 1.1X. We also show that the lifetime can be improved by a factor of 114X using the proposed endurance optimization schemes.


title: Technology Adoption among Small and Medium Enterprises (SME's): A Research Agenda

abstract: This paper presents the research agenda that has been proposed to develop an integrated model to explain technology adoption of SMEs in Malaysia. SMEs form over 90% of all business entities in Malaysia and they have been contributing to the development of the nation. Technology adoption has been a thorn issue among SMEs as they require big outlay which might not be available to the SMEs. Although resource has been an issue among SMEs they cannot lie low and ignore the technological advancements that are taking place at a rapid pace. With that in mind this paper proposes a model to explain the technology adoption issue among SMEs.