title: Agile and hackathons: a case study of emergent practices at the FNB codefest

abstract: Hackathons and similar innovation contests can accelerate the development of software prototypes to help large corporates such as banks experiment with new technology. These companies may also be adopting Agile in their existing software development practices and it is worth exploring the usage of Agile principles at such events and whether hackathons can assist overall Agile adoption. FNB is one of the largest and most innovative banks in South Africa and runs an internal hackathon called Codefest to enhance IT innovation in product design and internal operations. The event attracts over 200 internal developers who compete in teams during a 48-hour coding marathon. South African banks, including FNB, are also adopting Agile practices to improve speed and quality in their software development lifecycle. Codefest was not intended to help drive FNB's Agile journey, however some of its principles and practices were observed as having naturally occurred during the event. This article explores the emergence of Agile practices at FNB Codefest as observed during publicly broadcast interviews with various participants and stakeholders. The spoken words of the interviewees were analysed for dominant concepts using the values and principles of the Agile Manifesto as a coding framework. The interviews provided practical observations of the environment at Codefest which was found to encourage certain Agile principles and practices. Adoption of Agile by teams also correlated with their level of success in the Codefest competition however more research would be needed to determine whether Codefest accelerated the bank's overall Agile journey. Three main Agile concepts were found to be naturally cultivated by the environment of Codefest; collaboration, motivation and elements of technical excellence. Collaboration was observed between IT teams, between business and IT teams and between business teams, while also creating a model of conditions for how teams could operate during business as usual. Intrinsic motivators such as autonomy, mastery and purpose were also observed at codefest, supporting the notion of knowledge worker motivation as being crucial in setting up successful software development teams. Elements of technical excellence correlated to Agile through methodologies such as Extreme Programming or Scrum while quality practices were enabled by team practices such as communication and planning. Codefest was also mapped to a proposed model of Agile environments while considerations for such contests and suggestions for next steps are also presented. These include (1) using Codefest to raise awareness of Agile, (2) understanding how extrinsic motivators affect Codefest and Agile, (3) using Codefest participants to share and drive technical excellence and (4) Agile training before Codefest.


title: Software Defect Prediction via Convolutional Neural Network

abstract: To improve software reliability, software defect prediction is utilized to assist developers in finding potential bugs and allocating their testing efforts. Traditional defect prediction studies mainly focus on designing hand-crafted features, which are input into machine learning classifiers to identify defective code. However, these hand-crafted features often fail to capture the semantic and structural information of programs. Such information is important in modeling program functionality and can lead to more accurate defect prediction.In this paper, we propose a framework called Defect Prediction via Convolutional Neural Network (DP-CNN), which leverages deep learning for effective feature generation. Specifically, based on the programs' Abstract Syntax Trees (ASTs), we first extract token vectors, which are then encoded as numerical vectors via mapping and word embedding. We feed the numerical vectors into Convolutional Neural Network to automatically learn semantic and structural features of programs. After that, we combine the learned features with traditional hand-crafted features, for accurate software defect prediction. We evaluate our method on seven open source projects in terms of F-measure in defect prediction. The experimental results show that in average, DP-CNN improves the state-of-the-art method by 12%.


title: Synchronization in complex oscillator networks and smart grids

abstract: The emergence of synchronization in a network of coupled oscillators is a fascinating topic in various scientific disciplines. A widely adopted model of a coupled oscillator network is characterized by a population of heterogeneous phase oscillators, a graph describing the interaction among them, and diffusive and sinusoidal coupling. It is known that a strongly coupled and sufficiently homogeneous network synchronizes, but the exact threshold from incoherence to synchrony is unknown. Here, we present a unique, concise, and closed-form condition for synchronization of the fully nonlinear, nonequilibrium, and dynamic network. Our synchronization condition can be stated elegantly in terms of the network topology and parameters or equivalently in terms of an intuitive, linear, and static auxiliary system. Our results significantly improve upon the existing conditions advocated thus far, they are provably exact for various interesting network topologies and parameters; they are statistically correct for almost all networks; and they can be applied equally to synchronization phenomena arising in physics and biology as well as in engineered oscillator networks, such as electrical power networks. We illustrate the validity, the accuracy, and the practical applicability of our results in complex network scenarios and in smart grid applications.


title: A 4D Light-Field Dataset and CNN Architectures for Material Recognition

abstract: We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid-size dataset for light-field images. Our main goal is to investigate whether the additional information in a light-field (such as multiple sub-aperture views and view-dependent reflectance effects) can aid material recognition. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light-field images. In our experiments, the best performing CNN architecture achieves a 7 % boost compared with 2D image classification (\(70\,\%\rightarrow 77\,\%\)). These results constitute important baselines that can spur further research in the use of CNNs for light-field applications. Upon publication, our dataset also enables other novel applications of light-fields, including object detection, image segmentation and view interpolation.


title: Pore-scale Flow Simulation in Anisotropic Porous Material via Fluid-structure Coupling

abstract: Abstract This paper describes a novel hybrid method for fluid simulation of saturating anisotropic porous material via fluid-structure coupling. Our framework employs particle finite element method (PFEM) that not only adopts Lagrangian scheme to model the motion of freely-moving particles, but also produces the extended Delaunay Tessellation to furnish the governing equations with FEM discretization. We first employ adaptive smoothed particle hydrodynamics (SPH) to simulate porous flow respecting the anisotropic permeability with little cost. Second, the extended Delaunay Tessellation is obtained to solve differential equations for skeletal deformation. Third, a hybrid particle system is adopted to track the surface and topological changes. At the physical level, we introduce dynamic permeability considering skeletal deformation via fluid-structure coupling. At the geometric level, PFEM reduces the computational cost and effectively tracks topological changes. Moreover, our implementation on CUDA improves the performance in high-quality physics-based graphics applications. Consequently, the proposed method realistically reproduces interactions between pore-scale flow and anisotropic porous material.


title: Applying deep learning to answer selection: A study and an open task

abstract: We apply a general deep learning framework to address the non-factoid question answering task. Our approach does not rely on any linguistic tools and can be applied to different languages or domains. Various architectures are presented and compared. We create and release a QA corpus and setup a new QA task in the insurance domain. Experimental results demonstrate superior performance compared to the baseline methods and various technologies give further improvements. For this highly challenging task, the top-1 accuracy can reach up to 65.3% on a test set, which indicates a great potential for practical use.


Using Smart Objects to build the Internet of Things


title: Reconstruction from Memory in Naturalistic Environments

abstract: Abstract Many aspects of our experiences do not have to be explicitly remembered, but can be inferred based on our knowledge of the regularities in our environment. In this chapter, we investigate the interaction between episodic memory and prior knowledge in naturalistic environments. In contrast to previous studies that suggest a detrimental effect of prior knowledge, we show that when using stimuli that are statistically representative of our environment, prior knowledge of the regularities of our environment can lead to very different outcomes. For example, simple “guessing” using prior knowledge alone—without using episodic memory—leads to relatively high accuracy. In addition, we find relatively few intrusion errors in studies involving natural scenes. We argue that it is important to use ecologically valid stimuli in memory studies, because the findings of memory studies using statistically unrepresentative stimulus material are unlikely to give insights about the operation of human memory in more natural settings.


title: Evolutionary undersampling for extremely imbalanced big data classification under apache spark

abstract: The classification of datasets with a skewed class distribution is an important problem in data mining. Evolutionary undersampling of the majority class has proved to be a successful approach to tackle this issue. Such a challenging task may become even more difficult when the number of the majority class examples is very big. In this scenario, the use of the evolutionary model becomes unpractical due to the memory and time constrictions. Divide-and-conquer approaches based on the MapReduce paradigm have already been proposed to handle this type of problems by dividing data into multiple subsets. However, in extremely imbalanced cases, these models may suffer from a lack of density from the minority class in the subsets considered. Aiming at addressing this problem, in this contribution we provide a new big data scheme based on the new emerging technology Apache Spark to tackle highly imbalanced datasets. We take advantage of its in-memory operations to diminish the effect of the small sample size. The key point of this proposal lies in the independent management of majority and minority class examples, allowing us to keep a higher number of minority class examples in each subset. In our experiments, we analyze the proposed model with several data sets with up to 17 million instances. The results show the goodness of this evolutionary undersampling model for extremely imbalanced big data classification.


title: Age Progression/Regression by Conditional Adversarial Autoencoder

abstract: If I provide you a face image of mine (without telling you the actual age when I took the picture) and a large amount of face images that I crawled (containing labeled faces of different ages but not necessarily paired), can you show me what I would look like when I am 80 or what I was like when I was 5? The answer is probably a No. Most existing face aging works attempt to learn the transformation between age groups and thus would require the paired samples as well as the labeled query image. In this paper, we look at the problem from a generative modeling perspective such that no paired samples is required. In addition, given an unlabeled image, the generative model can directly produce the image with desired age attribute. We propose a conditional adversarial autoencoder (CAAE) that learns a face manifold, traversing on which smooth age progression and regression can be realized simultaneously. In CAAE, the face is first mapped to a latent vector through a convolutional encoder, and then the vector is projected to the face manifold conditional on age through a deconvolutional generator. The latent vector preserves personalized face features (i.e., personality) and the age condition controls progression vs. regression. Two adversarial networks are imposed on the encoder and generator, respectively, forcing to generate more photo-realistic faces. Experimental results demonstrate the appealing performance and flexibility of the proposed framework by comparing with the state-of-the-art and ground truth.


title: On the Performance of Byzantine Fault-Tolerant MapReduce

abstract: MapReduce is often used for critical data processing, e.g., in the context of scientific or financial simulation. However, there is evidence in the literature that there are arbitrary (or Byzantine) faults that may corrupt the results of MapReduce without being detected. We present a Byzantine fault-tolerant MapReduce framework that can run in two modes: nonspeculative and speculative. We thoroughly evaluate experimentally the performance of these two versions of the framework, showing that they use around twice more resources than Hadoop MapReduce, instead of the three times more of alternative solutions. We believe this cost is acceptable for many critical applications.


title: Miniature rectenna design

abstract: For powering small wireless IoT nodes, compact, efficient rectifying antennas have been realized. A first design for 868 and 915 MHz is described. Based on the lessons learned, a new design for 2.45 GHz is realized that is a factor 13 smaller in size.


title: A SICK cure for the evaluation of compositional distributional semantic models

abstract: Shared and internationally recognized benchmarks are fundamental for the development of any computational system. We aim to help the research community working on compositional distributional semantic models (CDSMs) by providing SICK (Sentences Involving Compositional Knowldedge), a large size English benchmark tailored for them. SICK consists of about 10,000 English sentence pairs that include many examples of the lexical, syntactic and semantic phenomena that CDSMs are expected to account for, but do not require dealing with other aspects of existing sentential data sets (idiomatic multiword expressions, named entities, telegraphic language) that are not within the scope of CDSMs. By means of crowdsourcing techniques, each pair was annotated for two crucial semantic tasks: relatedness in meaning (with a 5-point rating scale as gold score) and entailment relation between the two elements (with three possible gold labels: entailment, contradiction, and neutral). The SICK data set was used in SemEval-2014 Task 1, and it freely available for research purposes.


title: Single point active alignment method (SPAAM) for optical see-through HMD calibration for AR

abstract: Augmented reality (AR) is a technology in which a user's view of the real world is enhanced or augmented with additional information generated from a computer model. In order to have a working AR system, the see-through display system must be calibrated so that the graphics is properly rendered. The optical see-through systems present an additional challenge because we do not have access to the image data directly as in video see-through systems. This paper reports on a method we developed for optical see-through head-mounted displays. The method integrates the measurements for the camera and the magnetic tracker which is attached to the camera in order to do the calibration. The calibration is based on the alignment of image points with a single 3D point in the world coordinate system from various viewpoints. The user interaction to do the calibration is extremely easy compared to prior methods, and there is no requirement for keeping the head static while doing the calibration.


title: Peak Cardiac Power Output, Measured Noninvasively, Is a Powerful Predictor of Outcome in Chronic Heart Failure

abstract: Background— The cardiac output (CO) response to exercise and other invasively derived hemodynamic variables has been variably described to provide better prognostication than peak Vo2 in patients with chronic heart failure. Using noninvasive measurements of CO during exercise, we compared the prognostic value of peak CO and cardiac power to peak Vo2 in chronic heart failure patients. Method and Results— One hundred seventy-one consecutive patients with chronic heart failure underwent symptom limited bicycle exercise with noninvasive estimation of CO using an inert gas rebreathing method. An accurate measure of peak CO was obtained in 148 patients (85% of patients; mean age, 53�14 years; 80% male; left ventricular ejection fraction, 24�12%; ischemic etiology, 34%). Peak cardiac power was derived from the product of the peak mean arterial blood pressure and CO divided by 451. End points consisted of death, urgent heart transplant, or left ventricular assist device implantation. Duration of follow-up average...


title: Fully Automated Road Defect Detection Using Street View Images

abstract: Road quality assessment is a crucial part in municipalities' work to maintain their infrastructure, plan upgrades, and manage their budgets. Properly maintaining this infrastructure relies heavily on consistently monitoring its condition and deterioration over time. This can be a challenge, especially in larger towns and cities where there is a lot of city property to keep an eye on. We review road quality assessment methods currently employed, and then describe our novel system, which integrates a collection of existing algorithms, aimed at identifying distressed road regions from street view images and pinpointing cracks within them. We predict distressed regions by computing Fisher vectors on local SIFT descriptors and classifying them with an SVM trained to distinguish between road qualities. We follow this step with a comparison to a weighed contour map within these distressed regions to identify exact crack and defect locations, and use the contour weights to predict the crack severity. Promising results are obtained on our manually annotated dataset, which indicate the viability of using this cost-effective system to perform road quality assessment at the municipal level.


title: Fast fluid dynamics simulation on the GPU

abstract: This chapter describes a method for fast, stable fluid simulation that runs entirely on the GPU. It introduces fluid dynamics and the associated mathematics, and it describes in detail the techniques to perform the simulation on the GPU. After reading this chapter, you should have a basic understanding of fluid dynamics and know how to simulate fluids using the GPU. The source code accompanying this book demonstrates the techniques described in this chapter.


title: Evaluation in Design-Oriented Research

abstract: Design has been recognized for a long time both as art and as science. In the sixties of the previous century design-oriented research began to draw the attention of scientific researchers and methodologists, not only in technical engineering but also in the social sciences. However, a rather limited methodology for design-oriented research has been developed, especially as to the social sciences. In this article we introduce evaluation methodology and research methodology as a systematic input in the process of designing. A designing cycle is formulated with six stages, and for each of these stages operations, guidelines and criteria for evaluation are defined. All this may be used for a considerable improvement of the process and product of designing.


title: OLAP and Statistical Databases : Similarities and Differences

abstract: A non-linear emphasis circuit comprising a high-pass filter receiving an input signal, a compressor processing the output of the filter with such a variable gain as to decrease or increase responsive to an increase or a decrease of the output of the filter and an adder for adding the input signal and the output of the compressor at a predetermined rate for producing a non-linearly emphasized signal. The circuit has a structure adapted for production in an IC form and easy to design.


title: A 4×4 MIMO antenna system for mobile tablets

abstract: This paper presents a 4×4 MIMO antenna system for mobile tablets. Each antenna of the MIMO system is capable of covering GSM 900(890-960MHz), GSM 1800(1710-1880 MHz), GSM 1900(1850-1990 MHz), UMTS (1920-2170 MHz), WLAN (2400-2480 MHz), WiMAX (3400-3600 MHz) and several LTE frequency bands. The MIMO antenna system is composed of four coupled fed monopole antennas which each antenna placed on the corner at the non-grounded portion of the circuit board. The substrate used for the circuit board is FR-4 substrate with relative permittivity of 4.4 and loss tangent of 0.02. The overall volume of the circuit board is 230 × 176 × 0.8 mm ::: 3 ::: with each antenna occupying an area of 75 × 11 mm ::: 2 ::: . To improve the isolation between antennas of the MIMO system a decoupling structure, consisting of ground slots and decoupling strips, was etched on the bottom layer of the circuit board. The isolation achieved is better than 15 dB over all the frequency bands covered by each antenna leading to an envelope correlation coefficient of less than 0.09. The simulation and measured results are in good agreement.


title: Data-driven grasping

abstract: This thesis introduces a new framework for data-driven grasping. We assume, with neuropsychological justification, that human grasping is fundamentally example based rather than rule based. This means that planning grasps for a novel object will usually reduce to identifying it as similar to known objects with known grasp affordances. Our framework is intended to allow robots to mimic this approach. ::: For robots to succeed in the real world, it is essential that they be able to grasp objects based on realistically available sensor data. However, most existing grasp planners assume that the robot has access to the full 3D geometry of all objects to be grasped, which is unscalable, or abandon 3D geometry entirely to plan grasps based on appearance, which is difficult to extend to dexterous hands. The core advantage of our data-driven framework is that it naturally allows grasps to be planned for partially sensed objects. We accomplish this by using the partial sensor data to find similar 3D models, which can be used as proxy geometries for grasp planning. ::: Along with the framework, we present a new set of shape descriptors suitable for matching partial sensor data to similar - but not identical - 3D models. This is in contrast to most previous descriptors for partial matching, which tend to rely on local feature correspondences that will often not exist in our problem setting. In a similar vein we also present new algorithms for aligning the pose and scale of partial sensor data to the best matching models, where no local correspondences may be assumed to exist. ::: Our grasp planner makes use of a grasp database, consisting of example grasps for a large number of 3D models. As no such database has previously existed, this thesis introduces the Columbia Grasp Database, a freely available collection of hundreds of thousands of grasps for thousands of 3D models using a variety of robotic hands. To construct this database we modified the Eigengrasp grasp planner, which uses a low dimensional control space to simplify the grasp search space. We also discuss some limitations of this planner and show how they can be addressed by model decomposition. ::: Our use of a database of 3D models annotated with precomputed grasps suggests the possibility of annotating the models with other forms of information as well. With this in mind, we show how to leverage noisy user data downloaded from the internet to suggest likely text tags for previously unlabeled 3D models. Although this work has not yet been applied to the problem of grasp planning, we demonstrate a content-based 3D model search engine that implements our automatic labeling algorithm. ::: The title of this thesis, “Data-Driven Grasping,” represents a vision of robotic grasping that is larger than our particular implementation. Instead, the major contribution of this thesis is bridging the worlds of robotics and 3D model similarity search. Content based search is an important area of research in its own right, but the combination of content based search and robotics is particularly exciting because it opens up the possibility of using the entire internet as a knowledge base for intelligent robots.


title: Motivating Individuals and Groups at Work: A Social Identity Perspective on Leadership and Group Performance

abstract: We argue that additional understanding of work motivation can be gained by incorporating current insights concerning self-categorization and social identity processes and by examining the way in which these processes influence the motivation and behavior of individuals and groups at work. This theoretical perspective that focuses on the conditions determining different self-definitions allows us to show how individual and group processes interact to determine work motivation. To illustrate the added value of this approach, we develop some specific propositions concerning motivational processes underpinning leadership and group performance.


title: A 3-D flowering simulation based on botany characteristics and random generation algorithm

abstract: In recent years, virtual reality is experiencing a rapid development, which is also applied in plant morphology simulation. With a variety dynamic process of blooming, you are not only able to create a virtual landscape but to decorate a virtual space. In the internet, people can enjoy flowering anytime and anywhere to promote flower exhibitions and business. This paper focuses on the botanical characteristics of blooming process and creates a vivid effect based on Bezier curves and surfaces theory.


title: Simulation of powerline communication with OMNeT++ and INET-Framework

abstract: This paper presents the work that is done embedded in the EEnergy project [1] “E-DeMa” [2] to simulate powerline communication systems (PLC) for access and inhouse applications in a smart grid scenario. The simulation is implemented in a C++ builder using OMNeT++ [3] and its sub module INET [4]. OMNeT is an event based network simulation tool, and INET is a collection of common network units and technologies, so far excluding powerline. First, the rudiments of the implementations are presented - second, a real network to verify the simulation is described and third, the results are compared and analyzed.


title: Design of a Hybrid Exploration Robot for Air and Land Deployment (H.E.R.A.L.D) for urban search and rescue applications

abstract: Disaster scenarios involve a multitude of obstacles that are difficult to traverse for humans and robots alike. Most robotic search and rescue solutions to this problem involve large, tank-like robots that use brute force to cross difficult terrain; however, these large robots may cause secondary damage. H.E.R.A.L.D, the Hybrid Exploration Robot for Air and Land Deployment, is a novel integrated system of three nimble, lightweight robots which can travel over difficult obstacles by air, but also travel through rubble. We present the design methodology and optimization of each robot, as well as design and testing of the physical integration of the system as a whole, and compare the performance of the robots to the state of the art.


title: Dynamic models and model validation for PEM fuel cells using electrical circuits

abstract: This paper presents the development of dynamic models for proton exchange membrane (PEM) fuel cells using electrical circuits. The models have been implemented in MATLAB/SIMULINK and PSPICE environments. Both the double-layer charging effect and the thermodynamic characteristic inside the fuel cell are included in the models. The model responses obtained at steady-state and transient conditions are validated by experimental data measured from an Avista Labs SR-12 500-W PEM fuel-cell stack. The models could be used in PEM fuel-cell control related studies.


title: Investigation on radio wave propagation in shallow seawater: Simulations and measurements

abstract: The authors present full wave simulations and experimental results of propagation of electromagnetic waves in shallow seawaters. Transmitter and receiver antennas are ten-turns loops placed on the seabed. Some propagation frameworks are presented and simulated. Finally, simulation results are compared with experimental ones.


title: Hashing tree-structured data: Methods and applications

abstract: In this article we propose a new hashing framework for tree-structured data. Our method maps an unordered tree into a multiset of simple wedge-shaped structures refered to as pivots. By coupling our pivot multisets with the idea of minwise hashing, we realize a fixed sized signature-sketch of the tree-structured datum yielding an effective mechanism for hashing such data. We discuss several potential pivot structures and study some of the theoretical properties of such structures, and discuss their implications to tree edit distance and properties related to perfect hashing. We then empirically demonstrate the efficacy and efficiency of the overall approach on a range of real-world datasets and applications.


title: A heuristic approach for the traveling purchaser problem

abstract: Abstract The Travelling Purchaser Problem (TPP) is a known generalization of the Travelling Salesman Problem, and is defined as follows. Let us consider a set of products and a set of markets. Each market is provided with a limited amount of each product at a known price. The TPP consists in selecting a subset of markets such that a given demand of each product can be purchased, minimizing the routing cost and the purchasing cost. This problem arises in several applications, mainly in routing and scheduling contexts, and it is NP -hard in the strong sense. A new heuristic approach based on a local-search scheme, exploring a new neighbourhood structure, is proposed. The key idea is to perform a k -exchange of markets instead of the classical 1-exchanges. A neighbour of a given TPP solution is another TPP solution obtained by removing a path of consecutive markets, and by inserting other markets so as to restore the feasibility. This proposal is evaluated on a broad class of instances from literature, where the routing costs are Euclidean distances. Computational results show that our proposal is favourably compared to previous heuristic algorithms in literature.


title: Fog Computing for Smart Living

abstract: Cloud-only models face serious challenges in latency, network bandwidth, geographic focus, reliability, and security. Fog computing reduces these challenges by providing a system level horizontal architecture to distribute computing, storage, control, and networking resources and services from the cloud to connected devices (“things”). Over time, fog and cloud computing will converge into unified end-to-end platforms offering integrated services and applications along the continuum from the cloud to things. Applications developed and deployed for the cloud will be able to run in fog and vice versa. Fog computing will be integral in developing and sustaining smart living.


title: Simulation-based design of dynamic controllers for humanoid balancing

abstract: Model-based trajectory optimization often fails to find a reference trajectory for under-actuated bipedal robots performing highly-dynamic, contact-rich tasks in the real world due to inaccurate physical models. In this paper, we propose a complete system that automatically designs a reference trajectory that succeeds on tasks in the real world with a very small number of real world experiments. We adopt existing system identification techniques and show that, with appropriate model parameterization and control optimization, an iterative system identification framework can be effective for designing reference trajectories. We focus on a set of tasks that leverage the momentum transfer strategy to rapidly change the whole-body from an initial configuration to a target configuration by generating large accelerations at the center of mass and switching contacts.


title: Extracting semantically enriched events from biomedical literature

abstract: BackgroundResearch into event-based text mining from the biomedical literature has been growing in popularity to facilitate the development of advanced biomedical text mining systems. Such technology permits advanced search, which goes beyond document or sentence-based retrieval. However, existing event-based systems typically ignore additional information within the textual context of events that can determine, amongst other things, whether an event represents a fact, hypothesis, experimental result or analysis of results, whether it describes new or previously reported knowledge, and whether it is speculated or negated. We refer to such contextual information as meta-knowledge. The automatic recognition of such information can permit the training of systems allowing finer-grained searching of events according to the meta-knowledge that is associated with them.ResultsBased on a corpus of 1,000 MEDLINE abstracts, fully manually annotated with both events and associated meta-knowledge, we have constructed a machine learning-based system that automatically assigns meta-knowledge information to events. This system has been integrated into EventMine, a state-of-the-art event extraction system, in order to create a more advanced system (EventMine-MK) that not only extracts events from text automatically, but also assigns five different types of meta-knowledge to these events. The meta-knowledge assignment module of EventMine-MK performs with macro-averaged F-scores in the range of 57-87% on the BioNLP’09 Shared Task corpus. EventMine-MK has been evaluated on the BioNLP’09 Shared Task subtask of detecting negated and speculated events. Our results show that EventMine-MK can outperform other state-of-the-art systems that participated in this task.ConclusionsWe have constructed the first practical system that extracts both events and associated, detailed meta-knowledge information from biomedical literature. The automatically assigned meta-knowledge information can be used to refine search systems, in order to provide an extra search layer beyond entities and assertions, dealing with phenomena such as rhetorical intent, speculations, contradictions and negations. This finer grained search functionality can assist in several important tasks, e.g., database curation (by locating new experimental knowledge) and pathway enrichment (by providing information for inference). To allow easy integration into text mining systems, EventMine-MK is provided as a UIMA component that can be used in the interoperable text mining infrastructure, U-Compare.


title: Characterizing and Predicting Viral-and-Popular Video Content

abstract: The proliferation of online video content has triggered numerous works on its evolution and popularity, as well as on the effect of social sharing on content propagation. In this paper, we focus on the observable dependencies between the virality of video content on a micro-blogging social network (in this case, Twitter) and the popularity of such content on a video distribution service (YouTube). To this end, we collected and analysed a corpus of Twitter posts containing links to YouTube clips and the corresponding video meta-data from YouTube. Our analysis highlights the unique properties of content that is both popular and viral, which allows such content to attract high number of views on YouTube and achieve fast propagation on Twitter. With this in mind, we proceed to the predictions of popular-and-viral clips and propose a framework that can, with high degree of accuracy and low amount of training data, predict videos that are likely to be popular, viral, and both. The key contribution of our work is the focus on cross-system dynamics between YouTube and Twitter. We conjecture and validate that cross-system prediction of both popularity and virality of videos is feasible, and can be performed with a reasonably high degree of accuracy. One of our key findings is that YouTube features capturing user engagement, have strong virality prediction capabilities. This findings allows to solely rely on data extracted from a video sharing service to predict popularity and virality aspects of videos.


title: Efficient Solar-Thermal Energy Harvest Driven by Interfacial Plasmonic Heating-Assisted Evaporation.

abstract: The plasmonic heating effect of noble nanoparticles has recently received tremendous attention for various important applications. Herein, we report the utilization of interfacial plasmonic heating-assisted evaporation for efficient and facile solar-thermal energy harvest. An airlaid paper-supported gold nanoparticle thin film was placed at the thermal energy conversion region within a sealed chamber to convert solar energy into thermal energy. The generated thermal energy instantly vaporizes the water underneath into hot vapors that quickly diffuse to the thermal energy release region of the chamber to condense into liquids and release the collected thermal energy. The condensed water automatically flows back to the thermal energy conversion region under the capillary force from the hydrophilic copper mesh. Such an approach simultaneously realizes efficient solar-to-thermal energy conversion and rapid transportation of converted thermal energy to target application terminals. Compared to conventional external photothermal conversion design, the solar-thermal harvesting device driven by the internal plasmonic heating effect has reduced the overall thermal resistance by more than 50% and has demonstrated more than 25% improvement of solar water heating efficiency.


title: Information gathering actions over human internal state

abstract: Much of estimation of human internal state (goal, intentions, activities, preferences, etc.) is passive: an algorithm observes human actions and updates its estimate of human state. In this work, we embrace the fact that robot actions affect what humans do, and leverage it to improve state estimation. We enable robots to do active information gathering, by planning actions that probe the user in order to clarify their internal state. For instance, an autonomous car will plan to nudge into a human driver's lane to test their driving style. Results in simulation and in a user study suggest that active information gathering significantly outperforms passive state estimation.


title: Case studies in network community detection

abstract: Community structure describes the organization of a network into subgraphs that contain a prevalence of edges within each subgraph and relatively few edges across boundaries between subgraphs. The development of community-detection methods has occurred across disciplines, with numerous and varied algorithms proposed to find communities. As we present in this Chapter via several case studies, community detection is not just an"end game"unto itself, but rather a step in the analysis of network data which is then useful for furthering research in the disciplinary domain of interest. These case-study examples arise from diverse applications, ranging from social and political science to neuroscience and genetics, and we have chosen them to demonstrate key aspects of community detection and to highlight that community detection, in practice, should be directed by the application at hand.


title: Development and Evaluation of a Bluetooth EKG Monitoring Sensor

abstract: With the adoption of 2G and 3G cellular network technologies, mobile phones now have the bandwidth capability to stream data back to monitoring stations in real-time. Our paper describes the design and evaluation of a Bluetooth electrocardiogram sensor that transmits medical data to a cell phone. This data is displayed and stored on the phone. Future development of the system will relay this data over a cellular GPRS network. The current system provides a low cost and lightweight alternative to existing EKG event monitors. The final GPRS connected system will provide continuous monitoring of a patient 's heart anywhere cellular coverage is available


title: Deep Cooperative Sensing: Cooperative Spectrum Sensing Based on Convolutional Neural Networks

abstract: In this paper, we investigate cooperative spectrum sensing (CSS) in a cognitive radio network (CRN) where multiple secondary users (SUs) cooperate in order to detect a primary user, which possibly occupies multiple bands simultaneously. Deep cooperative sensing (DCS), which constitutes the first CSS framework based on a convolutional neural network (CNN), is proposed. In DCS, instead of the explicit mathematical modeling of CSS, the strategy for combining the individual sensing results of the SUs is learned autonomously with a CNN using training sensing samples regardless of whether the individual sensing results are quantized or not. Moreover, both spectral and spatial correlation of individual sensing outcomes are taken into account such that an environment-specific CSS is enabled in DCS. Through simulations, we show that the performance of CSS can be greatly improved by the proposed DCS.


title: Modelling of solar-wind hybrid renewable energy system architectures

abstract: Solar and wind are the most promising among the renewable energy sources. The objective of this paper is to investigate the optimal techno-economically feasible model to mitigate a large scale demand in decentralized scenario. Solar, and wind energy potential in a hybrid scenario is closely assessed with an intermittent support from diesel generator and battery banks. Based on the general usage of appliances in a residential demand for 200 families that comprises of 1000 people a suitable load pattern is developed for this study. Demand in northern Europe predominantly constitutes of heating requirement in the form of water and space heating. The proposed model demonstrates convincing reliability in terms of techno-economical prospective. EnergyPRO, HOMER, and Balmorel software environments were used to simulate the models, results are assessed in the paper.


title: Hierarchical-Pointer Generator Memory Network for Task Oriented Dialog

abstract: End-to-end networks trained for task-oriented dialog, such as for recommending restaurants to a user, suffer from out-of-vocabulary (OOV) problem -- the entities in the Knowledge Base (KB) may not be seen by the network at training time, making it hard to use them in dialog. We propose a novel Hierarchical Pointer Generator Memory Network (HyP-MN), in which the next word may be generated from the decode vocabulary or copied from a hierarchical memory maintaining KB results and previous utterances. This hierarchical memory layout along with a novel KB dropout helps to alleviate the OOV problem. Evaluating over the dialog bAbI tasks, we find that HyP-MN outperforms state-of-the-art results, with considerable improvements (10% on OOV test set). HyP-MN also achieves competitive performances on various real-world datasets such as CamRest676 and In-car assistant dataset.


title: Fully Unsupervised Convolutional Learning for Fast Image Retrieval

abstract: In this paper, we propose a fully unsupervised method for optimizing the Convolutional Neural Network (CNN) representations towards image retrieval. To accomplish this goal, we obtain the feature representations from the activations of the convolutional layers of a CNN pretrained model, and then we adapt it to a lightweight fully convolutional network, which allows for producing compact image representations, reducing the storage requirements. Subsequently, we retrain the weights of the convolutional layers in order to produce more efficient compact image descriptors, which improve the retrieval performance both in terms of time and precision, in a fully unsupervised fashion. The experimental evaluation on three publicly available image retrieval datasets indicates the effectiveness of the proposed method in learning more efficient representations for the retrieval task, outperforming other unsu-pervised CNN-based retrieval techniques, as well as conventional hand-crafted feature-based approaches in all the used datasets.


title: Parallel Implementation of Classification Algorithms Based on Cloud Computing Environment

abstract: As an important task of data mining, Classification has been received considerable attention in many applications, such as information retrieval, web searching, etc. The enlarging volumes of information emerging by the progress of technology and the growing individual needs of data mining, makes classifying of very large scale of data a challenging task. In order to deal with the problem, many researchers try to design efficient parallel classification algorithms. This paper introduces the classification algorithms and cloud computing briefly, based on it analyses the bad points of the present parallel classification algorithms, then addresses a new model of parallel classifying algorithms. And it mainly introduces a parallel Naive Bayes classification algorithm based on MapReduce, which is a simple yet powerful parallel programming technique. The experimental results demonstrate that the proposed algorithm improves the original algorithm performance, and it can process large datasets efficiently on commodity hardware.


title: Angiogenesis: Tumorigenesis and the angiogenic switch

abstract: It has become evident that we cannot understand tumour growth without considering components of the stromal microenvironment, such as the vasculature. At the same time, the tumour phenotype determines the nature of the tumour vasculature. Much research is now devoted to determining the impact of angiogenesis on tumour development and progression, and the reciprocal influences of tumour products on the microvasculature. A more detailed understanding of the complex parameters that govern the interactions between the tumour and vascular compartments will help to improve anti-angiogenic strategies-- not only for cancer treatment, but also for preventing recurrence.


title: Survey of Techniques and Architectures for Designing Energy-Efficient Data Centers

abstract: Cloud computing has emerged as the leading paradigm for information technology businesses. Cloud computing provides a platform to manage and deliver computing services around the world over the Internet. Cloud services have helped businesses utilize computing services on demand with no upfront investments. The cloud computing paradigm has sustained its growth, which has led to increase in size and number of data centers. Data centers with thousands of computing devices are deployed as back end to provide cloud services. Computing devices are deployed redundantly in data centers to ensure 24/7 availability. However, many studies have pointed out that data centers consume large amount of electricity, thus calling for energy-efficiency measures. In this survey, we discuss research issues related to conflicting requirements of maximizing quality of services (QoSs) (availability, reliability, etc.) delivered by the cloud services while minimizing energy consumption of the data center resources. In this paper, we present the concept of inception of data center energy-efficiency controller that can consolidate data center resources with minimal effect on QoS requirements. We discuss software- and hardware-based techniques and architectures for data center resources such as server, memory, and network devices that can be manipulated by the data center controller to achieve energy efficiency.


title: A Search Engine Backed by Internet-Wide Scanning

abstract: Fast Internet-wide scanning has opened new avenues for security research, ranging from uncovering widespread vulnerabilities in random number generators to tracking the evolving impact of Heartbleed. However, this technique still requires significant effort: even simple questions, such as, "What models of embedded devices prefer CBC ciphers?", require developing an application scanner, manually identifying and tagging devices, negotiating with network administrators, and responding to abuse complaints. In this paper, we introduce Censys, a public search engine and data processing facility backed by data collected from ongoing Internet-wide scans. Designed to help researchers answer security-related questions, Censys supports full-text searches on protocol banners and querying a wide range of derived fields (e.g., 443.https.cipher). It can identify specific vulnerable devices and networks and generate statistical reports on broad usage patterns and trends. Censys returns these results in sub-second time, dramatically reducing the effort of understanding the hosts that comprise the Internet. We present the search engine architecture and experimentally evaluate its performance. We also explore Censys's applications and show how questions asked in recent studies become simple to answer.


title: Neural nets versus conventional techniques in credit scoring in Egyptian banking

abstract: Neural nets have become one of the most important tools using in credit scoring. Credit scoring is regarded as a core appraised tool of commercial banks during the last few decades. The purpose of this paper is to investigate the ability of neural nets, such as probabilistic neural nets and multi-layer feed-forward nets, and conventional techniques such as, discriminant analysis, probit analysis and logistic regression, in evaluating credit risk in Egyptian banks applying credit scoring models. The credit scoring task is performed on one bank's personal loans' data-set. The results so far revealed that the neural nets-models gave a better average correct classification rate than the other techniques. A one-way analysis of variance and other tests have been applied, demonstrating that there are some significant differences amongst the means of the correct classification rates, pertaining to different techniques.


title: High impedance fault detection: Discrete wavelet transform and fuzzy function approximation

abstract: This paper presents a method including a combination of the wavelet transform and fuzzy function approximation (FFA) for high impedance fault (HIF) detection in distribution electricity network. Discrete wavelet transform (DWT) is used in this paper as a tool for a signal analysis and after studying different types of mother signals, detailed types and feeder signal, the best case is selected. In the next step, the DWT is used to extract the features. The extracted features are used as the FFA Systems inputs. The FFA system uses the input-output pairs to create a function approximation of the features. The FFA system is able to classify the new features. The combined model is used to model the HIF. This combined model has the high ability to model different types of HIF. In the proposed method, different kind of loads including nonlinear and asymmetric loads and HIF types are studied. The results show that the proposed method has high capability to distinguish between no fault and HIF states accurately.


title: Data replication for mobile computers

abstract: Users of mobile computers will soon have online access to a large number of databases via wireless networks. Because of limited bandwidth, wireless communication is more expensive than wire communication. In this paper we present and analyze various static and dynamic data allocation methods. The objective is to optimize the communication cost between a mobile computer and the stationary computer that stores the online database. Analysis is performed in two cost models. One is connection (or time) based, as in cellular telephones, where the user is charged per minute of connection. The other is message based, as in packet radio networks, where the user is charged per message. Our analysis addresses both, the average case and the worst case for determining the best allocation method.


title: Birdwatching Tourism in Australia

abstract: The Sustainable Tourism Cooperative Research Centre, established and supported under the Australian Government's Cooperative Research Centres Program, funded this research.


title: PeerTrust: supporting reputation-based trust for peer-to-peer electronic communities

abstract: Peer-to-peer (P2P) online communities are commonly perceived as an environment offering both opportunities and threats. One way to minimize threats in such communities is to use community-based reputations to help estimate the trustworthiness of peers. We present PeerTrust - a reputation-based trust supporting framework, which includes a coherent adaptive trust model for quantifying and comparing the trustworthiness of peers based on a transaction-based feedback system, and a decentralized implementation of such a model over a structured P2P network. PeerTrust model has two main features. First, we introduce three basic trust parameters and two adaptive factors in computing trustworthiness of peers, namely, feedback a peer receives from other peers, the total number of transactions a peer performs, the credibility of the feedback sources, transaction context factor, and the community context factor. Second, we define a general trust metric to combine these parameters. Other contributions of the paper include strategies used for implementing the trust model in a decentralized P2P environment, evaluation mechanisms to validate the effectiveness and cost of PeerTrust model, and a set of experiments that show the feasibility and benefit of our approach.


CS 224 N HW 4 : SQuAD Reading Comprehension Learning


title: School context, achievement motivation, and academic engagement: A longitudinal study of school engagement using a multidimensional perspective

abstract: This longitudinal study adopts a multidimensional perspective to examine the relationships between middle school students’ perceptions of the school environment (structure support, provision of choice, teaching for relevance, teacher and peer emotional support), achievement motivation (academic selfconcept and subjective task value), and school engagement (behavioral, emotional, and cognitive engagement). Participants were from an ethnically diverse, urban sample of 1157 adolescents. The findings indicated that student perceptions of distinct aspects of the school environment contributed differentially to the three types of school engagement. In addition, these associations were fully or partially mediated by achievement motivation. Specifically, student perceptions of the school environment influenced their achievement motivation and in turn influenced all three types of school engagement, although in different ways. Moderation effects of gender, ethnicity, and academic ability were also discussed.


title: XML Goes Native: Run-Time Representations for Xtatic

abstract: Xtatic is a lightweight extension of C# offering native support for statically typed XML processing. XML trees are built-in values in Xtatic, and static analysis of the trees manipulated by programs is part of the ordinary job of the typechecker. “Tree grep” pattern matching is used to investigate and transform XML trees. Xtatic's surface syntax and type system are tightly integrated with those of C#. Beneath the hood, however, an implementation of Xtatic must address a number of issues common to any language supporting a declarative style of XML processing (e.g., XQuery, XSLT, XDuce, CDuce, Xact, Xen, etc.). In particular, it must provide representations for XML tags, trees, and textual data that use memory efficiently, support efficient pattern matching, allow maximal sharing of common substructures, and permit separate compilation. We analyze these representation choices in detail and describe the solutions used by the Xtatic compiler.


title: 4D Cubism: Modeling, Animation, and Fabrication of Artistic Shapes

abstract: This article deals with creating artistic shapes in a cubist style. The authors propose adding cubist features to (or cubification of) time-variant sculptural shapes. A new concept of a 4D cubist camera is introduced for multiple projections from 4D space-time to 3D space, and 3D printing for stop-motion animation is applied.


title: Does cyberbullying overlap with school bullying when taking modality of involvement into account?

abstract: Elevated proportions of students are involved in cyber and school-bullying.Cyber and school bullying overlap very little.More than half of students engaged in cyberbullying were neutral in school-bullying.Cyberbullies appear to be less aggressive than school bullies.Cybervictims exhibit lower levels of internalizing problem than school victims. Education professionals and researchers are concerned by school bullying and cyberbullying because of its repercussions on students' health and the school climate. However, only a few studies investigating the impact of school versus cyberbullying have systematically explored whether student victims and perpetrators are involved in school bullying only, cyberbullying only, or both. The aim of the present study was thus to examine the possible overlap, as well as the similarities and/or differences, between these two forms of bullying when taking modality of involvement into account. Individual interviews were conducted with 1422 junior high- and high-school students (girls=43%, boys=57%, mean age=14.3?2.7years). Results showed that cyberbullying and school bullying overlapped very little. The majority of students involved in cyberbullying were not simultaneously involved in school bullying. Moreover, results indicated that psychosocial problems (psychological distress, social disintegration, general aggression) varied according to the form of bullying. Victims of school bullying had greater internalizing problems than cybervictims, while school bullies were more aggressive than cyberbullies. Given the sizable proportion of adolescents involved in bullying (school and cyber) and its significant relationship with mental health, the issue warrants serious attention from school and public health authorities.


title: Psychological, cognitive, and personal variables that predict college academic achievement among health sciences students.

abstract: Summary Background There are many factors that affect college academic achievement among health sciences students. Aim The aim of this study was to examine selected psychological, cognitive, and personal variables that affect students' academic achievement among health sciences college students in Saudi Arabia. Method A correlational descriptive cross-sectional design was employed to collect data on the studied variables from 510 health sciences students (Medicine, Nursing, Respiratory Therapy, and Pharmacy Doctor) employing self-administered questionnaire. Results Results showed that students experienced low level of self-esteem and low level of student–faculty interaction; and high level of achievement motivation and satisfaction with life. Also, they reported mild levels of depression and stress and a moderate level of anxiety. Female students reported higher level of achievement motivation, depression, anxiety, and stress; while male students reported a higher level of self-esteem. Results also showed that achievement motivation, mothers' educational level, working besides studying, gender, aptitude test score, and depression level were the best predictors of academic achievement and accounting for 43% of the total variance. Conclusions Several psychological, cognitive, and personal variables were found to affect college academic achievement among health sciences students. Recommendations and implications to enhance students' academic achievement are discussed.


title: Personalized Web search for improving retrieval effectiveness

abstract: Current Web search engines are built to serve all users, independent of the special needs of any individual user. Personalization of Web search is to carry out retrieval for each user incorporating his/her interests. We propose a novel technique to learn user profiles from users' search histories. The user profiles are then used to improve retrieval effectiveness in Web search. A user profile and a general profile are learned from the user's search history and a category hierarchy, respectively. These two profiles are combined to map a user query into a set of categories which represent the user's search intention and serve as a context to disambiguate the words in the user's query. Web search is conducted based on both the user query and the set of categories. Several profile learning and category mapping algorithms and a fusion algorithm are provided and evaluated. Experimental results indicate that our technique to personalize Web search is both effective and efficient.


title: Interactive Image Search by Color Map

abstract: The availability of large-scale images from the Internet has made the research on image search attract a lot of attention. Text-based image search engines, for example, Google/Microsoft Bing/Yahoo! image search engines using the surrounding text, have been developed and widely used. However, they suffer from an inability to search image content. In this article, we present an interactive image search system, image search by color map, which can be applied to, but not limited to, enhance text-based image search. This system enables users to indicate how the colors are spatially distributed in the desired images, by scribbling a few color strokes, or dragging an image and highlighting a few regions of interest in an intuitive way. In contrast to the conventional sketch-based image retrieval techniques, our system searches images based on colors rather than shapes, and we, technically, propose a simple but effective scheme to mine the latent search intention from the user’s input, and exploit the dominant color filter strategy to make our system more efficient. We integrate our system to existing Web image search engines to demonstrate its superior performance over text-based image search. The user study shows that our system can indeed help users conveniently find desired images.


B p-Tree : A Predictive B +-Tree for Reducing Writes on Phase Change Memory


title: Silicon acquisition by bananas (c.V. Grande Naine) is increased in presence of the arbuscular mycorrhizal fungus Rhizophagus irregularis MUCL 41833

abstract: Aim ::: This work aimed to investigate the role of arbuscular mycorrhizal fungi (AMF) in the uptake and accumulation of silicon (Si) in banana plants. Si is recognized as a significant element that helps plants resist stresses.


title: A Comprehensive Review of Smart Wheelchairs: Past, Present, and Future

abstract: A smart wheelchair (SW) is a power wheelchair (PW) to which computers, sensors, and assistive technology are attached. In the past decade, there has been little effort to provide a systematic review of SW research. This paper aims to provide a complete state-of-the-art overview of SW research trends. We expect that the information gathered in this study will enhance awareness of the status of contemporary PW as well as SW technology and increase the functional mobility of people who use PWs. We systematically present the international SW research effort, starting with an introduction to PWs and the communities they serve. Then, we discuss in detail the SW and associated technological innovations with an emphasis on the most researched areas, generating the most interest for future research and development. We conclude with our vision for the future of SW research and how to best serve people with all types of disabilities.


Introduction to the Doubly-Fed Induction Generator for Wind Power Applications


title: Searching for Activation Functions

abstract: The choice of activation functions in deep networks has a significant effect on the training dynamics and task performance. Currently, the most successful and widely-used activation function is the Rectified Linear Unit (ReLU). Although various hand-designed alternatives to ReLU have been proposed, none have managed to replace it due to inconsistent gains. In this work, we propose to leverage automatic search techniques to discover new activation functions. Using a combination of exhaustive and reinforcement learning-based search, we discover multiple novel activation functions. We verify the effectiveness of the searches by conducting an empirical evaluation with the best discovered activation function. Our experiments show that the best discovered activation function, $f(x) = x \cdot \text{sigmoid}(\beta x)$, which we name Swish, tends to work better than ReLU on deeper models across a number of challenging datasets. For example, simply replacing ReLUs with Swish units improves top-1 classification accuracy on ImageNet by 0.9\% for Mobile NASNet-A and 0.6\% for Inception-ResNet-v2. The simplicity of Swish and its similarity to ReLU make it easy for practitioners to replace ReLUs with Swish units in any neural network.


title: High-speed railway catenary components detection using the cascaded convolutional neural networks

abstract: With the massive construction of the China high-speed railway, it is of a great significance to propose an automatic approach to inspect the defects of the catenary support devices. Based on the obtained high resolution images, the detection and extraction of the components on the catenary support devices are the vital steps prior to their defect report. Inspired by the existing object detection Faster R-CNN framework, a cascaded convolutional neural network (CNN) architecture is built to successively detect the various components and the tiny fasteners in the complex catenary support device structures. Meanwhile, some missing states of the fasteners on the cantilever joints are directly reported via our proposed architecture. Experiments on the Wuhan-Guangzhou high-speed railway dataset demonstrate a practical performance of the component detection with good adaptation and robustness in complex environments, feasible to accurately inspect the extremely tiny defects on the various catenary components.


title: Parallel CRC Realization

abstract: This paper presents a theoretical result in the context of realizing high-speed hardware for parallel CRC checksums. Starting from the serial implementation widely reported in the literature, we have identified a recursive formula from which our parallel implementation is derived. In comparison with previous works, the new scheme is faster and more compact and is independent of the technology used in its realization. In our solution, the number of bits processed in parallel can be different from the degree of the polynomial generator. Last, we have also developed high-level parametric codes that are capable of generating the circuits autonomously when only the polynomial is given.


title: Relevance Auras : Macro Patterns and Micro Scatter

abstract: Empirical analysis of relevance assessments can illuminate how different groups of readers perceive the relationship between bibliographic records and index terms. This experiment harvested relevance assessments from two groups: engineering students (here after "engineers") and library school students ("librarians"). These groups assessed the relevance relationships between bibliographic records and index terms for three literatures: engineering, psychology and education. Assessment included the indexer-selected term (the topically relevant term) as well as broader, narrower and related terms. Figures 1 - 8 show these terms arranged as twodimensional term domains. Positive relevance assessments plotted across the twodimensional term domains revealed regular patterns, here called "relevance auras." A relevance aura is a penumbra ofpositive relevance, emanating from a bibliographic records across a term domain ofbroader, narrower and related index terms. This experiment attempted to compare the relevance auras produced by engineers and librarians at both a macro and micro level of aggregation.


title: User Identity Linkage by Latent User Space Modelling

abstract: User identity linkage across social platforms is an important problem of great research challenge and practical value. In real applications, the task often assumes an extra degree of difficulty by requiring linkage across multiple platforms. While pair-wise user linkage between two platforms, which has been the focus of most existing solutions, provides reasonably convincing linkage, the result depends by nature on the order of platform pairs in execution with no theoretical guarantee on its stability. In this paper, we explore a new concept of ``Latent User Space'' to more naturally model the relationship between the underlying real users and their observed projections onto the varied social platforms, such that the more similar the real users, the closer their profiles in the latent user space. We propose two effective algorithms, a batch model(ULink) and an online model(ULink-On), based on latent user space modelling. Two simple yet effective optimization methods are used for optimizing objective function: the first one based on the constrained concave-convex procedure(CCCP) and the second on accelerated proximal gradient. To our best knowledge, this is the first work to propose a unified framework to address the following two important aspects of the multi-platform user identity linkage problem --- (I) the platform multiplicity and (II) online data generation. We present experimental evaluations on real-world data sets for not only traditional pairwise-platform linkage but also multi-platform linkage. The results demonstrate the superiority of our proposed method over the state-of-the-art ones.


title: Identification of large-scale networks in the brain using fMRI

abstract: Abstract Cognition is thought to result from interactions within large-scale networks of brain regions. Here, we propose a method to identify these large-scale networks using functional magnetic resonance imaging (fMRI). Regions belonging to such networks are defined as sets of strongly interacting regions, each of which showing a homogeneous temporal activity. Our method of large-scale network identification (LSNI) proceeds by first detecting functionally homogeneous regions. The networks of functional interconnections are then found by comparing the correlations among these regions against a model of the correlations in the noise. To test the LSNI method, we first evaluated its specificity and sensitivity on synthetic data sets. Then, the method was applied to four real data sets with a block-designed motor task. The LSNI method correctly recovered the regions whose temporal activity was locked to the stimulus. In addition, it detected two other main networks highly reproducible across subjects, whose activity was dominated by slow fluctuations (0–0.1 Hz). One was located in medial and dorsal regions, and mostly overlapped the “default” network of the brain at rest [Greicius, M.D., Krasnow, B., Reiss, A.L., Menon, V., 2003. Functional connectivity in the resting brain: a network analysis of the default mode hypothesis. Proceedings of the National Academy of Sciences of the U.S.A. 100, 253–258]; the other was composed of lateral frontal and posterior parietal regions. The LSNI method we propose allows to detect in an exploratory and systematic way all the regions and large-scale networks activated in the working brain.


title: Conjugate Symmetric Sequency-Ordered Complex Hadamard Transform

abstract: A new transform known as conjugate symmetric sequency-ordered complex Hadamard transform (CS-SCHT) is presented in this paper. The transform matrix of this transform possesses sequency ordering and the spectrum obtained by the CS-SCHT is conjugate symmetric. Some of its important properties are discussed and analyzed. Sequency defined in the CS-SCHT is interpreted as compared to frequency in the discrete Fourier transform. The exponential form of the CS-SCHT is derived, and the proof of the dyadic shift invariant property of the CS-SCHT is also given. The fast and efficient algorithm to compute the CS-SCHT is developed using the sparse matrix factorization method and its computational load is examined as compared to that of the SCHT. The applications of the CS-SCHT in spectrum estimation and image compression are discussed. The simulation results reveal that the CS-SCHT is promising to be employed in such applications.


title: Mitigating anti-forensics in the Cloud via resource-based privacy preserving activity attribution

abstract: The multi-tenant Cloud environment creates a plethora of both technical and legal difficulties for digital forensics. Digital forensics typically relies on the analysis of evidence images or residual data. Due to the distributed nature of cloud environments can cause the required dataset to scale rapidly. When coupled with diversely heterogeneous environments, the widespread uptake of anti-forensic data and encryption, privacy requirements and a non-standardised architecture across Cloud Service Providers (CSPs), attribution of any activity for a forensic investigation becomes a tedious task. This paper presents an architecture agnostic, privacy-preserving solution to reducing the digital forensics target search space of a investigation within cloud and edge computing environments which will leverage standard metering and network logs for efficient activity attribution.


title: Video summarisation: A conceptual framework and survey of the state of the art

abstract: Video summaries provide condensed and succinct representations of the content of a video stream through a combination of still images, video segments, graphical representations and textual descriptors. This paper presents a conceptual framework for video summarisation derived from the research literature and used as a means for surveying the research literature. The framework distinguishes between video summarisation techniques (the methods used to process content from a source video stream to achieve a summarisation of that stream) and video summaries (outputs of video summarisation techniques). Video summarisation techniques are considered within three broad categories: internal (analyse information sourced directly from the video stream), external (analyse information not sourced directly from the video stream) and hybrid (analyse a combination of internal and external information). Video summaries are considered as a function of the type of content they are derived from (object, event, perception or feature based) and the functionality offered to the user for their consumption (interactive or static, personalised or generic). It is argued that video summarisation would benefit from greater incorporation of external information, particularly user based information that is unobtrusively sourced, in order to overcome longstanding challenges such as the semantic gap and providing video summaries that have greater relevance to individual users.


title: On dynamicity of expert finding in community question answering

abstract: Abstract Community Question Answering is one of the valuable information resources which provide users with a platform to share their knowledge. Finding potential experts in CQA is beneficial to several problems like low participation rate of the users, long waiting time to receive answers and to the low quality of answers. Many research papers focused on retrieving the expert users of CQAs. Most of them are taking expertise into consideration at the query time and ignore the temporal aspects of the expert finding problem. However, considering the evolution of personal expertise over time can improve the quality of expert finding. In many applications, it is beneficial to find the potential experts in future. The proper identification of potential experts in CQA can improve their skills and the overall user participation and engagement. Considering dynamic aspects of the expert finding problem, we introduce the new problem of Future Expert Finding in this paper.Here, given the expertise evidence in current time, we aim to predict the best ranking of experts in future. We proposed a learning framework to predict such ranking on StackOverflow which is currently one of the most successful CQAs. We examine the impact of various features to predict the probability of becoming an expert user in future time. Specifically, we consider four feature groups; namely, topic similarity, emerging topics, user behavior and topic transition . The experimental results indicate the efficiency of the proposed models in comparison with several baseline models. Our experiments show that the performance of our proposed models can improve the MAP measure up to 39.7% in comparison with our best baseline method. Moreover, we found that among all of these feature groups, user behaviors have the most influence in the estimation of future expertise probability.


title: Software aging in mobile devices: Partial computation offloading as a solution

abstract: Long running applications on resource-constrained mobile devices can lead to software aging, which is a critical impediment to the mobile users due to its pervasive nature. Mobile offloading that migrates computation-intensive parts of applications from mobile devices onto resource-rich cloud servers, is an effective way for enhancing the availability of mobile services as it can postpone or prevent the software aging in mobile devices. Through partitioning the execution between the device side and the cloud side, the mobile device can have the most benefit from offloading in reducing utilisation of the device and increasing its lifetime. In this paper, we propose a path-based offloading partitioning (POP) algorithm to determine which portions of the application tasks to run on mobile devices and which portions on cloud servers with different cost models in mobile environments. The evaluation results show that the partial offloading scheme can significantly improve performance and reduce energy consumption by optimally distributing tasks between mobile devices and cloud servers, and can well adapt to changes in the environment.


title: Attention-deficit/hyperactivity disorder, joint hypermobility-related disorders and pain: expanding body-mind connections to the developmental age

abstract: Attention-deficit/hyperactivity disorder (ADHD) and generalized joint hypermobility (JH) are two separated conditions, assessed, and managed by different specialists without overlapping interests. Recently, some researchers highlighted an unexpected association between these two clinical entities. This happens in a scenario of increasing awareness on the protean detrimental effects that congenital anomalies of the connective tissue may have on human health and development. To review pertinent literature to identify possible connections between ADHD and GJH, special emphasis was put on musculoskeletal pain and syndromic presentations of GJH, particularly the hypermobile Ehlers–Danlos syndrome. A comprehensive search of scientific databases and references lists was conducted, encompassing publications based on qualitative and quantitative research. Impaired coordination and proprioception, fatigue, chronic pain, and dysautonomia are identified as potential bridges between ADHD and JH. Based on these findings, a map of the pathophysiological and psychopathological pathways connecting both conditions is proposed. Although ADHD and JH are traditionally separated human attributes, their association may testify for the dyadic nature of mind-body connections during critical periods of post-natal development. Such a mixed picture has potentially important consequences in terms of disability and deserves more clinical and research attention.


title: Cover your ACKs: pitfalls of covert channel censorship circumvention

abstract: In response to increasingly sophisticated methods of blocking access to censorship circumvention schemes such as Tor, recently proposed systems such as Skypemorph, FreeWave, and CensorSpoofer have used voice and video conferencing protocols as "cover channels" to hide proxy connections. We demonstrate that even with perfect emulation of the cover channel, these systems can be vulnerable to attacks that detect or disrupt the covert communications while having no effect on legitimate cover traffic. Our attacks stem from differences in the channel requirements for the cover protocols, which are peer-to-peer and loss tolerant, and the covert traffic, which is client-proxy and loss intolerant. These differences represent significant limitations and suggest that such protocols are a poor choice of cover channel for general censorship circumvention schemes.


title: MANGO - Mobile Augmented Reality with Functional Eating Guidance and Food Awareness

abstract: The prevention of cardiovascular diseases becomes more and more important, as malnutrition accompanies today’s fast moving society. While most people know the importance of adequate nutrition, information on advantageous food is often not at hand, such as in daily activities. Decision making on individual dietary management is closely linked to the food shopping decision. Since food shopping often requires fast decision making, due to stressful and crowded situations, the user needs a meaningful assistance, with clear and rapidly available associations from food items to dietary recommendations. This paper presents first results of the Austrian project (MANGO) which develops mobile assistance for instant, situated information access via Augmented Reality (AR) functionality to support the user during everyday grocery shopping. Within a modern diet - the functional eating concept - the user is advised which fruits and vegetables to buy according to his individual profile. This specific oxidative stress profile is created through a short in-app survey. Using a built-in image recognition system, the application automatically classifies video captured food using machine learning and computer vision methodology, such as Random Forests classification and multiple color feature spaces. The user can decide to display additional nutrition information along with alternative proposals. We demonstrate, that the application is able to recognize food classes in real-time, under real world shopping conditions, and associates dietary recommendations using situated AR assistance.


title: User Clustering in Online Advertising via Topic Models

abstract: In the domain of online advertising, our aim is to serve the best ad to a user who visits a certain webpage, to maximize the chance of a desired action to be performed by this user after seeing the ad. While it is possible to generate a different prediction model for each user to tell if he/she will act on a given ad, the prediction result typically will be quite unreliable with huge variance, since the desired actions are extremely sparse, and the set of users is huge (hundreds of millions) and extremely volatile, i.e., a lot of new users are introduced everyday, or are no longer valid. In this paper we aim to improve the accuracy in finding users who will perform the desired action, by assigning each user to a cluster, where the number of clusters is much smaller than the number of users (in the order of hundreds). Each user will fall into the same cluster with another user if their event history are similar. For this purpose, we modify the probabilistic latent semantic analysis (pLSA) model by assuming the independence of the user and the cluster id, given the history of events. This assumption helps us to identify a cluster of a new user without re-clustering all the users. We present the details of the algorithm we employed as well as the distributed implementation on Hadoop, and some initial results on the clusters that were generated by the algorithm.


title: Color Based Hand and Finger Detection Technology for User Interaction

abstract: The aim of this paper is to present the methodology for hand detection and propose the finger detection method. The detected hand and finger can be used to implement the non-contact mouse. This technology can be used to control the home devices such as curtain and television. Skin color is used to segment the hand region from background and counter is extracted from the segmented hand. Analysis of counter gives us the location of finger tip in the hand. We have performed extensive experiment and achieve very encouraging result.


title: Design of a Low-Power Bandgap Current Reference

abstract: A low-power complementary metal oxide semiconduct-or (CMOS) bandgap current reference is proposed under the 1.8V supply voltage. The temperature compensation current generator was used in order to obtain an accurate 5µA current with lower temperature coefficient. Sub-threshold technology and advanced startup circuit were adopted to decrease the circuit power consumption. The current reference has been simulated based on SMIC 0.18µm CMOS technology. The simulation results show that it has significantly low power and low sensitivity to the temperature. The power consumption is only 35.22µW. The temperature coefficient is 42.62ppm/℃ under typical process with the temperature range from -40℃ to 125 ℃.


title: Natural history and manifestations of the hypermobility type Ehlers-Danlos syndrome: A pilot study on 21 patients

abstract: Hypermobility type Ehlers-Danlos syndrome (HT-EDS) is a relatively frequent, although commonly misdiagnosed variant of Ehlers-Danlos syndrome, mainly characterized by marked joint instability and mild cutaneous involvement. Chronic pain, asthenia, and gastrointestinal and pelvic dysfunction are characteristic additional manifestations. We report on 21 HT-EDS patients selected from a group of 40 subjects with suspected mild hereditary connective tissue disorder. General, mucocutaneous, musculoskeletal, cardiovascular, neurologic, gastrointestinal, urogynecological, and ear-nose-throat abnormalities are investigated systematically and tabulated. Six distinct clinical presentations of HT-EDS are outlined, whose tabulation is a mnemonic for the practicing clinical geneticist in an attempt to diagnose this condition accurately. With detailed clinical records and phenotype comparison among patients of different ages, the natural history of the disorder is defined. Three phases (namely, hypermobility, pain, and stiffness) are delineated based on distinguishing manifestations. A constellation of additional, apparently uncommon abnormalities is also identified, including dolichocolon, dysphonia, and Arnold-Chiari type I malformation. Their further investigation may contribute to an understanding of the pathogenesis of the protean manifestations of HT-EDS, and a more effective approach to the evaluation and management of affected individuals.


title: Selection, de-selection and progression in German football talent promotion

abstract: This study explored to which extent the development of German professional football players is based on early talent identification (TID) and long-term nurture in talent promotion (TP) programmes or on their emergence in the course of repeated procedures of player selection and de-selection in these programmes through childhood and youth. The annual turnover of squad members in national junior teams (2001–2013) and youth elite academies was calculated; national U-team members were followed up with regard to nominations through subsequent seasons and to their success level eventually achieved at senior age; and all current Bundesliga players were analysed retrospectively regarding their earlier involvement in TID/TP programmes. Analyses revealed that the mean annual turnover of squad members was 24.5% (youth academies) and 41.0% (national U-teams), respectively. At any age, the probability of persisting in the programme three years later was <50%. Among current Bundesliga players, the age of recruitment into the TID/TP programme was widely evenly distributed across childhood and youth, respectively. Accordingly, the number of (future) Bundesliga players who were involved in TID/TP was built up continuously through all age categories. The observations suggest that the collective of professional players emerged from repeated procedures of selection and de-selection through childhood and youth rather than from early selection and long-term continuous nurture in TID/TP programmes. The findings are discussed with regard to the uncertainty of TID and of interventions applied to the selected players, and they are related to the individualistic and collectivistic approach in TP.


title: Machine Learning for the Detection of Oil Spills in Satellite Radar Images

abstract: During a project examining the use of machine learning techniques for oil spill detection, we encountered several essential questions that we believe deserve the attention of the research community. We use our particular case study to illustrate such issues as problem formulation, selection of evaluation measures, and data preparation. We relate these issues to properties of the oil spill application, such as its imbalanced class distribution, that are shown to be common to many applications. Our solutions to these issues are implemented in the Canadian Environmental Hazards Detection System (CEHDS), which is about to undergo field testing.


A schema theory of discrete motor skill learning.


title: Risk of progression in complex and atypical endometrial hyperplasia: clinicopathologic analysis in cases with and without progestogen treatment.

abstract: In most cases, the endometrioid adenocarcinoma of the endometrium is preceded by hyperplasia with different risk of progression into carcinoma. The original histologic slides from 560 consecutive cases with complex and atypical hyperplasia were re-examined to assess the interobserver-correlation. The hyperplasias were analyzed separately for their likelihood of progression to carcinoma in patients with and without progestogen hormonal therapy. In all cases, a fractional re-curreting was performed to establish the state of the disease. The leading symptom was vaginal bleeding in 65.5% of the cases in the postmenopausal period. Eighty-six percent of the patients presented with obesity (BMI > 30 kg/m(2)), 23% had had an exogeneous use of estrogens. Twenty-two cases were reclassified as simple hyperplasia and excluded from further analysis. The interobserver-correlation was 91% for complex, 92% for atypical hyperplasia, and 89% for endometrioid carcinoma, representing an overall correlation of 90%. Two percent of the cases with complex hyperplasia (8/390) progressed into carcinoma and 10.5% into atypical hyperplasia. Fifty-two percent of the atypical hyperplasias (58/112) progressed into carcinomas. In the case of progestogen treatment (n = 208; P < 0.0001) 61.5% showed remission confirmed by re-curetting, compared with 20.3% of the cases without hormonal treatment (n = 182; P < 0.0001). Endometrial hyperplasia without atypia is likely to respond to hormonal treatment. Especially in postmenopausal situation, atypical hyperplasia should be treated with total hysterectomy.


title: Preamble-based frequency-domain joint CFO and STO estimation for OQAM-based filter bank multicarrier

abstract: Filter bank multicarrier systems, similarly to orthogonal frequency division multiplexing (OFDM), are very sensitive to carrier frequency offset (CFO) and symbol timing offset (STO). In this paper, a low-complexity preamble-based joint CFO and STO technique is presented. It is based on a relatively long preamble in order to improve the CFO estimation performance as well as avoid interference coming from the data following this preamble. After CFO and STO correction, the preamble can be reused to estimate the channel. Unlike most current techniques, the CFO and STO estimation occurs in the frequency domain. This allows for a low-complexity estimation with respect to time-domain techniques and, as will be shown by simulations, provide even better performance in a reasonable range. The drawback however is that the estimation range is shorter. Specifically, for large STOs (and to a smaller extent large CFOs), the performance decreases below time-domain estimations. Two versions of the STO estimation technique will be presented, the second one being an approximation of the first one, making it less complex yet also less precise. The performance is assessed by means of computer simulations, testing for both large and small STOs, and compared with existing techniques.


title: Clustering stock price time series data to generate stock trading recommendations: An empirical study

abstract: Abstract Predicting the stock market is considered to be a very difficult task due to its non-linear and dynamic nature. Our proposed system is designed in such a way that even a layman can use it. It reduces the burden on the user. The user's job is to give only the recent closing prices of a stock as input and the proposed Recommender system will instruct him when to buy and when to sell if it is profitable or not to buy share in case if it is not profitable to do trading. Using soft computing based techniques is considered to be more suitable for predicting trends in stock market where the data is chaotic and large in number. The soft computing based systems are capable of extracting relevant information from large sets of data by discovering hidden patterns in the data. Here regression trees are used for dimensionality reduction and clustering is done with the help of Self Organizing Maps (SOM). The proposed system is designed to assist stock market investors identify possible profit-making opportunities and also help in developing a better understanding on how to extract the relevant information from stock price data.


abstract: From the Publisher: ::: Some Web sites "work" and some don't. Good Web site consultants know that you can't just jump in and start writing HTML, the same way you can't build a house by just pouring a foundation and putting up some walls. You need to know who will be using the site, and what they'll be using it for. You need some idea of what you'd like to draw their attention to during their visit. Overall, you need a strong, cohesive vision for the site that makes it both distinctive and usable. ::: Information Architecture for the World Wide Web is about applying the principles of architecture and library science to Web site design. Each Web site is like a public building, available for tourists and regulars alike to breeze through at their leisure. The job of the architect is to set up the framework for the site to make it comfortable and inviting for people to visit, relax in, and perhaps even return to someday. ::: Most books on Web development concentrate either on the aesthetics or the mechanics of the site. This book is about the framework that holds the two together. With this book, you learn how to design Web sites and intranets that support growth, management, and ease of use. Special attention is given to: ::: The process behind architecting a large, complex site Web site hierarchy design and organization Techniques for making your site easier to search ::: ::: ::: Information Architecture for the World Wide Web is for Webmasters, designers, and anyone else involved in building a Web site. It's for novice Web designers who, from the start, want to avoid the traps that result in poorly designed sites. It's for experienced Web designers who have already created sites but realize that something "is missing" from their sites and want to improve them. It's for programmers and administrators who are comfortable with HTML, CGI, and Java but want to understand how to organize their Web pages into a cohesive site. ::: The authors are two of the principals of Argus Associates, a Web consulting firm. At Argus, they have created information architectures for Web sites and intranets of some of the largest companies in the United States, including Chrysler Corporation, Barron's, and Dow Chemical.


Comparative Analysis of ADS-B Verification Techniques


title: Performance of a Distribution Intelligent Universal Transformer under Source and Load Disturbances

abstract: A bench model of the new generation Intelligent Universal Transformer (IUT) has been recently developed for distribution applications. The distribution IUT employs high-voltage semiconductor device technologies along with multilevel converter circuits for medium-voltage grid connection. This paper briefly describes the basic operation of the IUT and its experimental setup. Performances under source and load disturbances are characterized with extensive tests using a voltage sag generator and various linear and nonlinear loads. Experimental results demonstrate that IUT input and output can avoid direct impact from its opposite side disturbances. The output voltage is well regulated when the voltage sag is applied to the input. The input voltage and current maintains clean sinusoidal and unity power factor when output is nonlinear load. Under load transients, the input and output voltages remain well regulated. These key features prove that the power quality performance of IUT is far superior to that of conventional copper-and-iron based transformers.