title: Chinese Image Text Recognition on grayscale pixels

abstract: This paper presents a novel scheme for Chinese text recognition in images and videos. It's different from traditional paradigms that binarize text images, fed the binarized text to an OCR engine and get the recognized results. The proposed scheme, named grayscale based Chinese Image Text Recognition (gCITR), implements the recognition directly on grayscale pixels via the following steps: image text over-segmentation, building recognition graph, Chinese character recognition and beam search determination. The advantages of gCITR lie in: (1) it does not heavily rely on the performance of binarization, which is not robust in practical and thus severely affects the performance of OCR, (2) grayscale image retains more information of the text thus facilitates the recognition. Experimental results on text from 13 TV news videos demonstrate the effectiveness of the proposed gCITR, from which significant performance gains are observed.


title: A New Intelligence Based Approach for Computer-Aided Diagnosis of Dengue Fever

abstract: Identification of the influential clinical symptoms and laboratory features that help in the diagnosis of dengue fever in early phase of the illness would aid in designing effective public health management and virological surveillance strategies. Keeping this as our main objective we develop in this paper, a new computational intelligence based methodology that predicts the diagnosis in real time, minimizing the number of false positives and false negatives. Our methodology consists of three major components (i) a novel missing value imputation procedure that can be applied on any data set consisting of categorical (nominal) and/or numeric (real or integer) (ii) a wrapper based features selection method with genetic search for extracting a subset of most influential symptoms that can diagnose the illness and (iii) an alternating decision tree method that employs boosting for generating highly accurate decision rules. The predictive models developed using our methodology are found to be more accurate than the state-of-the-art methodologies used in the diagnosis of the dengue fever.


title: An empirical study of customers' perceptions of security and trust in e-payment systems

abstract: Abstract It is commonly believed that good security improves trust, and that the perceptions of good security and trust will ultimately increase the use of electronic commerce. In fact, customers’ perceptions of the security of e-payment systems have become a major factor in the evolution of electronic commerce in markets. In this paper, we examine issues related to e-payment security from the viewpoint of customers. This study proposes a conceptual model that delineates the determinants of consumers’ perceived security and perceived trust, as well as the effects of perceived security and perceived trust on the use of e-payment systems. To test the model, structural equation modeling is employed to analyze data collected from 219 respondents in Korea. This research provides a theoretical foundation for academics and also practical guidelines for service providers in dealing with the security aspects of e-payment systems.


Histograms of Oriented Gradients


title: Inverting face embeddings with convolutional neural networks

abstract: Deep neural networks have dramatically advanced the state of the art for many areas of machine learning. Recently they have been shown to have a remarkable ability to generate highly complex visual artifacts such as images and text rather than simply recognize them. ::: In this work we use neural networks to effectively invert low-dimensional face embeddings while producing realistically looking consistent images. Our contribution is twofold, first we show that a gradient ascent style approaches can be used to reproduce consistent images, with a help of a guiding image. Second, we demonstrate that we can train a separate neural network to effectively solve the minimization problem in one pass, and generate images in real-time. We then evaluate the loss imposed by using a neural network instead of the gradient descent by comparing the final values of the minimized loss function.


title: An Overview of Process and Product Requirements for Next Generation Thin Film Electronics, Advanced Touch Panel Devices, and Ultra High Barriers

abstract: Roll-to-Roll (R2R) production of thin film based display components (e.g., active matrix TFT backplanes and touch screens) combine the advantages of the use $>$ of inexpensive, lightweight, and flexible substrates with high throughput production. Significant cost reduction opportunities can also be found in terms of processing tool capital cost, utilized substrate area, and process gas flow when compared with batch processing systems. Applied Materials has developed a variety of different web handling and coating technologies/platforms to enable high volume R2R manufacture of thin film silicon solar cells, TFT active matrix backplanes, touch screen devices, and ultra-high barriers for organic electronics. The work presented in this chapter therefore describes the latest advances in R2R PVD processing and principal challenges inherent in moving from lab and pilot scale manufacturing to high volume manufacturing of flexible display devices using CVD for the deposition of active semiconductors layers, gate insulators, and high performance barrier/passivation layers. This chapter also includes brief description of the process and cost advantage of the use of rotatable PVD source technologies (primarily for use in flexible touch panel manufacture) and a summary of the current performance levels obtained for R2R processed amorphous silicon and IGZO TFT backplanes. Results will also be presented for barrier film for final device/frontplane encapsulation for display applications.


title: Online Learning to Rank for Information Retrieval: SIGIR 2016 Tutorial

abstract: During the past 10--15 years offline learning to rank has had a tremendous influence on information retrieval, both scientifically and in practice. Recently, as the limitations of offline learning to rank for information retrieval have become apparent, there is increased attention for online learning to rank methods for information retrieval in the community. Such methods learn from user interactions rather than from a set of labeled data that is fully available for training up front. Below we describe why we believe that the time is right for an intermediate-level tutorial on online learning to rank, the objectives of the proposed tutorial, its relevance, as well as more practical details, such as format, schedule and support materials.


title: Persisting big-data: The NoSQL landscape

abstract: Abstract The growing popularity of massively accessed Web applications that store and analyze large amounts of data, being Facebook, Twitter and Google Search some prominent examples of such applications, have posed new requirements that greatly challenge traditional RDBMS. In response to this reality, a new way of creating and manipulating data stores, known as NoSQL databases, has arisen. This paper reviews implementations of NoSQL databases in order to provide an understanding of current tools and their uses. First, NoSQL databases are compared with traditional RDBMS and important concepts are explained. Only databases allowing to persist data and distribute them along different computing nodes are within the scope of this review. Moreover, NoSQL databases are divided into different types: Key-Value, Wide-Column, Document-oriented and Graph-oriented. In each case, a comparison of available databases is carried out based on their most important features.


title: Systemic resilience model

abstract: It has been realized that resilience as a concept involves several contradictory definitions, both for instance resilience as agile adjustment and as robust resistance to situations. Our analysis of resilience concepts and models suggest that beyond simplistic definitions, it is possible to draw up a systemic resilience model (SyRes) that maintains these opposing characteristics without contradiction. We outline six functions in a systemic model, drawing primarily on resilience engineering, and disaster response: anticipation, monitoring, response, recovery, learning, and self-monitoring. The model consists of four areas: Event-based constraints, Functional Dependencies, Adaptive Capacity and Strategy. The paper describes dependencies between constraints, functions and strategies. We argue that models such as SyRes should be useful both for envisioning new resilience methods and metrics, as well as for engineering and evaluating resilient systems.


CAUSES AND EFFECTS OF OCCUPATIONAL STRESS IN NURSING


Weld : A Common Runtime for High Performance Data Analytics


title: Modeling LTE Protocol for Mobile Terminals using a Formal Description Technique

abstract: The Long Term Evolution (LTE) radio communication is the upgrade of the current 3G mobile technology with a more complex protocol in order to enable very high data rates. The usage of Model Driven Development (MDD) has arisen as a promising way of dealing with the increasing complexity of next generation mobile protocols. In this paper, a light version of the LTE protocol for the access stratum user plane is modeled using the SDL Suite™ tool. The tool shows easy understanding of the model as well as easy testing of its functionality using simulation in cooperation with Message Sequence Chart (MSC). The simulation result shows that the implemented Specification and Description Language (SDL) guarantees a good consistency with the target scenarios. The system implementation is mapped to multiple threads and integrated with an operating system to enable execution in multi core hardware platforms.


Focus : Metabolism Brain metabolism in health , aging , and neurodegeneration


title: Dynamic integration of classifiers for handling concept drift

abstract: In the real world concepts are often not stable but change with time. A typical example of this in the biomedical context is antibiotic resistance, where pathogen sensitivity may change over time as new pathogen strains develop resistance to antibiotics that were previously effective. This problem, known as concept drift, complicates the task of learning a model from data and requires special approaches, different from commonly used techniques that treat arriving instances as equally important contributors to the final concept. The underlying data distribution may change as well, making previously built models useless. This is known as virtual concept drift. Both types of concept drifts make regular updates of the model necessary. Among the most popular and effective approaches to handle concept drift is ensemble learning, where a set of models built over different time periods is maintained and the best model is selected or the predictions of models are combined, usually according to their expertise level regarding the current concept. In this paper we propose the use of an ensemble integration technique that would help to better handle concept drift at an instance level. In dynamic integration of classifiers, each base classifier is given a weight proportional to its local accuracy with regard to the instance tested, and the best base classifier is selected, or the classifiers are integrated using weighted voting. Our experiments with synthetic data sets simulating abrupt and gradual concept drifts and with a real-world antibiotic resistance data set demonstrate that dynamic integration of classifiers built over small time intervals or fixed-sized data blocks can be significantly better than majority voting and weighted voting, which are currently the most commonly used integration techniques for handling concept drift with ensembles.


title: Anatomically distinct dopamine release during anticipation and experience of peak emotion to music

abstract: Combining positron emission tomography and functional magnetic resonance imaging, the authors investigate the effects of emotionally arousing music on the dopamine system. They find that the anticipation of this abstract reward can result in dopamine release in an anatomical pathway that is distinct from that associated with the peak pleasure itself.


WorldFlow: A System for Building Global Transactional Workflows


title: Diagnosing Error in Object Detectors

abstract: This paper shows how to analyze the influences of object characteristics on detection performance and the frequency and impact of different types of false positives. In particular, we examine effects of occlusion, size, aspect ratio, visibility of parts, viewpoint, localization error, and confusion with semantically similar objects, other labeled objects, and background. We analyze two classes of detectors: the Vedaldi et al. multiple kernel learning detector and different versions of the Felzenszwalb et al. detector. Our study shows that sensitivity to size, localization error, and confusion with similar objects are the most impactful forms of error. Our analysis also reveals that many different kinds of improvement are necessary to achieve large gains, making more detailed analysis essential for the progress of recognition research. By making our software and annotations available, we make it effortless for future researchers to perform similar analysis.


title: iCyrus: A Semantic Framework for Biomedical Image Discovery

abstract: Images have an irrefutably central role in scientific discovery and discourse. However, the issues associated with knowledge management and utility operations unique to image data are only recently gaining recognition. In our previous work, we have developed Yale Image finder (YIF), which is a novel Biomedical image search engine that indexes around two million biomedical image data, along with associated metadata. While YIF is considered to be a veritable source of easily accessible biomedical images, there are still a number of usability and interoperability challenges that have yet to be addressed. To overcome these issues and to accelerate the adoption of the YIF for next generation biomedical applications, we have developed a publically accessible semantic API for biomedical images with multiple modalities. The core API called iCyrus is powered by a dedicated semantic architecture that exposes the YIF content as linked data, permitting integration with related information resources and consumption by linked data-aware data services. To facilitate the adhoc integration of image data with other online data resources, we also built semantic web services for iCyrus, such that it is compatible with the SADI semantic web service framework. The utility of the combined infrastructure is illustrated with a number of compelling use cases and further extended through the incorporation of Domeo, a well known tool for open annotation. Domeo facilitates enhanced search over the images using annotations provided through crowdsourcing. The iCyrus triplestore currently holds more than thirty-five million triples and can be accessed and operated through syntactic or semantic query interfaces. Core features of the iCyrus API, namely: data reusability, system interoperability, semantic image search, automatic update and dedicated semantic infrastructure make iCyrus a state of the art resource for image data discovery and retrieval. Demo Page: http://cbakerlab.unbsj.ca:8080/icyrus/


title: SDCA without Duality, Regularization, and Individual Convexity

abstract: Stochastic Dual Coordinate Ascent is a popular method for solving regularized loss minimization for the case of convex losses. We describe variants of SDCA that do not require explicit regularization and do not rely on duality. We prove linear convergence rates even if individual loss functions are non-convex, as long as the expected loss is strongly convex.


title: Distributed Coordinate Descent for L1-regularized Logistic Regression

abstract: Logistic regression is a widely used technique for solving classification and class probability estimation problems in text mining, biometrics and clickstream data analysis. Solving logistic regression with L1-regularization in distributed settings is an important problem. This problem arises when training dataset is very large and cannot fit the memory of a single machine. We present d-GLMNET, a new algorithm solving logistic regression with L1-regularization in the distributed settings. We empirically show that it is superior over distributed online learning via truncated gradient.


title: Brain-computer interfaces for communication and control

abstract: The brain's electrical signals enable people without muscle control to physically interact with the world.


title: Evaluation of GaN transistors in a phase-leg configuration for motor drive applications

abstract: This paper presents the evaluation of a 650 V GaN transistor which is used for a motor drive application. The influence of parasitic effects, e.g. cross conduction and stray inductances, in a GaN phase-leg configuration are analyzed by means of simulation. Additionally, experimental results of the switching transients are shown. Furthermore, measurement issues and other practical challenges are discussed.


title: A compact multiband microstrip patch antenna loaded with CSRR for wireless applications

abstract: The purpose of this paper i s to design a compact size multiband antenna for wireless applications. The proposed antenna is loaded with circular CSRR on the radiating patch whereas the ground plane is loaded with multiple rings of rectangular CSRR. The dimension of proposed antenna is 16mm × 19mm × 1.6mm, which resonates at 3.1 GHz, 4.4GHz and 4.8GHz frequencies with a return loss of −11.96dB, −12.46dB and − 16.15 dB respectively. These frequency bands are assigned for Wi-Max, military and commercial applications which are highly demanding areas in wireless field. The finite element based High Frequency Structural Simulator (HFSS) version 14.0 is used to design and simulate the proposed antenna. The proposed antenna has an acceptable return loss and also achieves stable radiation pattern for all resonating frequencies. The material used in the design of substrate is FR4 with the relative permittivity of 4.4. The conductive material chosen for the design of patch and ground plane is copper.


title: Iris recognition under alcohol influence: A preliminary study

abstract: Iris recognition has been used mainly to recognize cooperative subjects in controlled environments. With the continuing improvements in iris matching performance and reduction in the cost of iris scanners, the technology will witness broader applications and may be confronted with newer challenges. In this research, we have investigated one such challenge, namely matching iris images captured before and after alcohol consumption. Due to alcohol consumption, the pupil dilates/constricts which causes deformation in iris pattern, possibly affecting iris recognition performance. The experiments performed on the “IIITD Iris Under Alcohol Influence” database show that in matching pre and post alcohol consumption images, the overlap between genuine and impostor match score distributions increases by approximately 20%. These results on a relatively small database suggest that about one in five subjects under alcohol influence may be able to evade identification by iris recognition.


Mobile Commerce (M-Commerce) Interface Design: A Review of Literature


title: Hybrid Data Visualization Based on Depth Complexity Histogram Analysis

abstract: In many cases, only the combination of geometric and volumetric data sets is able to describe a single phenomenon under observation when visualizing large and complex data. When semi-transparent geometry is present, correct rendering results require sorting of transparent structures. Additional complexity is introduced as the contributions from volumetric data have to be partitioned according to the geometric objects in the scene. The A-buffer, an enhanced framebuffer with additional per-pixel information, has previously been introduced to deal with the complexity caused by transparent objects. In this paper, we present an optimized rendering algorithm for hybrid volume-geometry data based on the A-buffer concept. We propose two novel components for modern GPUs that tailor memory utilization to the depth complexity of individual pixels. The proposed components are compatible with modern A-buffer implementations and yield performance gains of up to eight times compared to existing approaches through reduced allocation and reuse of fast cache memory. We demonstrate the applicability of our approach and its performance with several examples from molecular biology, space weather and medical visualization containing both, volumetric data and geometric structures.


title: Indoor and outdoor depth imaging of leaves with time-of-flight and stereo vision sensors: Analysis and comparison

abstract: Abstract In this article we analyze the response of Time-of-Flight (ToF) cameras (active sensors) for close range imaging under three different illumination conditions and compare the results with stereo vision (passive) sensors. ToF cameras are sensitive to ambient light and have low resolution but deliver high frame rate accurate depth data under suitable conditions. We introduce metrics for performance evaluation over a small region of interest. Based on these metrics, we analyze and compare depth imaging of leaf under indoor (room) and outdoor (shadow and sunlight) conditions by varying exposure times of the sensors. Performance of three different ToF cameras (PMD CamBoard, PMD CamCube and SwissRanger SR4000) is compared against selected stereo-correspondence algorithms (local correlation and graph cuts). PMD CamCube has better cancelation of sunlight, followed by CamBoard, while SwissRanger SR4000 performs poorly under sunlight. Stereo vision is comparatively more robust to ambient illumination and provides high resolution depth data but is constrained by texture of the object along with computational efficiency. Graph cut based stereo correspondence algorithm can better retrieve the shape of the leaves but is computationally much more expensive as compared to local correlation. Finally, we propose a method to increase the dynamic range of ToF cameras for a scene involving both shadow and sunlight exposures at the same time by taking advantage of camera flags (PMD) or confidence matrix (SwissRanger).


Amodel of HIV / AIDS population dynamics including ARV treatment and pre-exposure prophylaxis


title: PSIM-based modeling of automotive power systems: conventional, electric, and hybrid electric vehicles

abstract: Automotive manufacturers have been taking advantage of simulation tools for modeling and analyzing various types of vehicles, such as conventional, electric, and hybrid electric vehicles. These simulation tools are of great assistance to engineers and researchers to reduce product-development cycle time, improve the quality of the design, and simplify the analysis without costly and time-consuming experiments. In this paper, a modeling tool that has been developed to study automotive systems using the power electronics simulator (PSIM) software is presented. PSIM was originally made for simulating power electronic converters and motor drives. This user-friendly simulation package is able to simulate electric/electronic circuits; however, it has no capability for simulating the entire system of an automobile. This paper discusses the PSIM validity as an automotive simulation tool by creating module boxes for not only the electrical systems, but also the mechanical, energy-storage, and thermal systems of the vehicles. These modules include internal combustion engines, fuel converters, transmissions, torque couplers, and batteries. Once these modules are made and stored in the library, the user can make the car model either a conventional, an electric, or a hybrid vehicle at will, just by dragging and dropping onto a schematic blank page.


title: Decision feedback equalization

abstract: As real world communication channels are stressed with higher data rates, intersymbol interference (ISI) becomes a dominant limiting factor. One way to combat this effect that has recently received considerable attention is the use of a decision feedback equalizer (DFE) in the receiver. The action of the DFE is to feed back a weighted sum of past decision to cancel the ISI they cause in the present signaling interval. This paper summarizes the work in this area beginning with the linear equalizer. Three performance criteria have been used to derive optimum systems; 1) minimize the noise variance under a "zero forcing" (ZF) constraint i.e., insist that all intersymbol interference is cancelled, 2) minimize the mean-square error (MMSE) between the true sample and the observed signal just prior to the decision threshold, and 3) minimize the probability of error (Min P e ). The transmitter can be fixed and the receiver optimized or one can obtain the joint optimum transmitter and receiver. The number of past decisions used in the feedback equalization can be finite or infinite. The infinite case is easier to handle analytically. In addition to reviewing the work done in the area, we show that the linear equalizer is in fact a portion of the DFE receiver and that the processing done by the DFE is exactly equivalent to the general problem of linear prediction. Other similarities in the various system structures are also shown. The effect of error propagation due to incorrect decisions is discussed, and the coaxial cable channel is used as an example to demonstrate the improvement available using DFE.


title: Comparative Survey on Traffic Sign Detection and Recognition: a Review

abstract: Developing real-time Advanced Driver Assistance Systems (ADAS) based on video aiming to extract reliable vehicle state information has attracted a lot of attention during the past decades. This ADAS system includes inter-vehicle communication, driver behavioral monitoring, and human-machine interactions. In these systems, robust and reliable traffic sign detection and recognition (TSDR) technique is a critical step for ensuring vehicle safety. This paper provides a comprehensive survey on traffic sign detection and recognition system based on image and video data. Our main focus is to present the current trends and challenges in the field of developing an efficient TSDR system followed by a detail comparative study between different renowned methods used by various researchers. Finally, conclusion followed by some future suggestion is provided to develop an efficient TSDR system is provided. This survey will hopefully lead to develop an effective traffic sign detection and recognition system which will ensure driver safety in future. Streszczenie. System ADAS (Advanced Driver Assistance System) obejmuje takze metody rozpoznawania znakow drogowych. W artykule przedstawiono przegląd metod detekcji i rozpoznawania znakow drogowych bazujących na obrazie video. W artykule dokonano oceny istniejących metod oraz zaproponowano środki poprawy ich efektywności. Studium porownawcze metod detekcji i rozpoznawania znakow drogowych


title: Multimodal Generative Models for Scalable Weakly-Supervised Learning

abstract: Multiple modalities often co-occur when describing natural phenomena. Learning a joint representation of these modalities should yield deeper and more useful representations. Previous generative approaches to multi-modal input either do not learn a joint distribution or require additional computation to handle missing data. Here, we introduce a multimodal variational autoencoder (MVAE) that uses a product-of-experts inference network and a sub-sampled training paradigm to solve the multi-modal inference problem. Notably, our model shares parameters to efficiently learn under any combination of missing modalities. We apply the MVAE on four datasets and match state-of-the-art performance using many fewer parameters. In addition, we show that the MVAE is directly applicable to weakly-supervised learning, and is robust to incomplete supervision. We then consider two case studies, one of learning image transformations---edge detection, colorization, segmentation---as a set of modalities, followed by one of machine translation between two languages. We find appealing results across this range of tasks.


title: Trends in the exploitation of novel drug targets

abstract: Schioth and colleagues examine the drugs approved by the US Food and Drug Administration over the past 30 years and analyse the interactions of these drugs with therapeutic targets encoded by the human genome, identifying 435 effect-mediating drug targets. They also analyse trends in the introduction of drugs that modulate previously unexploited targets, and discuss the network pharmacology of the drugs in the data set.


title: Accurate Angular Velocity Estimation With an Event Camera

abstract: We present an algorithm to estimate the rotational motion of an event camera. In contrast to traditional cameras, which produce images at a fixed rate, event cameras have independent pixels that respond asynchronously to brightness changes, with microsecond resolution. Our method leverages the type of information conveyed by these novel sensors (i.e., edges) to directly estimate the angular velocity of the camera, without requiring optical flow or image intensity estimation. The core of the method is a contrast maximization design. The method performs favorably against ground truth data and gyroscopic measurements from an Inertial Measurement Unit, even in the presence of very high-speed motions (close to 1000 deg/s).


title: Learning in Auctions: Regret is Hard, Envy is Easy

abstract: A line of recent work provides welfare guarantees of simple combinatorial auction formats, such as selling m items via simultaneous second price auctions (SiSPAs) (Christodoulou et al. 2008, Bhawalkar and Roughgarden 2011, Feldman et al. 2013). These guarantees hold even when the auctions are repeatedly executed and players use no-regret learning algorithms. Unfortunately, off-the-shelf no-regret algorithms for these auctions are computationally inefficient as the number of actions is exponential. We show that this obstacle is insurmountable: there are no polynomial-time no-regret algorithms for SiSPAs, unless RP$\supseteq$ NP, even when the bidders are unit-demand. Our lower bound raises the question of how good outcomes polynomially-bounded bidders may discover in such auctions. ::: To answer this question, we propose a novel concept of learning in auctions, termed "no-envy learning." This notion is founded upon Walrasian equilibrium, and we show that it is both efficiently implementable and results in approximately optimal welfare, even when the bidders have fractionally subadditive (XOS) valuations (assuming demand oracles) or coverage valuations (without demand oracles). No-envy learning outcomes are a relaxation of no-regret outcomes, which maintain their approximate welfare optimality while endowing them with computational tractability. Our results extend to other auction formats that have been studied in the literature via the smoothness paradigm. ::: Our results for XOS valuations are enabled by a novel Follow-The-Perturbed-Leader algorithm for settings where the number of experts is infinite, and the payoff function of the learner is non-linear. This algorithm has applications outside of auction settings, such as in security games. Our result for coverage valuations is based on a novel use of convex rounding schemes and a reduction to online convex optimization.


title: EventRadar: A Real-Time Local Event Detection Scheme Using Twitter Stream

abstract: Twitter has become popular among researchers as a means to detect various kinds of events. Several attempts were made to detect trends, real world events, news, earthquakes and others with satisfying results. However they do not perform well on finding local events such as release parties, musicians in a park, or art exhibitions. Many of the local events that were found by algorithms of existing work were not related to an event but to locations, global events, or just common words. In this paper, we introduce Event Radar, a novel local event detection method to improve the precision by analyzing seven day historic Tweet data. We estimate the average Tweet frequency of keywords per day in and around a potential event area and use these estimations to classify whether the keywords are related to a local event. The proposed scheme achieves a precision rate of 68% which is a significant improvement compared to related work that states a precision rate of 25.5%.


title: Comparing continual task learning in minds and machines

abstract: Humans can learn to perform multiple tasks in succession over the lifespan (“continual” learning), whereas current machine learning systems fail. Here, we investigated the cognitive mechanisms that permit successful continual learning in humans and harnessed our behavioral findings for neural network design. Humans categorized naturalistic images of trees according to one of two orthogonal task rules that were learned by trial and error. Training regimes that focused on individual rules for prolonged periods (blocked training) improved human performance on a later test involving randomly interleaved rules, compared with control regimes that trained in an interleaved fashion. Analysis of human error patterns suggested that blocked training encouraged humans to form “factorized” representation that optimally segregated the tasks, especially for those individuals with a strong prior bias to represent the stimulus space in a well-structured way. By contrast, standard supervised deep neural networks trained on the same tasks suffered catastrophic forgetting under blocked training, due to representational interference in the deeper layers. However, augmenting deep networks with an unsupervised generative model that allowed it to first learn a good embedding of the stimulus space (similar to that observed in humans) reduced catastrophic forgetting under blocked training. Building artificial agents that first learn a model of the world may be one promising route to solving continual task performance in artificial intelligence research.


title: Cloud motion and stability estimation for intra-hour solar forecasting

abstract: Abstract Techniques for estimating cloud motion and stability for intra-hour forecasting using a ground-based sky imaging system are presented. A variational optical flow (VOF) technique was used to determine the sub-pixel accuracy of cloud motion for every pixel. Cloud locations up to 15 min ahead were forecasted by inverse mapping of the cloud map. A month of image data captured by a sky imager at UC San Diego was analyzed to compare the accuracy of VOF forecast with cross-correlation method (CCM) and image persistence method. The VOF forecast with a fixed smoothness parameter was found to be superior to image persistence forecast for all forecast horizons for almost all days and outperform CCM forecast with an average error reduction of 39%, 21%, 19%, and 19% for 0, 5, 10, and 15 min forecasts respectively. Optimum forecasts may be achieved with forecast-horizon-dependent smoothness parameters. In addition, cloud stability and forecast confidence was evaluated by correlating point trajectories with forecast error. Point trajectories were obtained by tracking sub-sampled pixels using optical flow field. Point trajectory length in mintues was shown to increase with decreasing forecast error and provide valuable information for cloud forecast confidence at forecast issue time.


title: The writers' workshop for youth programmers: digital storytelling with scratch in middle school classrooms

abstract: This study investigates the potential to introduce basic programming concepts to middle school children within the context of a classroom writing-workshop. In this paper we describe how students drafted, revised, and published their own digital stories using the introductory programming language Scratch and in the process learned fundamental CS concepts as well as the wider connection between programming and writing as interrelated processes of composition.


title: Role of social media in information-seeking behaviour of international students: A systematic literature review

abstract: Purpose ::: ::: ::: ::: ::: The purpose of this paper is to investigate the information-seeking behaviour of international students in terms of their information needs and to highlight the role of social media. ::: ::: ::: ::: ::: Design/methodology/approach ::: ::: ::: ::: ::: In this paper, a systematic literature survey was conducted in order to investigate information-seeking trends among international students while using social media. As a result, an exhaustive systematic literature review (SLR) was carried out in order to investigate social media as a source for the observation of the behaviours of international students. For this purpose, 71 articles were selected from various well-known sources after an intensive SLR process of searching, filtering and enforcing the inclusion and exclusion criteria. ::: ::: ::: ::: ::: Findings ::: ::: ::: ::: ::: As an outcome of this study, the information-seeking behaviour of international students was highlighted with respect to social media as a source of information. In addition, this research identifies the information needs of the international students and categorizes them by the roles played by the social media in fulfilling the information needs. ::: ::: ::: ::: ::: Practical implications ::: ::: ::: ::: ::: A comparative study that highlighted the dearth of studies which merge the social media and information-seeking behaviour of international students as well as identify the future direction for the researchers and for benefits of international students. ::: ::: ::: ::: ::: Originality/value ::: ::: ::: ::: ::: A detail SLR which highlights the need of shifting the information seeking behaviour from libraries to social media in regard to the new environment for international students.


title: Fingerprint Image-Quality Estimation and its Application to Multialgorithm Verification

abstract: Signal-quality awareness has been found to increase recognition rates and to support decisions in multisensor environments significantly. Nevertheless, automatic quality assessment is still an open issue. Here, we study the orientation tensor of fingerprint images to quantify signal impairments, such as noise, lack of structure, blur, with the help of symmetry descriptors. A strongly reduced reference is especially favorable in biometrics, but less information is not sufficient for the approach. This is also supported by numerous experiments involving a simpler quality estimator, a trained method (NFIQ), as well as the human perception of fingerprint quality on several public databases. Furthermore, quality measurements are extensively reused to adapt fusion parameters in a monomodal multialgorithm fingerprint recognition environment. In this study, several trained and nontrained score-level fusion schemes are investigated. A Bayes-based strategy for incorporating experts' past performances and current quality conditions, a novel cascaded scheme for computational efficiency, besides simple fusion rules, is presented. The quantitative results favor quality awareness under all aspects, boosting recognition rates and fusing differently skilled experts efficiently as well as effectively (by training).


title: Hash-SVM: Scalable Kernel Machines for Large-Scale Visual Classification

abstract: This paper presents a novel algorithm which uses compact hash bits to greatly improve the efficiency of non-linear kernel SVM in very large scale visual classification problems. Our key idea is to represent each sample with compact hash bits, over which an inner product is defined to serve as the surrogate of the original nonlinear kernels. Then the problem of solving the nonlinear SVM can be transformed into solving a linear SVM over the hash bits. The proposed Hash-SVM enjoys dramatic storage cost reduction owing to the compact binary representation, as well as a (sub-)linear training complexity via linear SVM. As a critical component of Hash-SVM, we propose a novel hashing scheme for arbitrary non-linear kernels via random subspace projection in reproducing kernel Hilbert space. Our comprehensive analysis reveals a well behaved theoretic bound of the deviation between the proposed hashing-based kernel approximation and the original kernel function. We also derive requirements on the hash bits for achieving a satisfactory accuracy level. Several experiments on large-scale visual classification benchmarks are conducted, including one with over 1 million images. The results show that Hash-SVM greatly reduces the computational complexity (more than ten times faster in many cases) while keeping comparable accuracies.


title: Towards reservation-based intersection coordination: an economic approach

abstract: Understanding and controlling a complex system like traffic is not a trivial task. To this aim, many market-based methods have been applied to the design and the management of such systems, by defining the "rules of the game" and trying to enforce a desired global outcome. We model traffic as a computational economy, where drivers trade with the intelligent infrastructure in a virtual marketplace, buying time and space to cross intersections when commuting through the city. We show how such mechanism influences the drivers' behaviour, producing benefits for both the drivers (i.e. lower average travel times) and the road network (i.e. less congestions).


title: Efficient Antenna Selection and User Scheduling in 5G Massive MIMO-NOMA System

abstract: To achieve extremely high spectral efficiency in the 5- th generation (5G) communication network, the combination of massive multiple input multiple output (MIMO) and non-orthogonal multiple access (NOMA) technologies becomes a promising solution. However, due to limited radio frequency (RF) chains and channel condition variation, it is important to develop efficient antenna selection and user scheduling algorithms in complex MIMO-NOMA system. In this paper, efficient antenna selection and user scheduling algorithms are investigated to maximize the sum rate in two MIMO-NOMA scenarios. In the first simple single- band two-user scenario, the proposed antenna selection algorithm achieves higher search efficiency by limiting the candidate antennas to those are beneficial to the relevant users. In the multi-band multi-user scenario, the proposed joint antenna and user (AU) contribution algorithm considers the contribution of each antenna's and user's channel gain to total channel gain jointly. Numerical results show that proposed antenna selection algorithm achieves near-optimal performance, and joint AU contribution algorithm achieves similar performance to existing methods with reduced complexity.


title: Type Prediction in RDF Knowledge Bases Using Hierarchical Multilabel Classification

abstract: Large Semantic Web knowledge bases are often noisy, incorrect, and incomplete with respect to type information. Automatic type prediction can help reduce such incompleteness, and, as previous works show, statistical methods are well-suited for this kind of data. Since most Semantic Web knowledge bases come with an ontology defining a type hierarchy, in this paper, we rephrase the type prediction problem as a hierarchical multilabel classification problem. We propose SLCN, a modification of the local classifier per node approach, which performs feature selection, instance sampling, and class balancing for each local classifier. Our approach improves scalability, facilitating its application on large Semantic Web datasets with high-dimensional feature and label spaces. We compare the performance of our proposed method with a state-of-the-art type prediction approach and popular hierarchical multilabel classifiers, and report on experiments with large-scale RDF datasets.


title: Touch & Interact: Touch-based Interaction of Mobile Phones with Displays

abstract: The limited screen size and resolution of current mobile devices can still be problematic for map, multimedia and browsing applications. In this paper we present Touch & Interact: an interaction technique in which a mobile phone is able to touch a display, at any position, to perform selections. Through the combination of the output capabilities of the mobile phone and display, applications can share the entire display space. Moreover, there is potential to realize new interaction techniques between the phone and display. For example, select & pick and select & drop are interactions whereby entities can be picked up onto the phone or dropped onto the display. We report the implementation of Touch & Interact, its usage for a tourist guide application and experimental comparison. The latter shows that the performance of Touch & Interact is comparable to approaches based on a touch screen; it also shows the advantages of our system regarding ease of use, intuitiveness and enjoyment.


title: Fast subsequence matching in time-series databases

abstract: We present an efficient indexing method to locate 1-dimensional subsequences within a collection of sequences, such that the subsequences match a given (query) pattern within a specified tolerance. The idea is to map each data sequences into a small set of multidimensional rectangles in feature space. Then, these rectangles can be readily indexed using traditional spatial access methods, like the R*-tree [9]. In more detail, we use a sliding window over the data sequence and extract its features; the result is a trail in feature space. We propose an efficient and effective algorithm to divide such trails into sub-trails, which are subsequently represented by their Minimum Bounding Rectangles (MBRs). We also examine queries of varying lengths, and we show how to handle each case efficiently. We implemented our method and carried out experiments on synthetic and real data (stock price movements). We compared the method to sequential scanning, which is the only obvious competitor. The results were excellent: our method accelerated the search time from 3 times up to 100 times.


title: An efficient dense and scale-invariant spatio-temporal interest point detector

abstract: Over the years, several spatio-temporal interest point detectors have been proposed. While some detectors can only extract a sparse set of scale-invariant features, others allow for the detection of a larger amount of features at user-defined scales. This paper presents for the first time spatio-temporal interest points that are at the same time scale-invariant (both spatially and temporally) and densely cover the video content. Moreover, as opposed to earlier work, the features can be computed efficiently. Applying scale-space theory, we show that this can be achieved by using the determinant of the Hessian as the saliency measure. Computations are speeded-up further through the use of approximative box-filter operations on an integral video structure. A quantitative evaluation and experimental results on action recognition show the strengths of the proposed detector in terms of repeatability, accuracy and speed, in comparison with previously proposed detectors.


title: An effective hybrid learning system for telecommunication churn prediction

abstract: Abstract Customer churn has emerged as a critical issue for Customer Relationship Management and customer retention in the telecommunications industry, thus churn prediction is necessary and valuable to retain the customers and reduce the losses. Moreover, high predictive accuracy and good interpretability of the results are two key measures of a classification model. More studies have shown that single model-based classification methods may not be good enough to achieve a satisfactory result. To obtain more accurate predictive results, we present a novel hybrid model-based learning system, which integrates the supervised and unsupervised techniques for predicting customer behaviour. The system combines a modified k-means clustering algorithm and a classic rule inductive technique (FOIL). Three sets of experiments were carried out on telecom datasets. One set of the experiments is for verifying that the weighted k-means clustering can lead to a better data partitioning results; the second set of experiments is for evaluating the classification results, and comparing it to other well-known modelling techniques; the last set of experiment compares the proposed hybrid-model system with several other recently proposed hybrid classification approaches. We also performed a comparative study on a set of benchmarks obtained from the UCI repository. All the results show that the hybrid model-based learning system is very promising and outperform the existing models.


title: Who Accepts Responsibility for Their Transgressions?

abstract: After committing an offense, transgressors can optimize their chances of reconciling with the victim by accepting responsibility. However, transgressors may be motivated to avoid admitting fault because it can feel threatening to accept blame for harmful behavior. Who, then, is likely to accept responsibility for a transgression? We examined how implicit theories of personality—whether people see personality as malleable (incremental theory) or fixed (entity theory)—influence transgressors’ likelihood of accepting responsibility. We argue that incremental theorists may feel less threatened by accepting responsibility because they are more likely to view the situation as an opportunity for them to grow as a person and develop their relationship with the victim. We found support for our predictions across four studies using a combination of real-world and hypothetical offenses, and correlational and experimental methods. These studies therefore identify an important individual difference factor that can lea...


title: Single Process Architecture for E-Learning Over Cloud Computing

abstract: A Cloud is a type of parallel and distributed system consisting of a collection of interconnected and virtualized computers that are dynamically provisioned and presented as one or more unified computing resources based on service-level agreements established through negotiation between the service provider and consumers. C loud Computing refers to both the applications delivered as services over the Internet and the hardware and systems software in the data centres that provide those services (Software as a Service - SaaS). The data center hardware and software is what we will call a Cloud. Fr om the studies of various research papers and works done by various researchers it has been found that the major areas of focus in the field of cloud computing are architecture definitions, security, integration of services on various layers, inclusion of Various network and communication devices being developed rapidly. E -Learning through cloud computing is a promising area for the ease of both faculties and students around the world. The work done in cloud computing based e-Learning is oriented on centralized server and further improvement in this can be done. I n this research, a new distributed architecture is being proposed to provide an opportunity to the learners around the world to use the resources being shared by the faculties and online communication between the faculties and students.


title: Fat content, energy value and fatty acid profile of donkey milk during lactation and implications for human nutrition

abstract: Background and aimsMilk contains numerous nutrients. The content of n-3 fatty acids, the n-6/n-3 ratio, and short- and medium-chain fatty acids may promote positive health effects. In Western societies, cow’s milk fat is perceived as a risk factor for health because it is a source of a high fraction of saturated fatty acids. Recently, there has been increasing interest in donkey’s milk. In this work, the fat and energetic value and acidic composition of donkey’s milk, with reference to human nutrition, and their variations during lactation, were investigated. We also discuss the implications of the acidic profile of donkey’s milk on human nutrition.MethodsIndividual milk samples from lactating jennies were collected 15, 30, 45, 60, 90, 120, 150, 180 and 210days after foaling, for the analysis of fat, proteins and lactose, which was achieved using an infrared milk analyser, and fatty acids composition by gas chromatography.ResultsThe donkey’s milk was characterised by low fat and energetic (1719.2kJ·kg-1) values, a high polyunsaturated fatty acids (PUFA) content of mainly α-linolenic acid (ALA) and linoleic acid (LA), a low n-6 to n-3 FA ratio or LA/ALA ratio, and advantageous values of atherogenic and thrombogenic indices. Among the minor PUFA, docosahesaenoic (DHA), eicosapentanoic (EPA), and arachidonic (AA) acids were present in very small amounts (<1%). In addition, the AA/EPA ratio was low (0.18). The fat and energetic values decreased (P < 0.01) during lactation.The fatty acid patterns were affected by the lactation stage and showed a decrease (P < 0.01) in saturated fatty acids content and an increase (P < 0.01) in the unsaturated fatty acids content. The n-6 to n-3 ratio and the LA/ALA ratio were approximately 2:1, with values <1 during the last period of lactation, suggesting the more optimal use of milk during this period.ConclusionsThe high level of unsaturated/saturated fatty acids and PUFA-n3 content and the low n-6/n-3 ratio suggest the use of donkey’s milk as a functional food for human nutrition and its potential utilisation for infant nutrition as well as adult diets, particular for the elderly.


title: Occupational therapy and the use of music tempo in the treatment of the mental health care user with psychosis

abstract: The mental health care user (MHCU) with psychosis has restrictions in occupational performance components and occupational performance areas, causing impaired activity participation, compromising the achievement of Occupational Therapy (OT) goals with this population. The effect of slow versus fast tempo music during OT treatment of the MHCU with psychosis was investigated by means of a quantitative, experimental study. One hundred and sixty MHCUs with either inhibited or agitated symptoms of psychosis were included. The activity participation of the MHCUs, in terms of attention, following of instructions, directedness towards activity and willingness to participate were evaluated by three assessors through a pre- and post-test. The study indicated that music tempo, together with activity, has the ability to improve attention, following of instructions and directedness towards activity in the inhibited MHCU and attention and following of instructions in the agitated MHCU. Occupational therapy with music elements might be useful when working with the MHCU with psychosis.


title: MeshGit: diffing and merging meshes for polygonal modeling

abstract: This paper presents MeshGit, a practical algorithm for diffing and merging polygonal meshes typically used in subdivision modeling workflows. Inspired by version control for text editing, we introduce the mesh edit distance as a measure of the dissimilarity between meshes. This distance is defined as the minimum cost of matching the vertices and faces of one mesh to those of another. We propose an iterative greedy algorithm to approximate the mesh edit distance, which scales well with model complexity, providing a practical solution to our problem. We translate the mesh correspondence into a set of mesh editing operations that transforms the first mesh into the second. The editing operations can be displayed directly to provide a meaningful visual difference between meshes. For merging, we compute the difference between two versions and their common ancestor, as sets of editing operations. We robustly detect conflicting operations, automatically apply non-conflicting edits, and allow the user to choose how to merge the conflicting edits. We evaluate MeshGit by diffing and merging a variety of meshes and find it to work well for all.


title: Adversarial Link Prediction in Social Networks

abstract: Link prediction is one of the fundamental tools in social network analysis, used to identify relationships that are not otherwise observed. Commonly, link prediction is performed by means of a similarity metric, with the idea that a pair of similar nodes are likely to be connected. However, traditional link prediction based on similarity metrics assumes that available network data is accurate. We study the problem of adversarial link prediction, where an adversary aims to hide a target link by removing a limited subset of edges from the observed subgraph. We show that optimal attacks on local similarity metrics---that is, metrics which use only the information about the node pair and their network neighbors---can be found in linear time. In contrast, attacking Katz and ACT metrics which use global information about network topology is NP-Hard. We present an approximation algorithm for optimal attacks on Katz similarity, and a principled heuristic for ACT attacks. Extensive experiments demonstrate the efficacy of our methods.


title: Understanding the Detection of View Fraud in Video Content Portals

abstract: While substantial effort has been devoted to understand fraudulent activity in traditional online advertising (search and banner), more recent forms such as video ads have received little attention. The understanding and identification of fraudulent activity (i.e., fake views) in video ads for advertisers, is complicated as they rely exclusively on the detection mechanisms deployed by video hosting portals. In this context, the development of independent tools able to monitor and audit the fidelity of these systems are missing today and needed by both industry and regulators. ::: ::: In this paper we present a first set of tools to serve this purpose. Using our tools, we evaluate the performance of the audit systems of five major online video portals. Our results reveal that YouTube's detection system significantly outperforms all the others. Despite this, a systematic evaluation indicates that it may still be susceptible to simple attacks. Furthermore, we find that YouTube penalizes its videos' public and monetized view counters differently, the former being more aggressive. This means that views identified as fake and discounted from the public view counter are still monetized. We speculate that even though YouTube's policy puts in lots of effort to compensate users after an attack is discovered, this practice places the burden of the risk on the advertisers, who pay to get their ads displayed.


title: From de novo mutations to personalized therapeutic interventions in autism.

abstract: The high heritability, early age at onset, and reproductive disadvantages of autism spectrum disorders (ASDs) are consistent with an etiology composed of dominant-acting de novo (spontaneous) mutations. Mutation detection by microarray analysis and DNA sequencing has confirmed that de novo copy-number variants or point mutations in protein-coding regions of genes contribute to risk, and some of the underlying causal variants and genes have been identified. As our understanding of autism genes develops, the spectrum of autism is breaking up into quanta of many different genetic disorders. Given the diversity of etiologies and underlying biochemical pathways, personalized therapy for ASDs is logical, and clinical genetic testing is a prerequisite.


title: View-Consistent 3D Scene Flow Estimation over Multiple Frames

abstract: We propose a method to recover dense 3D scene flow from stereo video. The method estimates the depth and 3D motion field of a dynamic scene from multiple consecutive frames in a sliding temporal window, such that the estimate is consistent across both viewpoints of all frames within the window. The observed scene is modeled as a collection of planar patches that are consistent across views, each undergoing a rigid motion that is approximately constant over time. Finding the patches and their motions is cast as minimization of an energy function over the continuous plane and motion parameters and the discrete pixel-to-plane assignment. We show that such a view-consistent multi-frame scheme greatly improves scene flow computation in the presence of occlusions, and increases its robustness against adverse imaging conditions, such as specularities. Our method currently achieves leading performance on the KITTI benchmark, for both flow and stereo.


title: Comparison of Naive Basian and K-NN Classifier ABSTRACT

abstract: In this paper comparison is done between k-nearest neighbor and naive basin classifier based on the subset of features. Sequential feature selection method is used to establish the subsets. Four categories of subsets are used like life and medical transcripts, arts and humanities transcripts, social science transcripts, physical science transcripts to show the experimental results to classify data and to show that K-NN classifier gets competition with naive basian classifier. The classification performance K-NN classifier is far better then naive basian classifier when learning parameters and number of samples are small. But as the number of samples increases the naive basian classifier performance is better K-NN classifier. On the other hand naive basian classifier is much better then K-NN classifier when computational demand and memory requirements are considered. This paper demonstrates the strength of naive basian classifier for classification and summarizes the some of the most important developments in naive basian classification and K- nearest neighbor classification research. Specifically, the issues of posterior probability estimation, the link between Naive basian and K-NN classifiers, learning and generalization tradeoff in classification, the feature variable selection, as well as the effect of misclassification costs are examined. The purpose is to provide a synthesis of the published research in this area and stimulate further research interests and efforts in the identified topics. General Terms


title: Joint Resource Partitioning and Offloading in Heterogeneous Cellular Networks

abstract: In heterogeneous cellular networks (HCNs), it is desirable to offload mobile users to small cells, which are typically significantly less congested than the macrocells. To achieve sufficient load balancing, the offloaded users often have much lower SINR than they would on the macrocell. This SINR degradation can be partially alleviated through interference avoidance, for example time or frequency resource partitioning, whereby the macrocell turns off in some fraction of such resources. Naturally, the optimal offloading strategy is tightly coupled with resource partitioning; the optimal amount of which in turn depends on how many users have been offloaded. In this paper, we propose a general and tractable framework for modeling and analyzing joint resource partitioning and offloading in a two-tier cellular network. With it, we are able to derive the downlink rate distribution over the entire network, and an optimal strategy for joint resource partitioning and offloading. We show that load balancing, by itself, is insufficient, and resource partitioning is required in conjunction with offloading to improve the rate of cell edge users in co-channel heterogeneous networks.


title: Content-Based Image Retrieval Using Error Diffusion Block Truncation Coding Features

abstract: This paper presents a new approach to index color images using the features extracted from the error diffusion block truncation coding (EDBTC). The EDBTC produces two color quantizers and a bitmap image, which are further processed using vector quantization (VQ) to generate the image feature descriptor. Herein two features are introduced, namely, color histogram feature (CHF) and bit pattern histogram feature (BHF), to measure the similarity between a query image and the target image in database. The CHF and BHF are computed from the VQ-indexed color quantizer and VQ-indexed bitmap image, respectively. The distance computed from CHF and BHF can be utilized to measure the similarity between two images. As documented in the experimental result, the proposed indexing method outperforms the former block truncation coding based image indexing and the other existing image retrieval schemes with natural and textural data sets. Thus, the proposed EDBTC is not only examined with good capability for image compression but also offers an effective way to index images for the content-based image retrieval system.


title: Quantitative cw Overhauser effect dynamic nuclear polarization for the analysis of local water dynamics

abstract: Abstract Liquid state Overhauser effect Dynamic Nuclear Polarization (ODNP) has experienced a recent resurgence of interest. The ODNP technique described here relies on the double resonance of electron spin resonance (ESR) at the most common, i.e. X-band (∼10 GHz), frequency and 1H nuclear magnetic resonance (NMR) at ∼15 MHz. It requires only a standard continuous wave (cw) ESR spectrometer with an NMR probe inserted or built into an X-band cavity. We focus on reviewing a new and powerful manifestation of ODNP as a high frequency NMR relaxometry tool that probes dipolar cross relaxation between the electron spins and the 1H nuclear spins at X-band frequencies. This technique selectively measures the translational mobility of water within a volume extending 0.5–1.5 nm outward from a nitroxide radical spin probe that is attached to a targeted site of a macromolecule. It allows one to study the dynamics of water that hydrates or permeates the surface or interior of proteins, polymers, and lipid membrane vesicles. We begin by reviewing the recent advances that have helped develop ODNP into a tool for mapping the dynamic landscape of hydration water with sub-nanometer locality. In order to bind this work coherently together and to place it in the context of the extensive body of research in the field of NMR relaxometry, we then rephrase the analytical model and extend the description of the ODNP-derived NMR signal enhancements. This extended model highlights several aspects of ODNP data analysis, including the importance of considering all possible effects of microwave sample heating, the need to consider the error associated with various relaxation rates, and the unique ability of ODNP to probe the electron–1H cross-relaxation process, which is uniquely sensitive to fast (tens of ps) dynamical processes. By implementing the relevant corrections in a stepwise fashion, this paper draws a consensus result from previous ODNP procedures and then shows how such data can be further corrected to yield clear and reproducible saturation of the NMR hyperpolarization process. Finally, drawing on these results, we broadly survey the previous ODNP dynamics literature. We find that the vast number of published, empirical hydration dynamics data can be reproducibly classified into regimes of surface, interfacial, vs. buried water dynamics.


title: Coordinating donors on crowdfunding websites

abstract: Crowdfunding websites like Kickstarter, Spot.Us and Donor's Choose seek to fund multiple projects simultaneously by soliciting donations from a large number of donors. Crowdfunding site designers must decide what to do with donations to projects that don't reach their goal by the deadline. Some crowdfunding sites use an all-or-nothing return rule in which donations are returned to donors if a project doesn't meet its goal. Other sites use a direct donation structure where all donations are kept by the project even if the total is insufficient. We simulated a crowdfunding site using a threshold public goods game in which a set of donors tries to fund multiple projects that vary in riskiness. We find that the return rule mechanism leads to a marginal improvement in productivity of a site -- more money is donated in total -- by eliciting more donations. However, the return rule also leads to a potential loss in efficiency (percentage of projects funded) because donations become spread across too many projects and are not coordinated to achieve the maximum possible impact. The direct donation model, though, encourages donors to coordinate to creates a more efficient but slightly less productive marketplace.


title: Human Social Interaction Modeling Using Temporal Deep Networks

abstract: We present a novel approach to computational modeling of social interactions based on modeling of essential social interaction predicates (ESIPs) such as joint attention and entrainment. Based on sound social psychological theory and methodology, we collect a new "Tower Game" dataset consisting of audio-visual capture of dyadic interactions labeled with the ESIPs. We expect this dataset to provide a new avenue for research in computational social interaction modeling. We propose a novel joint Discriminative Conditional Restricted Boltzmann Machine (DCRBM) model that combines a discriminative component with the generative power of CRBMs. Such a combination enables us to uncover actionable constituents of the ESIPs in two steps. First, we train the DCRBM model on the labeled data and get accurate (76\%-49\% across various ESIPs) detection of the predicates. Second, we exploit the generative capability of DCRBMs to activate the trained model so as to generate the lower-level data corresponding to the specific ESIP that closely matches the actual training data (with mean square error 0.01-0.1 for generating 100 frames). We are thus able to decompose the ESIPs into their constituent actionable behaviors. Such a purely computational determination of how to establish an ESIP such as engagement is unprecedented.


title: Modelling Compression with Discourse Constraints

abstract: Sentence compression holds promise for many applications ranging from summarisation to subtitle generation and subtitle generation. The task is typically performed on isolated sentences without taking the surrounding context into account, even though most applications would operate over entire documents. In this paper we present a discourse informed model which is capable of producing document compressions that are coherent and informative. Our model is inspired by theories of local coherence and formulated within the framework of Integer Linear Programming. Experimental results show significant improvements over a stateof-the-art discourse agnostic approach.


title: Limited Preemptive Scheduling for Real-Time Systems. A Survey

abstract: The question whether preemptive algorithms are better than nonpreemptive ones for scheduling a set of real-time tasks has been debated for a long time in the research community. In fact, especially under fixed priority systems, each approach has advantages and disadvantages, and no one dominates the other when both predictability and efficiency have to be taken into account in the system design. Recently, limited preemption models have been proposed as a viable alternative between the two extreme cases of fully preemptive and nonpreemptive scheduling. This paper presents a survey of the existing approaches for reducing preemptions and compares them under different metrics, providing both qualitative and quantitative performance evaluations.


title: Open-minded Environmental Education in the Science Classroom

abstract: In this paper we will discuss the issue of environmental advocacy in science education in light of William Hare’s concept of open-mindedness. Although we shall assume that science teaching and learning must go beyond the scientific facts and theories and deal with the implications of science for society, we shall argue that science education should also demand an open-mindedness about environmental concerns such that all proposals for sustainability and the like are weighed against the alternatives using the best scientific knowledge available. Our approach will be to describe two examples of environmental education that recommend insufficiently open-minded forms of teaching and a third example that avoids this shortcoming yet provides a sound basis in environmental education.


title: A study of Prediction models for football player valuations by quantifying statistical and economic attributes for the global transfer market

abstract: The global transfer market in the field of football has long been devoid of technology. Over the last two decades, the economic dynamics have completely been changed, and the amount of money which has been pumped has increased exponentially. Valuations for any physical entity with a well-defined set of parameters is a challenge in itself and depends on numerous other things. And doing the same for an individual with varying parameters, based on the source of data, is even more challenging. With this paper, we attempt to find the most optimum way of fetching data of football players and applying the right model on it so as to extract meaningful information, thus reducing the gap between the estimated prices and the Final price. We do so by crowdsourcing the data and applying regression techniques along with Opta index. We also try to find the results using Neural Networks and conclude with a comparison between our models


title: 2′-Bispyrene-Modified 2′-O-Methyl RNA Probes as Useful Tools for the Detection of RNA: Synthesis, Fluorescent Properties, and Duplex Stability

abstract: The synthesis and properties two series of new 2′-O-methyl RNA probes, each containing a single insertion of a 2′-bispyrenylmethylphosphorodiamidate derivative of a nucleotide (U, C, A, and G), are described. As demonstrated by UV melting studies, the probes form stable complexes with model RNAs and DNAs. Significant increases (up to 21-fold) in pyrene excimer fluorescence intensity were observed upon binding of most of the probes with complementary RNAs, but not with DNAs. The fluorescence spectra are independent of the nature of the modified nucleotides. The nucleotides on the 5′-side of the modified nucleotide have no effect on the fluorescence spectra, whereas the natures of the two nucleotides on the 3′-side are important: CC, CG, and UC dinucleotide units on the 3′-side of the modified nucleotide provide the maximum increases in excimer fluorescence intensity. This study suggests that these 2′-bispyrene-labeled 2′-O-methyl RNA probes might be useful tools for detection of RNAs.


title: An improved neural network model for joint POS tagging and dependency parsing

abstract: We propose a novel neural network model for joint part-of-speech (POS) tagging and dependency parsing. Our model extends the well-known BIST graph-based dependency parser (Kiperwasser and Goldberg, 2016) by incorporating a BiLSTM-based tagging component to produce automatically predicted POS tags for the parser. On the benchmark English Penn treebank, our model obtains strong UAS and LAS scores at 94.51% and 92.87%, respectively, producing 1.5+% absolute improvements to the BIST graph-based parser, and also obtaining a state-of-the-art POS tagging accuracy at 97.97%. Furthermore, experimental results on parsing 61 "big" Universal Dependencies treebanks from raw texts show that our model outperforms the baseline UDPipe (Straka and Strakova, 2017) with 0.8% higher average POS tagging score and 3.6% higher average LAS score. In addition, with our model, we also obtain state-of-the-art downstream task scores for biomedical event extraction and opinion analysis applications. Our code is available together with all pre-trained models at: this https URL


title: Effectiveness of long-term psychodynamic psychotherapy: a meta-analysis.

abstract: CONTEXT ::: The place of long-term psychodynamic psychotherapy (LTPP) within psychiatry is controversial. Convincing outcome research for LTPP has been lacking. ::: ::: ::: OBJECTIVE ::: To examine the effects of LTPP, especially in complex mental disorders, ie, patients with personality disorders, chronic mental disorders, multiple mental disorders, and complex depressive and anxiety disorders (ie, associated with chronic course and/or multiple mental disorders), by performing a meta-analysis. ::: ::: ::: DATA SOURCES ::: Studies of LTPP published between January 1, 1960, and May 31, 2008, were identified by a computerized search using MEDLINE, PsycINFO, and Current Contents, supplemented by contact with experts in the field. ::: ::: ::: STUDY SELECTION ::: Only studies that used individual psychodynamic psychotherapy lasting for at least a year, or 50 sessions; had a prospective design; and reported reliable outcome measures were included. Randomized controlled trials (RCTs) and observational studies were considered. Twenty-three studies involving a total of 1053 patients were included (11 RCTs and 12 observational studies). ::: ::: ::: DATA EXTRACTION ::: Information on study characteristics and treatment outcome was extracted by 2 independent raters. Effect sizes were calculated for overall effectiveness, target problems, general psychiatric symptoms, personality functioning, and social functioning. To examine the stability of outcome, effect sizes were calculated separately for end-of-therapy and follow-up assessment. ::: ::: ::: RESULTS ::: According to comparative analyses of controlled trials, LTPP showed significantly higher outcomes in overall effectiveness, target problems, and personality functioning than shorter forms of psychotherapy. With regard to overall effectiveness, a between-group effect size of 1.8 (95% confidence interval [CI], 0.7-3.4) indicated that after treatment with LTPP patients with complex mental disorders on average were better off than 96% of the patients in the comparison groups (P = .002). According to subgroup analyses, LTPP yielded significant, large, and stable within-group effect sizes across various and particularly complex mental disorders (range, 0.78-1.98). ::: ::: ::: CONCLUSIONS ::: There is evidence that LTPP is an effective treatment for complex mental disorders. Further research should address the outcome of LTPP in specific mental disorders and should include cost-effectiveness analyses.


ELECTRICAL DISTRIBUTION POWER SYSTEMS OF MODERN CIVIL AIRCRAFTS


title: Geometric and radiometric estimation in a structured-light 3D scanner

abstract: The key problems in a structured-light 3D scanner using a projector and camera are its geometric and radiometric calibration. A projector can be effectively represented using the familiar pin-hole model for cameras. However, since we cannot directly observe the projector plane, it cannot be calibrated like a camera. The first contribution of our paper is to develop a geometric calibration and 3D estimation method that utilises the projective geometric relationships available in a projector---camera pair, i.e., homography induced by a plane and invariance of cross-ratios. The low-dimensional parametric form of the homography averages out individual errors, resulting in a geometric calibration approach that is both simple to use and highly accurate. We present an extensive set of results to demonstrate the effectiveness of our approach and also characterise its accuracy. Second, we present a method for correcting systematic errors introduced due to the radiometric non-linearities present in commercial projectors. These errors are pronounced when only a few phase shifts are used in the sinusoidal coding scheme for structured-light scanners. We develop a cubic spline-based method to model and remove the effects due to these non-linearities. The efficacy of our model is demonstrated on real datasets.


title: Deep Learning for Reliable Mobile Edge Analytics in Intelligent Transportation Systems: An Overview

abstract: Intelligent transportation systems (ITSs) will be a major component of tomorrow's smart cities. However, realizing the true potential of ITSs requires ultralow latency and reliable data analytics solutions that combine, in real time, a heterogeneous mix of data stemming from the ITS network and its environment. Such data analytics capabilities cannot be provided by conventional cloud-centric data processing techniques whose communication and computing latency can be high. Instead, edge-centric solutions that are tailored to the unique ITS environment must be developed. In this article, an edge analytics architecture for ITSs is introduced in which data is processed at the vehicle or roadside smart sensor level to overcome the ITS's latency and reliability challenges. With a higher capability of passengers' mobile devices and intravehicle processors, such a distributed edge computing architecture leverages deep-learning techniques for reliable mobile sensing in ITSs. In this context, the ITS mobile edge analytics challenges pertaining to heterogeneous data, autonomous control, vehicular platoon control, and cyberphysical security are investigated. Then, different deep-learning solutions for such challenges are revealed. The discussed deep-learning solutions enable ITS edge analytics by endowing the ITS devices with powerful computer vision and signal processing functions. Preliminary results show that the introduced edge analytics architecture, coupled with the power of deep-learning algorithms, provides a reliable, secure, and truly smart transportation environment.


title: A hierarchical clustering-based routing protocol for wireless sensor networks supporting multiple data aggregation qualities

abstract: Wireless Sensor Networks (WSNs) consist of a large number of energy-limited sensor nodes that are densely deployed in a large geographical region. For WSNs, energy efficiency is always a key design issue to improve the life span of the network. In this paper, we propose a routing protocol, called the Clustering-Based Expanding Ring Routing Protocol (CBERRP), which mainly focuses on the network layer while integrating factors from other layers to gain the preferred performance. CBERRP is a centralised scheme which is controlled by the Base Station (BS) and utilises the two-level hierarchical structure of clusters and chains to route the sensed data to the BS. In addition, CBERRP can be fine-tuned to support multiple Data Aggregation Qualities. The performance of CBERRP is compared to Low-Energy Adaptive Clustering Hierarchy (LEACH) and LEACH-Centralised (LEACH-C). Simulation results show that CBERRP presents significant improvement on power consumption, throughput and the network lifetime.


title: Compact LTCC Rat-Race Couplers Using Multilayered Phase-Delay and Phase-Advance T-Equivalent Sections

abstract: In this paper, compact low-temperature co-fired ceramic (LTCC) rat-race couplers are developed using multilayered phase-delay and phase-advance T-equivalent sections. By taking advantage of the multilayered LTCC layout capability, compact 3-D phase-delay and phase-advance T-equivalent sections are designed, and then treated as fundamental elements to construct two types of LTCC rat-race couplers. One type of coupler consists of three phase-delay and a phase-advance sections, whereas the other type coupler is integrated by a phase-delay and three phase-advance sections. At the design frequency 2 GHz, both two types of couplers occupy 3.8 × 3.8 mm2 circuit sizes, namely areas of 0.025 ?0 × 0.025 ?0 , where ?0 is the free-space wavelength. The measured results of the developed couplers agree the simulated results very well. It demonstrates that using multilayered phase-delay and phase-advance T-equivalent sections is an effective approach to miniaturize the circuit size of the rat-race coupler.


title: Roll up stator development for 56 frame PM synchronous motor

abstract: The paper reports on a project which sought to implement a version of the segmented stator concept on the 56 Frame permanent magnet synchronous motor platform. The motivation behind the project was to increase the slot fill, improve material usage, and reduce motor manufacturing cost. In fact, the new product not only met initial expectations, it challenged the engineering, manufacturing, and product development teams to rethink and redesign the entire product and how it is manufactured.


title: LogTM: log-based transactional memory

abstract: Transactional memory (TM) simplifies parallel programming by guaranteeing that transactions appear to execute atomically and in isolation. Implementing these properties includes providing data version management for the simultaneous storage of both new (visible if the transaction commits) and old (retained if the transaction aborts) values. Most (hardware) TM systems leave old values "in place" (the target memory address) and buffer new values elsewhere until commit. This makes aborts fast, but penalizes (the much more frequent) commits. In this paper, we present a new implementation of transactional memory, log-based transactional memory (LogTM), that makes commits fast by storing old values to a per-thread log in cacheable virtual memory and storing new values in place. LogTM makes two additional contributions. First, LogTM extends a MOESI directory protocol to enable both fast conflict detection on evicted blocks and fast commit (using lazy cleanup). Second, LogTM handles aborts in (library) software with little performance penalty. Evaluations running micro- and SPLASH-2 benchmarks on a 32-way multiprocessor support our decision to optimize for commit by showing that only 1-2% of transactions abort.


title: An Open and Scalable Web-Based Interactive Live-Streaming architecture: The WILSP Platform

abstract: Interactive live-streaming applications and platforms face particular challenges: the actions of the viewer’s affect the content of the stream. A minimal capture-render delay is critical. This is the case of applications, such as remote laboratories, which allow students to view specific hardware through a webcam, and interact with it remotely in close to real time. It is also the case of other applications, such as videoconferencing or remote rendering. In the latest years, several commercial live-streaming platforms have appeared. However, the most of them have two significant limitations. First, because they are oriented toward standard live-streaming, their capture-render delay tends to be too high for interactive live-streaming. Second, their architectures and sources are closed. That makes them unsuitable for many research and practical purposes, especially when customization is required. This paper presents the requirements for an interactive live-streaming platform, focusing on remote lab needs as a case study. Then, it proposes an architecture to satisfy those requirements that relies on Redis to achieve high scalability. The architecture is based on open technologies, and has been implemented and published as open source. From a client-side perspective, it is web-based and mobile-friendly. It is intended to be useful for both research and practical purposes. Finally, this paper experimentally evaluates the proposed architecture through its contributed implementation, analyzing its performance and scalability.


title: A Systems-Based Approach to Technology Integration Using Mentoring and Communities of Practice.

abstract: The purpose of this article is to present a systems-based mentoring model of technology integration that follows a research-based path. The model moves teachers through four specific stages of technology adoption toward using technology to support learning in more student-centered ways. The model describes how a mentor can negotiate the interplay of multiple barriers (time, beliefs, access, professional development, culture) on teachers who are learning to integrate technology and suggests a number of strategies for integrating technology, such as establishing a culture of technology integration, modeling technology use, and creating teacher leaders. Unlike previous mentoring approaches to integrating technology into the classroom, this model culminates with the establishment of a teacher-led community of practice that uses the resources currently available at a school to support and sustain the implementation of the system. Suggestions for implementing the model in a variety of K-12 and higher education settings are discussed.


title: Recovering Shading from Color Images

abstract: Existing shape-from-shading algorithms assume constant reflectance across the shaded surface. Multi-colored surfaces are excluded because both shading and reflectance affect the measured image intensity. Given a standard RGB color image, we describe a method of eliminating the reflectance effects in order to calculate a shading field that depends only on the relative positions of the illuminant and surface. Of course, shading recovery is closely tied to lightness recovery and our method follows from the work of Land [10, 9], Horn [7] and Blake [1]. In the luminance image, R+G+B, shading and reflectance are confounded. Reflectance changes are located and removed from the luminance image by thresholding the gradient of its logarithm at locations of abrupt chromaticity change. Thresholding can lead to gradient fields which are not conservative (do not have zero curl everywhere and are not integrable) and therefore do not represent realizable shading fields. By applying a new curl-correction technique at the thresholded locations, the thresholding is improved and the gradient fields are forced to be conservative. The resulting Poisson equation is solved directly by the Fourier transform method. Experiments with real images are presented.


title: An Empirical Study of a Highly Available File System

abstract: In this paper we present results from a six-month empirical study of the high availability aspects of the Coda File System. We report on the service failures experienced by Coda clients, and show that such failures are masked successfully. We also explore the effectiveness and resource costs of key aspects of server replication and disconnected operation , the two high availability mechanisms of Coda. Wherever possible, we compare our measurements to simulation-based predictions from earlier papers and to anecdotal evidence from users. Finally, we explore how users take advantage of the support provided by Coda for mobile computing.


title: Change-impact analysis of firewall policies

abstract: Firewalls are the mainstay of enterprise security and the most widely adopted technology for protecting private networks. The quality of protection provided by a firewall directly depends on the quality of its policy (i.e., configuration). Due to the lack of tools for analyzing firewall policies, most firewalls on the Internet have been plagued with policy errors. A firewall policy error either creates security holes that will allow malicious traffic to sneak into a private network or blocks legitimate traffic and disrupts normal business processes, which in turn could lead to irreparable, if not tragic, consequences. ::: ::: A major source of policy errors stem from policy changes. Firewall policies often need to be changed as networks evolve and new threats emerge. In this paper, we first present the theory and algorithms for firewall policy change-impact analysis. Our algorithms take as input a firewall policy and a proposed change, then output the accurate impact of the change. Thus, a firewall administrator can verify a proposed change before committing it.


title: A Framework for Classifying Unstructured Data of Cardiac Patients: A Supervised Learning Approach

abstract: Data mining has recently emerged as an important field that helps in extracting useful knowledge from the huge amount of unstructured and apparently un-useful data. Data mining in health organization has highest potential in this area for mining the unknown patterns in the datasets and disease prediction. The amount of work done for cardiovascular patients in Pakistan is scarcely very less. In this research study, using classification approach of machine learning we have proposed a framework to classify unstructured data of cardiac patients of the Armed Forces Institute of Cardiology (AFIC), Pakistan to four important classes. The focus of this study is to structure the unstructured medical data/reports manually, as there was no structured database available for the specific data under study. Multi-nominal Logistic Regression (LR) is used to perform multi-class classification and 10-fold cross validation is used to validate the classification models. In order to analyze the results and the performance of Logistic Regression models. The performance-measuring criterion that is used includes precision, f-measure, sensitivity, specificity, classification error, area under the curve and accuracy. This study will provide a road map for future research in the field of Bioinformatics in Pakistan.


title: Programmable Networks—From Software-Defined Radio to Software-Defined Networking

abstract: Current implementations of Internet systems are very hard to be upgraded. The ossification of existing standards restricts the development of more advanced communication systems. New research initiatives, such as virtualization, software-defined radios, and software-defined networks, allow more flexibility for networks. However, until now, those initiatives have been developed individually. We advocate that the convergence of these overlying and complementary technologies can expand the amount of programmability on the network and support different innovative applications. Hence, this paper surveys the most recent research initiatives on programmable networks. We characterize programmable networks, where programmable devices execute specific code, and the network is separated into three planes: data, control, and management planes. We discuss the modern programmable network architectures, emphasizing their research issues, and, when possible, highlight their practical implementations. We survey the wireless and wired elements on the programmable data plane. Next, on the programmable control plane, we survey the divisor and controller elements. We conclude with final considerations, open issues and future challenges.


title: A Cooperative MIMO Radar Network Using Highly Integrated FMCW Radar Sensors

abstract: In this paper, an incoherent radar network based on integrated radar sensors is presented. It consists of radar sensor nodes with independently generated frequency-modulated continuous wave-modulated transmit signals, performing both monostatic and bistatic distance measurements. An architecture to operate such a cooperative bistatic network is introduced and an optimal parameterization of the system is presented. Several nonidealities of frequency synthesizers, that are important for such cooperative radar networks, are discussed and their influence on system performance is evaluated. A prototype of the cooperative bistatic network has been realized using highly integrated radar sensors at 122 GHz. Radar measurements using the prototype prove the feasibility and potential of the network approach. The complete signal processing chain is presented, including an adapted multilateration algorithm for target position estimation using all measured distances.


title: Multimodal Neuroimaging Feature Learning With Multimodal Stacked Deep Polynomial Networks for Diagnosis of Alzheimer's Disease

abstract: The accurate diagnosis of Alzheimer's disease (AD) and its early stage, i.e., mild cognitive impairment, is essential for timely treatment and possible delay of AD. Fusion of multimodal neuroimaging data, such as magnetic resonance imaging (MRI) and positron emission tomography (PET), has shown its effectiveness for AD diagnosis. The deep polynomial networks (DPN) is a recently proposed deep learning algorithm, which performs well on both large-scale and small-size datasets. In this study, a multimodal stacked DPN (MM-SDPN) algorithm, which MM-SDPN consists of two-stage SDPNs, is proposed to fuse and learn feature representation from multimodal neuroimaging data for AD diagnosis. Specifically speaking, two SDPNs are first used to learn high-level features of MRI and PET, respectively, which are then fed to another SDPN to fuse multimodal neuroimaging information. The proposed MM-SDPN algorithm is applied to the ADNI dataset to conduct both binary classification and multiclass classification tasks. Experimental results indicate that MM-SDPN is superior over the state-of-the-art multimodal feature-learning-based algorithms for AD diagnosis.


title: CLOUD COMPUTING AND SECURITY ISSUES IN THE CLOUD

abstract: Cloud computing has formed the conceptual and infrastructural basis for tomorrow’s computing. The global computing infrastructure is rapidly moving towards cloud based architecture. While it is important to take advantages of could based computing by means of deploying it in diversified sectors, the security aspects in a cloud based computing environment remains at the core of interest. Cloud based services and service providers are being evolved which has resulted in a new business trend based on cloud technology. With the introduction of numerous cloud based services and geographically dispersed cloud service providers, sensitive information of different entities are normally stored in remote servers and locations with the possibilities of being exposed to unwanted parties in situations where the cloud servers storing those information are compromised. If security is not robust and consistent, the flexibility and advantages that cloud computing has to offer will have little credibility. This paper presents a review on the cloud computing concepts as well as security issues inherent within the context of cloud computing and cloud infrastructure.


title: Object detection in pleiades images using deep features

abstract: Extracting and identifying objects in very high resolution imagery has been a popular research topic in remote sensing. Since the beginning of this decade, deep learning techniques have revolutionized computer vision providing significant performance gains compared to traditional “shallow” techniques in various challenging vision problems. The training of deep neural networks usually requires very large training datasets. The advantage of using deep features is to exploit already trained Convolutional Neural Networks (CNN) in order to produce high level features without the burden of having to train a CNN from scratch. In this paper, we are investigating the use of deep features for the detection of small objects (cars and individual trees) in high resolution Pleiades imagery. Preliminary results show good detection performance and are very encouraging for future applications.