title: User modeling with neural network for review rating prediction

abstract: We present a neural network method for review rating prediction in this paper. Existing neural network methods for sentiment prediction typically only capture the semantics of texts, but ignore the user who expresses the sentiment. This is not desirable for review rating prediction as each user has an influence on how to interpret the textual content of a review. For example, the same word (e.g. "good") might indicate different sentiment strengths when written by different users. We address this issue by developing a new neural network that takes user information into account. The intuition is to factor in user-specific modification to the meaning of a certain word. Specifically, we extend the lexical semantic composition models and introduce a userword composition vector model (UWCVM), which effectively captures how user acts as a function affecting the continuous word representation. We integrate UWCVM into a supervised learning framework for review rating prediction, and conduct experiments on two benchmark review datasets. Experimental results demonstrate the effectiveness of our method. It shows superior performances over several strong baseline methods.


title: El paisaje en la ordenación del territorio. La experiencia del Observatorio del Paisaje de Cataluña

abstract: Given the progressive degradation of our landscapes there is a need, on the one hand, to include the landscape in planning and, on the other, to raise public awareness of landscape values. To achieve this double goal, the year 2005 saw the creation of the Landscape Observatory of Catalonia, an independent public consortium that simultaneously acts as both an advisory body to the public authorities and an organisation that aims to raise society’s awareness of landscape-related issues. One of its main functions is to draw up landscape catalogues of Catalonia, an innovative tool for incorporating the landscape into planning and an element of town and country planning with regard to the implementation of landscape policy. The catalogues define the strategic lines and specific directives that can help improve landscape policy and, in short, the public’s quality of life.


title: Why Minimally Guided Teaching Techniques Do Not Work: A Reply to Commentaries

abstract: In this reply to commentaries on the Kirschner, Sweller, and Clark (2006) paper, we not only reemphasize the importance of randomized, controlled experimental tests of competing instructional procedures, but also indicate that altering one variable at a time is an essential feature of a properly controlled experiment. Furthermore, we also emphasize that variable must be relevant to the issue at hand with its effects explainable by our knowledge of human cognitive architecture. We reject the view that the presentation of relevant information should be reduced in favor of teaching learners how to find information. Lastly, we indicate that we believe a new educational psychology has been developed that has the potential to rapidly change our field.


title: Which tone-mapping operator is the best? A comparative study of perceptual quality

abstract: Tone-mapping operators (TMOs) are designed to generate perceptually similar low-dynamic-range images from high-dynamic-range ones. We studied the performance of 15 TMOs in two psychophysical experiments where observers compared the digitally generated tone-mapped images to their corresponding physical scenes. All experiments were performed in a controlled environment, and the setups were designed to emphasize different image properties: in the first experiment we evaluated the local relationships among intensity levels, and in the second one we evaluated global visual appearance among physical scenes and tone-mapped images, which were presented side by side. We ranked the TMOs according to how well they reproduced the results obtained in the physical scene. Our results show that ranking position clearly depends on the adopted evaluation criteria, which implies that, in general, these tone-mapping algorithms consider either local or global image attributes but rarely both. Regarding the question of which TMO is the best, KimKautz ["Consistent tone reproduction," in Proceedings of Computer Graphics and Imaging (2008)] and Krawczyk ["Lightness perception in tone reproduction for high dynamic range images," in Proceedings of Eurographics (2005), p. 3] obtained the better results across the different experiments. We conclude that more thorough and standardized evaluation criteria are needed to study all the characteristics of TMOs, as there is ample room for improvement in future developments.


title: Ranking Saliency

abstract: Most existing bottom-up algorithms measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of only considering the contrast between salient objects and their surrounding regions, we consider both foreground and background cues in this work. We rank the similarity of image elements with foreground or background cues via graph-based manifold ranking. The saliency of image elements is defined based on their relevances to the given seeds or queries. We represent an image as a multi-scale graph with fine superpixels and coarse regions as nodes. These nodes are ranked based on the similarity to background and foreground queries using affinity matrices. Saliency detection is carried out in a cascade scheme to extract background regions and foreground salient objects efficiently. Experimental results demonstrate the proposed method performs well against the state-of-the-art methods in terms of accuracy and speed. We also propose a new benchmark dataset containing 5,168 images for large-scale performance evaluation of saliency detection methods.


Keyframe-based Video Summarization with Human in the Loop


Fraud Detection Using Data Mining Techniques


title: Machine Learning Approach for Prediction of Learning Disabilities in School-Age Children

abstract: his paper highlights the two machine learning approa ches, viz. Rough Sets and Decision Trees (DT), for the prediction of Learning Disabilities (LD) in school-age children, with an emphasis on applications of data mining. Learning disability prediction is a very complicated task. By using these two approaches, we can easily and accurately predict LD in any child and also we can determine the best classification method. In this study, in rough sets the attribute reduction and classification are performed using Johnson's reduction algorithm and Naive Bayes algorithm respectively for rule mining and in construction of decision trees, J48 algorithm is used. From this study, it is concluded that, the performance of decision trees are considerably poorer in several important aspects compared to rough sets. It is found that, for selection of attributes, rough sets is very useful especially in the case of inconsistent data and it also gives the information about the attribute correlation which is very important in the case of learning disability.


title: The use of the area under the ROC curve in the evaluation of machine learning algorithms

abstract: In this paper we investigate the use of the area under the receiver operating characteristic (ROC) curve (AUC) as a performance measure for machine learning algorithms. As a case study we evaluate six machine learning algorithms (C4.5, Multiscale Classifier, Perceptron, Multi-layer Perceptron, k-Nearest Neighbours, and a Quadratic Discriminant Function) on six ''real world'' medical diagnostics data sets. We compare and discuss the use of AUC to the more conventional overall accuracy and find that AUC exhibits a number of desirable properties when compared to overall accuracy: increased sensitivity in Analysis of Variance (ANOVA) tests; a standard error that decreased as both AUC and the number of test samples increased; decision threshold independent; and it is invariant to a priori class probabilities. The paper concludes with the recommendation that AUC be used in preference to overall accuracy for ''single number'' evaluation of machine learning algorithms.


title: MDD inclusion of navigational, structural and RBAC elements for JSF and ASP.NET MVC frameworks in UML models

abstract: Educational applications have become complex enterprise web applications. During the design and development of this type of software we have realized the existing modeling approaches were not well suited for the modeling of JSF or ASP.NET MVC applications. This paper extends UML for the modeling of the presentation tier of enterprise applications based on JSF or ASP.NET MVC frameworks. Our main aim was to include navigational, structural and role-based access control (RBAC) elements for JSF/ASP.NET MVC applications in UML class and sequence diagrams built according to multitier patterns. Accordingly, our approach: (i) characterizes the navigational, structural and RBAC elements of JSF/ASP.NET MVC frameworks; (ii) does not define a proprietary notation but provides two platform-specific UML profiles that extend UML-WAE; (iii) is able to include any multitier, service-oriented architecture and security design-level pattern in the models; (iv) provides models whose semantics can be directly translated into code; and therefore, (v) decouples code maintenance from specific tools. This approach has been successfully used in the design of a complex e-learning web application deployed by our research group.


Learning to think: The challenges of teaching thinking


title: Statistical Threat Assessment for General Road Scenes Using Monte Carlo Sampling

abstract: This paper presents a threat-assessment algorithm for general road scenes. A road scene consists of a number of objects that are known, and the threat level of the scene is based on their current positions and velocities. The future driver inputs of the surrounding objects are unknown and are modeled as random variables. In order to capture realistic driver behavior, a dynamic driver model is implemented as a probabilistic prior, which computes the likelihood of a potential maneuver. A distribution of possible future scenarios can then be approximated using a Monte Carlo sampling. Based on this distribution, different threat measures can be computed, e.g., probability of collision or time to collision. Since the algorithm is based on the Monte Carlo sampling, it is computationally demanding, and several techniques are presented to increase performance without increasing computational load. The algorithm is intended both for online safety applications in a vehicle and for offline data analysis.


title: Nonlinear design optimization of electric machines by using parametric Fourier coefficients of air gap flux density

abstract: Weight and efficiency are conflicting top requirements for all kind of mobile applications of electrical machines. Utilizing the design freedom of a novel machine design, based on a slotless air gap winding while providing high torque and efficient cooling, this paper presents a design optimization approach that allows for precise and fast design of that type of machine. Design speed is achieved by building a simplified parametric model of the electrical machine that combines very well with a nonlinear optimization formulation of the design problem, where machine mass and losses are balanced out to find a Pareto optimal design set. Additional design requirements are treated via constraints. The parametric machine model was built upon a numerical finite elements method analysis of magnetic flux density in the air gap with ANSYS Maxwell, which is used to formulate parametric Fourier series based models of flux acting on a phase, flux acting on a six-step commutated winding and effective flux useful to define machine torque constant. Except the numerical optimization procedure, all model and design equations can be formulated analytically exploiting Maple's symbolic computation features, thus simplifying analysis and speeding up design. Finally, an optimal motor design study for a 15-inch rim wheel-hub drive is presented. The range of a tradeoff between demands for weight and efficiency while delivering the required torque at nominal speed is shown.


A Framework for Highly-Available Composed Real-Time Internet Services


title: Reviewing the Concept of Brand Equity and Evaluating Consumer-Based Brand Equity (CBBE) Models

abstract: IFRS cut across all segments of industries. However, concerning the Oil and Gas industry sectors, IFRS has significant impact. IFRS has various guidelines that help to make the convergence to the new system, especially, in Oil and Gas accounting and reporting easy. Interestingly, accounting for Oil and Gas activities presents many difficulties. Significant upfront investment, uncertainty over prospects and long project lives have led to a variety of approaches being developed by companies and a range of country-specific guidance for the sector. The main objective of this paper is to critically look at the recent IFRS pronouncements on specific issues concerning the Oil and Gas industries accounting in order to simplify these difficulties. This paper looks at some of the main accounting issues across Oil and Gas Companies IFRS guideline has provided. Specifically, the paper looks at the provisions of IFRS 6 and 13 concerning the Evaluation and Exploration of Oil and Gas; certain elements such as Joint Arrangements, Fair value representations, deferred taxation as well as Impairments issues. It considers current effective standards and notes future developments that can impact accounting in the sector as reflected on their financial statements.


title: Computational thinking in compulsory education: Towards an agenda for research and practice

abstract: Computational Thinking is considered a universal competence, which should be added to every child’s analytical ability as a vital ingredient of their school learning. In this article we further elaborate on what Computational Thinking is and present examples of what needs to be taught and how. First we position Computational Thinking in Papert’s work with LOGO. We then discuss challenges in defining Computational Thinking and discuss the core and peripheral aspects of a definition. After that we offer examples of how Computational Thinking can be addressed in both formal and informal educational settings. In the conclusion and discussion section an agenda for research and practice is presented.


title: Visualizing Change over Time Using Dynamic Hierarchies: TreeVersity2 and the StemView

abstract: To analyze data such as the US Federal Budget or characteristics of the student population of a University it is common to look for changes over time. This task can be made easier and more fruitful if the analysis is performed by grouping by attributes, such as by Agencies, Bureaus and Accounts for the Budget, or Ethnicity, Gender and Major in a University. We present TreeVersity2, a web based interactive data visualization tool that allows users to analyze change in datasets by creating dynamic hierarchies based on the data attributes. TreeVersity2 introduces a novel space filling visualization (StemView) to represent change in trees at multiple levels - not just at the leaf level. With this visualization users can explore absolute and relative changes, created and removed nodes, and each node's actual values, while maintaining the context of the tree. In addition, TreeVersity2 provides overviews of change over the entire time period, and a reporting tool that lists outliers in textual form, which helps users identify the major changes in the data without having to manually setup filters. We validated TreeVersity2 with 12 case studies with organizations as diverse as the National Cancer Institute, Federal Drug Administration, Department of Transportation, Office of the Bursar of the University of Maryland, or eBay. Our case studies demonstrated that TreeVersity2 is flexible enough to be used in different domains and provide useful insights for the data owners. A TreeVersity2 demo can be found at https://treeversity.cattlab.umd.edu.


title: Deep scattering: rendering atmospheric clouds with radiance-predicting neural networks

abstract: We present a technique for efficiently synthesizing images of atmospheric clouds using a combination of Monte Carlo integration and neural networks. The intricacies of Lorenz-Mie scattering and the high albedo of cloud-forming aerosols make rendering of clouds---e.g. the characteristic silverlining and the "whiteness" of the inner body---challenging for methods based solely on Monte Carlo integration or diffusion theory. We approach the problem differently. Instead of simulating all light transport during rendering, we pre-learn the spatial and directional distribution of radiant flux from tens of cloud exemplars. To render a new scene, we sample visible points of the cloud and, for each, extract a hierarchical 3D descriptor of the cloud geometry with respect to the shading location and the light source. The descriptor is input to a deep neural network that predicts the radiance function for each shading configuration. We make the key observation that progressively feeding the hierarchical descriptor into the network enhances the network's ability to learn faster and predict with higher accuracy while using fewer coefficients. We also employ a block design with residual connections to further improve performance. A GPU implementation of our method synthesizes images of clouds that are nearly indistinguishable from the reference solution within seconds to minutes. Our method thus represents a viable solution for applications such as cloud design and, thanks to its temporal stability, for high-quality production of animated content.


title: Developing predictive models for early detection of at-risk students on distance learning modules

abstract: Not all students who fail or drop out would have done so if they had been offered help at the right time. This is particularly true on distance learning modules where there is no direct tutor/student contact, but where it has been shown that making contact at the right time can improve a student’s chances. This paper explores the latest work conducted at the Open University, one of Europe’s largest distance learning institutions, to identify when is the optimum time to make student interventions and to develop models to identify the at-risk students in this time frame. This work in progress is taking real time data and feeding it back to module teams as the module is running. Module teams will be indicating which of the predicted at-risk students have received an intervention, and the nature of the intervention.


title: Reactive Nonholonomic Trajectory Generation via Parametric Optimal Control

abstract: There are many situations for which a feasible nonholonomic motion plan must be generated immediately based on real-time perceptual information. Parametric trajectory representations limit computation because they reduce the search space for solutions (at the cost of potentially introducing suboptimality). The use of any parametric trajectory model converts the optimal control formulation into an equivalent nonlinear programming problem. In this paper, curvature polynomials of arbitrary order are used as the assumed form of solution. Polynomials sacrifice little in terms of spanning the set of feasible controls while permitting an expression of the general solution to the system dynamics in terms of decoupled quadratures. These quadratures are then readily linearized to express the necessary conditions for optimality. Resulting trajectories are convenient to manipulate and execute in vehicle controllers and they can be computed with a straightforward numerical procedure in real time. KEY WORDS—mobile robots, car-like robots, trajectory generation, curve generation, nonholonomic, clothoid, cornu spiral, optimal control


title: Lose the Views: Limited Angle CT Reconstruction via Implicit Sinogram Completion

abstract: Computed Tomography (CT) reconstruction is a fundamental component to a wide variety of applications ranging from security, to healthcare. The classical techniques require measuring projections, called sinograms, from a full 180$^\circ$ view of the object. This is impractical in a limited angle scenario, when the viewing angle is less than 180$^\circ$, which can occur due to different factors including restrictions on scanning time, limited flexibility of scanner rotation, etc. The sinograms obtained as a result, cause existing techniques to produce highly artifact-laden reconstructions. In this paper, we propose to address this problem through implicit sinogram completion, on a challenging real world dataset containing scans of common checked-in luggage. We propose a system, consisting of 1D and 2D convolutional neural networks, that operates on a limited angle sinogram to directly produce the best estimate of a reconstruction. Next, we use the x-ray transform on this reconstruction to obtain a "completed" sinogram, as if it came from a full 180$^\circ$ measurement. We feed this to standard analytical and iterative reconstruction techniques to obtain the final reconstruction. We show with extensive experimentation that this combined strategy outperforms many competitive baselines. We also propose a measure of confidence for the reconstruction that enables a practitioner to gauge the reliability of a prediction made by our network. We show that this measure is a strong indicator of quality as measured by the PSNR, while not requiring ground truth at test time. Finally, using a segmentation experiment, we show that our reconstruction preserves the 3D structure of objects effectively.


title: Geometrical Insights for Implicit Generative Modeling

abstract: Learning algorithms for implicit generative models can optimize a variety of criteria that measure how the data distribution differs from the implicit model distribution, including the Wasserstein distance, the Energy distance, and the Maximum Mean Discrepancy criterion. A careful look at the geometries induced by these distances on the space of probability measures reveals interesting differences. In particular, we can establish surprising approximate global convergence guarantees for the 1-Wasserstein distance, even when the parametric generator has a nonconvex parametrization.


title: Proactive kinodynamic planning using the Extended Social Force Model and human motion prediction in urban environments

abstract: This paper presents a novel approach for robot navigation in crowded urban environments where people and objects are moving simultaneously while a robot is navigating. Avoiding moving obstacles at their corresponding precise moment motivates the use of a robotic planner satisfying both dynamic and nonholonomic constraints, also referred as kynodynamic constraints. We present a proactive navigation approach with respect its environment, in the sense that the robot calculates the reaction produced by its actions and provides the minimum impact on nearby pedestrians. As a consequence, the proposed planner integrates seamlessly planning and prediction and calculates a complete motion prediction of the scene for each robot propagation. Making use of the Extended Social Force Model (ESFM) allows an enormous simplification for both the prediction model and the planning system under differential constraints. Simulations and real experiments have been carried out to demonstrate the success of the proactive kinodynamic planner.


title: Identification of microplastics by FTIR and Raman microscopy: a novel silicon filter substrate opens the important spectral range below 1300 cm−1 for FTIR transmission measurements

abstract: The presence of microplastics in aquatic ecosystems is a topical problem and leads to the need of appropriate and reliable analytical methods to distinctly identify and to quantify these particles in environmental samples. As an example transmission, Fourier transform infrared (FTIR) imaging can be used to analyze samples directly on filters without any visual presorting, when the environmental sample was afore extracted, purified, and filtered. However, this analytical approach is strongly restricted by the limited IR transparency of conventional filter materials. Within this study, we describe a novel silicon (Si) filter substrate produced by photolithographic microstructuring, which guarantees sufficient transparency for the broad mid-infrared region of 4000–600 cm-1. This filter type features holes with a diameter of 10 μm and exhibits adequate mechanical stability. Furthermore, it will be shown that our Si filter substrate allows a distinct identification of the most common microplastics, polyethylene (PE), and polypropylene (PP), in the characteristic fingerprint region (1400–600 cm-1). Moreover, using the Si filter substrate, a differentiation of microparticles of polyesters having quite similar chemical structure, like polyethylene terephthalate (PET) and polybutylene terephthalate (PBT), is now possible, which facilitates a visualization of their distribution within a microplastic sample by FTIR imaging. Finally, this Si filter can also be used as substrate for Raman microscopy—a second complementary spectroscopic technique—to identify microplastic samples.


title: Microwave breast cancer detection via cost-sensitive ensemble classifiers: Phantom and patient investigation

abstract: Abstract Microwave breast screening has been proposed as a complementary modality to the current standard of X-ray mammography. In this work, we design three ensemble classification structures that fuse information from multiple sensors to detect abnormalities in the breast. A principled Neyman–Pearson approach is developed to allow control of the trade-off between false positive rate and the false negative rate. We evaluate performance using data derived from measurements of heterogeneous breast phantoms. We also use data collected in a clinical trial that monitored 12 healthy patients monthly over an eight-month period. In order to assess the efficacy of the proposed algorithms we model scans of breasts with malignant lesions by artificially adding simulated tumour responses to existing scans of healthy volunteers. Tumour responses are constructed based on measured properties of breast tissues and real breast measurements, thus the simulation model takes into account the heterogeneity of the breast tissue. The algorithms we present take advantage of breast scans from other patients or tissue-mimicking breast phantoms to learn about breast content and what constitutes a “tumour-free” and “tumour-bearing” set of measurements. We demonstrate that the ensemble selection-based algorithm, which constructs an ensemble of the most informative classifiers, significantly outperforms other detection techniques for the clinical trial data set.


title: Increased levels of laminin in ascitic fluid of patients with ovarian cancer.

abstract: Laminin is a component of the extracellular matrix and is associated with tumor cell metastasis. Present studies show that the ovarian cancer cell lines produce significant amounts of laminin (54-140 ng/ml) in culture. Since ovarian cancer is associated with ascites production, laminin levels were then determined in ascites and serum. The results indicate that the ascites from patients with serous adenocarcinoma of the ovary had higher levels of laminin than the normal peritoneal fluid (P < 0.0001). However, the serum levels of laminin did not differ significantly between the control population and ovarian cancer patients.


title: The Foot-and-Mouth Epidemic in Great Britain: Pattern of Spread and Impact of Interventions

abstract: We present an analysis of the current foot-and-mouth disease epidemic in Great Britain over the first 2 months of the spread of the virus. The net transmission potential of the pathogen and the increasing impact of control measures are estimated over the course of the epidemic to date. These results are used to parameterize a mathematical model of disease transmission that captures the differing spatial contact patterns between farms before and after the imposition of movement restrictions. The model is used to make predictions of future incidence and to simulate the impact of additional control strategies. Hastening the slaughter of animals with suspected infection is predicted to slow the epidemic, but more drastic action, such as “ring” culling or vaccination around infection foci, is necessary for more rapid control. Culling is predicted to be more effective than vaccination.


An Empirical Study of Hyperparameter Importance Across Datasets


title: Textual analysis of stock market prediction using breaking financial news: The AZFin text system

abstract: Our research examines a predictive machine learning approach for financial news articles analysis using several different textual representations: bag of words, noun phrases, and named entities. Through this approach, we investigated 9,211 financial news articles and 10,259,042 stock quotes covering the SP directional accuracy) and the highest return using a simulated trading engine (2.06p return). We further investigated the different textual representations and found that a Proper Noun scheme performs better than the de facto standard of Bag of Words in all three metrics.


title: Weed Detection Using Fractal-Based Low Cost Commodity Hardware Raspberry Pi

abstract: Conventional weed control system is usually used by spraying herbicides uniformly throughout the land. Excessive use of herbicides on an ongoing basis can produce chemical waste that is harmful to plants and soil. The application of precision agriculture farming in the detection process in order to control weeds using Computer Vision On Farm becomes interesting, but it still has some problems due to computer size and power consumption. Raspberry Pi is one of the minicomputer with low price and low power consumption. Having computing like a desktop computer with the open source Linux operating system can be used for image processing and weed fractal dimension processing using OpenCV library and C programming. This research results the best fractal computation time when performing the image with dimension size of 128 x 128 pixels. It is about 7 milliseconds. Furthermore, the average speed ratio between personal computer and Raspberry Pi is 0.04 times faster. The use of Raspberry Pi is cost and power consumption efficient compared to personal computer.


title: End-To-End Ego Lane Estimation Based on Sequential Transfer Learning for Self-Driving Cars

abstract: Autonomous cars establish driving strategies using the positions of ego lanes. The previous methods detect lane points and select ego lanes with heuristic and complex postprocessing with strong geometric assumptions. We propose a sequential end-to-end transfer learning method to estimate left and right ego lanes directly and separately without any postprocessing. We redefined a point-detection problem as a region-segmentation problem; as a result, the proposed method is insensitive to occlusions and variations of environmental conditions, because it considers the entire content of an input image during training. Also, we constructed an extensive dataset that is suitable for a deep neural network training by collecting a variety of road conditions, annotating ego lanes, and augmenting them systematically. The proposed method demonstrated improved accuracy and stability on input variations compared with a recent method based on deep learning. Our approach does not involve postprocessing, and is therefore flexible to change of target domain.


title: Comparative Study of Caffe, Neon, Theano, and Torch for Deep Learning

abstract: Deep learning methods have resulted in significant performance improvements in several application domains and as such several software frameworks have been developed to facilitate their implementation. This paper presents a comparative study of four deep learning frameworks, namely Caffe, Neon, Theano, and Torch, on three aspects: extensibility, hardware utilization, and speed. The study is performed on several types of deep learning architectures and we evaluate the performance of the above frameworks when employed on a single machine for both (multi-threaded) CPU and GPU (Nvidia Titan X) settings. The speed performance metrics used here include the gradient computation time, which is important during the training phase of deep networks, and the forward time, which is important from the deployment perspective of trained networks. For convolutional networks, we also report how each of these frameworks support various convolutional algorithms and their corresponding performance. From our experiments, we observe that Theano and Torch are the most easily extensible frameworks. We observe that Torch is best suited for any deep architecture on CPU, followed by Theano. It also achieves the best performance on the GPU for large convolutional and fully connected networks, followed closely by Neon. Theano achieves the best performance on GPU for training and deployment of LSTM networks. Finally Caffe is the easiest for evaluating the performance of standard deep architectures.


title: A Comparison of the Readability of Graphs Using Node-Link and Matrix-Based Representations

abstract: In this paper, we describe a taxonomy of generic graph related tasks and an evaluation aiming at assessing the readability of two representations of graphs: matrix-based representations and node-link diagrams. This evaluation bears on seven generic tasks and leads to important recommendations with regard to the representation of graphs according to their size and density. For instance, we show that when graphs are bigger than twenty vertices, the matrix-based visualization performs better than node-link diagrams on most tasks. Only path finding is consistently in favor of node-link diagrams throughout the evaluation


title: Study on hydrodynamic performance of hubless rim-driven propulsors with variable parameters

abstract: Hubless Rim-Driven Propulsor (HRDP) is a new-concept electric thruster which integrates the motor and propeller directly without intermediate transmission mechanism. As the methods for designing this kind of propeller has not been brought up yet, a CFD (computational fluid dynamics) simulation method is used in this paper. The comparison between the calculated results and the experimental results of NO.19A+Ka4−70 ducted propeller was carried out. Thus, the accuracy and application of the numerical method were thus validated. Then an improved Ka-series propeller was introduced and utilized. Finally, the effects on propeller's hydrodynamic performance of the number of blade, rake angle and pitch were analyzed.


title: A modern Bayesian look at the multi-armed bandit

abstract: A multi-armed bandit is an experiment with the goal of accumulating rewards from a payoff distribution with unknown parameters that are to be learned sequentially. This article describes a heuristic for managing multi-armed bandits called randomized probability matching, which randomly allocates observations to arms according the Bayesian posterior probability that each arm is optimal. Advances in Bayesian computation have made randomized probability matching easy to apply to virtually any payoff distribution. This flexibility frees the experimenter to work with payoff distributions that correspond to certain classical experimental designs that have the potential to outperform methods that are ‘optimal’ in simpler contexts. I summarize the relationships between randomized probability matching and several related heuristics that have been used in the reinforcement learning literature. Copyright © 2010 John Wiley & Sons, Ltd.


title: Early androgen exposure and human gender development

abstract: During early development, testosterone plays an important role in sexual differentiation of the mammalian brain and has enduring influences on behavior. Testosterone exerts these influences at times when the testes are active, as evidenced by higher concentrations of testosterone in developing male than in developing female animals. This article critically reviews the available evidence regarding influences of testosterone on human gender-related development. In humans, testosterone is elevated in males from about weeks 8 to 24 of gestation and then again during early postnatal development. Individuals exposed to atypical concentrations of testosterone or other androgenic hormones prenatally, for example, because of genetic conditions or because their mothers were prescribed hormones during pregnancy, have been consistently found to show increased male-typical juvenile play behavior, alterations in sexual orientation and gender identity (the sense of self as male or female), and increased tendencies to engage in physically aggressive behavior. Studies of other behavioral outcomes following dramatic androgen abnormality prenatally are either too small in their numbers or too inconsistent in their results, to provide similarly conclusive evidence. Studies relating normal variability in testosterone prenatally to subsequent gender-related behavior have produced largely inconsistent results or have yet to be independently replicated. For studies of prenatal exposures in typically developing individuals, testosterone has been measured in single samples of maternal blood or amniotic fluid. These techniques may not be sufficiently powerful to consistently detect influences of testosterone on behavior, particularly in the relatively small samples that have generally been studied. The postnatal surge in testosterone in male infants, sometimes called mini-puberty, may provide a more accessible opportunity for measuring early androgen exposure during typical development. This approach has recently begun to be used, with some promising results relating testosterone during the first few months of postnatal life to later gender-typical play behavior. In replicating and extending these findings, it may be important to assess testosterone when it is maximal (months 1 to 2 postnatal) and to take advantage of the increased reliability afforded by repeated sampling.


title: Forecasting hourly corona losses applying statistical approach

abstract: Based on physics laws, overhead lines parameters of the Bulgarian transmission grid, weather and electrical historical data, the main correlations between weather conditions and corona losses are detected. Corona losses are defined by weighting factors for different weather conditions of twelve typical Bulgarian meteorological regions presenting parts of entire transmission grid. A forecasting model based on a statistical approach concerning hourly corona losses is tested and proposed in order to decrease the imbalances costs of Bulgarian transmission system operator.


title: Three new graphical models for statistical language modelling

abstract: The supremacy of n-gram models in statistical language modelling has recently been challenged by parametric models that use distributed representations to counteract the difficulties caused by data sparsity. We propose three new probabilistic language models that define the distribution of the next word in a sequence given several preceding words by using distributed representations of those words. We show how real-valued distributed representations for words can be learned at the same time as learning a large set of stochastic binary hidden features that are used to predict the distributed representation of the next word from previous distributed representations. Adding connections from the previous states of the binary hidden features improves performance as does adding direct connections between the real-valued distributed representations. One of our models significantly outperforms the very best n-gram models.


title: Contrast-enhanced dual energy mammography with a novel anode/filter combination and artifact reduction: a feasibility study

abstract: Objectives ::: To demonstrate the feasibility of contrast-enhanced dual-energy mammography (CEDEM) using titanium (Ti) filtering at 49 kVp for high-energy images and a novel artefact reducing image-subtraction post-processing algorithm.


title: Subjective and objective quality evaluation of 3D point cloud denoising algorithms

abstract: The increasing availability of point cloud data in recent years is demanding for high performance denoising methods and compression schemes. When point cloud data is directly obtained from depth sensors or extracted from images acquired from different viewpoints, imprecisions on the depth acquisition or in the 3D reconstruction techniques result in noisy point clouds which may include a significant number of outliers. Moreover, the quality assessment of point clouds is a challenging problem since this 3D representation format is unstructured and it is typically not directly visualized. In this paper, selected objective quality metrics are evaluated regarding their correlation with human quality assessment and thus human perception. As far as the authors know, this is the first paper performing the subjective assessment of point cloud denoising algorithms and the evaluation of most used point cloud objective quality metrics. Experimental results show that graph-based denoising algorithms can improve significantly the point cloud quality data and that objective metrics that model the underlying point cloud surface can correlate better with human perception.


title: Graph Convolutional Encoders for Syntax-aware Neural Machine Translation

abstract: We present a simple and effective approach to incorporating syntactic structure into neural attention-based encoder-decoder models for machine translation. We rely on graph-convolutional networks (GCNs), a recent class of neural networks developed for modeling graph-structured data. Our GCNs use predicted syntactic dependency trees of source sentences to produce representations of words (i.e. hidden states of the encoder) that are sensitive to their syntactic neighborhoods. GCNs take word representations as input and produce word representations as output, so they can easily be incorporated as layers into standard encoders (e.g., on top of bidirectional RNNs or convolutional neural networks). We evaluate their effectiveness with English-German and English-Czech translation experiments for different types of encoders and observe substantial improvements over their syntax-agnostic versions in all the considered setups.


title: Measurement of spectral characteristics of semiconductor laser diodes-effect of injected current modulation and optical feedback

abstract: Precision optical spectral lineshape measurements on semiconductor lasers using Michelson and Fabry-Perot interferometers are presented. Measurements ranging from 30 MHz up to 100 GHz give the spectral behavior above and below threshold even for very small emitted optical powers. Variations of the linewidth above and below threshold as a function of injected current are measured and used to evaluate the linewidth enhancement factor and behavior at threshold. The lineshape can differ from the modified Schawlow-Townes almost Lorentzian form for semiconductor lasers above threshold when submitted to operating system conditions such as modulation and optical feedback. Under modulation, line frequency and signal form are modified because of chirping effects. A simple method for chirp evaluation at low frequencies based on lineshape evaluation is proposed. Optical feedback effects are studied as a function of the feedback coefficient C showing various regimes differing for single-mode and multimode laser diodes. >


title: Sector-Disk (SD) Erasure Codes for Mixed Failure Modes in RAID Systems

abstract: Traditionally, when storage systems employ erasure codes, they are designed to tolerate the failures of entire disks. However, the most common types of failures are latent sector failures, which only affect individual disk sectors, and block failures which arise through wear on SSD’s. This article introduces SD codes, which are designed to tolerate combinations of disk and sector failures. As such, they consume far less storage resources than traditional erasure codes. We specify the codes with enough detail for the storage practitioner to employ them, discuss their practical properties, and detail an open-source implementation.


title: Utility of flow cytometry immunophenotyping and DNA ploidy studies for diagnosis and characterization of blood involvement in CD4+ Sézary's syndrome.

abstract: BACKGROUND AND OBJECTIVES ::: The exact immunophenotypic criteria for the identification of Sézary cells in the blood are still poorly defined. ::: ::: ::: DESIGN AND METHODS ::: We analyzed the immunophenotype and DNA cell content of blood T cells in a series of 18 consecutive cases of Sézary's syndrome (SS), 21 normal individuals and 10 patients with reactive erythroderma, and correlated them with molecular and morphological findings. ::: ::: ::: RESULTS ::: Phenotypically abnormal CD3+/TCRalphabeta+/CD4+ T cells were found in all SS patients but in none of the reactive erythroderma cases; small diploid, or less frequently hypodiploid Sézary's cells coexisted with large nearly tetraploid Sézary's cells in some cases. The most frequent phenotypic aberrations consisted in decreased expression of CD3/TCRalphabeta (94%), CD4 (94%), CD7 (100%) and/or CD2 (83%). In addition, Sézary's cells were constantly CD28+ and CD5+ and they did not express natural-killer associated (NKa) antigens. Phenotypic heterogeneity was a common finding and phenotypic changes over time were frequently observed. In contrast to what was found in patients with reactive erythroderma, flow cytometry analysis of the T-cell receptor (TCR) repertoire revealed a major TCR-Vbeta expansion in all SS cases. ::: ::: ::: INTERPRETATION AND CONCLUSIONS ::: The presence of CD28+/CD5+/NKa-/CD4+ T cells expressing abnormally low levels of CD3, TCRalphabeta, CD4, CD7 and/or CD2 would support the diagnosis of SS in patients with erythroderma. Further analyses on larger series of patients are necessary in order to cover less frequent phenotypic patterns, establish the preferential usage of specific TCR-Vb families and investigate the specificity of these phenotypic abnormalities for diagnosing SS.


title: Co-training for Statistical Machine Translation

abstract: I propose a novel co-training method for statistical machine translation. As co-training requires multiple learners trained on views of the data which are disjoint and sufficient for the labeling task, I use multiple source documents as views on translation. Co-training for statistical machine translation is therefore a type of multi-source translation. Unlike previous mutli-source methods, it improves the overall quality of translations produced by a model, rather than single translations. This is achieved by augmenting the parallel corpora on which the statistical translation models are trained. Experiments suggest that co-training is especially effective for languages with highly impoverished parallel corpora.


title: From Library Management to Knowledge Management: A Conceptual Change

abstract: In the knowledge economy era, the library will play a very crucial role in the further extension and modification of knowledge. The growing need for knowledge management has influenced every component and operation of a library. Knowledge management requires more effective methods of information handling, speedy transfer of information and linking of information with individuals and their activities. It demands library patron centred development of information systems and services and customisation of information at the individual level. This opinion paper briefly outlines the nature and significance of conceptual changes involved in the practices of library management to their transition of knowledge management and discusses the competencies required for managing knowledge resources.


title: The danger of losing situation awareness

abstract: Saying that our constructs are more operational than representational is an ethical cop-out. Researchers and lay people alike use constructs such as complacency and situation awareness as reflective of an empirical reality. Instances of “loss of situation awareness” are counted by researchers and given causal status. Practitioners now get accused of “losing situation awareness” in investigations, court cases and inquests. As a human factors community, we cannot walk away from our responsibility for helping create that possibility.


title: A Mobile Asset Tracking System Architecture under Mobile-Stationary Co-Existing WSNs

abstract: The tracking of multiple wireless mobile nodes is not easy with current legacy WSN technologies, due to their inherent technical complexity, especially when heavy traffic and frequent movement of mobile nodes are encountered. To enable mobile asset tracking under these legacy WSN systems, it is necessary to design a specific system architecture that can manage numerous mobile nodes attached to mobile assets. In this paper, we present a practical system architecture including a communication protocol, a three-tier network, and server-side middleware for mobile asset tracking in legacy WSNs consisting of mobile-stationary co-existing infrastructures, and we prove the functionality of this architecture through careful evaluation in a test bed. Evaluation was carried out in a microwave anechoic chamber as well as on a straight road near our office. We evaluated communication mobility performance between mobile and stationary nodes, location-awareness performance, system stability under numerous mobile node conditions, and the successful packet transfer rate according to the speed of the mobile nodes. The results indicate that the proposed architecture is sufficiently robust for application in realistic mobile asset tracking services that require a large number of mobile nodes.


title: Adaptivity and Optimism: An Improved Exponentiated Gradient Algorithm

abstract: We present an adaptive variant of the exponentiated gradient algorithm. Leveraging the optimistic learning framework of Rakhlin & Sridharan (2012), we obtain regret bounds that in the learning from experts setting depend on the variance and path length of the best expert, improving on results by Hazan & Kale (2008) and Chiang et al. (2012), and resolving an open problem posed by Kale (2012). Our techniques naturally extend to matrix-valued loss functions, where we present an adaptive matrix exponentiated gradient algorithm. To obtain the optimal regret bound in the matrix case, we generalize the Follow-the-Regularized-Leader algorithm to vector-valued payoffs, which may be of independent interest.


title: Probabilistic linear discriminant analysis with bottleneck features for speech recognition.

abstract: We have recently proposed a new acoustic model based on probabilistic linear discriminant analysis (PLDA) which enjoys the flexibility of using higher dimensional acoustic features, and is more capable to capture the intra-frame feature correlations. In this paper, we investigate the use of bottleneck features obtained from a deep neural network (DNN) for the PLDA-based acoustic model. Experiments were performed on the Switchboard dataset — a large vocabulary conversational telephone speech corpus. We observe significant word error reduction by using the bottleneck features. In addition, we have also compared the PLDA-based acoustic model to three others using Gaussian mixture models (GMMs), subspace GMMs and hybrid deep neural networks (DNNs), and PLDA can achieve comparable or slightly higher recognition accuracy from our experiments.


title: Relational Collaborative Topic Regression for Recommender Systems

abstract: Due to its successful application in recommender systems, collaborative filtering (CF) has become a hot research topic in data mining and information retrieval. In traditional CF methods, only the feedback matrix, which contains either explicit feedback (also called ratings) or implicit feedback on the items given by users, is used for training and prediction. Typically, the feedback matrix is sparse, which means that most users interact with few items. Due to this sparsity problem, traditional CF with only feedback information will suffer from unsatisfactory performance. Recently, many researchers have proposed to utilize auxiliary information, such as item content (attributes), to alleviate the data sparsity problem in CF. Collaborative topic regression (CTR) is one of these methods which has achieved promising performance by successfully integrating both feedback information and item content information. In many real applications, besides the feedback and item content information, there may exist relations (also known as networks) among the items which can be helpful for recommendation. In this paper, we develop a novel hierarchical Bayesian model called Relational Collaborative Topic Regression (RCTR), which extends CTR by seamlessly integrating the user-item feedback information, item content information, and network structure among items into the same model. Experiments on real-world datasets show that our model can achieve better prediction accuracy than the state-of-the-art methods with lower empirical training time. Moreover, RCTR can learn good interpretable latent structures which are useful for recommendation.


title: Endangered childhoods: how consumerism is impacting child and youth identity

abstract: Modern-day children are immersed in cultures of consumption such that every aspect of their lives is touched by a buy-and-consume modality. In particular, children in North America are increasingly experiencing the effects of consumer culture at unprecedented levels of involvement. It becomes necessary, therefore, to examine the impact of consumerism in order to assess identity formation and development in youth. Young people are receiving an endless barrage of material messages encouraging purchasing behavior and consumption that impacts the self-image. Indeed, children from the ages of 4 to 12 have increasingly been defined and viewed by their spending capacity. Girls especially are targeted by marketers to sell them a whole line of products they ‘need’ to emulate a feminine ideal. There is mounting evidence to suggest that the structure of childhood is eroding and children are suffering from serious physical, emotional and social deficits directly related to consumerism.


The Best of Both Worlds : Sentiment Analysis in Call Center Conversations using Audio Search Techniques


title: Self-sensing of a solenoid valve via phase detection

abstract: This article presents a self-sensing method suitable for high-resolution positioning of a switching-solenoid valve. An external capacitor is connected to the solenoid coil, which is excited by a signal with a near-resonant frequency. At this frequency the phase between the input and output signals is most sensitive to variations of the coil inductance, which is dependent on the core position. Displacement of the iron core is estimated by observing the phase angles. Experimental results on an industrial solenoid indicate satisfactory repeatability of the self-sensing scheme. To explain for the frequency-dependent characteristic of the measured inductance and the phase-frequency curves, an equivalent circuit for the solenoid is proposed and analyzed. Preliminary results on closed-loop position control are also reported.


title: Image Visual Realism: From Human Perception to Machine Computation

abstract: Visual realism is defined as the extent to which an image appears to people as a photo rather than computer generated. Assessing visual realism is important in applications like computer graphics rendering and photo retouching. However, current realism evaluation approaches use either labor-intensive human judgments or automated algorithms largely dependent on comparing renderings to reference images. We develop a reference-free computational framework for visual realism prediction to overcome these constraints. First, we construct a benchmark dataset of 2,520 images with comprehensive human annotated attributes. From statistical modeling on this data, we identify image attributes most relevant for visual realism. We propose both empirically-based (guided by our statistical modeling of human data) and deep convolutional neural network models to predict visual realism of images. Our framework has the following advantages: (1) it creates an interpretable and concise empirical model that characterizes human perception of visual realism; (2) it links computational features to latent factors of human image perception.


title: 3DTV at home: eulerian-lagrangian stereo-to-multiview conversion

abstract: Stereoscopic 3D (S3D) movies have become widely popular in the movie theaters, but the adoption of S3D at home is low even though most TV sets support S3D. It is widely believed that S3D with glasses is not the right approach for the home. A much more appealing approach is to use automulti-scopic displays that provide a glasses-free 3D experience to multiple viewers. A technical challenge is the lack of native multiview content that is required to deliver a proper view of the scene for every viewpoint. Our approach takes advantage of the abundance of stereoscopic 3D movies. We propose a real-time system that can convert stereoscopic video to a high-quality multiview video that can be directly fed to automultiscopic displays. Our algorithm uses a wavelet-based decomposition of stereoscopic images with per-wavelet disparity estimation. A key to our solution lies in combining Lagrangian and Eulerian approaches for both the disparity estimation and novel view synthesis, which leverages the complementary advantages of both techniques. The solution preserves all the features of Eulerian methods, e.g., subpixel accuracy, high performance, robustness to ambiguous depth cases, and easy integration of inter-view aliasing while maintaining the advantages of Lagrangian approaches, e.g., robustness to large disparities and possibility of performing non-trivial disparity manipulations through both view extrapolation and interpolation. The method achieves real-time performance on current GPUs. Its design also enables an easy hardware implementation that is demonstrated using a field-programmable gate array. We analyze the visual quality and robustness of our technique on a number of synthetic and real-world examples. We also perform a user experiment which demonstrates benefits of the technique when compared to existing solutions.


title: Clinical management of the acrylic splint Herbst appliance.

abstract: This article describes one variation in Herbst appliance design--the acrylic splint Herbst. Topics discussed include early fixed appliance treatment before Herbst therapy, impression taking, bite registration, and evaluation of appliance fabrication. The specifics of appliance delivery, including bonding of the maxillary appliance when indicated, are also discussed as are the techniques of appliance advancement and removal.


title: Adolescent Psychosocial, Social, and Cognitive Development

abstract: 1. Renata Arrington Sanders, MD, MPH, ScM ::: ::: ::: ::: ::: 1. Division of General Pediatrics & Adolescent Medicine, Johns Hopkins School of Medicine, Baltimore, MD. ::: ::: The cognitive and psychosocial development of adolescents is variable. Asynchrony among physical, cognitive, and psychosocial development may limit the adolescents’ ability to perceive and judge risk effectively and may result in adolescent views that are incongruous with parents or guardians. Pediatricians can help adolescents to transition through this important developmental period while simultaneously providing parents with appropriate guidance and support. ::: ::: After completing this article, readers should be able to: ::: ::: 1. Understand the stages of cognitive and psychosocial adolescent development. ::: ::: 2. Understand the role of the imaginary audience and the personal fable in adolescent development. ::: ::: 3. Recognize the implications of early pubertal timing. ::: ::: 4. Be able to communicate effectively with adolescents and address developmental concerns that may arise. ::: ::: Adolescence marks the transition from childhood into adulthood. It is characterized by cognitive, psychosocial, and emotional development. Cognitive development is the progression of thinking from the way a child does to the way an adult does. ::: ::: There are 3 main areas of cognitive development that occur during adolescence. First, adolescents develop more advanced reasoning skills, including the ability to explore a full range of possibilities inherent in a situation, think hypothetically (contrary-fact situations), and use a logical thought process. ::: ::: Second, adolescents develop the ability to think abstractly. Adolescents move from being concrete thinkers, who think of things that they have direct contact with or knowledge about, to abstract thinkers, who can imagine things not seen or experienced. This allows adolescents to have the capacity to love, think about spirituality, and participate in more advanced mathematics. Youth who remain at the level of a concrete thinker focus largely on physically present or real objects in problem solving and, as a result, may …


title: Optimizing the Use of Soccer Drills for Physiological Development

abstract: SUMMARY: USING SOCCER DRILLS FOR CONDITIONING HAS CONSIDERABLE BENEFITS. HOWEVER, THE PRACTICALITIES OF CONDUCTING SUCH SESSIONS ARE MORE CHALLENGING THAN NON-SOCCER-SPECIFIC CONDITIONING METHODS. THIS ARTICLE EXAMINES FACTORS THAT AFFECT THE PHYSICAL NATURE OF SOCCER DRILLS AND PROVIDES GUIDANCE ON HOW TO OPTIMIZE TRAINING STRUCTURE WHEN USING SOCCER DRILLS FOR PHYSIOLOGICAL DEVELOPMENT


title: Nordic case reader in Logistics and Supply Chain Management

abstract: While supply chain management (SCM) and logistics are universal concepts, they are certainly not implemented the same worldwide. This case reader provides audience with a diversity of topics, industries and applications that are unique to the European region, yet with lessons for firms located anywhere in the world. With 20 cases written by a prestigious list of contributors, this case reader provides students with SCM and logistics strategies of a variety of companies in the Nordic region.


title: A general theory of bibliometric and other cumulative advantage processes

abstract: A Cumulative Advantage Distribution is proposed which models statistically the situation in which success breeds success. It differs from the Negative Binomial Distribution in that lack of success, being a non-event, is not punished by increased chance of failure. It is shown that such a stochastic law is governed by the Beta Function, containing only one free parameter, and this is approximated by a skew or hyperbolic distribution of the type that is widespread in bibliometrics and diverse social science phenomena. In particular, this is shown to be an appropriate underlying probabilistic theory for the Bradford Law, the Lotka Law, the Pareto and Zipf Distributions, and for all the empirical results of citation frequency analysis. As side results one may derive also the obsolescence factor for literature use. The Beta Function is peculiarly elegant for these manifold purposes because it yields both the actual and the cumulative distributions in simple form, and contains a limiting case of an inverse square law to which many empirical distributions conform.


Improving Regressors using Boosting Techniques


title: A SCOR-Based Method for Supply Chain Process Reengineering with Applications in Chinese Automotive Industry

abstract: In the era of economic globalization, the competition of product in the market does not depend on a certain enterprise. The traditional competition among different enterprises has transitioned to the competition among different supply chains. In order to win in the global competition, enterprises tend to implement supply chain process reengineering to enhance the coordination among all supply chain members. This study focuses on how to systematically use the methodology and tools provided by SCOR to improve supply chain performance. The contribution of the paper is putting forward a systematic SCOR-based method for supply chain process reengineering, and applying it in Chinese automotive industry to analyze, optimize and redesign the logistic process of automobile supply chain.


title: Detecting Obfuscated Viruses Using Cosine Similarity Analysis

abstract: Virus writers are getting smarter by the day. They are coming up with new, innovative ways to evade signature detection by anti-virus software. One such evasion technique used by polymorphic and metamorphic viruses is their ability to morph code so that signature based detection techniques fail. These viruses change form such that every new infected file has different strings, rendering string based signature detection practically useless against such viruses. Our work is based on the premise that given a variant of morphed code, we can detect any obfuscated version of this code with high probability using some simple statistical techniques. We use the cosine similarity function to compare two files based on static analysis of the portable executable (PE) format. Our results show that for certain evasion techniques, it is possible to identify polymorphic/metamorphic versions of files based on cosine similarity


title: When Networks Disagree: Ensemble Methods for Hybrid Neural Networks

abstract: Abstract : This paper presents a general theoretical framework for ensemble methods of constructing significantly improved regression estimates. Given a population of regression estimators, the authors construct a hybrid estimator that is as good or better in the mean square error sense than any estimator in the population. They argue that the ensemble method presented has several properties: (1) it efficiently uses all the networks of a population -- none of the networks need to be discarded; (2) it efficiently uses all of the available data for training without over-fitting; (3) it inherently performs regularization by smoothing in functional space, which helps to avoid over-fitting; (4) it utilizes local minima to construct improved estimates whereas other neural network algorithms are hindered by local minima; (5) it is ideally suited for parallel computation; (6) it leads to a very useful and natural measure of the number of distinct estimators in a population; and (7) the optimal parameters of the ensemble estimator are given in closed form. Experimental results show that the ensemble method dramatically improves neural network performance on difficult real-world optical character recognition tasks.


title: Attribute based access control for APIs in spring security

abstract: The widespread adoption of Application Programming Interfaces (APIs) by enterprises is changing the way business is done by permitting the implementation of a multitude of apps, customized to user needs. While supporting a more flexible exploitation of available data, services and applications developed on top of APIs are vulnerable to a variety of attacks, ranging from SQL injection to unauthorized access of sensitive data. Available security solutions must be re-used and/or adapted to work with APIs. In this paper, we focus on the development of a flexible access control mechanism for APIs. This is an important security mechanism to guarantee the enforcement of authorization constraints on resources while invoking their API functions. We have developed an extension of the Spring Security framework, the standard for securing services and apps built in the popular (open source) Spring framework, for the specification and enforcement of Attribute-Based Access Control (ABAC) policies. We demonstrate our work with scenarios arising in a smart energy eco-system.


title: Roombots—Towards decentralized reconfiguration with self-reconfiguring modular robotic metamodules

abstract: This paper presents our work towards a decentralized reconfiguration strategy for self-reconfiguring modular robots, assembling furniture-like structures from Roombots (RB) metamodules. We explore how reconfiguration by locomotion from a configuration A to a configuration B can be controlled in a distributed fashion. This is done using Roombots metamodules—two Roombots modules connected serially—that use broadcast signals, lookup tables of their movement space, assumptions about their neighborhood, and connections to a structured surface to collectively build desired structures without the need of a centralized planner.


title: Effectiveness of comprehensive fixed appliance treatment used with the Forsus Fatigue Resistant Device in Class II patients

abstract: OBJECTIVE ::: To assess the dental, skeletal, and soft tissue effects of comprehensive fixed appliance treatment combined with the Forsus Fatigue Resistant Device (FRD) in Class II patients. ::: ::: ::: MATERIALS AND METHODS ::: Thirty-two Class II patients (mean age 12.7 ± 1.2 years) were treated consecutively with the FRD protocol and compared with a matched sample of 27 untreated Class II subjects (mean age 12.8 ± 1.3 years). Lateral cephalograms were taken before therapy and at the completion of comprehensive therapy. The mean duration of comprehensive treatment was 2.4 ± 0.4 years. Statistical comparisons were carried out with the Student's t-test (P < .05). ::: ::: ::: RESULTS ::: The success rate was 87.5%. The FRD group showed a significant restraint in the sagittal skeletal position of the maxilla (also at the soft tissue level), a significant increase in mandibular length, and a significant improvement in maxillo-mandibular sagittal skeletal relationships. The treated group exhibited a significant reduction in overjet and a significant increase in molar relationship. The lower incisors were significantly proclined and intruded, while the lower first molars moved significantly in a mesial and vertical direction. ::: ::: ::: CONCLUSIONS ::: The FRD protocol is effective in correcting Class II malocclusion with a combination of skeletal (mainly maxillary) and dentoalveolar (mainly mandibular) modifications.


title: Google protocol buffers research and application in online game

abstract: This pager put Google protocol buffers algorithm in online game's data transmission, aiming is to increase the efficiency of data transmission. Firstly, desion data protocol and formulate relevant agreement format file. Later, serialize the data and transfer it to the client. Last, the client anti-sequentially unpacks the data. Through the above algorithm, after been serialized, data quantity is compressed to traditional data quantity's 30% or smaller, so the data transmission efficiency is greatly improved.


title: Hydrocele of the Canal of Nuck: Value of Radiological Diagnosis

abstract: Hydrocele of the canal of Nuck (cyst of the canal of Nuck) is the female homologue of hydrocele of the spermatic cord in males and is a rarely encountered entity, mostly diagnosed on the operating table at the time of suspected inguinal hernia surgery, as the clinical presentation usually is subtle and inconclusive. Diagnosis is mainly by ultrasound and has shown varied presentation of the same over the years. We discuss the sonographic findings of a case of hydrocele of the canal of Nuck.


title: Distance browsing in spatial databases

abstract: We compare two different techniques for browsing through a collection of spatial objects stored in an R-tree spatial data structure on the basis of their distances from an arbitrary spatial query object. The conventional approach is one that makes use of a k -nearest neighbor algorithm where k is known prior to the invocation of the algorithm. Thus if m k neighbors are needed, the k -nearest neighbor algorithm has to be reinvoked for m neighbors, thereby possibly performing some redundant computations. The second approach is incremental in the sense that having obtained the k nearest neighbors, the k + 1 st neighbor can be obtained without having to calculate the k + 1 nearest neighbors from scratch. The incremental approach is useful when processing complex queries where one of the conditions involves spatial proximity (e.g., the nearest city to Chicago with population greater than a million), in which case a query engine can make use of a pipelined strategy. We present a general incremental nearest neighbor algorithm that is applicable to a large class of hierarchical spatial data structures. This algorithm is adapted to the R-tree and its performance is compared to an existing k -nearest neighbor algorithm for R-trees [Rousseopoulos et al. 1995]. Experiments show that the incremental nearest neighbor algorithm significantly outperforms the k -nearest neighbor algorithm for distance browsing queries in a spatial database that uses the R-tree as a spatial index. Moreover, the incremental nearest neighbor algorithm usually outperforms the k -nearest neighber algorithm when applied to the k -nearest neighbor problem for the R-tree, although the improvement is not nearly as large as for distance browsing queries. In fact, we prove informally that at any step in its execution the incremental nearest neighbor algorithm is optimal with respect to the spatial data structure that is employed. Furthermore, based on some simplifying assumptions, we prove that in two dimensions the number of distance computations and leaf nodes accesses made by the algorithm for finding k neighbors is O ( k + k ).


title: A rational model of function learning

abstract: Theories of how people learn relationships between continuous variables have tended to focus on two possibilities: one, that people are estimating explicit functions, or two that they are performing associative learning supported by similarity. We provide a rational analysis of function learning, drawing on work on regression in machine learning and statistics. Using the equivalence of Bayesian linear regression and Gaussian processes, which provide a probabilistic basis for similarity-based function learning, we show that learning explicit rules and using similarity can be seen as two views of one solution to this problem. We use this insight to define a rational model of human function learning that combines the strengths of both approaches and accounts for a wide variety of experimental results.


Min Max Normalization Based Data Perturbation Method for Privacy Protection


title: A quaternion-based model for optimal control of the SkySails airborne wind energy system

abstract: Airborne wind energy systems are capable of extracting energy from higher wind speeds at higher altitudes. The configuration considered in this paper is based on a tethered kite flown in a pumping orbit. This pumping cycle generates energy by winching out at high tether forces and driving a generator while flying figures-of-eight, or lemniscates, as crosswind pattern. Then, the tether is reeled in while keeping the kite at a neutral position, thus leaving a net amount of generated energy. In order to achieve an economic operation, optimization of pumping cycles is of great interest. In this paper, first the principles of airborne wind energy will be briefly revisited. The first contribution is a singularity-free model for the tethered kite dynamics in quaternion representation, where the model is derived from first principles. The second contribution is an optimal control formulation and numerical results for complete pumping cycles. Based on the developed model, the setup of the optimal control problem (OCP) is described in detail along with its numerical solution based on the direct multiple shooting method in the CasADi optimization environment. Optimization results for a pumping cycle consisting of six lemniscates show that the approach is capable to find an optimal orbit in a few minutes of computation time. For this optimal orbit, the power output is increased by a factor of two compared to a sophisticated initial guess for the considered test scenario.


title: Effects of Interparental Violence on the Psychological Adjustment and Competencies of Young Children.

abstract: Preschool children (N = 107) were divided into 4 groups on the basis of maternal report; home and shelter groups exposed to verbal and physical conflict, a home group exposed to verbal conflict only, and a home control group. Parental ratings of behavior problems and competencies and children's self-report data were collected. Results show that verbal conflict only was associated with a moderate level of conduct problems: verbal plus physical conflict was associated with clinical levels of conduct problems and moderate levels of emotional problems; and verbal plus physical conflict plus shelter residence was associated with clinical levels of conduct problems, higher level of emotional problems, and lower levels of social functioning and perceived maternal acceptance. Findings suggests a direct relationship between the nature of the conflict and residence and type and extent of adjustment problems. Language: en


title: Triage: diagnosing production run failures at the user's site

abstract: Diagnosing production run failures is a challenging yet importanttask. Most previous work focuses on offsite diagnosis, i.e.development site diagnosis with the programmers present. This is insufficient for production-run failures as: (1) it is difficult to reproduce failures offsite for diagnosis; (2) offsite diagnosis cannot provide timely guidance for recovery or security purposes; (3)it is infeasible to provide a programmer to diagnose every production run failure; and (4) privacy concerns limit the release of information(e.g. coredumps) to programmers. To address production-run failures, we propose a system, called Triage, that automatically performs onsite software failure diagnosis at the very moment of failure. It provides a detailed diagnosis report, including the failure nature, triggering conditions, related code and variables, the fault propagation chain, and potential fixes. Triage achieves this by leveraging lightweight reexecution support to efficiently capture the failure environment and repeatedly replay the moment of failure, and dynamically--using different diagnosis techniques--analyze an occurring failure. Triage employs afailure diagnosis protocol that mimics the steps a human takes in debugging. This extensible protocol provides a framework to enable the use of various existing and new diagnosis techniques. We also propose a new failure diagnosis technique, delta analysis, to identify failure related conditions, code, and variables. We evaluate these ideas in real system experiments with 10 real software failures from 9 open source applications including four servers. Triage accurately diagnoses the evaluated failures, providing likely root causes and even the fault propagation chain, while keeping normal-run overhead to under 5%. Finally, our user study of the diagnosis and repair of real bugs shows that Triagesaves time (99.99% confidence), reducing the total time to fix by almost half.


title: Generalized stacking of layerwise-trained Deep Convolutional Neural Networks for document image classification

abstract: This article presents our recent study of a lightweight Deep Convolutional Neural Network (DCNN) architecture for document image classification. Here, we concentrated on training of a committee of generalized, compact and powerful base DCNNs. A support vector machine (SVM) is used to combine the outputs of individual DCNNs. The main novelty of the present study is introduction of supervised layerwise training of DCNN architecture in document classification tasks for better initialization of weights of individual DCNNs. Each DCNN of the committee is trained for a specific part or the whole document. Also, here we used the principle of generalized stacking for combining the normalized outputs of all the members of the DCNN committee. The proposed document classification strategy has been tested on the well-known Tobacco3482 document image dataset. Results of our experimentations show that the proposed strategy involving a considerably smaller network architecture can produce comparable document classification accuracies in competition with the state-of-the-art architectures making it more suitable for use in comparatively low configuration mobile devices.


title: Single or Multiple Frames Content Delivery for Next-Generation Networks?

abstract: This paper addresses the four enabling technologies, namely multi-user sparse code multiple access (SCMA), content caching, energy harvesting, and physical layer security for proposing an energy and spectral efficient resource allocation algorithm for the access and backhaul links in heterogeneous cellular networks. Although each of the above mentioned issues could be a topic of research, in a real situation, we would face a complicated scenario where they should be considered jointly, and hence, our target is to consider these technologies jointly in a unified framework. Moreover, we propose two novel content delivery scenarios: 1) single frame content delivery (SFCD), and 2) multiple frames content delivery (MFCD), where the time duration of serving user requests is divided into several frames. In the first scenario, the requested content by each user is served over one frame. However, in the second scenario, the requested content by each user can be delivered over several frames. We formulate the resource allocation for the proposed scenarios as optimization problems where our main aim is to maximize the energy efficiency of access links subject to the transmit power and rate constraints of access and backhaul links, caching and energy harvesting constraints, and SCMA codebook allocation limitations. Due to the practical limitations, we assume that the channel state information values between eavesdroppers and base stations are uncertain and design the network for the worst case scenario. Since the corresponding optimization problems are mixed integer non-linear and nonconvex programming, NP-hard, and intractable, we propose an iterative algorithm based on the well-known alternate and successive convex approximation methods. In addition, the proposed algorithms are studied from the computational complexity, convergence, and performance perspectives. Moreover, the proposed caching scheme outperforms the existing traditional caching schemes like random caching and most popular caching. We also study the effect of joint and disjoint considerations of enabling technologies for the performance of next-generation networks. On the one hand, our proposed caching strategy increases slightly the computational complexity by 3%, 1.1%, and 2% compared to no caching, SFCD, and disjoint solution, respectively. On the other hand, the proposed caching scheme achieves a performance gain of 43%, 9.4%, and 51.3% compared to the three state-of-the-art schemes.


title: Stanford's 2014 Slot Filling Systems

abstract: We describe Stanford’s entry in the TACKBP 2014 Slot Filling challenge. We submitted two broad approaches to Slot Filling: one based on the DeepDive framework (Niu et al., 2012), and another based on the multi-instance multi-label relation extractor of Surdeanu et al. (2012). In addition, we evaluate the impact of learned and hard-coded patterns on performance for slot filling, and the impact of the partial annotations described in Angeli et al. (2014).


title: Retinal image analysis using multidirectional functors based on geodesic conversions

abstract: A retinal image contains vital information. Extracting these features is the first and most important step in the analysis of retinal images for various applications of medical or human recognition. In this study, a morphological-based blood vessel extraction algorithm using adaptive-local analysis from colored retinal images is proposed. In this algorithm, by applying the appropriate morphology functors and local histogram stretching on the retinal images, the brightness of the images is considerably uniformed. Furthermore, curvelet transform (CT) is used to enhance the retinal images by highlighting the edges of the images in various scales and directions and by the adaptive and local improving of the CT coefficients. Since the blood vessels in retinal images are distributed in various directions, we use the morphology functors with multidirectional structure elements to extract the blood vessels from the retinal images. Geodesic conversion-based morphology functors are used to properly refine the appeared frills with a size smaller than those of the arterioles in the images. Finally, by locally applying the connected component analysis in the images and locally applying an adaptive filter on the connected components, all of the residual frills are refined from the images. The obtained results of the proposed algorithm show that the blood vessels are extracted from the background of the images with high accuracy, which in turn shows the high ability of the proposed algorithm in extracting the retinal blood vessels.


title: Deep Convolutional Neural Networks for Anomaly Event Classification on Distributed Systems

abstract: The increasing popularity of server usage has brought a plenty of anomaly log events, which have threatened a vast collection of machines. Recognizing and categorizing the anomalous events thereby is a much salient work for our systems, especially the ones generate the massive amount of data and harness it for technology value creation and business development. To assist in focusing on the classification and the prediction of anomaly events, and gaining critical insights from system event records, we propose a novel log preprocessing method which is very effective to filter abundant information and retain critical characteristics. Additionally, a competitive approach for automated classification of anomalous events detected from the distributed system logs with the state-of-the-art deep (Convolutional Neural Network) architectures is proposed in this paper. We measure a series of deep CNN algorithms with varied hyper-parameter combinations by using standard evaluation metrics, the results of our study reveals the advantages and potential capabilities of the proposed deep CNN models for anomaly event classification tasks on real-world systems. The optimal classification precision of our approach is 98.14%, which surpasses the popular traditional machine learning methods.


title: Public beliefs about and attitudes towards people with mental illness: a review of population studies

abstract: Objective: To provide a review of population-based attitude research in psychiatry during the past 15 years. ::: ::: Method: An electronic search using PubMed, Medline, and Academic Search Premier plus a hand search of the literature was carried out for studies on public beliefs about mental illness and attitudes towards the mentally ill published between 1990 and 2004. ::: ::: Results: Thirty-three national studies and 29 local and regional studies were identified, mostly from Europe. Although the majority are of descriptive nature, more recent publications include studies testing theory-based models of the stigmatization of mentally ill people, analyses of time trends and cross-cultural comparisons, and evaluations of antistigma interventions. ::: ::: Conclusion: Attitude research in psychiatry made considerable progress over the past 15 years. However, there is still much to be done to provide an empirical basis for evidence-based interventions to reduce misconceptions about mental illness and improve attitudes towards persons with mental illness.


title: An Overview of Robust Subspace Recovery

abstract: This paper will serve as an introduction to the body of work on robust subspace recovery. Robust subspace recovery involves finding an underlying low-dimensional subspace in a dataset that is possibly corrupted with outliers. While this problem is easy to state, it has been difficult to develop optimal algorithms due to its underlying nonconvexity. This work will emphasize advantages and disadvantages of proposed approaches and unsolved problems in the area.


title: A PCM-based TCAM cell using NDR

abstract: This paper presents a new design of a ternary content addressable memory (TCAM) cell. This design exploits two emerging technology devices: a phase change memory (PCM) as storage element and a negative differential resistance (NDR) device as control element. The PCM stores a multi-value resistance to account for the ternary nature of the equality function to be performed in a TCAM. The CMOS-NDR device uses a macroscopic model made of three transistors to exhibit a very high peak-to-valley current ratio, a single symmetric peak and excellent switching speed. The CMOS-NDR device is embedded in the cell such that specific operational points are placed on the I-V curve for generating the match/mismatch outcome. A common-source amplifier with source degeneration is used for decoding the PCM. Extensive simulation results at nanoscale feature sizes (45, 32 and 22 nm) are presented. These results show that the proposed TCAM cell outperforms a previously presented non-volatile TCAM cell based on MTJ in terms of power dissipation and power delay product (PDP).


title: Guitar Tab Mining, Analysis and Ranking

abstract: With over 4.5 million tablatures and chord sequences (collectively known as tabs), the web holds vast quantities of hand annotated scores in non-standardised text files. These scores are typically error-prone and incomplete, and tab collections contain many duplicates, making retrieval of high quality tabs difficult. Despite this, tabs are by far the most popular means of sharing musical instructions on the internet. We have developed tools that use text analysis and alignment for the automatic retrieval, interpretation and analysis of such tabs in order to filter and estimate the most accurate tabs from the multitude available. We show that the standard means of ranking tabs, such as search engine ranks or user ratings, have little correlation with the accuracy of a tab and that a better ranking method is to use features such as the concurrency between tabs of the same song. We also compare the quality of top-ranked tabs with state-of-the-art chord transcription output and find that the latter provides a more reliable source of chord symbols with an accuracy rate 10% higher than the ranked hand annotations.


title: Spelling Correction As An Iterative Process That Exploits The Collective Knowledge Of Web Users

abstract: Logs of user queries to an internet search engine provide a large amount of implicit and explicit information about language. In this paper, we investigate their use in spelling correction of search queries, a task which poses many additional challenges beyond the traditional spelling correction problem. We present an approach that uses an iterative transformation of the input query strings into other strings that correspond to more and more likely queries according to statistics extracted from internet search query logs.


title: Dual Sentiment Analysis: Considering Two Sides of One Review

abstract: Bag-of-words (BOW) is now the most popular way to model text in statistical machine learning approaches in sentiment analysis. However, the performance of BOW sometimes remains limited due to some fundamental deficiencies in handling the polarity shift problem. We propose a model called dual sentiment analysis (DSA), to address this problem for sentiment classification. We first propose a novel data expansion technique by creating a sentiment-reversed review for each training and test review. On this basis, we propose a dual training algorithm to make use of original and reversed training reviews in pairs for learning a sentiment classifier, and a dual prediction algorithm to classify the test reviews by considering two sides of one review. We also extend the DSA framework from polarity (positive-negative) classification to 3-class (positive-negative-neutral) classification, by taking the neutral reviews into consideration. Finally, we develop a corpus-based method to construct a pseudo-antonym dictionary, which removes DSA's dependency on an external antonym dictionary for review reversion. We conduct a wide range of experiments including two tasks, nine datasets, two antonym dictionaries, three classification algorithms, and two types of features. The results demonstrate the effectiveness of DSA in supervised sentiment classification.


Acceleration of Binning Nearest Neighbor Methods


title: Exploring smartphone sensors for fall detection

abstract: Falling, and the fear of falling, is a serious health problem among the elderly. It often results in physical and mental injuries that have the potential to severely reduce their mobility, independence and overall quality of life. Nevertheless, the consequences of a fall can be largely diminished by providing fast assistance. These facts have lead to the development of several automatic fall detection systems. Recently, many researches have focused particularly on smartphone-based applications. In this paper, we study the capacity of smartphone built-in sensors to differentiate fall events from activities of daily living. We explore, in particular, the information provided by the accelerometer, magnetometer and gyroscope sensors. A collection of features is analyzed and the efficiency of different sensor output combinations is tested using experimental data. Based on these results, a new, simple, and reliable algorithm for fall detection is proposed. The proposed method is a threshold-based algorithm and is designed to require a low battery power consumption. The evaluation of the performance of the algorithm in collected data indicates 100 % for sensitivity and 93 % for specificity. Furthermore, evaluation conducted on a public dataset, for comparison with other existing smartphone-based fall detection algorithms, shows the high potential of the proposed method.


title: Understanding Mobile Banking Usage: An Integrative Perspective

abstract: Increasingly, smartphones and mobile commerce are changing consumer transaction behaviors and shifting the ways in which users interact with business. Similarly, the banking and financial industry is now empowered by FinTech applications to improve their consumer experience and banking services to solidify the role for mobile banking (MB) for years to come. In this study, we are taking an integrated approach by combining two IT adoption models: UTAUT and IS Success, and using both subjective and objective measures for mobile banking usage. Our regression results for a sample of 472 bank users in a mid-size US bank find a strong support for satisfaction, objective actual use, and loyalty but not for subjective actual use. These results provide interesting implications for both IS adoption theory and banking institutions for improving satisfaction and use of MB.


title: Effect of Calorie Restriction with or without Exercise on Body Composition and Fat Distribution

abstract: CONTEXT ::: There is debate over the independent and combined effects of dieting and increased physical activity on improving metabolic risk factors (body composition and fat distribution). ::: ::: ::: OBJECTIVE ::: The objective of the study was to conduct a randomized, controlled trial (CALERIE) to test the effect of a 25% energy deficit by diet alone or diet plus exercise for 6 months on body composition and fat distribution. ::: ::: ::: DESIGN ::: This was a randomized, controlled trial. ::: ::: ::: SETTING ::: The study was conducted at an institutional research center. ::: ::: ::: PARTICIPANTS ::: Thirty-five of 36 overweight but otherwise healthy participants (16 males, 19 females) completed the study. ::: ::: ::: INTERVENTION ::: Participants were randomized to either control (healthy weight maintenance diet, n = 11), caloric restriction (CR; 25% reduction in energy intake, n = 12), or caloric restriction plus exercise (CR+EX; 12.5% reduction in energy intake + 12.5% increase in exercise energy expenditure, n = 12) for 6 months. ::: ::: ::: MAIN OUTCOME MEASURES ::: Changes in body composition by dual-energy x-ray absorptiometry and changes in abdominal fat distribution by multislice computed tomography were measured. ::: ::: ::: RESULTS ::: The calculated energy deficit across the intervention was not different between CR and CR+EX. Participants lost approximately 10% of body weight (CR: - 8.3 +/- 0.8, CR+EX: - 8.1 +/- 0.8 kg, P = 1.00), approximately 24% of fat mass (CR: - 5.8 +/- 0.6, CR+EX: - 6.4 +/- 0.6 kg, P = 0.99), and 27% of abdominal visceral fat (CR: 0.9 +/- 0.2, CR+EX: 0.8 +/- 0.2 kg, P = 1.00). Both whole-body and abdominal fat distribution were not altered by the intervention. ::: ::: ::: CONCLUSION ::: Exercise plays an equivalent role to CR in terms of energy balance; however, it can also improve aerobic fitness, which has other important cardiovascular and metabolic implications.


Filtering Corrupted Image and Edge Detection in Restored Grayscale Image Using Derivative Filters


title: Multi-lingual Knowledge Graph Embeddings for Cross-lingual Knowledge Alignment

abstract: Many recent works have demonstrated the benefits of knowledge graph embeddings in completing monolingual knowledge graphs. Inasmuch as related knowledge bases are built in several different languages, achieving cross-lingual knowledge alignment will help people in constructing a coherent knowledge base, and assist machines in dealing with different expressions of entity relationships across diverse human languages. Unfortunately, achieving this highly desirable crosslingual alignment by human labor is very costly and errorprone. Thus, we propose MTransE, a translation-based model for multilingual knowledge graph embeddings, to provide a simple and automated solution. By encoding entities and relations of each language in a separated embedding space, MTransE provides transitions for each embedding vector to its cross-lingual counterparts in other spaces, while preserving the functionalities of monolingual embeddings. We deploy three different techniques to represent cross-lingual transitions, namely axis calibration, translation vectors, and linear transformations, and derive five variants for MTransE using different loss functions. Our models can be trained on partially aligned graphs, where just a small portion of triples are aligned with their cross-lingual counterparts. The experiments on cross-lingual entity matching and triple-wise alignment verification show promising results, with some variants consistently outperforming others on different tasks. We also explore how MTransE preserves the key properties of its monolingual counterpart TransE.