title: A total data quality management for credit risk: new insights and challenges

abstract: Recent studies indicated that companies are increasingly experiencing data quality (DQ) related problems resulting from their increased data collection efforts. Addressing these concerns requires a clear definition of DQ but typically, DQ is only broadly defined as ‘fitness for use’. While capturing its essence, a more precise interpretation of DQ is required during measurement. While there is a growing consensus on the multi-dimensional nature of DQ, no exact DQ definition has been put forward due to its context dependency. On the contrary, it is often stated that its constituting dimensions should be identified and defined in relation to the task at hand. Answering this call, we identify the DQ dimensions important to the credit risk assessment environment. In addition, we explore key DQ challenges and report on the causes of DQ problems in financial institutions. Statistical tests indicated nine most important DQ dimensions.


title: A Secured Access Control Technique for Cloud Computing Environment Using Attribute Based Hierarchical Structure and Token Granting System

abstract: Cloud computing has drastically condensed the computational and storage costs of outsourced data. The existing access control techniques offer users access provisions centered on the common user attributes like Roles, which reduces the fine-grained access measure. The paper defines a Storage Correctness and Fine-grained Access Provision (SCFAP) scheme, that provides the user an exclusive access through the use of a hierarchical structure which is a combination of users unique and common attributes. Also, we deploy the concept of Token Granting system that allows the users to verify the correctness of outsourced data without the retrieval of the respective files. The tokens are derived from the metadata containing file location that helps in the process of storage correctness verification and improvises the storage efficiency. The experimental results show SCFAP has improved storage efficiency and error recovery measures than existing techniques.


title: A Logical Analysis of Burdens of Proof 1

abstract: The starting point of this article is the claim that logics for defeasible argumentation provide the means to logically characterise the difference between several kinds of proof burdens, but only if they are embedded in a dynamic setting that captures the various stages of a legal proceeding. It is also argued that estandardi argumentation logics for AI must be adapted in order to model shifts in burdens of proof. Thus this analysis illustrates in two ways that logics cannot be simply imposed on the law but that features of legal systems must be taken into account. First there is the claim that the burden of persuasion, which legally is the burden to prove a statement to a specified degree (the standard of proof) on the penalty of losing on the issue, can be verified by applying an argumentation logic to the evidence available at the final stage of a proceeding. Then a precise distinction is made between two burdens that are sometimes confused, namely the burden of production and the tactical burden. In this analysis, the tactical burden of proof is automatically induced by the defeasible nature of the reasoning. The burden of production, by contrast, concerns the legal question whether an issue can be submitted to trial or must be decided as a matter of law against the one who fails to produce any evidence. Finally the issue is raised to what extent this account can be generalised to statistical and story-based approaches.


title: Anatomical study of the palatine aponeurosis: application to posterior palatal seal of the complete maxillary denture

abstract: The palatine aponeurosis is a thin, fibrous lamella comprising the extended tendons of the tensor veli palatini muscles, attached to the posterior border and inferior surface of the palatine bone. In dentistry, the relationship between the "vibrating line" and the border of the hard and soft palate has long been discussed. However, to our knowledge, there has been no discussion of the relationship between the palatine aponeurosis and the vibrating line(s). Twenty sides from ten fresh frozen White cadaveric heads (seven males and three females) whose mean age at death was 79 years) were used in this study. The thickness of the mucosa including the submucosal tissue was measured. The maximum length of the palatine aponeurosis on each side and the distance from the posterior nasal spine to the posterior border of the palatine aponeurosis in the midline were also measured. The relationship between the marked borderlines and the posterior border of the palatine bone was observed. The thickness of the mucosa and submucosal tissue on the posterior nasal spine and the maximum length of the palatine aponeurosis were 3.4 mm, and 12.2 mm on right side and 12.8 mm on left, respectively. The length of the palatine aponeurosis in the midline was 4.9 mm. In all specimens, the borderline between the compressible and incompressible parts corresponded to the posterior border of the palatine bone.


title: Social flocks: a crowd simulation framework for social network generation, community detection, and collective behavior modeling

abstract: This work combines the central ideas from two different areas, crowd simulation and social network analysis, to tackle some existing problems in both areas from a new angle. We present a novel spatio-temporal social crowd simulation framework, Social Flocks, to revisit three essential research problems, (a) generation of social networks, (b) community detection in social networks, (c) modeling collective social behaviors in crowd simulation. Our framework produces social networks that satisfy the properties of high clustering coefficient, low average path length, and power-law degree distribution. It can also be exploited as a novel dynamic model for community detection. Finally our framework can be used to produce real-life collective social behaviors over crowds, including community-guided flocking, leader following, and spatio-social information propagation. Social Flocks can serve as visualization of simulated crowds for domain experts to explore the dynamic effects of the spatial, temporal, and social factors on social networks. In addition, it provides an experimental platform of collective social behaviors for social gaming and movie animations. Social Flocks demo is at http://mslab.csie.ntu.edu.tw/socialflocks/ .


title: Gemini: a computation-centric distributed graph processing system

abstract: Traditionally distributed graph processing systems have largely focused on scalability through the optimizations of inter-node communication and load balance. However, they often deliver unsatisfactory overall processing efficiency compared with shared-memory graph computing frameworks. We analyze the behavior of several graph-parallel systems and find that the added overhead for achieving scalability becomes a major limiting factor for efficiency, especially with modern multi-core processors and high-speed interconnection networks. ::: ::: Based on our observations, we present Gemini, a distributed graph processing system that applies multiple optimizations targeting computation performance to build scalability on top of efficiency. Gemini adopts (1) a sparse-dense signal-slot abstraction to extend the hybrid push-pull computation model from shared-memory to distributed scenarios, (2) a chunk-based partitioning scheme enabling low-overhead scaling out designs and locality-preserving vertex accesses, (3) a dual representation scheme to compress accesses to vertex indices, (4) NUMA-aware sub-partitioning for efficient intra-node memory accesses, plus (5) locality-aware chunking and fine-grained work-stealing for improving both internode and intra-node load balance, respectively. Our evaluation on an 8-node high-performance cluster (using five widely used graph applications and five real-world graphs) shows that Gemini significantly outperforms all well-known existing distributed graph processing systems, delivering up to 39.8× (from 8.91×) improvement over the fastest among them.


title: AWSS: An Algorithm for Measuring Arabic Word Semantic Similarity

abstract: Semantic similarity is an essential component of numerous applications in fields such as natural language processing, artificial intelligence, linguistics, and psychology. Most of the reported work has been done in English. To the best of our knowledge, there is no word similarity measure developed specifically for Arabic. This paper presents a method to measure the semantic similarity between two Arabic words in the Arabic knowledge base. The semantic similarity is calculated through the combination of the common and different attributes between the Arabic words in the hierarchy semantic net. We use a previously developed Arabic word benchmark dataset to optimize and evaluate the Arabic measure. Experimental evaluation indicates that the Arabic measure is performing well. It has achieved a correlation value of 0.894 compared with the average value of human participants of 0.893 on evaluation dataset.


title: Work climate, work values and professional commitment as predictors of job satisfaction in nurses

abstract: AIM ::: To investigate the effect of some psychosocial variables on nurses' job satisfaction. ::: ::: ::: BACKGROUND ::: Nurses' job satisfaction is one of the most important factors in determining individuals' intention to stay or leave a health-care organisation. Literature shows a predictive role of work climate, professional commitment and work values on job satisfaction, but their conjoint effect has rarely been considered. ::: ::: ::: METHODS ::: A cross-sectional questionnaire survey was adopted. Participants were hospital nurses and data were collected in 2011. ::: ::: ::: RESULTS ::: Professional commitment and work climate positively predicted nurses' job satisfaction. The effect of intrinsic vs. extrinsic work value orientation on job satisfaction was completely mediated by professional commitment. ::: ::: ::: CONCLUSIONS ::: Nurses' job satisfaction is influenced by both contextual and personal variables, in particular work climate and professional commitment. According to a more recent theoretical framework, work climate, work values and professional commitment interact with each other in determining nurses' job satisfaction. ::: ::: ::: IMPLICATIONS FOR NURSING MANAGEMENT ::: Nursing management must be careful to keep the context of work tuned to individuals' attitude and vice versa. Improving the work climate can have a positive effect on job satisfaction, but its effect may be enhanced by favouring strong professional commitment and by promoting intrinsic more than extrinsic work values.


title: Low-Power link quality estimation in smart grid environments

abstract: Several Link Quality Estimators (LQEs) have been proposed for Wireless Sensor Networks. However, their adequacy to smart grid environments has not been properly investigated. This paper addresses the problem of efficient low-power link quality estimation for smart grid environments. The first part of this paper presents a performance study of representative LQEs, namely ETX, four-bit and F-LQE, in three typical smart grid environments. These LQEs are evaluated in terms of reliability, stability and reactivity, by analyzing their statistical behavior. This study shows that F-LQE is more reliable and more stable than ETX and four-bit. However, it is not the most efficient for smart grid due to the lack of reactivity and also its higher complexity. Hence, the second part of this paper introduces Opt-FLQE, an optimized version of F-LQE that overcomes its limitations. The performance analysis of Opt-FLQE shows that it is more reactive than F-LQE while still being more reliable.


An Annotated German-Language Medical Text Corpus as Language Resource


title: Income inequality and population health: A review and explanation of the evidence

abstract: Whether or not the scale of a society's income inequality is a determinant of population health is still regarded as a controversial issue. We decided to review the evidence and see if we could find a consistent interpretation of both the positive and negative findings. We identified 168 analyses in 155 papers reporting research findings on the association between income distribution and population health, and classified them according to how far their findings supported the hypothesis that greater income differences are associated with lower standards of population health. Analyses in which all adjusted associations between greater income equality and higher standards of population health were statistically significant and positive were classified as "wholly supportive"; if none were significant and positive they were classified as "unsupportive"; and if some but not all were significant and supportive they were classified as "partially supportive". Of those classified as either wholly supportive or unsupportive, a large majority (70 per cent) suggest that health is less good in societies where income differences are bigger. There were substantial differences in the proportion of supportive findings according to whether inequality was measured in large or small areas. We suggest that the studies of income inequality are more supportive in large areas because in that context income inequality serves as a measure of the scale of social stratification, or how hierarchical a society is. We suggest three explanations for the unsupportive findings reported by a minority of studies. First, many studies measured inequality in areas too small to reflect the scale of social class differences in a society; second, a number of studies controlled for factors which, rather than being genuine confounders, are likely either to mediate between class and health or to be other reflections of the scale of social stratification; and third, the international relationship was temporarily lost (in all but the youngest age groups) during the decade from the mid-1980s when income differences were widening particularly rapidly in a number of countries. We finish by discussing possible objections to our interpretation of the findings.


title: Epitope analysis of the allergen ovalbumin (Gal d II) with monoclonal antibodies and patients' IgE.

abstract: Ovalbumin (OVA) is a major allergen (Gal d II) of hen egg white and is often the cause of hypersensitivity reactions to food. Further knowledge of the antigenic and allergenic epitopes of allergens will provide better treatment of this disease. To analyse these epitopes we produced a panel of monoclonal antibodies (mAbs) against native OVA. The initial information about the epitopes was obtained with the binding patterns of these mAbs in IEF-immunoprints and western blots of OVA under reducing and non-reducing conditions. It was possible to demonstrate that the different conformations of OVA exhibit different epitopes, and that there are other epitopes which are shared by each conformation. Seven different, although sometimes overlapping epitopes, could be determined on native OVA; four different epitopes on denaturated non-reduced OVA by means of immunoblots of the intact molecule. The number of epitopes which could be differentiated by the mAbs was increased by the use of peptide blots after CNBr fragmentation of the molecule. IgE binding to different OVA conformations and to CNBr-fragments of OVA was also detectable and appears in the same regions as the reactivity of some mAbs. Western blots of OVA and CNBr-peptides demonstrate that some antigenic/allergenic binding sites seem at least partly to be continuous epitopes. The identification of the CNBr-fragments was performed by a microsequence analysis of blotted CNBr-fragments after a 2-dimensional electrophoresis. IgE was found to bind the two largest CNBr-fragments (residues 41-172 and 301-385), but not the fragment corresponding to residues 173-196. A number of monoclonal antibodies also reacted with the two large fragments, especially with fragment 301-385, and some bind also to shorter peptides, such as fragment 173-196, which were not reactive to patients' IgE. Most of the monoclonal antibodies and patients' IgE bind to the fragments 41-172 and 301-385 in 2D-PAGE blots suggesting that these fragments are involved in an immunogenic structure.


title: Two case studies of open source software development: Apache and Mozilla

abstract: According to its proponents, open source style software development has the capacity to compete successfully, and perhaps in many cases displace, traditional commercial development methods. In order to begin investigating such claims, we examine data from two major open source projects, the Apache web server and the Mozilla browser. By using email archives of source code change history and problem reports we quantify aspects of developer participation, core team size, code ownership, productivity, defect density, and problem resolution intervals for these OSS projects. We develop several hypotheses by comparing the Apache project with several commercial projects. We then test and refine several of these hypotheses, based on an analysis of Mozilla data. We conclude with thoughts about the prospects for high-performance commercial/open source process hybrids.


title: Training Deep Convolutional Neural Networks for Land–Cover Classification of High-Resolution Imagery

abstract: Deep convolutional neural networks (DCNNs) have recently emerged as a dominant paradigm for machine learning in a variety of domains. However, acquiring a suitably large data set for training DCNN is often a significant challenge. This is a major issue in the remote sensing domain, where we have extremely large collections of satellite and aerial imagery, but lack the rich label information that is often readily available for other image modalities. In this letter, we investigate the use of DCNN for land–cover classification in high-resolution remote sensing imagery. To overcome the lack of massive labeled remote-sensing image data sets, we employ two techniques in conjunction with DCNN: transfer learning (TL) with fine-tuning and data augmentation tailored specifically for remote sensing imagery. TL allows one to bootstrap a DCNN while preserving the deep visual feature extraction learned over an image corpus from a different image domain. Data augmentation exploits various aspects of remote sensing imagery to dramatically expand small training image data sets and improve DCNN robustness for remote sensing image data. Here, we apply these techniques to the well-known UC Merced data set to achieve the land–cover classification accuracies of 97.8 ± 2.3%, 97.6 ± 2.6%, and 98.5 ± 1.4% with CaffeNet, GoogLeNet, and ResNet, respectively.


title: Joint Effects of Smoking and Alcohol Drinking on Esophageal Cancer Mortality in Japanese Men: Findings from the Japan Collaborative Cohort Study

abstract: Background: The purpose of our study was to elucidate the joint effects of combined smoking and alcohol intake on esophageal cancer mortality in Japanese men through a large cohort study with a 20-year follow-up period. Materials and Methods: The Japan Collaborative Cohort Study for Evaluation of Cancer Risk (JACC Study) was established in the late 1980s, including 46,395 men and 64,190 women aged 40 years and older and younger than 80. Follow-up of these participants was conducted until 2009. We used the Cox proportional hazards model to analyze data for 42,408 people excluding female participants, 411 people with histories of malignant neoplasms, and 3,576 with unclear smoking and drinking data. Results: The joint effects of age at start of smoking and amount of alcohol consumed per day were compared with non-smokers and non-drinkers or those consuming less than one unit of alcohol per day. The mortality risk was 9.33 (95% confidence interval, 2.55-34.2) for those who started smoking between ages 10 and 19 years and drinking at least three units of alcohol per day. Regarding the joint effects of cumulative amount of smoking and alcohol intake, the risk was high when both smoking and alcohol intake were above a certain level. Conclusions: In this Japanese cohort study, increased cancer mortality risks were observed, especially for people who both started smoking early and drank alcohol. Quitting smoking or not starting to smoke at any age and reducing alcohol consumption are important for preventing esophageal cancer in Japan.


title: SDCNet: Video Prediction Using Spatially-Displaced Convolution

abstract: We present an approach for high-resolution video frame prediction by conditioning on both past frames and past optical flows. Previous approaches rely on resampling past frames, guided by a learned future optical flow, or on direct generation of pixels. Resampling based on flow is insufficient because it cannot deal with disocclusions. Generative models currently lead to blurry results. Recent approaches synthesis a pixel by convolving input patches with a predicted kernel. However, their memory requirement increases with kernel size. Here, we spatially-displaced convolution (SDC) module for video frame prediction. We learn a motion vector and a kernel for each pixel and synthesize a pixel by applying the kernel at a displaced location in the source image, defined by the predicted motion vector. Our approach inherits the merits of both vector-based and kernel-based approaches, while ameliorating their respective disadvantages. We train our model on 428K unlabelled 1080p video game frames. Our approach produces state-of-the-art results, achieving an SSIM score of 0.904 on high-definition YouTube-8M videos, 0.918 on Caltech Pedestrian videos. Our model handles large motion effectively and synthesizes crisp frames with consistent motion.


title: Traffic Scene Prediction via Deep Learning: Introduction of Multi-Channel Occupancy Grid Map as a Scene Representation

abstract: When predicting future motions of surrounding vehicles for autonomous vehicles, the inter-vehicular interaction must be considered in order to predict future risks and to make safe and intelligent decisions. This becomes critical when it comes to conflicting driving situation such as lane merge, tollgate area, and unsignalized intersections. Previously developed future prediction algorithms show limited performance when handling interactions and conflicts between vehicles because they focused on predicting individual vehicle motion and/or interaction between a single pair of vehicles rather than the entire traffic scene. In this paper, a scene representation method, namely multi-channel Occupancy Grid Map (OGM), is proposed to describe the entire traffic scene, which is then utilized for the deep learning architecture that predicts the future traffic scene or OGM. Multi-channel OGM represents entire traffic scene as a manner of image-like structure from bird’s eye view composed with dynamic layer and static layer depicting the occupancy of the dynamic and static objects. By using this 2D traffic scene representation, future prediction can be modeled as a video processing problem, where future time-serial image sequence need to be predicted. In order to predict future traffic scenes based on past traffic scenes, a deep learning architecture is proposed using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Networks. With the proposed deep learning architecture, future prediction accuracy in highly conflicting traffic situation is guaranteed up to 90 percent with 3 seconds of prediction horizon. A video ofthe traffic scene prediction results is available online [1].


title: Wheeled inverted pendulum type assistant robot: inverted mobile, standing, and sitting motions

abstract: This paper describes the mobile control and the standing and sitting motions of an Inverted PENdulum type assistant robot-(I-PENTAR) aiming at the coexistence of safety and work capability. I-PENTAR consists of a body with a high powered waist joint, arms designed for safety, and a wheeled inverted pendulum mobile platform. It is modeled as a three-dimensional robot with controls for inclination angle, linear position, and steering angle, and is controlled by state feedback control based on the LQR method. The motion planning of standing and sitting-important motions for an inverted pendulum type robot in practical use-is proposed. It was experimentally confirmed that I-PENTAR could realize a series of fundamental motions required in practical use, which are standing, running, turning, and sitting stably.


title: A Reconfigurable Antenna With Frequency and Polarization Agility

abstract: A new antenna with both frequency and polarization reconfigurability is presented. The antenna consists of a square microstrip patch with a single probe feed located along the diagonal line. The center of each edge of the patch is connected to a shorting post via a p-i-n diode for polarization switching and two varactor diodes for frequency tuning. By switching between the different states of the p-i-n diodes, the proposed antenna can produce radiation patterns with horizontal, vertical, or 45° linear polarization. By varying the dc bias voltage, the operating frequency of each polarization of the antenna can be independently tuned. The frequency tuning range is from 1.35 to 2.25 GHz (|S11| <; -10 dB) for either horizontal or vertical polarization and from 1.35 to 1.9 GHz for the 45° linear polarization. Measured results on frequency tuning ranges and radiation patterns agree well with numerical simulations.


title: Measures Of Distributional Similarity

abstract: We study distributional similarity measures for the purpose of improving probability estimation for unseen cooccurrences. Our contributions are three-fold: an empirical comparison of a broad range of measures; a classification of similarity functions based on the information that they incorporate; and the introduction of a novel function that is superior at evaluating potential proxy distributions.


Artificial intelligence - structures and strategies for complex problem solving (2. ed.)


title: DimBoost: Boosting Gradient Boosting Decision Tree to Higher Dimensions

abstract: Gradient boosting decision tree (GBDT) is one of the most popular machine learning models widely used in both academia and industry. Although GBDT has been widely supported by existing systems such as XGBoost, LightGBM, and MLlib, one system bottleneck appears when the dimensionality of the data becomes high. As a result, when we tried to support our industrial partner on datasets of the dimension up to 330K, we observed suboptimal performance for all these aforementioned systems. In this paper, we ask "Can we build a scalable GBDT training system whose performance scales better with respect to dimensionality of the data?" The first contribution of this paper is a careful investigation of existing systems by developing a performance model with respect to the dimensionality of the data. We find that the collective communication operations in many existing systems only implement the algorithm designed for small messages. By just fixing this problem, we are able to speed up these systems by up to 2X. Our second contribution is a series of optimizations to further optimize the performance of collective communications. These optimizations include a task scheduler, a two-phase split finding method, and low-precision gradient histograms. Our third contribution is a sparsity-aware algorithm to build gradient histograms and a novel index structure to build histograms in parallel. We implement these optimizations in DimBoost and show that it can be 2-9X faster than existing systems.


title: Homophily and Latent Attribute Inference: Inferring Latent Attributes of Twitter Users from Neighbors

abstract: In this paper, we extend existing work on latent attribute inference by leveraging the principle of homophily: we evaluate the inference accuracy gained by augmenting the user features with features derived from the Twitter profiles and postings of her friends. We consider three attributes which have varying degrees of assortativity: gender, age, and political affiliation. Our approach yields a significant and robust increase in accuracy for both age and political affiliation, indicating that our approach boosts performance for attributes with moderate to high assortativity. Furthermore, different neighborhood subsets yielded optimal performance for different attributes, suggesting that different subsamples of the user's neighborhood characterize different aspects of the user herself. Finally, inferences using only the features of a user's neighbors outperformed those based on the user's features alone. This suggests that the neighborhood context alone carries substantial information about the user.


title: Topically Administered Ketamine Reduces Capsaicin-Evoked Mechanical Hyperalgesia

abstract: Background: The n-methyl-d-aspartate receptor antagonists such as ketamine relieve chronic pain but their oral and parenteral use is limited by the adverse effects. Experimental studies indicate that the peripheral n-methyl-d-aspartate receptors are involved in nociception. Recent clinical findings suggest that ketamine gel alleviates neuropathic pain, but no placebo-controlled randomized studies are available on the neurosensory effects of ketamine gel in experimental neurogenic pain. Objectives: The aim of this study was to assess the effects of topically applied ketamine using the intradermal capsaicin model in healthy volunteers. Methods: Nine healthy subjects received ketamine and placebo gel on 3 occasions in a randomized, double-blind, and crossover manner. Theconcentration of ketaminewas 50 mg/mL.One milliliterofgelwas rubbed into the skin of both forearms 10 minutes before the intradermal injection of capsaicin (250 mg). Thereafter, the intensity and unpleasantness of spontaneous and evoked pain and dysesthesia was assessed up to 60 minutes using a 10-cm visual analog scale. Pain and dysesthesia were evoked using cotton gauze, a von Frey microfilament, and 38C, 42C, and 47C heat. Side effects were recorded, and individuals’subjective experiences were assessed with a standard questionnaire. Results: Ketamine gel had no effect on immediate burning pain followed by the capsaicin injection. Both the intensity and unpleasantness of mechanical hyperalgesia was statistically significantly reduced by ketamine gel applied both on the left and right side. Neither tactile allodynia evoked by a brush nor thermal hyperalgesia were observed in any volunteer. No local or systemic side effects were observed. No patient reported any drug effects. Discussion: A significant reduction of mechanical hyperalgesia was produced by topically and pre-emptively applied ketamine in healthy patients. We propose that the mechanism of action would be the reduction of central sensitization caused by the absorption of ketamine in circulation.


title: Detection and resolution of feature interactions in IP multimedia subsystem

abstract: The IP Multimedia Subsystem (IMS) enables the delivery of multimedia services through different access networks. 3GPP adopted the Session Initiation Protocol (SIP) as the IMS signalling protocol. However, IMS interaction with these services faces the 'feature interaction' problem encountered in traditional telephony networks. This problem occurs as features invoked during a session behave correctly when processed separately from each other, but not when running together. Although much research work has explored managing feature interactions, applying these solutions in IMS remains challenging and further investigation is needed. This paper aims to analyze the feature interaction issue in IMS and to propose a new solution. We defined a SIP-based algorithm and the associated mechanisms to enhance IMS service invocation. This algorithm is the core of the Service Broker, a new functional entity in charge of managing feature interaction. We validated our proposal through a performance evaluation and prototyped the Service Broker on an open source IMS platform.


title: Multi-Modal Knowledge Representation Learning via Webly-Supervised Relationships Mining

abstract: Knowledge representation learning (KRL) encodes enormous structured information with entities and relations into a continuous low-dimensional semantic space. Most conventional methods solely focus on learning knowledge representation from single modality, yet neglect the complementary information from others. The more and more rich available multi-modal data on Internet also drive us to explore a novel approach for KRL in multi-modal way, and overcome the limitations of previous single-modal based methods. This paper proposes a novel multi-modal knowledge representation learning (MM-KRL) framework which attempts to handle knowledge from both textual and visual modal web data. It consists of two stages, i.e., webly-supervised multi-modal relationship mining, and bi-enhanced cross-modal knowledge representation learning. Compared with existing knowledge representation methods, our framework has several advantages: (1) It can effectively mine multi-modal knowledge with structured textual and visual relationships from web automatically. (2) It is able to learn a common knowledge space which is independent to both task and modality by the proposed Bi-enhanced Cross-modal Deep Neural Network (BC-DNN). (3) It has the ability to represent unseen multi-modal relationships by transferring the learned knowledge with isolated seen entities and relations into unseen relationships. We build a large-scale multi-modal relationship dataset (MMR-D) and the experimental results show that our framework achieves excellent performance in zero-shot multi-modal retrieval and visual relationship recognition.


title: Structured Compressive Sensing Based Superimposed Pilot Design in Downlink Large-Scale MIMO Systems

abstract: Large-scale multiple-input multiple-output (MIMO) with high spectrum and energy efficiency is a very promising key technology for future 5G wireless communications. For large-scale MIMO systems, accurate channel state information (CSI) acquisition is a challenging problem, especially when each user has to distinguish and estimate numerous channels coming from a large number of transmit antennas in the downlink. Unlike the conventional orthogonal pilots whose pilot overhead prohibitively increases with the number of transmit antennas, we propose a spectrum-efficient superimposed pilot design for downlink large-scale MIMO scenarios, where frequency-domain pilots of different transmit antennas occupy the completely same subcarriers in the freqency domain. Meanwhile, spatial-temporal common sparsity of large-scale MIMO channels motivates us to exploit the emerging theory of structured compressive sensing (CS) for reliable MIMO channel estimation, which is realized by the proposed structured subspace pursuit (SSP) algorithm to simultaneously recover multiple channels with low pilot overhead. Simulation results demonstrate that the proposed scheme performs well and can approach the performance bound.


title: Thermo-hydraulic performance optimization of wavy fin heat exchanger by combining delta winglet vortex generators

abstract: Abstract Wavy fins and vortex generators are effective methods for enhancing heat transfer of heat exchangers. In general, wavy fins and vortex generators are studied separately in the literature. A novel combination of a wavy fin and vortex generators is proposed in the present study. The effects of different corrugation angles of the wavy fin and different attack angles of the vortex generators are numerically studied to optimize the thermo-hydraulic performance of the novel combination. The Nusselt number, friction factor, and thermal performance factor are compared between different corrugation angles and attack angles and between wavy fins with and without vortex generators. The heat transfer performance is improved considerably by the novel combination. The maximum thermal performance factor increases by 26.4% owing to the combination of the vortex generator and wavy fin. The optimum attack angle of the vortex generator, which yields the highest heat transfer performance of the wavy fin plate heat exchanger, is reported as 45°.


title: Real-Time Removing of Outliers and Noise in 3D Point Clouds Applied in Robotic Applications

abstract: Nowadays, robots are able to carry out a complex series of actions, to take decisions, to interact with their environment and generally to perform plausible reactions. Robots’ visual ability plays an important role to their behavior, helping them to efficiently manage the received information. In this paper, we present a real time method for removing outliers and noise of 3D point clouds which are captured by the optical system of robots having depth camera at their disposal. Using our method, the final result of the created 3D object is smoothed providing an ideal form for using it in further processing techniques; namely navigation, object recognition and segmentation. In our experiments, we investigate real scenarios where the robot moves while it acquires the point cloud in natural light environment, so that unpleasant noise and outliers become apparent.


title: An Emerging Strategic Counterculture? Pierre Elliott Trudeau, Canadian Intellectuals and the Revision of Liberal Defence Policy concerning NATO (1968-1969)

abstract: Michel Fortmann is professor of political science at the Universite de Montreal. Martin Larose is a doctoral student in the history department of the same institution. The authors express their gratitude to SSHRC for the support that made this study possible.INTRODUCTIONAs Stephane Roussel reminds us in this special issue, government leaders must from time to time make hard choices in security matters. Pierre Elliott Trudeau, who became prime minister in 1968, is a case in point. After ordering, in the spring of 1968, a general review of Canada's foreign and defence policies, the government announced, on 3 April 1969, its intention to gradually reduce, in consultation with allies, the country's military presence in Europe. The Atlantic alliance thereby effectively lost the pre-eminent position that it had occupied in Canadian foreign policy since 1949. Although the actual troop reductions turned out to be less drastic than envisioned, the Liberal government's choice in 1969 represented a clear break with previous policy.What explains this departure from well-established policies? Structural-realist theory would argue that a new configuration of power in the international system, or a decline of Canada's position therein, might have been the cause of the decision. A strengthening of Europe or a weakening of the Soviet threat might have thrown into question Canada's role in the North Atlantic Treaty Organization (NATO). Alternatively, a significant reduction in the resources that Canada could commit to its defence policy might have motivated the choice. Neither factor, however, provides a convincing explanation in the context of the late 1960s. To the contrary, the 1968 Soviet invasion of Czechoslovakia reinforced the reality of the threat posed by the Warsaw Pact in Europe, and NATO's 1967 adoption of the doctrine of flexible response reactivated the debate over conventional defence. Europe's political division on defence issues was aggravated by France's decision, the previous year, to withdraw its forces from NATO's integrated command. Finally, Canada's decision to reduce its military presence came at the very time that the east and west were preparing for negotiations over reciprocal reductions in their military forces in Europe. How to explain this decision, which is in apparent contradiction to the strategic reality of the time?The cultural approach provides a partial answer. According to this approach, strategic choices are not merely the reflection of a rigorous and objective analysis of the international environment; they also result from the interpretation of that environment in the light of the prevailing strategic culture. This "culture," as defined by Alastair Iain Johnston, has two components: the first consists of basic assumptions about the international order, its stability and the usefulness of military force; the second reflects the operational preferences of the decision-makers (i.e., those choices that appear most rational given one's perception of the international system).(1) Thus, strategic culture is a filter through which reality is perceived, limiting, in the eyes of the decision-makers, the available options. The Liberal government's decision to reduce the Canadian military presence in Europe is thus a reflection of its vision of the world and its resulting preferences. One must therefore refer to the source of the government's perceptions in order to explain the 1968-1970 change in defence policy.The cultural approach also helps us understand how policies change. In the case under consideration, two Weltanschauungen came into conflict, one reflecting the dominant values and preferences of the internationalist or Pearsonian tradition (1945-1968), the other the emerging worldview of Pierre Elliott Trudeau and intellectuals from his generation. The revision of Canada's NATO policy thus provides an opportunity to examine the conditions under which a new approach to security questions (a strategic counterculture) can arise and challenge the dominant culture. …


title: Face Recognition on Consumer Devices: Reflections on Replay Attacks

abstract: Widespread deployment of biometric systems supporting consumer transactions is starting to occur. Smart consumer devices, such as tablets and phones, have the potential to act as biometric readers authenticating user transactions. However, the use of these devices in uncontrolled environments is highly susceptible to replay attacks, where these biometric data are captured and replayed at a later time. Current approaches to counter replay attacks in this context are inadequate. In order to show this, we demonstrate a simple replay attack that is 100% effective against a recent state-of-the-art face recognition system; this system was specifically designed to robustly distinguish between live people and spoofing attempts, such as photographs. This paper proposes an approach to counter replay attacks for face recognition on smart consumer devices using a noninvasive challenge and response technique. The image on the screen creates the challenge, and the dynamic reflection from the person’s face as they look at the screen forms the response. The sequence of screen images and their associated reflections digitally watermarks the video. By extracting the features from the reflection region, it is possible to determine if the reflection matches the sequence of images that were displayed on the screen. Experiments indicate that the face reflection sequences can be classified under ideal conditions with a high degree of confidence. These encouraging results may pave the way for further studies in the use of video analysis for defeating biometric replay attacks on consumer devices.


title: Understanding Tutor Learning: Knowledge- Building and Knowledge-Telling in Peer Tutors’ Explanations and Questions

abstract: Prior research has established that peer tutors can benefit academically from their tutoring experiences. However, although tutor learning has been observed across diverse settings, the magnitude of these gains is often underwhelming. In this review, the authors consider how analyses of tutors’ actual behaviors may help to account for variation in learning outcomes and how typical tutoring behaviors may create or undermine opportunities for learning. The authors examine two tutoring activities that are commonly hypothesized to support tutor learning: explaining and questioning. These activities are hypothesized to support peer tutors’ learning via reflective knowledge-building, which includes self-monitoring of comprehension, integration of new and prior knowledge, and elaboration and construction of knowledge. The review supports these hypotheses but also finds that peer tutors tend to exhibit a pervasive knowledge-telling bias. Peer tutors, even when trained, focus more on delivering knowledge rather th...


title: The Impacts of Representing the Correlation of Errors in Radar Data Assimilation. Part I: Experiments with Simulated Background and Observation Estimates

abstract: AbstractIn radar data assimilation, statistically optimal analyses are sought by minimizing a cost function in which the variance and covariance of background and observation errors are correctly represented. Radar observations are particular in that they are often available at spatial resolution comparable to that of background estimates. Because of computational constraints and lack of information, it is impossible to perfectly represent the correlation of errors. In this study, the authors characterize the impact of such misrepresentations in an idealized framework where the spatial correlations of background and observation errors are each described by a homogeneous and isotropic exponential decay. Analyses obtained with perfect representation of correlations are compared to others obtained by neglecting correlations altogether. These two sets of analyses are examined from a theoretical and an experimental perspective. The authors show that if the spatial correlations of background and observation err...


title: Learning Representations for Counterfactual Inference

abstract: Observational studies are rising in importance due to the widespread accumulation of data in fields such as healthcare, education, employment and ecology. We consider the task of answering counterfactual questions such as, "Would this patient have lower blood sugar had she received a different medication?". We propose a new algorithmic framework for counterfactual inference which brings together ideas from domain adaptation and representation learning. In addition to a theoretical justification, we perform an empirical comparison with previous approaches to causal inference from observational data. Our deep learning algorithm significantly outperforms the previous state-of-the-art.


title: Towards Maturity Models as Methods to Manage IT for Business Value – A Resource-based View Foundation

abstract: For more than three decades business value of IT attracts attention in Information Systems Research (ISR) and practice. Despite the fact, that corporate management considers enterprise IT increasingly as a strategic factor, the question on how to manage IT effectively for business value remains widely unsolved. With the goal to support this management task we suggest maturity models (MMs) as a new method. We found MMs in the resource-based view in order to provide maturity models with a theoretical basis in general and to lay out the feasibility of MMs as methods to manage IT resources for business value in particular. To accomplish these goals, we conduct an in-depth analysis using an argumentative-deductive research approach. We contribute to the knowledge base by adopting a well-proven approach, transferring it to a new application domain, and grounding it in theory. Taking an example from corporate management research we illustrate our findings schematically.


L O ] 2 0 Ju l 2 01 8 Learning Heuristics for Automated Reasoning through Deep Reinforcement Learning


Motor nerve conduction velocity and electromyogram in carbon disulphide poisoning.


title: Substrate Integrated Waveguide-to-Microstrip Transition in Multilayer Substrate

abstract: This paper presents a novel transition between a microstrip line and a substrate integrated waveguide (SIW) in a multilayer substrate design environment. In order to achieve a low-loss broadband response, the transition, consisting of a tapered or multisectional ridged SIW and a tapered microstrip line, is modeled and designed by simultaneously considering both impedance matching and field matching. Characteristic impedance and guided wavelength calculated by using closed-form expressions based on a transverse resonant method are used to develop our design procedure. Effective broad bandwidth is obtained in two examples developed in this study, which are validated with simulated and measured results. This transition provides a simple way to design substrate integrated circuits with buried microstrip circuits in the multilayer substrate in which any ratio of impedance transform can be anticipated.


title: Social Spammer Detection in Microblogging

abstract: The availability of microblogging, like Twitter and Sina Weibo, makes it a popular platform for spammers to unfairly overpower normal users with unwanted content via social networks, known as social spamming. The rise of social spamming can significantly hinder the use of microblogging systems for effective information dissemination and sharing. Distinct features of microblogging systems present new challenges for social spammer detection. First, unlike traditional social networks, microblogging allows to establish some connections between two parties without mutual consent, which makes it easier for spammers to imitate normal users by quickly accumulating a large number of "human" friends. Second, microblogging messages are short, noisy, and unstructured. Traditional social spammer detection methods are not directly applicable to microblogging. In this paper, we investigate how to collectively use network and content information to perform effective social spammer detection in microblogging. In particular, we present an optimization formulation that models the social network and content information in a unified framework. Experiments on a real-world Twitter dataset demonstrate that our proposed method can effectively utilize both kinds of information for social spammer detection.


title: Broadband four-way power divider for active antenna array application

abstract: A novel four-way power divider based on substrate integrated waveguide is presented. Broadband performance is obtained by using a stepped coaxial line transformer together with tapered substrate integrated waveguides. Simulated and measured results show that good performance of insertion loss and impedance matching is achieved over a broad bandwidth from 4 GHz to 11 GHz. When the proposed power divider is applied for active antenna array, it can be shown that by using appropriate matching networks, gain fluctuation caused by non-ideal return loss and isolation can be alleviated.


title: Technology as experience

abstract: In Technology as Experience, John McCarthy and Peter Wright argue that any account of what is often called the user experience must take into consideration the emotional, intellectual, and sensual aspects of our interactions with technology. We don't just use technology, they point out; we live with it. They offer a new approach to understanding human-computer interaction through examining the felt experience of technology. Drawing on the pragmatism of such philosophers as John Dewey and Mikhail Bakhtin, they provide a framework for a clearer analysis of technology as experience. Just as Dewey, in Art as Experience, argued that art is part of everyday lived experience and not isolated in a museum, McCarthy and Wright show how technology is deeply embedded in everyday life. The "zestful integration" or transcendent nature of the aesthetic experience, they say, is a model of what human experience with technology might become. McCarthy and Wright illustrate their theoretical framework with real-world examples that range from online shopping to ambulance dispatch. Their approach to understanding human-computer interactionseeing it as creative, open, and relational, part of felt experienceis a measure of the fullness of technologys potential to be more than merely functional.


title: Practical and flexible path planning for car-like mobile robot using maximal-curvature cubic spiral

abstract: Abstract This paper presents a nonholonomic path planning method, aiming at taking into considerations of curvature constraint, length minimization, and computational demand, for car-like mobile robot based on cubic spirals. The generated path is made up of at most five segments: at most two maximal-curvature cubic spiral segments with zero curvature at both ends in connection with up to three straight line segments. A numerically efficient process is presented to generate a Cartesian shortest path among the family of paths considered for a given pair of start and destination configurations. Our approach is resorted to minimization via linear programming over the sum of length of each path segment of paths synthesized based on minimal locomotion cubic spirals linking start and destination orientations through a selected intermediate orientation. The potential intermediate configurations are not necessarily selected from the symmetric mean circle for non-parallel start and destination orientations. The novelty of the presented path generation method based on cubic spirals is: (i) Practical: the implementation is straightforward so that the generation of feasible paths in an environment free of obstacles is efficient in a few milliseconds; (ii) Flexible: it lends itself to various generalizations: readily applicable to mobile robots capable of forward and backward motion and Dubins’ car (i.e. car with only forward driving capability); well adapted to the incorporation of other constraints like wall-collision avoidance encountered in robot soccer games; straightforward extension to planning a path connecting an ordered sequence of target configurations in simple obstructed environment.


title: Google analytics for time behavior measurement in Moodle

abstract: The shift to e-learning and online education requires new and different approaches for tracking student performance and behavior. Moodle is currently the leading virtual learning environment in Spain, and a lot of different plugins have appeared to complement the lack of native tools to track the learning process in all its aspects. However, none of these take the temporal dimension as a possible behavior measurement into consideration. In this case study, a new technical and statistical approach to time tracking of students in Moodle will be discussed, so that time as a behavioral aspect can be considered in further assessments. Furthermore, this approach could not only be applied to Moodle, but to all other online virtual learning environments.


title: Memristor Bridge Synapses

abstract: In this paper, we propose a memristor bridge circuit consisting of four identical memristors that is able to perform zero, negative, and positive synaptic weightings. Together with three additional transistors, the memristor bridge weighting circuit is able to perform synaptic operation for neural cells. It is compact as both weighting and weight programming are performed in a memristor bridge synapse. It is power efficient, since the operation is based on pulsed input signals. Its input terminals are utilized commonly for applying both weight programming and weight processing signals via time sharing. In this paper, features of the memristor bridge synapses are investigated using the TiO memristor model via simulations.


title: Chronic urticaria merits serum vitamin D evaluation and supplementation; a randomized case control study

abstract: BackgroundSeveral studies suggest that Vitamin D (Vit-D3) supplementation reduces Chronic Urticaria (CU) symptoms.ObjectivesEvaluation of serum 25-hydroxyvitamin-D (25 (OH)2D) level and assessment of therapeutic effect of VitD3in CU patients.Methods192 subjects were stratified according to the baseline 25(OH)2D levels and subsequently randomized into three subgroups to receive Vit-D3 alone (VD) or antihistamine and systemic corticosteroid (H+S) or VitD3 with antihistamine and systemic corticosteroid (VD+H+S) for 6 weeks between July 2012 to Oct 2014. 130 healthy controls (HC) were followed without any intervention. The patients were evaluated for reduction in urticarial symptoms using visual analogue scale (VAS) and 5-D itch score.ResultsLow serum levels of 25 (OH)2D was observed in 91% of CU patients and 64% of the healthy controls (P < 0.0001). VAS and 5-D Score in subgroups VD, H + S and VD + H + S decreased significantly from 6 · 7 ± 0 · 043, 6 · 6 ± 0 · 42 and 6 · 68 ± 0 · 40 at baseline to 5 · 2 ± 0 · 70 (P = 0 · 0088), 3 · 3 ± 0 · 50 (P < 0 · 0001) and1 · 86 ± 0 · 39 (P < 0 · 0001) after treatment and from 14 · 5 ± 0 · 72, 13 · 9 ± 0 · 77 and 13 · 9 ± 0 · 221 to 12 · 06 ± 1 · 10 (P = 0 · 0072), 8 · 1 ± 1 · 13 (P < 0 · 0001) and 5 · 01 ± 0 · 94 (P < 0 · 0001) respectively.ConclusionsCU patients have low serum 25(OH)2D levels and Vit-D3 supplementation in combination with antihistamine and systemic corticosteroid show elevated response in resolving the symptoms of CU. This study also warrants that each subject with CU should be screened for serum 25 (OH)2D levels before starting a treatment.


title: Assessing agreement level between forced alignment models with data from endangered language documentation corpora

abstract: Automatic forced alignment between transcriptions has achieved high levels of agreement for languages with large corpora, but the technique holds great promise for work on all languages. Here, we apply two forced alignment programs to data from an endangered Mixtecan language of Mexico. Both yielded a majority of boundaries within 20 ms of hand-labeled ones. Phonemes with fairly steady-state elements (e.g. nasals, fricatives) were more accurately labeled than others. Forced alignment thus may increase efficiency of labeling texts from smaller languages, at least in cases where the phoneme inventories are similar to those of the languages of the training.


title: Method for measuring stereo camera depth accuracy based on stereoscopic vision

abstract: We present a method to evaluate stereo camera depth accuracy in human centered applications. It enables the comparison ::: between stereo camera depth resolution and human depth resolution. Our method uses a multilevel test target which can ::: be easily assembled and used in various studies. Binocular disparity enables humans to perceive relative depths ::: accurately, making a multilevel test target applicable for evaluating the stereo camera depth accuracy when the accuracy ::: requirements come from stereoscopic vision. ::: The method for measuring stereo camera depth accuracy was validated with a stereo camera built of two SLRs (singlelens ::: reflex). The depth resolution of the SLRs was better than normal stereo acuity at all measured distances ranging ::: from 0.7 m to 5.8 m. The method was used to evaluate the accuracy of a lower quality stereo camera. Two parameters, ::: focal length and baseline, were varied. Focal length had a larger effect on stereo camera's depth accuracy than baseline. ::: The tests showed that normal stereo acuity was achieved only using a tele lens. ::: However, a user's depth resolution in a video see-through system differs from direct naked eye viewing. The same test ::: target was used to evaluate this by mixing the levels of the test target randomly and asking users to sort the levels ::: according to their depth. The comparison between stereo camera depth resolution and perceived depth resolution was ::: done by calculating maximum erroneous classification of levels.


title: 2-D Entropy and Short-Time Fourier Transform to Leverage GPR Data Analysis Efficiency

abstract: Accurate detection of singular region using ground-penetrating radar (GPR) is very useful in assessing roadway pavement, bridge deck concrete structure, and railroad ballast conditions. To locate an object within the large radargram involves extensive computational resources and time, especially when the data of interests only possess a small portion of the whole big data set. Therefore, an efficient GPR signal-processing technique is highly demanded. This paper proposes the use of 2-D entropy analysis to narrow down the data scope to the interested regions, which can considerably reduce computational cost for more sophisticated data postprocessing. Joint time-frequency analysis using short-time Fourier transform is then performed for singular region location detection and refinement. The proposed methodology is tested with different laboratory setups. The analysis results show good agreement with physical configurations.


title: Using deep learning to segment breast and fibroglandular tissue in MRI volumes

abstract: Purpose: Automated segmentation of breast and fibroglandular tissue (FGT) is required for various computer‐aided applications of breast MRI. Traditional image analysis and computer vision techniques, such atlas, template matching, or, edge and surface detection, have been applied to solve this task. However, applicability of these methods is usually limited by the characteristics of the images used in the study datasets, while breast MRI varies with respect to the different MRI protocols used, in addition to the variability in breast shapes. All this variability, in addition to various MRI artifacts, makes it a challenging task to develop a robust breast and FGT segmentation method using traditional approaches. Therefore, in this study, we investigated the use of a deep‐learning approach known as “U‐net.” Materials and methods: We used a dataset of 66 breast MRI's randomly selected from our scientific archive, which includes five different MRI acquisition protocols and breasts from four breast density categories in a balanced distribution. To prepare reference segmentations, we manually segmented breast and FGT for all images using an in‐house developed workstation. We experimented with the application of U‐net in two different ways for breast and FGT segmentation. In the first method, following the same pipeline used in traditional approaches, we trained two consecutive (2C) U‐nets: first for segmenting the breast in the whole MRI volume and the second for segmenting FGT inside the segmented breast. In the second method, we used a single 3‐class (3C) U‐net, which performs both tasks simultaneously by segmenting the volume into three regions: nonbreast, fat inside the breast, and FGT inside the breast. For comparison, we applied two existing and published methods to our dataset: an atlas‐based method and a sheetness‐based method. We used Dice Similarity Coefficient (DSC) to measure the performances of the automated methods, with respect to the manual segmentations. Additionally, we computed Pearson's correlation between the breast density values computed based on manual and automated segmentations. Results: The average DSC values for breast segmentation were 0.933, 0.944, 0.863, and 0.848 obtained from 3C U‐net, 2C U‐nets, atlas‐based method, and sheetness‐based method, respectively. The average DSC values for FGT segmentation obtained from 3C U‐net, 2C U‐nets, and atlas‐based methods were 0.850, 0.811, and 0.671, respectively. The correlation between breast density values based on 3C U‐net and manual segmentations was 0.974. This value was significantly higher than 0.957 as obtained from 2C U‐nets (P < 0.0001, Steiger's Z‐test with Bonferoni correction) and 0.938 as obtained from atlas‐based method (P = 0.0016). Conclusions: In conclusion, we applied a deep‐learning method, U‐net, for segmenting breast and FGT in MRI in a dataset that includes a variety of MRI protocols and breast densities. Our results showed that U‐net‐based methods significantly outperformed the existing algorithms and resulted in significantly more accurate breast density computation.


title: Moddicom: a complete and easily accessible library for prognostic evaluations relying on image features

abstract: Decision Support Systems (DSSs) are increasingly ::: exploited in the area of prognostic evaluations. For predicting ::: the effect of therapies on patients, the trend is now to use image ::: features, i.e. information that can be automatically computed by ::: considering images resulting by analysis. The DSSs application ::: as predictive tools is particularly suitable for cancer treatment, ::: given the peculiarities of the disease –which is highly localised ::: and lead to significant social costs– and the large number of ::: images that are available for each patient. ::: At the state of the art, there exists tools that allow to handle ::: image features for prognostic evaluations, but they are not ::: designed for medical experts. They require either a strong ::: engineering or computer science background since they do not ::: integrate all the required functions, such as image retrieval and ::: storage. In this paper we fill this gap by proposing Moddicom, ::: a user-friendly complete library specifically designed to be ::: exploited by physicians. A preliminary experimental analysis, ::: performed by a medical expert that used the tool, demonstrates ::: the efficiency and the effectiveness of Moddicom.


Parsing as Language Modeling


title: Diagnostic Visualization for Deep Neural Networks Using Stochastic Gradient Langevin Dynamics

abstract: The internal states of most deep neural networks are difficult to interpret, which makes diagnosis and debugging during training challenging. Activation maximization methods are widely used, but lead to multiple optima and are hard to interpret (appear noise-like) for complex neurons. Image-based methods use maximally-activating image regions which are easier to interpret, but do not provide pixel-level insight into why the neuron responds to them. In this work we introduce an MCMC method: Langevin Dynamics Activation Maximization (LDAM), which is designed for diagnostic visualization. LDAM provides two affordances in combination: the ability to explore the set of maximally activating pre-images, and the ability to trade-off interpretability and pixel-level accuracy using a GAN-style discriminator as a regularizer. We present case studies on MNIST, CIFAR and ImageNet datasets exploring these trade-offs. Finally we show that diagnostic visualization using LDAM leads to a novel insight into the parameter averaging method for deep net training.


title: Multi-label image classification with a probabilistic label enhancement model

abstract: In this paper, we present a novel probabilistic label enhancement model to tackle multi-label image classification problem. Recognizing multiple objects in images is a challenging problem due to label sparsity, appearance variations of the objects and occlusions. We propose to tackle these difficulties from a novel perspective by constructing auxiliary labels in the output space. Our idea is to exploit label combinations to enrich the label space and improve the label identification capacity in the original label space. In particular, we identify a set of informative label combination pairs by constructing a tree-structured graph in the label space using the maximum spanning tree algorithm, which naturally forms a conditional random field. We then use the produced label pairs as auxiliary new labels to augment the original labels and perform piecewise training under the framework of conditional random fields. In the test phase, max-product message passing is used to perform efficient inference on the tree graph, which integrates the augmented label pair classifiers and the standard individual binary classifiers for multi-label prediction. We evaluate the proposed approach on several image classification datasets. The experimental results demonstrate the superiority of our label enhancement model in terms of both prediction performance and running time comparing to the-state-of-the-art multi-label learning methods.


title: Conceptual issues concerning mediation, interventions and composition

abstract: Concepts concerning mediation in the causal inference literature are reviewed. Notions of direct and indirect effects from a counterfactual approach to mediation are compared with those arising from the standard regression approach to mediation of Baron and Kenny (1986), commonly utilized in the social science literature. It is shown that concepts of direct and indirect effect from causal inference generalize those described by Baron and Kenny and that under appropriate identification assumptions these more general direct and indirect effects from causal inference can be estimated using regression even when there are interactions between the primary exposure of interest and the mediator. A number of conceptual issues are discussed concerning the interpretation of identification conditions for mediation, the notion of counterfactuals based on hypothetical interventions and the so called consistency and composition assumptions.


title: Clothes detection and classification using convolutional neural networks

abstract: In this paper we describe development of a computer vision system for accurate detection and classification of clothes for e-commerce images. We present a set of experiments on well established architectures of convolutional neural networks, including Residual networks, SqueezeNet and Single Shot MultiBox Detector (SSD). The clothes detection network was trained and tested on DeepFashion dataset, which contains box annotations for locations of clothes. Classification task was evaluated on a set of images of dresses that were collected from online shops. Ground truth labels were inferred from shop items metadata for five different attributes, including color, pattern, sleeve, neckline and hemline, each consisting of several possible classes. Automatic gathering of labels resulted in an average of 83% rate of correct labels. In the experiments we evaluate the impact on classification accuracy of a set of potential improvements, including data augmentation by generating diverse backgrounds, increasing the size of the network and using ensembles. We analyse the accuracy improvements with respect to the processing efficiency. Finally, we present the achieved accuracy rates in the clothes detection task and outline the most successful network configurations for dresses classification.


The Research and Design of Web-based Intelligent Tutoring System


title: Toroidal skin drive for snake robot locomotion

abstract: Small robots have the potential to access confined spaces where humans cannot go. However, the mobility of wheeled and tracked systems is severely limited in cluttered environments. Snake robots using biologically inspired gaits for locomotion can provide better access in many situations, but are slow and can easily snag. This paper introduces an alternative approach to snake robot locomotion, in which the entire surface of the robot provides continuous propulsive force to significantly improve speed and mobility in many environments.


title: Cellular-Enabled UAV Communication: Trajectory Optimization under Connectivity Constraint

abstract: In this paper, we study a cellular-enabled unmanned aerial vehicle (UAV) communication system consisting of one UAV and multiple ground base stations (GBSs). The UAV has a mission of flying from an initial location to a final location, during which it needs to maintain reliable wireless connection with the cellular network by associating with one of the GBSs at each time instant. We aim to minimize the UAV mission completion time by optimizing its trajectory, subject to a quality of connectivity constraint of the GBS-UAV link specified by a minimum received signal-to-noise ratio (SNR) target, which needs to be satisfied throughout the mission. This problem is non-convex and difficult to be optimally solved. We first propose an effective approach to check its feasibility based on graph connectivity verification. Then, by examining the GBS-UAV association sequence during the UAV mission, we obtain useful insights on the optimal UAV trajectory, based on which an efficient algorithm is proposed to find an approximate solution to the trajectory optimization problem by leveraging techniques in convex optimization and graph theory. Numerical results show that our proposed trajectory design achieves near-optimal performance.


title: Ad Hoc Table Retrieval using Semantic Similarity

abstract: We introduce and address the problem of ad hoc table retrieval: answering a keyword query with a ranked list of tables. This task is not only interesting on its own account, but is also being used as a core component in many other table-based information access scenarios, such as table completion or table mining. The main novel contribution of this work is a method for performing semantic matching between queries and tables. Specifically, we (i) represent queries and tables in multiple semantic spaces (both discrete sparse and continuous dense vector representations) and (ii) introduce various similarity measures for matching those semantic representations. We consider all possible combinations of semantic representations and similarity measures and use these as features in a supervised learning model. Using a purpose-built test collection based on Wikipedia tables, we demonstrate significant and substantial improvements over a state-of-the-art baseline.


title: Game level layout from design specification

abstract: The design of video game environments, or levels, aims to control gameplay by steering the player through a sequence of designer-controlled steps, while simultaneously providing a visually engaging experience. Traditionally these levels are painstakingly designed by hand, often from pre-existing building blocks, or space templates. In this paper, we propose an algorithmic approach for automatically laying out game levels from user-specified blocks. Our method allows designers to retain control of the gameplay flow via user-specified level connectivity graphs, while relieving them from the tedious task of manually assembling the building blocks into a valid, plausible layout. Our method produces sequences of diverse layouts for the same input connectivity, allowing for repeated replay of a given level within a visually different, new environment. We support complex graph connectivities and various building block shapes, and are able to compute complex layouts in seconds. The two key components of our algorithm are the use of configuration spaces defining feasible relative positions of building blocks within a layout and a graph-decomposition based layout strategy that leverages graph connectivity to speed up convergence and avoid local minima. Together these two tools quickly steer the solution toward feasible layouts. We demonstrate our method on a variety of real-life inputs, and generate appealing layouts conforming to user specifications.


Singular Causation


title: Anthocyanins , total polyphenols and antioxidant activity in amaranth and quinoa seeds and sprouts during their growth

abstract: Total antioxidant capacity, total phenolic contents (TP) and anthocyanins contents (ANT) were determined in Amaranthus cruentus and Chenopodium quinoa seeds and sprouts. Antioxidant activity of the investigated seeds decreased in the following order: quinoa, amaranth v. Rawa, amaranth v. Aztek for FRAP and quinoa, amaranth v. Aztek, amaranth v. Rawa for both ABTS and DPPH. Sprouts activity depended on the length of their growth, and the peak values were reached on the fourth day in the case of amaranth and on the sixth day in the case of quinoa. The data obtained by the three methods showed significant correlation between TP content in seeds and sprouts. In sprouts grown in the daylight and in the darkness we observed some significant changes of TP, ANT and antioxidant activity. Amaranth and quinoa seeds and sprouts can be used in food, because it is a good source of ANT and TP with high antioxidant activity.


title: General Control Considerations for Input-Series Connected DC/DC Converters

abstract: This paper discusses the general control problems of dc/dc converters connected in series at the input. As the input voltage is shared by a number of dc/dc converters, the resulting converter relieves the voltage stresses of individual devices and hence is suitable for high input-voltage applications. At the output side, parallel connection provides current sharing and is suitable for high output-current applications. Moreover, series connection at the output side is also possible, resulting in output voltage sharing. Theoretically, from a power balance consideration, one can show that fulfillment of input-voltage sharing implies fulfillment of output-current or of output-voltage sharing, and vice versa. However, the presence of right-half-plane poles can cause instability when the sharing is implemented at the output side. As a consequence, control should be directed to input-voltage sharing in order to ensure a stable sharing of the input voltage and of the output current (parallel connection at output) or output voltage (series connection at output). In this paper, general problems in input-series connected converter systems are addressed. Minimal control structures are then derived and some practical design considerations are discussed in detail. Illustrative examples are given for addressing these general control considerations. Finally, experimental prototypes are built to validate these considerations.


title: A novel image matting method using sparse manual clicks

abstract: Traditional image matting methods often have strict requirements on user input. This paper proposes a new matting method based on spectral clustering, which generates a well matte using a sparse user input. Firstly, connected components are obtained using spectral clustering, which actually utilizes a linear transformation of the smallest eigenvectors of the matting Laplacian matrix. An accurate trimap is obtained via user input and threshold segmentation. Secondly, sample sets are gathered by two-level hierarchical clustering and Fast Approximate Nearest Neighbors algorithm and unknown pixels are evaluated by the samples. Finally, an optimal matte is obtained by constructing an energy function with local smoothness constraint. Experiments show that the proposed method outperforms most of the state-of-the-art methods with a sparse user input and our method has fewer requirements to get a robust matte.


Free newspaper readership


title: Baby-led weaning did not significantly impact body mass index when compared with traditional spoon-feeding

abstract: Single-centre blinded randomised control trial. Randomisation occurred after stratification for parity and maternal education using random-length blocks. ::: ::: Patients : All expectant mothers were considered for recruitment in late pregnancy. Exclusion criteria included prematurity or congenital anomalies likely to affect feeding or growth. ::: ::: Intervention : Baby-led introduction to solids (BLISS) group received eight additional contact points up to 9 months of age to provide education and support in keeping with baby-led weaning, compared with traditional spoon feeding. ::: ::: Primary outcome : Differences in body mass index (BMI). ::: ::: Secondary outcomes : Energy self-regulation, eating behaviours and energy intake. ::: ::: Follow-up period : Twenty-four months. …


title: Detection of malicious payload distribution channels in DNS

abstract: Botmasters are known to use different protocols to hide their activities. Throughout the past few years, several protocols have been abused, and recently Domain Name System (DNS) also became a target of such malicious activities. In this paper, we study the use of DNS as a malicious payload distribution channel. We present a system to analyze the resource record activities of domain names and build DNS zone profiles to detect payload distribution channels. Our work is based on an extensive analysis of malware datasets for one year, and a near real-time feed of passive DNS traffic. The experimental results reveal a few previously unreported long-running hidden domains used by the Morto worm for distributing malicious payloads. Our experiments on passive DNS traffic indicate that our system can detect these channels regardless of the payload format.


title: Complex Video Scene Analysis Using Kernelized-Collaborative Behavior Pattern Learning Based on Hierarchical Representative Object Behaviors

abstract: This paper considers an unsupervised learning algorithm that can automatically discover key behavior patterns to characterize a complex video scene. For behavior features (bFs) extracted at multiple spatial-temporal scales, an optimization problem is formulated to cluster bFs in their scales while establishing a collaborative nonlinear relationship in the form of a kernel regression function among clustered bFs across different spatial scales. The relationship allows features extracted in one scale to be considered as contextual information in the analysis of another scale. This optimization problem is solved using linear programming to reduce computational complexity. The proposed algorithm is evaluated on four crowded traffic scenes and two sports video data sets. Experimental results show that the proposed algorithm achieves a better performance compared with the current state-of-the-art algorithms in terms of video segmentation accuracy.


title: The Evaluation of Accessibility, Usability, and User Experience

abstract: This chapter introduces a range of evaluation methods that assist developers in the creation of interactive electronic products, services and environments (eSystems) that are both easy and pleasant to use for the target audience. The target audience might be the broadest range of people, including people with disabilities and older people or it might be a highly specific audience, such as university students studying biology. The chapter will introduce the concepts of accessibility, usability and user experience as the criteria against which developers should be evaluating their eSystems, and the iterative user- centred design lifecycle as the framework within which the development and evaluation of these eSystems can take place. Then a range of methods for evaluating accessibility, usability and user experience will be outlined, with information about their appropriate use and strengths and weaknesses.


title: On Fairness and Calibration

abstract: The machine learning community has become increasingly concerned with the potential for bias and discrimination in predictive models, and this has motivated a growing line of work on what it means for a classification procedure to be"fair."In particular, we investigate the tension between minimizing error disparity across different population groups while maintaining calibrated probability estimates. We show that calibration is compatible only with a single error constraint (i.e. equal false-negatives rates across groups), and show that any algorithm that satisfies this relaxation is no better than randomizing a percentage of predictions for an existing classifier. These unsettling findings, which extend and generalize existing results, are empirically confirmed on several datasets.


title: A Cooperative Coevolution Framework for Parallel Learning to Rank

abstract: We propose CCRank, the first parallel framework for learning to rank based on evolutionary algorithms (EA), aiming to significantly improve learning efficiency while maintaining accuracy. CCRank is based on cooperative coevolution (CC), a divide-and-conquer framework that has demonstrated high promise in function optimization for problems with large search space and complex structures. Moreover, CC naturally allows parallelization of sub-solutions to the decomposed sub-problems, which can substantially boost learning efficiency. With CCRank, we investigate parallel CC in the context of learning to rank. We implement CCRank with three EA-based learning to rank algorithms for demonstration. Extensive experiments on benchmark datasets in comparison with the state-of-the-art algorithms show the performance gains of CCRank in efficiency and accuracy.


title: An Implicit ELGamal Digital Signature Scheme

abstract: This paper introduced a detail ElGamal digital signature scheme, and mainly analyzed the existing problems of the ElGamal digital signature scheme. Then improved the scheme according to the existing problems of ElGamal digital signature scheme, and proposed an implicit ElGamal type digital signature scheme with the function of message recovery. As for the problem that message recovery not being allowed by ElGamal signature scheme, this article approached a method to recover message. This method will make ElGamal signature scheme have the function of message recovery. On this basis, against that part of signature was used on most attacks for ElGamal signature scheme, a new implicit signature scheme with the function of message recovery was formed, after having tried to hid part of signature message and refining forthcoming implicit type signature scheme. The safety of the refined scheme was anlyzed, and its results indicated that the new scheme was better than the old one.


title: Profiting from an inefficient association football gambling market: Prediction, risk and uncertainty using Bayesian networks

abstract: We present a Bayesian network (BN) model for forecasting Association Football match outcomes. Both objective and subjective information are considered for prediction, and we demonstrate how probabilities transform at each level of model component, whereby predictive distributions follow hierarchical levels of Bayesian inference. The model was used to generate forecasts for each match of the 2011/2012 English Premier League (EPL) season, and forecasts were published online prior to the start of each match. Profitability, risk and uncertainty are evaluated by considering various unit-based betting procedures against published market odds. Compared to a previously published successful BN model, the model presented in this paper is less complex and is able to generate even more profitable returns.


title: Self-Correction of Commutation Point for High-Speed Sensorless BLDC Motor With Low Inductance and Nonideal Back EMF

abstract: This paper presents a novel self-correction method of commutation point for high-speed sensorless brushless dc motors with low inductance and nonideal back electromotive force (EMF) in order to achieve low steady-state loss of magnetically suspended control moment gyro. The commutation point before correction is obtained by detecting the phase of EMF zero-crossing point and then delaying 30 electrical degrees. Since the speed variation is small between adjacent commutation points, the difference of the nonenergized phase's terminal voltage between the beginning and the end of commutation is mainly related to the commutation error. A novel control method based on model-free adaptive control is proposed, and the delay degree is corrected by the controller in real time. Both the simulation and experimental results show that the proposed correction method can achieve ideal commutation effect within the entire operating speed range.


title: Cloud computing as technological solutions for higher education institutions: Adoption readiness assessment model: Reseach in-progress

abstract: Cloud computing recently offers immense benefits. But higher education institutions (HEIs) are hesitating to adopt. As a result, they are not benefited more from such technology. This is because key adoption capabilities are not explored in detail in HEIs context. In addition to this, there is no comprehensive empirically validated adoption readiness assessment model which helps them to adopt Cloud solutions efficiently. Hence, to fill this gap this study identifies the critical capabilities and develops a model to assess Cloud computing adoption readiness of higher education institutions before initiating the adoption. Resource based view theory was used as theoretical lens to identify capabilities and to build the model. The conceptual model considers adoption readiness and adoption success from technological, human, organizational, financial and external environment dimensions. The model proposes that the degree to which an organization holds these capabilities is related to the degree to which they are ready to deploy and use computing resources, thereby increasing the possibility of adoption success. Further empirical research will be used to validate and refine the conceptual model.


title: Investigating a quantitative radiomics approach for brain tumor classification

abstract: Differentiating a solitary brain metastasis (METS) from glioblastoma multiforme (GBM) is an important yet difficult task using current MR imaging techniques. A final diagnosis is obtained by performing a stereotactic brain biopsy, which carries a small but not insignificant risk. Distinguishing between primary and secondary malignant neoplasms is critical for providing appropriate patient prognosis, management and treatment planning. Devising non-invasive means of distinguishing between the two would be clinically useful, as patients may forego a surgical biopsy. In this study, we propose a radiomics approach for enhanced characterization and classification of such tumors. The final dataset for this study consisted of pre-treatment MRI scans acquired from 52 patients (aged 61 +/- 7 years; 31M, 21F; 35 GBM, 17 METS, 3T MRI scanner) consisting of Contrast-Enhanced (CE) T1 and T2 FLAIR sequences. The extracted features were used with an Adaboost classifier with a 10-fold cross validation scheme. The best results (AUC=0.84) were obtained using Local Binary Patterns extracted from the CE T1 sequences and also a high-dimensional feature vector from the wavelet-transformed image at the lowest frequency (AUC=0.84). Combining all the features from both the sequences resulted in a classification performance of 0.71. Our results suggest that radiomics-based machine learning analysis can accurately differentiate glioblastomas from metastatic brain tumors. Improvements in classification of such tumors could potentially reduce the need for an invasive stereotactic brain biopsy.


Treatment of posterolateral tibial plateau fractures with modified Frosch approach : a cadaveric study and case series


title: Optimizing performance through intrinsic motivation and attention for learning: The OPTIMAL theory of motor learning

abstract: Effective motor performance is important for surviving and thriving, and skilled movement is critical in many activities. Much theorizing over the past few decades has focused on how certain practice conditions affect the processing of task-related information to affect learning. Yet, existing theoretical perspectives do not accommodate significant recent lines of evidence demonstrating motivational and attentional effects on performance and learning. These include research on (a) conditions that enhance expectancies for future performance, (b) variables that influence learners’ autonomy, and (c) an external focus of attention on the intended movement effect. We propose the OPTIMAL (Optimizing Performance through Intrinsic Motivation and Attention for Learning) theory of motor learning. We suggest that motivational and attentional factors contribute to performance and learning by strengthening the coupling of goals to actions. We provide explanations for the performance and learning advantages of these variables on psychological and neuroscientific grounds. We describe a plausible mechanism for expectancy effects rooted in responses of dopamine to the anticipation of positive experience and temporally associated with skill practice. Learner autonomy acts perhaps largely through an enhanced expectancy pathway. Furthermore, we consider the influence of an external focus for the establishment of efficient functional connections across brain networks that subserve skilled movement. We speculate that enhanced expectancies and an external focus propel performers’ cognitive and motor systems in productive “forward” directions and prevent “backsliding” into self- and non-task focused states. Expected success presumably breeds further success and helps consolidate memories. We discuss practical implications and future research directions.


title: Learning multiple-question decision trees for cold-start recommendation

abstract: For cold-start recommendation, it is important to rapidly profile new users and generate a good initial set of recommendations through an interview process --- users should be queried adaptively in a sequential fashion, and multiple items should be offered for opinion solicitation at each trial. In this work, we propose a novel algorithm that learns to conduct the interview process guided by a decision tree with multiple questions at each split. The splits, represented as sparse weight vectors, are learned through an L_1-constrained optimization framework. The users are directed to child nodes according to the inner product of their responses and the corresponding weight vector. More importantly, to account for the variety of responses coming to a node, a linear regressor is learned within each node using all the previously obtained answers as input to predict item ratings. A user study, preliminary but first in its kind in cold-start recommendation, is conducted to explore the efficient number and format of questions being asked in a recommendation survey to minimize user cognitive efforts. Quantitative experimental validations also show that the proposed algorithm outperforms state-of-the-art approaches in terms of both the prediction accuracy and user cognitive efforts.


title: Deconstructing episodic memory with construction

abstract: It has recently been observed that the brain network supporting recall of episodic memories shares much in common with other cognitive functions such as episodic future thinking, navigation and theory of mind. It has been speculated that 'self-projection' is the key common process. However, in this Opinion article, we note that other functions (e.g. imagining fictitious experiences) not explicitly connected to either the self or a subjective sense of time, activate a similar brain network. Hence, we argue that the process of 'scene construction' is better able to account for the commonalities in the brain areas engaged by an extended range of disparate functions. In light of this, we re-evaluate our understanding of episodic memory, the processes underpinning it and other related cognitive functions.


title: Regularizing Long Short Term Memory with 3D Human-Skeleton Sequences for Action Recognition

abstract: This paper argues that large-scale action recognition in video can be greatly improved by providing an additional modality in training data – namely, 3D human-skeleton sequences – aimed at complementing poorly represented or missing features of human actions in the training videos. For recognition, we use Long Short Term Memory (LSTM) grounded via a deep Convolutional Neural Network (CNN) onto the video. Training of LSTM is regularized using the output of another encoder LSTM (eLSTM) grounded on 3D human-skeleton training data. For such regularized training of LSTM, we modify the standard backpropagation through time (BPTT) in order to address the wellknown issues with gradient descent in constraint optimization. Our evaluation on three benchmark datasets – Sports-1M, HMDB-51, and UCF101 – shows accuracy improvements from 1.7% up to 14.8% relative to the state of the art.


title: How deep learning works --The geometry of deep learning

abstract: Why and how that deep learning works well on different tasks remains a mystery from a theoretical perspective. In this paper we draw a geometric picture of the deep learning system by finding its analogies with two existing geometric structures, the geometry of quantum computations and the geometry of the diffeomorphic template matching. In this framework, we give the geometric structures of different deep learning systems including convolutional neural networks, residual networks, recursive neural networks, recurrent neural networks and the equilibrium prapagation framework. We can also analysis the relationship between the geometrical structures and their performance of different networks in an algorithmic level so that the geometric framework may guide the design of the structures and algorithms of deep learning systems.


title: Cybersecurity for Critical Infrastructures: Attack and Defense Modeling

abstract: Disruption of electric power operations can be catastrophic on national security and the economy. Due to the complexity of widely dispersed assets and the interdependences among computer, communication, and power infrastructures, the requirement to meet security and quality compliance on operations is a challenging issue. In recent years, the North American Electric Reliability Corporation (NERC) established a cybersecurity standard that requires utilities' compliance on cybersecurity of control systems. This standard identifies several cyber-related vulnerabilities that exist in control systems and recommends several remedial actions (e.g., best practices). In this paper, a comprehensive survey on cybersecurity of critical infrastructures is reported. A supervisory control and data acquisition security framework with the following four major components is proposed: (1) real-time monitoring; (2) anomaly detection; (3) impact analysis; and (4) mitigation strategies. In addition, an attack-tree-based methodology for impact analysis is developed. The attack-tree formulation based on power system control networks is used to evaluate system-, scenario -, and leaf-level vulnerabilities by identifying the system's adversary objectives. The leaf vulnerability is fundamental to the methodology that involves port auditing or password strength evaluation. The measure of vulnerabilities in the power system control framework is determined based on existing cybersecurity conditions, and then, the vulnerability indices are evaluated.