title: High-level fuzzy logic guidance system for an unmanned surface vehicle (USV) tasked to perform autonomous launch and recovery (ALR) of an autonomous underwater vehicle (AUV)

abstract: There have been much technological advances and research in Unmanned Surface Vehicles (USV) as a support and delivery platform for Autonomous/Unmanned Underwater Vehicles (AUV/UUV) or Remotely Operated Vehicles (ROV). Advantages include extending underwater search and survey operations time and reach, improving underwater positioning and mission awareness, in addition to minimizing the costs and risks associated with similar manned vessel operations. The objective of this paper is to present the design and development a high-level fuzzy logic guidance controller for a WAM-V 14 unmanned surface vehicle (USV) in order to autonomously launch and recover a REMUS 100 autonomous underwater vehicle (AUV). The approach to meeting this objective is to develop ability for the USV to intercept and rendezvous with an AUV that is in transit in order to maximize the probability of a final mobile docking maneuver. Specifically, a fuzzy logic Rendezvous-Docking controller has been developed that generates Waypoint-Heading goals for the USV to minimize the cross-track errors between the USV and AUV. A subsequent fuzzy logic Waypoint-Heading controller has been developed to provide the desired heading and speed commands to the low-level controller given the Waypoint-Heading goals. High-level mission control has been extensively simulated using Matlab and partially characterized in real-time during testing. Detailed simulation, experimental results and findings will be reported in this paper.


title: Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion

abstract: Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.


Explorer Dependency Parsing as Head Selection


title: Extreme Lateral Interbody Fusion (XLIF) in the Thoracic and Thoracolumbar Spine: Technical Report and Early Outcomes

abstract: Background ::: Previous studies have demonstrated the distinct advantages of thoracoscopically assisted spinal fusion compared to traditional open thoracotomy. However, these techniques are limited by a steep learning curve, prolonged operative time, and lack of three-dimensional visualization of the surgical field.


The Graphic Novel and the Age of Transition: A Survey and Analysis


title: A novel segmentation algorithm for MRI brain tumor images

abstract: A novel segmentation algorithm for MRI Brain tumor images is proposed. The proposed algorithm is compared with Thresholding and Region Grow methods. Testing was performed by generating two datasets of real MRI images of brain tumors. Criteria for assessment of the quality of the segmentation results were: the Dice score, sensitivity, specificity and accuracy. Analysis of results obtained using this algorithm to solve the brain tumor MRI image segmentation task showed levels of sensitivity and specificity of 91% to 99%, which is evidence that assessment of the position and boundaries of brain pathology is highly effective.


title: Visualizing and Understanding Convolutional Networks

abstract: Large Convolutional Network models have recently demonstrated impressive classification performance on the ImageNet benchmark Krizhevsky et al. [18]. However there is no clear understanding of why they perform so well, or how they might be improved. In this paper we explore both issues. We introduce a novel visualization technique that gives insight into the function of intermediate feature layers and the operation of the classifier. Used in a diagnostic role, these visualizations allow us to find model architectures that outperform Krizhevsky et al on the ImageNet classification benchmark. We also perform an ablation study to discover the performance contribution from different model layers. We show our ImageNet model generalizes well to other datasets: when the softmax classifier is retrained, it convincingly beats the current state-of-the-art results on Caltech-101 and Caltech-256 datasets.


title: Analyzing the Problem of Employee Internal Social Network Site Avoidance: Are Users Resistant due to Their Privacy Concerns?

abstract: I investigate the phenomenon of user resistance behavior concerning internal social networking sites through an empirical analysis of the behavioral attitudes of 253 working professionals from various sectors and all company sizes. Results from linear regression analysis indicates the importance the role of privacy concerns play in explaining user resistance behavior phenomenon. In addition, I found considerable negative interrelations between privacy concerns and perceived usefulness (rPC = -- 0.421) as well as privacy concerns and perceived ease of use (rPC -- PE = -- 0.459). Results from structural equation modeling using privacy concerns, usefulness and ease of use reveals an impressive predictive power (R2 = 0.731).


title: DATS - Data Containers for Web Applications

abstract: Data containers enable users to control access to their data while untrusted applications compute on it. However, they require replicating an application inside each container - compromising functionality, programmability, and performance. We propose DATS - a system to run web applications that retains application usability and efficiency through a mix of hardware capability enhanced containers and the introduction of two new primitives modeled after the popular model-view-controller (MVC) pattern. (1) DATS introduces a templating language to create views that compose data across data containers. (2) DATS uses authenticated storage and confinement to enable an untrusted storage service, such as memcached and deduplication, to operate on plain-text data across containers. These two primitives act as robust declassifiers that allow DATS to enforce non-interference across containers, taking large applications out of the trusted computing base (TCB). We showcase eight different web applications including Gitlab and a Slack-like chat, significantly improve the worst-case overheads due to application replication, and demonstrate usable performance for common-case usage.


title: Performance improvements of multicast RFID systems using phased array antennas and phase diversity

abstract: Data supporting Performance Improvements of Multicast RFID Systems using Phased Array Antennas and Phase Diversity


title: Time optimal multi-agent path planning on graphs

abstract: Significant progress has been made in the area of multiagent path finding/planning in the past decade (Silver 2005; van den Berg et al. 2009; Standley 2010; Luna and Bekris 2011; Wang and Botea 2011). In this work, we introduce a multi-agent path planning problem similar to that of (Standley 2010) and aim at maximizing parallelism among the agents. That is, we seek a feasible plan that minimizes the time it takes the last agent to reach its goal. To solve the problem, we convert it into an equivalent multiflow problem, from which an integer linear programming (ILP) problem is readily obtained. Our algorithm is complete.


title: Equality of Opportunity in Supervised Learning

abstract: We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group are available, we show how to optimally adjust any learned predictor so as to remove discrimination according to our definition. Our framework also improves incentives by shifting the cost of poor classification from disadvantaged groups to the decision maker, who can respond by improving the classification accuracy. We enourage readers to consult the more complete manuscript on the arXiv.


title: Determination of the pulse repetition interval (PRI) agility of an incoming radar emitter signal using instantaneous power analysis

abstract: Determining the level of pulse repetition interval (PRI) agility is important in the field of Electronic Intelligence (ELINT) in order to provide warning receiver and jammers with the appropriate information in order to take action. This paper uses smoothed instantaneous power in order to estimate the PRIs of an incoming radar emitter signal. The test signals considered for this work is made up of simple pulsed and staggered PRI radar signal of various intervals and position. The algorithm designed uses the Hilbert transform to obtain the analytic version of the incoming signal before the Hamming smoothed instantaneous power is applied to the signal. Thereafter, first, second and third PRI are obtained and used to form rules for the rule-based Classifier. The rule-based classifier classifies the signals in order to determine the level of PRI agility. Performance analysis is carried out in the presence of additive white Gaussian noise (AWGN) for 100 iterations. Results obtained showed that a 100 percent probability of correct classification is obtained at signal-to-noise ratio (SNR) equal or greater than 3dB irrespective of the incoming signal.


title: A Hybrid Microfluidic/CMOS Capacitive Sensor Dedicated to Lab-on-Chip Applications

abstract: A hybrid microfluidic/IC capacitive sensor is presented in this paper for highly integrated lab-on-chips (LoCs). We put forward the design and implementation of a charge based capacitive sensor array in 0.18-mum CMOS process. This sensor chip is incorporated with a microfluidic channel using direct-write microfluidic fabrication process (DWFP). The design, construction and experimental results as well are demonstrated using four different chemical solutions with known dielectric constants. The proposed highly sensitive CMOS capacitive sensor (ap530 mV/fF) along with low complexity DWFP emerges as clear favorite for LoC applications.


title: Robust Nonlinear Fusion of Inertial and Visual Data for position, velocity and attitude estimation of UAV

abstract: This paper presents a coupled observer that uses accelerometer, gyrometer and vision sensors to provide estimates of pose and linear velocity for an aerial robotic vehicle. The observer is based on a non-linear complimentary filter framework and incorporates adaptive estimates of measurement bias in gyrometers and accelerometers commonly encountered in low-cost inertial measurement systems. Asymptotic stability of the observer estimates is proved as well as bounded energy of the observer error signals. Experimental data is provided for the proposed filter run on data obtained from an experiment involving a remotely controlled helicopter.


A Privacy Analysis of Cross-device Tracking.


title: Automatic attendance management system using face detection

abstract: This paper is about the biometric attendance management. The automatic attendance management will replace the manual method, which takes a lot of time consuming and difficult to maintain. There are many biometric processes [10], in that face recognition is the best method. In this paper we are going to describe the attendance without human interference. In this method the camera is fixed in the classroom and it will capture the image, the faces are detected and then it is recognized with the database and finally the attendance is marked. If the attendance is marked as absent the message about the student's absent is send to their parents. There are various methods for comparing the faces. The Eigen face is the one of the method. Eigen faces is set of Eigen vectors which are used in computer vision problem of face recognition.


title: Uni-omnidirectional radial waveguide slot array antenna design for wireless local area network

abstract: This paper has considered the development of a low profile unidirectional antenna for WLAN application, performed with frequency range 2.4-2.4835 GHz. The motivation for this work was the need for an effective directional antenna to be used with the access point. The Radial Waveguide Slot Array Antenna (RWSA) is investigated as a low profile, lightly weight, simple but effective, easy to design and fabricate with reasonable cost efficient. The initial element design was first optimized in the structure based on Zeland Fidelity, FDTD (Finite-Difference Time-Domain) with Full-3D EM Simulation for the radiation pattern, and return loss. The second part of this paper deals with producing design relying on optimum simulation results, the antenna tested in terms of return loss, gain and radiation pattern. Finally, experimental trial had been consociate and compared RWSA Antenna with a monopole antenna on an Access point for WLAN by using AirMagnet software. Measurements are conformed to results presented by the prototype simulation.


title: Semantic Role Labeling Via FrameNet, VerbNet And PropBank

abstract: This article describes a robust semantic parser that uses a broad knowledge base created by interconnecting three major resources: FrameNet, VerbNet and PropBank. The FrameNet corpus contains the examples annotated with semantic roles whereas the VerbNet lexicon provides the knowledge about the syntactic behavior of the verbs. We connect VerbNet and FrameNet by mapping the FrameNet frames to the VerbNet Intersective Levin classes. The PropBank corpus, which is tightly connected to the VerbNet lexicon, is used to increase the verb coverage and also to test the effectiveness of our approach. The results indicate that our model is an interesting step towards the design of more robust semantic parsers.


title: Ruminations on Multi-Tenant Databases

abstract: This is a position paper on multi-tenant databases. As motivation, it first describes the emerging marketplace of hosted enterprise services and the importance of using multi-tenancy to handle high traffic volumes at low cost. It then outlines the main requirements on multi-tenant databases: scale up by consolidating multiple tenants onto the same server and scale out by providing an administrative framework that manages a farm of such servers. Finally it describes three approaches to implementing multi-tenant databases and compares them based on some simple experiments. The main conclusion is that existing database vendors need to enhance their products to better support multi-tenancy.


title: The comparisons of data mining techniques for the predictive accuracy of probability of default of credit card clients

abstract: This research aimed at the case of customers' default payments in Taiwan and compares the predictive accuracy of probability of default among six data mining methods. From the perspective of risk management, the result of predictive accuracy of the estimated probability of default will be more valuable than the binary result of classification - credible or not credible clients. Because the real probability of default is unknown, this study presented the novel ''Sorting Smoothing Method'' to estimate the real probability of default. With the real probability of default as the response variable (Y), and the predictive probability of default as the independent variable (X), the simple linear regression result (Y=A+BX) shows that the forecasting model produced by artificial neural network has the highest coefficient of determination; its regression intercept (A) is close to zero, and regression coefficient (B) to one. Therefore, among the six data mining techniques, artificial neural network is the only one that can accurately estimate the real probability of default.


title: The Internet and Adolescent Girls’ Weight Satisfaction and Drive for Thinness

abstract: The primary aim of the study was to examine the relationship between media exposure and body image in adolescent girls, with a particular focus on the ‘new’ and as yet unstudied medium of the Internet. A sample of 156 Australian female high school students (mean age = 14.9 years) completed questionnaire measures of media consumption and body image. Internet appearance exposure and magazine reading, but not television exposure, were found to be correlated with greater internalization of thin ideals, appearance comparison, weight dissatisfaction, and drive for thinness. Regression analyses indicated that the effects of magazines and Internet exposure were mediated by internalization and appearance comparison. It was concluded that the Internet represents a powerful sociocultural influence on young women’s lives.


title: Multiview Spectral Embedding

abstract: In computer vision and multimedia search, it is common to use multiple features from different views to represent an object. For example, to well characterize a natural scene image, it is essential to find a set of visual features to represent its color, texture, and shape information and encode each feature into a vector. Therefore, we have a set of vectors in different spaces to represent the image. Conventional spectral-embedding algorithms cannot deal with such datum directly, so we have to concatenate these vectors together as a new vector. This concatenation is not physically meaningful because each feature has a specific statistical property. Therefore, we develop a new spectral-embedding algorithm, namely, multiview spectral embedding (MSE), which can encode different features in different ways, to achieve a physically meaningful embedding. In particular, MSE finds a low-dimensional embedding wherein the distribution of each view is sufficiently smooth, and MSE explores the complementary property of different views. Because there is no closed-form solution for MSE, we derive an alternating optimization-based iterative algorithm to obtain the low-dimensional embedding. Empirical evaluations based on the applications of image retrieval, video annotation, and document clustering demonstrate the effectiveness of the proposed approach.


title: Some New Thoughts on Conceptualizing Perceived Service Quality: A Hierarchical Approach

abstract: Abstract Through qualitative and empirical research, the authors find that the service quality construct conforms to the structure of a third-order factor model that ties service quality perceptions to distinct and actionable dimensions: outcome, interaction, and environmental quality. In turn, each has three subdimensions that define the basis of service quality perceptions. The authors further suggest that for each of these subdimensions to contribute to improved service quality perceptions, the quality received by consumers must be perceived to be reliable, responsive, and empathetic. The authors test and support this conceptualization across four service industries. They consider the research and managerial implications of the study and its limitations.


title: The City Browser: Utilizing Massive Call Data to Infer City Mobility Dynamics

abstract: This paper presents the City Browser, a tool developed to analyze the complexities underlying human mobility at the city scale. The tool uses data generated from mobile phones as a proxy to provide several insights with regards to the commuting patterns of the population within the bounds of a city. The three major components of the browser are the data warehouse, modules and algorithm, and the visualization interface. The modules and algorithm component utilizes Call Detail Records (CDRs) stored within the data warehouse to infer mobility patterns that are then communicated through the visualization interface. The modules and algorithm component consists of four modules: the spatial-temporal decomposition module, the home/work capturing module, the community detection module, and the flow estimation module. The visualization interface manages the output of each module to provide a comprehensive view of a city’s mobility dynamics over varying time scales. A case study is presented on the city of Riyadh in Saudi Arabia, where the browser was developed to better understand city mobility patterns.


title: Exploring Learners' Sequential Behavioral Patterns, Flow Experience, and Learning Performance in an Anti-Phishing Educational Game

abstract: Introduction As of 2014, there were 2.4 billion Internet users globally (Internet World Stats, 2014). However, studies show that Internet users are often unaware of the dangers of the phishing attacks they are exposed to (Arachchilage & Love, 2013). As of 2013, Internet phishing through email scams had decreased by 16% from 2012 (Fossi et al., 2013); however, Internet scams using social sites had increased by 3% (Anti-Phishing Working Group, 2013). Social networking websites are gradually replacing emails as the new primary means for Internet scams (Fossi et al., 2013). Victims of Internet phishing include people of all age groups, among which young people constitute the most vulnerable group (Kumaraguru, Sheng, Acquisti, Cranor, & Hong, 2010). Statistics from Livingstone and Haddon (2009) showed that 60% of all social site users were between 9-16 years old, while more recent data from 2013 further showed that the number of young people who were social sites users increased by 29% in one year and was continuing to increase (Harper, 2014). This heavy use of social sites accentuates young people's vulnerability to phishing through these sites. Tools for detecting phishing may not provide effective defenses (Kumaraguru et al., 2007a). Therefore, some academics deem anti-phishing education, in which users learn correct Internet phishing concepts and defense methods and are equipped with basic defense abilities, to be the most effective means of combating phishing (Kumaraguru et al., 2007b; Kumaraguru et al., 2010; Sheng, Holbrook, Kumaraguru, Cranor, & Downs, 2010; Sheng et al., 2007) as well as fostering good general Internet safety, operating habits, and awareness (Wirth, Rifon, LaRose, & Lewis, 2007). Defense against Internet phishing is a problem-solving process that requires judgment and reasoning abilities (Kumaraguru et al., 2007a). The advantage of game-based learning materials is the construction of life-like situations of phishing and game-based learning scenarios; therefore, game-based learning is an effective teaching method to help learners enhance their anti-phishing abilities (Kiili, 2005; Sheng et al., 2007). The game-based learning method affects learners' flow experience and problem solving abilities, and can promote effective learning behaviors; for example, simulation games help learners enter a flow state during the computational problem solving activities (Liu, Cheng, & Huang, 2011) and facilitate the flow experience when playing games as a group in an interactive social game environment (Inal & Cagiltay, 2007). There have been previous studies focusing on the use of anti-phishing games in teaching among university students (Yang, Tseng, Lee, Weng, & Chen, 2012), but there has been no study exploring the effective learning behavior of children or teenagers through anti-phishing educational games. Therefore, the present study, using young people as the research subjects and an anti-phishing game as the teaching method, sought to understand whether the flow experience in games can contribute to effective learning behaviors. Mayer (1992) proposed that it is relatively difficult for teachers to understand learners' metacognitive processes such as learning strategies through summative assessment (e.g., learning achievement tests), and that it is therefore difficult to improve learners' learning states or enhance their learning achievement using these methods. Thus, it may be more effective to help educators understand and improve learners' learning states and strategies using quantified dynamic learning behavior, presented in the form of a time order (Liu et al., 2011), as well as applying sequential analysis to explore the correlation between learners' behaviors and to infer overall behavioral patterns (Bakeman & Gottman, 1997). Learning behavior patterns are beneficial in terms of identifying the reason for unsatisfactory learning states and learning achievement, which can in turn help teachers provide learners with suitable guidance for better learning outcomes (Hou, 2012b). …


Smart Parking Space Detection Using MATLAB and Internet of Things


title: Scaffolded writing and rewriting in the discipline: A web-based reciprocal peer review system

abstract: This paper describes how SWoRD (scaffolded writing and rewriting in the discipline), a web-based reciprocal peer review system, supports writing practice, particularly for large content courses in which writing is considered critical but not feasibly included. To help students gain content knowledge as well as writing and reviewing skills, SWoRD supports the whole cycle of writing, reviews, back-reviews, and rewriting by scaffolding the journal publication process as its authentic practice model. In addition, SWoRD includes algorithms that compute individual reviewer's review accuracy, which is in turn used to support the various drawbacks of reciprocal peer reviews (e.g., variation in motivation or ability of reviewers). Finally, this paper describes an empirical evaluation showing that the SWoRD approach is effective in improving writing quality in content classes.


title: Classification of Imbalance Data using Tomek Link(T-Link) Combined with Random Under-sampling (RUS) as a Data Reduction Method

abstract: The problem of classifying subjects into disease categories is of common occurrence in medical research. Machine learning tools such as Artificial Neural Network (ANN), Support Vector Machine (SVM) and Logistic Regression (LR) and Fisher’s Linear Discriminant Analysis (LDA) are widely used in the areas of prediction and classification. The main objective of these competing classification strategies is to predict a dichotomous outcome (e.g. disease/healthy) based on several features. Like any of the well-known statistical inferential models; machine learning tools are faced with a problem known as “class imbalance”. A data set is imbalanced if the classification categories are not approximately equally represented. When learning from highly imbalanced data, most classifiers are affected by the majority class leading to an increase in the false negative rate. Increased interests in applying machine learning techniques to "realworld" problems, whose data are characterized by severe imbalance, have emerged as can be seen in numerous publications in medicine and biology. Predictive accuracy, a popular choice for evaluating performance of a classifier, might not be appropriate when the data is imbalanced and/or when the costs of different errors vary markedly. In this paper, we use the T-Link algorithm in the preprocessing phase as a method of data cleaning in order to remove noise. We combine T-Link with other sampling method such as RUS, ROS and Synthetic Minority Oversampling Technique (SMOTE) in order to maintain a balanced class distribution. Classification was then utilized using several ML algorithms such as ANN, RF and LR. Classifiers performance was evaluated using several performance measures deemed more appropriate for classifying data with sever imbalance. These methods are applied to arterial blood pressures data and Ecoli2 data set. Results: Using TLink in combination with RUS and SMOTE demonstrated a superior performance compared to resampling techniques such among different classification algorithms such as SVM, ANN, RF and LR.


title: Webcam-Based Eye Movement Analysis Using CNN

abstract: Due to its low price, webcam has become one of the most promising sensors with the rapid development of computer vision. However, the accuracies of eye tracking and eye movement analysis are largely limited by the quality of the webcam videos. To solve this issue, a novel eye movement analysis model is proposed based on five eye feature points rather than a single point (such as the iris center). First, a single convolutional neural network (CNN) is trained for eye feature point detection, and five eye feature points are detected for obtaining more useful eye movement information. Subsequently, six types of original time-varying eye movement signals can be constructed by feature points of each frame, which can reduce the dependency of the iris center in low quality videos. Finally, behaviors-CNN can be trained by the time-varying eye movement signals for recognizing different eye movement patterns, which is capable of avoiding the influence of errors from the basic eye movement type detection and artificial eye movement feature construction. To validate the performance, a webcam-based visual activity data set was constructed, which contained almost 0.5 million frames collected from 38 subjects. The experimental results on this database have demonstrated that the proposed model can obtain promising results for natural and convenient eye movement-based applications.


title: DBMS security audit function test case generation

abstract: Data base management system(DBMS) security audit conformance testing requires evaluators to design test cases which effectively cover all DBMS security target specifications.This paper describes a DBMS security audit test case generation approach based on security audit specifications using labeled transition system(LTS) modeling.This approach first builds the LTS model for the security audit function components for the DBMS security target(DBMS ST),produces audit event sequences based on the path output from the component behavior model using a heuristic path search algorithm,and generates test scenarios with testing data in a target database for the scenarios according to TOE security constraints to form test case suites.Test with about two hundred test cases were produced using this approach and transformed into scripts through variable configurations or statements.These were run against a national DBMS,during which several security flaws were found.Tests show that this approach can generate useful test cases,which cover all security audit component function test requirements.


title: A 16 watt X-band GaN high power amplifier MMIC for phased array applications

abstract: For the next generation HPA MMIC, a 16 watts two-stage X-band GaN HPA MMIC is described in this paper. The MMIC has been designed and fabricated by using a 0.25 um GaN HEMT process. The MMIC under a drain voltage of 28 V exhibits pulsed output power of 42.1 dBm (16 watts) with an associated PAE from 37.75 % to 41.55 % over the frequency range of 8.5 ∼ 10.5 GHz. The fabricated MMIC is as small as 3.65 mm × 2.5 mm (area of 9.125 mm2).


title: The development of peer support in the Netherlands, Brazil, and Israel.

abstract: My recent communication and travels related to peer support efforts in The Netherlands, Brazil, and Israel provide a glimpse of the integration of peer support into existing mental health services in these three countries. Country by country, peer support is evolving in unique ways specific to each culture. Based on my communication with peers and colleagues, peer support is fairly well established in The Netherlands, but has not gained as strong a foothold in countries like Brazil and Israel, where governments and mental health authorities have yet to fully embrace the concepts and practice of peer support. Also volunteerism, so essential in the start-up of peer support and self-help groups in the United States in the early 1970s, does not yet exist in Brazil or Israel where there are not as well established nongovernmental (nonprofit) organizations. After speaking with informed advocates of peer support in these three countries, the question, for me, remains whether peer support will take root in countries other than English-speaking countries. Can these voluntary, grass roots, democratic associations have a viable future in countries like the Netherlands, Brazil, and Israel? The hope is that once mental health officials see the success of peer support programs and gain confidence in peer leaders, they will provide the funding and support necessary for peer support programs to grow and thrive.


title: A Framework for Expressing and Enforcing Purpose-Based Privacy Policies

abstract: Purpose is a key concept in privacy policies. Although some models have been proposed for enforcing purpose-based privacy policies, little has been done in defining formal semantics for purpose, and therefore an effective enforcement mechanism for such policies has remained a challenge. We have developed a framework for expressing and enforcing such policies by giving a formal definition of purpose and proposing a modal-logic language for formally expressing purpose constraints. The semantics of this language are defined over an abstract model of workflows. Based on this formal framework, we discuss some properties of purpose, show how common forms of purpose constraints can be formalized, how purpose-based constraints can be connected to more general access control policies, and how they can be enforced in a workflow-based information system by extending common access control technologies.


title: A Scalable and Efficient Outlier Detection Strategy for Categorical Data

abstract: Outlier detection has received significant attention in many applications, such as detecting credit card fraud or network intrusions. Most existing research focuses on numerical datasets, and cannot directly apply to categorical sets where there is little sense in calculating distances among data points. Furthermore, a number of outlier detection methods require quadratic time with respect to the dataset size and usually multiple dataset scans. These characteristics are undesirable for large datasets, potentially scattered over multiple distributed sites. In this paper, we introduce Attribute Value Frequency (A VF), a fast and scalable outlier detection strategy for categorical data. A VF scales linearly with the number of data points and attributes, and relies on a single data scan. AVF is compared with a list of representative outlier detection approaches that have not been contrasted against each other. Our proposed solution is experimentally shown to be significantly faster, and as effective in discovering outliers.


title: Accurate De Novo Prediction of Protein Contact Map by Ultra-Deep Learning Model

abstract: MOTIVATION ::: Protein contacts contain key information for the understanding of protein structure and function and thus, contact prediction from sequence is an important problem. Recently exciting progress has been made on this problem, but the predicted contacts for proteins without many sequence homologs is still of low quality and not very useful for de novo structure prediction. ::: ::: ::: METHOD ::: This paper presents a new deep learning method that predicts contacts by integrating both evolutionary coupling (EC) and sequence conservation information through an ultra-deep neural network formed by two deep residual neural networks. The first residual network conducts a series of 1-dimensional convolutional transformation of sequential features; the second residual network conducts a series of 2-dimensional convolutional transformation of pairwise information including output of the first residual network, EC information and pairwise potential. By using very deep residual networks, we can accurately model contact occurrence patterns and complex sequence-structure relationship and thus, obtain higher-quality contact prediction regardless of how many sequence homologs are available for proteins in question. ::: ::: ::: RESULTS ::: Our method greatly outperforms existing methods and leads to much more accurate contact-assisted folding. Tested on 105 CASP11 targets, 76 past CAMEO hard targets, and 398 membrane proteins, the average top L long-range prediction accuracy obtained by our method, one representative EC method CCMpred and the CASP11 winner MetaPSICOV is 0.47, 0.21 and 0.30, respectively; the average top L/10 long-range accuracy of our method, CCMpred and MetaPSICOV is 0.77, 0.47 and 0.59, respectively. Ab initio folding using our predicted contacts as restraints but without any force fields can yield correct folds (i.e., TMscore>0.6) for 203 of the 579 test proteins, while that using MetaPSICOV- and CCMpred-predicted contacts can do so for only 79 and 62 of them, respectively. Our contact-assisted models also have much better quality than template-based models especially for membrane proteins. The 3D models built from our contact prediction have TMscore>0.5 for 208 of the 398 membrane proteins, while those from homology modeling have TMscore>0.5 for only 10 of them. Further, even if trained mostly by soluble proteins, our deep learning method works very well on membrane proteins. In the recent blind CAMEO benchmark, our fully-automated web server implementing this method successfully folded 6 targets with a new fold and only 0.3L-2.3L effective sequence homologs, including one β protein of 182 residues, one α+β protein of 125 residues, one α protein of 140 residues, one α protein of 217 residues, one α/β of 260 residues and one α protein of 462 residues. Our method also achieved the highest F1 score on free-modeling targets in the latest CASP (Critical Assessment of Structure Prediction), although it was not fully implemented back then. ::: ::: ::: AVAILABILITY ::: http://raptorx.uchicago.edu/ContactMap/.


title: Automatic Relocalization and Loop Closing for Real-Time Monocular SLAM

abstract: Monocular SLAM has the potential to turn inexpensive cameras into powerful pose sensors for applications such as robotics and augmented reality. We present a relocalization module for such systems which solves some of the problems encountered by previous monocular SLAM systems-tracking failure, map merging, and loop closure detection. This module extends recent advances in keypoint recognition to determine the camera pose relative to the landmarks within a single frame time of 33 ms. We first show how this module can be used to improve the robustness of these systems. Blur, sudden motion, and occlusion can all cause tracking to fail, leading to a corrupted map. Using the relocalization module, the system can automatically detect and recover from tracking failure while preserving map integrity. Extensive tests show that the system can then reliably generate maps for long sequences even in the presence of frequent tracking failure. We then show that the relocalization module can be used to recognize overlap in maps, i.e., when the camera has returned to a previously mapped area. Having established an overlap, we determine the relative pose of the maps using trajectory alignment so that independent maps can be merged and loop closure events can be recognized. The system combining all of these abilities is able to map larger environments and for significantly longer periods than previous systems.


title: Designing and Implementation of Stable Sinusoidal Rough-Neural Identifier

abstract: A rough neuron is defined as a pair of conventional neurons that are called the upper and lower bound neurons. In this paper, the sinusoidal rough-neural networks (SR-NNs) are used to identify the discrete dynamic nonlinear systems (DDNSs) with or without noise in series–parallel configuration. In the identification of periodic nonlinear systems, sinusoidal activation functions provide more efficient neural networks than the sigmoidal activation functions. Based on the Lyapunov stability theory, an online learning algorithm is developed to train the SR-NNs. The asymptotically convergence of the identification error to zero and the boundedness of parameters as well as predictions are proved. SR-NNs are used to identify some DDNSs and the cement rotary kiln (CRK). CRK is a complex nonlinear system in the cement factory, which produces the cement clinker. The experiments show that the SR-NNs in the identification of nonlinear systems have better performances than multilayer perceptrons (MLPs), sinusoidal neural networks, and rough MLPs, particularly in the presence of noise.


title: Active ESD Protection Design for Interface Circuits Between Separated Power Domains Against Cross-Power-Domain ESD Stresses

abstract: Several complex electrostatic discharge (ESD) failure mechanisms have been found in the interface circuits of an IC product with multiple separated power domains. In this case, the machine-model (MM) ESD robustness cannot achieve 150 V in this IC product with separated power domains, although it can pass the 2-kV human-body-model (HBM) ESD test. The negative-to-VDD (ND) mode MM ESD currents were discharged by circuitous current paths through interface circuits to cause the gate oxide damage, the junction filament, and the contact destruction of the internal transistors. The detailed discharging paths of ND-mode ESD failures were analyzed in this paper. In addition, some ESD protection designs have been illustrated and reviewed to further comprehend the protection strategies for cross-power-domain ESD events. Moreover, one new active ESD protection design for the interface circuits between separated power domains has been proposed and successfully verified in a 0.13-mum CMOS technology. The HBM and MM ESD robustness of the separated- power-domain interface circuits with the proposed active ESD protection design can achieve over 4 kV and 400 V, respectively.


title: Social Isolation and Loneliness in Old Age: Review and Model Refinement

abstract: This paper reviews the empirical literature on social isolation and loneliness and identifies a wide range of published correlates. Using data from a study conducted in North Wales, which included many of the same correlated variables, a statistical modelling technique is used to refine models of isolation and loneliness by controlling for co-variance. The resulting models indicate that the critical factors for isolation are: marital status, network type and social class; and, for loneliness: network type, household composition and health.


Merging Datasets for Aggressive Text Identification.


title: A NOVEL APPROACH TO GENERATE TEST CASES FROM UML ACTIVITY DIAGRAMS

abstract: Model-based test case generation is gaining acceptance to the software practitioners. Advantages of this are the early detection of faults, reducing software development time etc. In recent times, researchers have considered difierent UML diagrams for generating test cases. Few work on the test case generation using activity diagrams is reported in literatures. However, the existing work consider activity diagrams in method scope and mainly follow UML 1:x for modeling. In this paper, we present an approach of generating test cases from activity diagrams using UML 2:0 syntax and with use case scope. We consider a test coverage criterion, called activity path coverage criterion. The test cases generated using our approach are capable of detecting more faults like synchronization faults, loop faults unlike the existing approaches.


title: Will Pyramids Built Of Nuggets Topple Over?

abstract: The present methodology for evaluating complex questions at TREC analyzes answers in terms of facts called "nuggets". The official F-score metric represents the harmonic mean between recall and precision at the nugget level. There is an implicit assumption that some facts are more important than others, which is implemented in a binary split between "vital" and "okay" nuggets. This distinction holds important implications for the TREC scoring model---essentially, systems only receive credit for retrieving vital nuggets---and is a source of evaluation instability. The upshot is that for many questions in the TREC testsets, the median score across all submitted runs is zero. In this work, we introduce a scoring model based on judgments from multiple assessors that captures a more refined notion of nugget importance. We demonstrate on TREC 2003, 2004, and 2005 data that our "nugget pyramids" address many shortcomings of the present methodology, while introducing only minimal additional overhead on the evaluation flow.


abstract: We propose three regularization techniques to overcome drawbacks of local winner-take-all methods used in deep convolutional networks. Channel-Max inherits the max activation unit from Maxout networks, but otherwise adopts complementary subsets of input and filters with different kernel sizes as better companions to the max function. To balance the training on different pathways, Channel-Drop is employed to randomly discard half pathways before their inputs are convolved respectively. Stochastic Max-pooling is defined to reduce the overfitting caused by conventional max-pooling, in which half activations are randomly dropped in each pooling region during training and top largest activations are probabilistically averaged during testing. Using Channel-Max, Channel-Drop and Stochastic Max-pooling, we demonstrate state-of-the-art performance on four benchmark datasets: CIFAR-10, CIFAR-100, STL-10 and SVHN.


title: Parallel implementation of artificial neural network training

abstract: In this paper we describe the implementation of a complete ANN training procedure for speech recognition using the block mode back-propagation learning algorithm. We exploit the high performance SIMD architecture of GPU using CUDA and its C-like language interface. We also compare the speed-up obtained implementing the training procedure only taking advantage of the multi-thread capabilities of multi-core processors. Our approach has been tested by training acoustic models for large vocabulary speech recognition tasks, showing a 6 times reduction of the time required to train real-world large size networks with respect to an already optimized implementation using the Intel MKL libraries.


title: Combining classifiers for bone fracture detection in X-ray images

abstract: In medical applications, sensitivity in detecting medical problems and accuracy of detection are often in conflict. A single classifier usually cannot achieve both high sensitivity and accuracy at the same time. Methods of combining classifiers have been proposed in the literature. This paper presents a study of probabilistic combination methods applied to the detection of bone fractures in X-ray images. Test results show that the effectiveness of a method in improving both accuracy and sensitivity depends on the nature of the method as well as the proportion of positive samples.


title: Digital business strategy and value creation: framing the dynamic cycle of control points

abstract: Within changing value networks, the profits and competitive advantages of participation reside dynamically at control points that are the positions of greatest value and/or power. The enterprises that hold these positions have a great deal of control over how the network operates, how the benefits are redistributed, and how this influences the execution of a digital business strategy. This article is based on a field study that provides preliminary, yet promising, empirical evidence that sheds light on the dynamic cycle of value creation and value capture points in digitally enabled networks in response to triggers related to technology and business strategy. The context used is that of the European and U.S. broadcasting industry. Specifically, the paper illustrates how incremental innovations may shift value networks from static, vertically integrated networks to more loosely coupled networks, and how cross-boundary industry disruptions may then, in turn, shift those to two-sided markets. Based on the analysis, insights and implications for digital business strategy research and practice are then provided.


title: A boosting approach to remove class label noise

abstract: Ensemble methods have been known to improve prediction accuracy over the base learning algorithm. AdaBoost is well-recognized for that in its class. However, it is susceptible to overfitting the training instances corrupted by class label noise. This paper proposes a modification to AdaBoost that is more tolerant to class label noise, which further enhances its ability to boost prediction accuracy. In particular, we observe that in Adaboost, the weight-hike of noisy examples can be constrained by careful application of a cut-off in their weights. Effectiveness of our algorithm is demonstrated empirically using some artificially generated data. We also corroborate this on a number of data sets from UCI repository (Blake and Mertz, 1998). In both experimental settings, the results obtained affirm the efficacy of our approach. Finally, some of the significant characteristics of our technique related to noisy environments have been investigated.


title: Detecting anomalies in cellular networks using an ensemble method

abstract: The Self-Organizing Networks (SON) concept includes the functional area known as self-healing, which aims to automate the detection and diagnosis of, and recovery from, network degradations and outages. This paper focuses on the problem of cell anomaly detection, addressing partial and complete degradations in cell-service performance, and it proposes an adaptive ensemble method framework for modeling cell behavior. The framework uses Key Performance Indicators (KPIs) to determine cell-performance status and is able to cope with legitimate system changes (i.e., concept drift). The results, generated using real cellular network data, suggest that the proposed ensemble method automatically and significantly improves the detection quality over univariate and multivariate methods, while using intrinsic system knowledge to enhance performance.


title: 80Days: Adaptive Digital Storytelling for Digital Educational Games

abstract: The overall aim of the European research project 80Days situated in the field of Technology-enhanced Learning is to combine adaptive learning, Storytelling and gaming technology in order to build intelligent, adaptive and exciting learning environments in the form of Storytelling-based digital educational games (DEGs). This paper presents the major results of the Serious Gaming group at TU Darmstadt achieved in the first development cycle of 80Days: Based on a short introduction in section 1 providing a brief overview of the 80Days approach and key challenges being addressed, section 2 introduces methods and concepts of an adaptive digital storytelling framework and indicates how this contributes to intelligent personalisation and adaptation in DEGs. Section 3 describes practical results in terms of a first technical platform of 80Days integrating an adaptive learning engine, story engine and game engine plus a content repository and StoryTec as authoring environment to create such Story-based DEGs. Section 4 summarizes the current status and main results of the 80Days approach achieved so far including first evaluation feedback, and points out further research and development activities.


title: Topic Modeling based Sentiment Analysis on Social Media for Stock Market Prediction

abstract: The goal of this research is to build a model to predict stock price movement using sentiments on social media. A new feature which captures topics and their sentiments simultaneously is introduced in the prediction model. In addition, a new topic model TSLDA is proposed to obtain this feature. Our method outperformed a model using only historical prices by about 6.07% in accuracy. Furthermore, when comparing to other sentiment analysis methods, the accuracy of our method was also better than LDA and JST based methods by 6.43% and 6.07%. The results show that incorporation of the sentiment information from social media can help to improve the stock prediction.


Resource-Lean Modeling of Coherence in Commonsense Stories


title: Attaining human-level performance for anatomical landmark detection in 3D CT data

abstract: We present an efficient neural network approach for locating anatomical landmarks, using a two-pass, two-resolution cascaded approach which leverages a mechanism we term atlas location autocontext. Location predictions are made by regression to Gaussian heatmaps, one heatmap per landmark. This system allows patchwise application of a shallow network, thus enabling the prediction of multiple volumetric heatmaps in a unified system, without prohibitive GPU memory requirements. Evaluation is performed for 22 landmarks defined on a range of structures in head CT scans and the neural network model is benchmarked against a previously reported decision forest model trained with the same cascaded system. Models are trained and validated on 201 scans. Over the final test set of 20 scans which was independently annotated by 2 observers, we show that the neural network reaches an accuracy which matches the annotator variability.


title: A Language for Function Signature Representations

abstract: Recent work by (Richardson and Kuhn, 2017a,b; Richardson et al., 2018) looks at semantic parser induction and question answering in the domain of source code libraries and APIs. In this brief note, we formalize the representations being learned in these studies and introduce a simple domain specific language and a systematic translation from this language to first-order logic. By recasting the target representations in terms of classical logic, we aim to broaden the applicability of existing code datasets for investigating more complex natural language understanding and reasoning problems in the software domain.


title: Direction sensitive fall detection using a triaxial accelerometer and a barometric pressure sensor

abstract: Falling is one of the leading causes of serious health decline or injury-related deaths in the elderly. For survivors of a fall, the resulting health expenses can be a devastating burden, largely because of the long recovery time and potential comorbidities that ensue. The detection of a fall is, therefore, important in care of the elderly for decreasing the reaction time by the care-givers especially for those in care who are particularly frail or living alone. Recent advances in motion-sensor technology have enabled wearable sensors to be used efficiently for pervasive care of the elderly. In addition to fall detection, it is also important to determine the direction of a fall, which could help in the location of joint weakness or post-fall fracture. This work uses a waist-worn sensor, encompassing a 3D accelerometer and a barometric pressure sensor, for reliable fall detection and the determination of the direction of a fall. Also assessed is an efficient analysis framework suitable for on-node implementation using a low-power micro-controller that involves both feature extraction and fall detection. A detailed laboratory analysis is presented validating the practical application of the system.


title: Mobilizing the Micro-Ops: Exploiting Context Sensitive Decoding for Security and Energy Efficiency

abstract: Modern instruction set decoders feature translation of native instructions into internal micro-ops to simplify CPU design and improve instruction-level parallelism. However, this translation is static in most known instances. This work proposes context-sensitive decoding, a technique that enables customization of the micro-op translation at the microsecond or faster granularity, based on the current execution context and/or preset hardware events. While there are many potential applications, this work demonstrates its effectiveness with two use cases: 1) as a novel security defense to thwart instruction/data cache-based side-channel attacks, as demonstrated on commercial implementations of RSA and AES and 2) as a power management technique that performs selective devectorization to enable efficient unit-level power gating. This architecture, first by allowing execution to transition between different translation modes rapidly, defends against a variety of attacks, completely obfuscating code-dependent cache access, only sacrificing 5% in steady-state performance - orders of magnitude less than prior art. By selectively disabling the vector units without disabling vector arithmetic, context-sensitive decoding reduces energy by 12.9% with minimal loss in performance. Both optimizations work with no significant changes to the pipeline or the external ISA.


title: Association mapping of six yield-related traits in rapeseed (Brassica napus L.)

abstract: Yield is one of the most important traits for rapeseed (Brassica napus L.) breeding, but its genetic basis remains largely ambiguous. Association mapping has provided a robust approach to understand the genetic basis of complex agronomic traits in crops. In this study, a panel of 192 inbred lines of B. napus from all over the world was genotyped using 451 single-locus microsatellite markers and 740 amplified fragment length polymorphism markers. Six yield-related traits of these inbred lines were investigated in three consecutive years with three replications, and genome-wide association studies were conducted for these six traits. Using the model controlling both population structure and relative kinship (Q + K), a total of 43 associations (P < 0.001) were detected using the means of the six yield-related traits across 3 years, with two to fourteen markers associated with individual traits. Among these, 18 markers were repeatedly detected in at least 2 years, and 12 markers were located within or close to QTLs identified in previous studies. Six markers commonly associated with correlated traits. Conditional association analysis indicated that five of the associations between markers and correlated traits are caused by one QTL with pleiotropic effects, and the remaining association is caused by linked but independent QTLs. The combination of favorable alleles of multiple associated markers significantly enhances trait performance, illustrating a great potential of utilization of the associations in rapeseed breeding programs.


title: Omni-Wrist III - a new generation of pointing devices. Part I. Laser beam steering devices - mathematical modeling

abstract: Omni-Wrist III is a new sensor mount developed with U.S. Air Force funding that emulates the kinematics of a human wrist. Driven by two linear motors and computer controlled, it is capable of a full 180/spl deg/ hemisphere of pitch/yaw motion. A comprehensive laboratory testing of one of the few existing devices of this type, installed in the Laser Communications Research Laboratory at Binghamton University, has resulted in the establishment of a complete mathematical model relating pitch/yaw coordinates of the sensor mount to the motor encoder signals. This paper presents the development of the model that incorporates inverse and forward pose kinematics solutions as well as the dynamics of the novel gimbal-like pointing system.


title: Detection and Removal of Rain from Video Using Predominant Direction of Gabor Filters

abstract: In this paper, we examine the visual effects of rain on the imaging system and present a new method for detection and removal of rain in a video sequences. In the proposed algorithm, to separate the moving foreground from the background in image sequences that are the frames of video with scenes recorded from the raindrops moving, a background subtraction technique is used. Then, rain streaks are detected using predominant direction of Gabor filters which contains maximum energy. To achieve this goal, the rainy image is partitioned to multiple sub images. Then, all directions of Gabor filter banks are applied to each sub image and the direction which maximizes the energy of the filtered sub image is selected as the predominant direction of that region. At the end, the rainy pixels diagnosed in per frame are replaced with non-rainy pixels background of other frames. As a result, we reconstruct a new video in which the rain streaks have been removed. According to the certain limitations and existence of textures variation during time, the proposed method is not sensitive to these changes and operates properly. Simulation results show that the proposed method can detect and locate the rain place as well.


title: Beam Switching Reflectarray Monolithically Integrated With RF MEMS Switches

abstract: A reflectarray antenna monolithically integrated with 90 RF MEMS switches has been designed and fabricated to achieve switching of the main beam. Aperture coupled microstrip patch antenna (ACMPA) elements are used to form a 10 × 10 element reconfigurable reflectarray antenna operating at 26.5 GHz. The change in the progressive phase shift between the elements is obtained by adjusting the length of the open ended transmission lines in the elements with the RF MEMS switches. The reconfigurable reflectarray is monolithically fabricated with the RF MEMS switches in an area of 42.46 cm2 using an in-house surface micromachining and wafer bonding process. The measurement results show that the main beam can be switched between broadside and 40° in the H-plane at 26.5 GHz.


title: Impact of an LPT(II) concept with Thin Wafer Process Technology for IGBT's vertical structure

abstract: In this paper, for the first time, 600 ∼ 6500 V IGBTs utilizing a new vertical structure of “Light Punch-Through (LPT) (II)” with Thin Wafer Process Technology demonstrate high total performance with low overall loss and high safety operating area (SOA) capability. This collector structure enables a wide position in the trade-off characteristics between on-state voltage (V CE (sat)) and turn-off loss (E OFF ) without utilizing any conventional carrier lifetime technique. In addition, this device concept achieves a wide operating junction temperature (@218 ∼ 423 K) of IGBT without the snap-back phenomena (≤298 K) and thermal destruction (≥398 K). From the viewpoint of the high performance of IGBT, the breaking limitation of any Si wafer size, the proposed LPT(II) concept that utilizes an FZ silicon wafer and Thin Wafer Technology is the most promising candidate as a vertical structure of IGBT for the any voltage class.


title: Effect of Mn and Ni on the Variation of the Microstructure and Mechanical Properties of Low-carbon Weld Metals

abstract: Low-carbon weld metals with various amounts of Mn and Ni addition were made using metal-cored wires and Ar-2%O 2 shielding gas, and their mechanical properties were evaluated. The objective of the research, aimed to develop welding consumables with better resistance to cold cracking, was to determine the optimum composition ranges of Mn and Ni, in the presence of carbon content less than 0.02 %. The hardness of weld metals were found to increase linearly with Mn and Ni, which was attributed mainly to solid solution strengthening and in part to formation of hard phases. Varying Ni content influenced Charpy impact energy, the extent of which depended on Mn content. For a low-Mn composition, Ni addition increased hardness without sacrificing impact toughness whereas for a high Mn composition, Ni deteriorated the impact toughness seriously and caused intergranular fracture. The fracture path followed columnar grain boundaries that are identical to prior austenite grain boundaries since no δ-ferrite phase formed during solidification. Accordingly, these boundaries without having ferrite phase were susceptible to cracking under dynamic loading. Based on hardness and impact resistance, the optimum levels of Mn and Ni were suggested to be 0.5-1 % and 4-5 %, respectively.


title: Realistic road path reconstruction from GIS data

abstract: We introduce a new approach to construct smooth piecewise curves representing realistic road paths. Given a GIS database of road networks in which sampled points are organized in 3D polylines, our method creates horizontal, then vertical curves, and finally combines them to produce 3D road paths. We first estimate the possibility of each point of being a junction between two separate primitive curve segments. Next, we design a tree-traversal algorithm to expand sequences of local best fit primitives which are then merged together with respect to the G1 continuity constraint and civil engineering rules. We apply the Levenberg-Marquardt method to minimize the error between the resulting curve and the sampled points while preserving the G1 continuity.


title: Automatic brain tumor segmentation and extraction in MR images

abstract: A brain tumor or intracranial neoplasm is formed when abnormal cells get accumulated within the brain. These cells multiply in an uncontrolled manner and damage the brain tissues. Magnetic Resonance Imaging (MRI) scans are commonly used to diagnose brain tumors. However, segmenting and detecting the brain tumor manually is a tedious task for the radiologists. Hence, there is a need for automatic systems which yield accurate results. In this paper, a fully automatic method is introduced to detect brain tumors. The proposed method consists of five stages, viz., Image Acquisition, Preprocessing, Segmentation using Fuzzy C Means technique, Tumor Extraction and Evaluation. Tumor extraction is carried out by using Area and Circularity as a criteria. The results are finally verified by comparing them with the manually segmented Ground Truth. Dice coefficient is also calculated and the average dice coefficient value obtained was 0.729.


title: A Fuzzy linguistic prolog and its applications

abstract: In this work a fuzzy linguistic prolog language is presented and its design, implementation and applications are detailed. A fuzzy linguistic prolog is a fuzzy prolog which allows to work with both fuzzy linguistic and linguistic tools in order to gear the prolog systems towards the computing with words paradigm in which the linguistic resources can be very useful.


title: Rear-Stitched View Panorama: A Low-Power Embedded Implementation for Smart Rear-View Mirrors on Vehicles

abstract: Automobiles are currently equipped with a three-mirror system for rear-view visualization. The two side-view mirrors show close the periphery on the left and right sides of the vehicle, and the center rear-view mirror is typically adjusted to allow the driver to see through the vehicle's rear windshield. This three-mirror system, however, imposes safety concerns in requiring drivers to shift their attention and gaze to look in each mirror to obtain a full visualization of the rear-view surroundings, which takes attention off the scene in front of the vehicle. We present an alternative to the three-mirror rear-view system, which we call Rear-Stitched View Panorama (RSVP). The proposed system uses four rear-facing cameras, strategically placed to overcome the traditional blind spot problem, and stitches the feeds from each camera together to generate a single panoramic view, which can display the entire rear surroundings. We project individually captured frames onto a single virtual view using precomputed system calibration parameters. Then we determine optimal seam lines, along which the images are fused together to form the single RSVP view presented to the driver. Furthermore, we highlight techniques that enable efficient embedded implementation of the system and showcase a real-time system utilizing under 2W of power, making it suitable for in-cabin deployment in vehicles.


title: Ensemble Feature for Person Re-Identification

abstract: In person re-identification (re-ID), the key task is feature representation, which is used to compute distance or similarity in prediction. Person re-ID achieves great improvement when deep learning methods are introduced to tackle this problem. The features extracted by convolutional neural networks (CNN) are more effective and discriminative than the hand-crafted features. However, deep feature extracted by a single CNN network is not robust enough in testing stage. To improve the ability of feature representation, we propose a new ensemble network (EnsembleNet) by dividing a single network into multiple end-to-end branches. The ensemble feature is obtained by concatenating each of the branch features to represent a person. EnsembleNet is designed based on ResNet-50 and its backbone shares most of the parameters for saving computation and memory cost. Experimental results show that our EnsembleNet achieves the state-of-the-art performance on the public Market1501, DukeMTMC-reID and CUHK03 person re-ID benchmarks.


title: Entropy on intuitionistic fuzzy soft sets and on interval-valued fuzzy soft sets

abstract: Molodtsov initiated the concept of soft set theory, which can be used as a generic mathematical tool for dealing with uncertainty. However, it has been pointed out that classical soft sets are not appropriate to deal with imprecise and fuzzy parameters. In order to handle these types of problem parameters, some fuzzy (or intuitionistic fuzzy, interval-valued fuzzy) extensions of soft set theory are presented, yielding fuzzy (or intuitionistic fuzzy, interval-valued fuzzy) soft set theory. In this paper, we define the distance measures between intuitionistic fuzzy soft sets and give an axiom definition of intuitionistic entropy for an intuitionistic fuzzy soft set and a theorem which characterizes it. Furthermore, we discuss the relationship between intuitionistic fuzzy soft sets and interval-valued fuzzy soft sets and transform the structure of entropy for intuitionistic fuzzy soft sets to the interval-valued fuzzy soft sets.


title: New Constructions of Optimal Locally Recoverable Codes via Good Polynomials

abstract: In recent literature, a family of optimal linear locally recoverable codes (LRC codes) that attain the maximum possible distance (given code length, cardinality, and locality) is presented. The key ingredient for constructing such optimal linear LRC codes is the so-called $r$ -good polynomials, where $r$ is equal to the locality of the LRC code. However, given a prime $p$ , known constructions of $r$ -good polynomials over some extension field of $\mathbb {F}_{p}$ exist only for some special integers $r$ , and the problem of constructing optimal LRC codes over small field for any given locality is still open. In this paper, by using function composition, we present two general methods of designing good polynomials, which lead to three new constructions of $r$ -good polynomials. Such polynomials bring new constructions of optimal LRC codes. In particular, our constructed polynomials as well as the power functions yield optimal $(n,k,r)$ LRC codes over $\mathbb {F}_{q}$ for all positive integers $r$ as localities, where $q$ is near the code length $n$ .


title: Alice in Warningland: A Large-Scale Field Study of Browser Security Warning Effectiveness

abstract: We empirically assess whether browser security warnings are as ineffective as suggested by popular opinion and previous literature. We used Mozilla Firefox and Google Chrome's in-browser telemetry to observe over 25 million warning impressions in situ. During our field study, users continued through a tenth of Mozilla Firefox's malware and phishing warnings, a quarter of Google Chrome's malware and phishing warnings, and a third of Mozilla Firefox's SSL warnings. This demonstrates that security warnings can be effective in practice; security experts and system architects should not dismiss the goal of communicating security information to end users. We also find that user behavior varies across warnings. In contrast to the other warnings, users continued through 70.2% of Google Chrome's SSL warnings. This indicates that the user experience of a warning can have a significant impact on user behavior. Based on our findings, we make recommendations for warning designers and researchers.


title: Folktale Classification using Learning to Rank

abstract: We present a learning to rank approach to classify folktales, such as fairy tales and urban legends, according to their story type, a concept that is widely used by folktale researchers to organize and classify folktales. A story type represents a collection of similar stories often with recurring plot and themes. Our work is guided by two frequently used story type classification schemes. Contrary to most information retrieval problems, the text similarity in this problem goes beyond topical similarity. We experiment with approaches inspired by distributed information retrieval and features that compare subject-verb-object triplets. Our system was found to be highly effective compared with a baseline system.


title: A General-Purpose Architecture for Replicated Metadata Services in Distributed File Systems

abstract: A large class of modern distributed file systems treat metadata services as an independent system component, separately from data servers. The availability of the metadata service is key to the availability of the overall system. Given the high rates of failures observed in large-scale data centers, distributed file systems usually incorporate high-availability (HA) features. A typical approach in the development of distributed file systems is to design and develop metadata services from the ground up, at significant cost in terms of complexity and time, often leading to functional shortcomings. Our motivation in this paper was to improve on this state of things by defining a general-purpose architecture for HA metadata services (which we call RMS) that can be easily incorporated and reused in new or existing file systems, reducing development time. Taking two prominent distributed file systems as case studies, PVFS and HDFS, we developed RMS variants that improve on functional shortcomings of the original HA solutions, while being easy to build and test. Our extensive evaluation of the RMS variant of HDFS shows that it does not incur an overall performance or availability penalty compared to the original implementation.


Atypical Presentation of Genital Herpes Simplex (HSV-2) in Two Patients with Chronic Lymphocytic Leukemia


title: Multiple Habit-Induced Nail Problems Including Diffuse Brownish Chromonychia

abstract: Dear Editor: ::: ::: Chromonychia is the discoloration of the nail plate or subungual soft tissue with various causes1. Exogenous causes such as occupational agents, trauma or infection may provoke nail color change, and rarely, overproduction or storage of endogenous pigments is also responsible for chromonychia1,2. External dye stains the nail plate along the proximal fold, whereas endogenous pigmentation follows the shape of the lunula2. Melanonychia is a specific type of chromonychia with brown or black pigmentation1. Several reports have indicated that nail associated habits induce longitudinal melanonychia3,4,5, but none mentioned diffuse brownish chromonychia. Herein, we report a case of brownish chromonychia occurred with other nail deformities because of nail irritating habits. This study is approved by Catholic Medical Center (IRB No. SC13RISI0039). ::: ::: A 15-year-old girl presented with diffuse dark brown discoloration of the fingernails for several months. The patient had no remarkable medical or family history. The physical examination revealed brownish chromonychia, periungual erythema and swelling, cuticle loss and nail pitting on the left 3rd, 4th, and 5th fingernails (Fig. 1A). Onychoschizia and onycholysis with irregular grooves on the left thumbnail were also detected (Fig. 1C). Moreover, her right 4th fingernail had a longitudinal depression on its medial side (Fig. 2A). She said that she had been poking under the proximal nail folds of the left 3rd, 4th, and 5th fingers with the lead of a mechanical pencil. She had been sucking and biting the left thumbnail and had a peculiar pencil grip that was supported by the right 4th fingernail (Fig. 2B). ::: ::: ::: ::: Fig. 1 ::: ::: (A) Brown chromonychia on the left 3rd, 4th, and 5th fingernails. (B) After 4 months of treatment. (C) Onychoschizia and onycholysis with irregular grooves on the left thumbnail. (D) Left thumbnail fully recovered after 4 months of treatment. ::: ::: ::: ::: ::: ::: Fig. 2 ::: ::: (A) A longitudinal groove on the right 4th fingernail. (B) Probably the effect of a peculiar pencil grip. ::: ::: ::: ::: Because the patient's nail trauma history and development of melanonychia presented sequentially, we considered a strong association between them and delayed nail matrix biopsy. We educated her on discontinuing all habits and applying diflucortolone valerate 0.3% ointment to the periungual area of the affected fingernails regularly. In 4 months of follow-up, the fingernails gradually restored their natural shapes and colors (Fig. 1B, D). ::: ::: Because of the diffuse brownish pigmentation along the proximal nail fold and obvious improvement after stopping habit, we presumed that the lead had stained the nail plate. ::: ::: Repetitive mechanical stimulation on the nail matrix such as picking, chewing, breaking or rubbing would fluctuate the nail formation and make nail thickness irregular1,3. These habitual behaviors are easily seen in children, as 60% of children and 45% of teenagers are indulged in onychophagia1. ::: ::: In our case, we observed diffuse brownish chromonychia with other forms of nail deformities caused soley by her habits. Observing multiple nail problems in different fingers of a patient simultaneously is notable, because habit induced nail problem usually occurs as one type at a time. Fortunately, she managed to stop all habits so that the disfigurements improved. Occasionally, children with onychophagia may need psychiatric consultation for the obsessive-compulsive features and treatment with a selective serotonin reuptake inhibitor1. ::: ::: Thorough history taking and physical examination would be enough for diagnosis in children with nail irritating behaviors, avoiding unnecessary invasive examination including nail matrix biopsy4,5.


title: The back squat: A proposed assessment of functional deficits and technical factors that limit performance.

abstract: Fundamental movement competency is essential for participation in physical activity and for mitigating the risk of injury, which are both key elements of health throughout life. The squat movement pattern is arguably one of the most primal and critical fundamental movements necessary to improve sport performance, to reduce injury risk and to support lifelong physical activity. Based on current evidence, this first (1 of 2) report deconstructs the technical performance of the back squat as a foundation training exercise and presents a novel dynamic screening tool that incorporates identification techniques for functional deficits that limit squat performance and injury resilience. The follow-up report will outline targeted corrective methodology for each of the functional deficits presented in the assessment tool.


title: A Survey on Wireless Body Area Networks

abstract: The increasing use of wireless networks and the constant miniaturization of electrical devices has empowered the development of Wireless Body Area Networks (WBANs). In these networks various sensors are attached on clothing or on the body or even implanted under the skin. The wireless nature of the network and the wide variety of sensors offer numerous new, practical and innovative applications to improve health care and the Quality of Life. The sensors of a WBAN measure for example the heartbeat, the body temperature or record a prolonged electrocardiogram. Using a WBAN, the patient experiences a greater physical mobility and is no longer compelled to stay in the hospital. This paper offers a survey of the concept of Wireless Body Area Networks. First, we focus on some applications with special interest in patient monitoring. Then the communication in a WBAN and its positioning between the different technologies is discussed. An overview of the current research on the physical layer, existing MAC and network protocols is given. Further, cross layer and quality of service is discussed. As WBANs are placed on the human body and often transport private data, security is also considered. An overview of current and past projects is given. Finally, the open research issues and challenges are pointed out.


title: Design and implementation prototype of a smart house system at low cost and multi-functional

abstract: Many smart house systems have been presented, but there are still some limitations in terms of the high cost, less functionality, difficulty of use that are not satisfactorily reliable and cannot be developed. Therefore, the aim of this research project was to design and develop a prototype for a smart house system that is low cost, has a user-friendly interface, and is scalable and reliable by using an integrated system of hardware and software. The hardware, such as Arduino Uno, servo motors, a temperature sensor, a motion sensor and a battery, was utilized to develop the prototype of a smart house system. The software included Arduino integrated development environment (IDE) to compile the code in hardware and using MIT App Inventor for Android mobile phones to interfacing a mobile handset with hardware. Moreover, In order to make the design of prototype more professional, Sweet Home 3D software was used. The system has been evaluated with the previous works and it was demonstrated to six experts to obtain some feedback on the prototype. The testing of the prototype demonstrated that the system was an integrated, practical and easy to use, and any new device could easily be installed into the system. The aim of this research project has been achieved successfully. However, the prototype requires further developments, which include the power supply reliability, use other wireless technologies such as Wi-Fi and ZigBee, and cross-platform apps to work with differently operated systems such as iOS for people use Apple devices by using MIT App Inventor.


title: Heterogeneous Cloud Framework for Big Data Genome Sequencing

abstract: The next generation genome sequencing problem with short (long) reads is an emerging field in numerous scientific and big data research domains. However, data sizes and ease of access for scientific researchers are growing and most current methodologies rely on one acceleration approach and so cannot meet the requirements imposed by explosive data scales and complexities. In this paper, we propose a novel FPGA-based acceleration solution with MapReduce framework on multiple hardware accelerators. The combination of hardware acceleration and MapReduce execution flow could greatly accelerate the task of aligning short length reads to a known reference genome. To evaluate the performance and other metrics, we conducted a theoretical speedup analysis on a MapReduce programming platform, which demonstrates that our proposed architecture have efficient potential to improve the speedup for large scale genome sequencing applications. Also, as a practical study, we have built a hardware prototype on the real Xilinx FPGA chip. Significant metrics on speedup, sensitivity, mapping quality, error rate, and hardware cost are evaluated, respectively. Experimental results demonstrate that the proposed platform could efficiently accelerate the next generation sequencing problem with satisfactory accuracy and acceptable hardware cost.


title: Uncertain IMM Estimator for Multi-Sensor Target Tracking

abstract: Interacting Multiple Model (IMM) estimator can provide better performance of target tracking than mono model Kalman filter. In multi-sensor system ordinarily, availability of measurement from different sensors is stochastic, and it is difficult to construct uniform global observation vector and observation matrix appropri-ately in existing method. An IMM estimator for uncertain measurement is presented. By the method invalid measurement is regarded as outlier, and approximation is reconstructed by feedback of system state estima-tion of fusion center. Then nominally generalized certain measurement can be obtained by substituting re-constructed one for invalid one. The generalized certain measurement can be centralized to construct global measurement and provided to IMM estimator, and existing multi-sensor IMM estimation method is general-ized to uncertain environment. Theoretical analysis and simulation results show the effectiveness of the method.


title: An ant colony algorithm for the multi-compartment vehicle routing problem

abstract: We demonstrate the use of Ant Colony System (ACS) to solve the capacitated vehicle routing problem associated with collection of recycling waste from households, treated as nodes in a spatial network. For networks where the nodes are concentrated in separate clusters, the use of k-means clustering can greatly improve the efficiency of the solution. The ACS algorithm is extended to model the use of multi-compartment vehicles with kerbside sorting of waste into separate compartments for glass, paper, etc. The algorithm produces high-quality solutions for two-compartment test problems.


title: A cutting plane method for solving linear generalized disjunctive programming problems

abstract: Raman and Grossmann [Raman, R., & Grossmann, I.E. (1994). Modeling and computational techniques for logic based integer programming. Computers and Chemical Engineering, 18(7), 563–578] and Lee and Grossmann [Lee, S., & Grossmann, I.E. (2000). New algorithms for nonlinear generalized disjunctive programming. Computers and Chemical Engineering, 24, 2125–2141] have developed a reformulation of Generalized Disjunctive Programming (GDP) problems that is based on determining the convex hull of each disjunction. Although the relaxation of the reformulated problem using this method will often produce a significantly tighter lower bound when compared with the traditional big-M reformulation, the limitation of this method is that the representation of the convex hull of every disjunction requires the introduction of new disaggregated variables and additional constraints that can greatly increase the size of the problem. In order to circumvent this issue, a cutting plane method that can be applied to linear GDP problems is proposed in this paper. The method relies on converting the GDP problem into an equivalent big-M reformulation that is successively strengthened by cuts generated from an LP or QP separation problem. The sequence of problems is repeatedly solved, either until the optimal integer solution is found, or else until there is no improvement within a specified tolerance, in which case one switches to a branch and bound method. The strip-packing, retrofit planning and zero-wait job-shop scheduling problems are presented as examples to illustrate the performance of the proposed cutting plane method.


title: Infrastructure Systems Interdependencies and Risk Informed Decision Making (RIDM): Impact Scenario Analysis of Infrastructure Risks Induced by Natural, Technological and Intentional Hazards

abstract: This paper reviews current research into infrastructure systems interdependencies with regard to safesty risks induced by natural, technological and intentional hazards. The paper further considers risk informed decision-making.


title: Extra robotic thumb and exoskeleton robotic fingers for patient with hand function disability

abstract: This paper presents the development of wearable robot to enhance and to assist people who lost the ability to grasp object. In the developing countries, most persons with hand disabilities still use manual and passive tools, especially for prosthetic and orthotic. We propose a wearable robot as an orthotic device namely extra robotic thumb and exoskeleton robotic fingers as new type of wearable robot for assisting person whose the thumb can not move anymore and the other four fingers has diminished fingers function. The motion of exoskeleton robotic finger has four DOF which each DOF represents the motion of flexion and extension on the fingers. For extra robotic thumb, it has two DOF to represent the motion of circumduction and flexion/extension on the thumb. The robot employs four linear actuators in exoskeleton robotic fingers and uses two micro servos in extra robotic thumb. The robot is designed based on 3D print technology. To assist person in Activity of Daily Living (ADL), five grip patterns are developed in this research. User can command the robot by using electromyography (EMG) sensor that attached on near healthy elbow. Based on the experimental results, the robot can successfully perform object grasping tasks by using the developed five grip modes.


title: Ensemble of Probabilistic Learning Networks for IoT Edge Intrusion Detection

abstract: This paper proposes an intelligent and compact machine learning model for IoT intrusion detection using an ensemble of semi-parametric models with Ada boost. The proposed model provides an adequate realtime intrusion detection at an affordable computational complexity suitable for the IoT edge networks. The ::: proposed model is evaluated against other comparable models using the benchmark data on IoT-IDS and shows comparable performance with reduced computations as required.


title: Decision trees: a recent overview

abstract: Decision tree techniques have been widely used to build classification models as such models closely resemble human reasoning and are easy to understand. This paper describes basic decision tree issues and current research points. Of course, a single article cannot be a complete review of all algorithms (also known induction classification trees), yet we hope that the references cited will cover the major theoretical issues, guiding the researcher in interesting research directions and suggesting possible bias combinations that have yet to be explored.


Demographic Attributes Prediction on the Real-World Mobile Data


Autonomous Vehicles Need Experimental Ethics: Are We Ready for Utilitarian Cars?


title: Improving multisensor estimation of heavy-to-extreme precipitation via conditional bias-penalized optimal estimation

abstract: Abstract A new technique for merging radar precipitation estimates and rain gauge data is developed and evaluated to improve multisensor quantitative precipitation estimation (QPE), in particular, of heavy-to-extreme precipitation. Unlike the conventional cokriging methods which are susceptible to conditional bias (CB), the proposed technique, referred to herein as conditional bias-penalized cokriging (CBPCK), explicitly minimizes Type-II CB for improved quantitative estimation of heavy-to-extreme precipitation. CBPCK is a bivariate version of extended conditional bias-penalized kriging (ECBPK) developed for gauge-only analysis. To evaluate CBPCK, cross validation and visual examination are carried out using multi-year hourly radar and gauge data in the North Central Texas region in which CBPCK is compared with the variant of the ordinary cokriging (OCK) algorithm used operationally in the National Weather Service Multisensor Precipitation Estimator. The results show that CBPCK significantly reduces Type-II CB for estimation of heavy-to-extreme precipitation, and that the margin of improvement over OCK is larger in areas of higher fractional coverage (FC) of precipitation. When FC > 0.9 and hourly gauge precipitation is > 60 mm, the reduction in root mean squared error (RMSE) by CBPCK over radar-only (RO) is about 12 mm while the reduction in RMSE by OCK over RO is about 7 mm. CBPCK may be used in real-time analysis or in reanalysis of multisensor precipitation for which accurate estimation of heavy-to-extreme precipitation is of particular importance.


title: Personality Development: Stability and Change

abstract: In this review, we evaluate four topics in the study of personality development where discernible progress has been made since 1995 (the last time the area of personality development was reviewed in this series). We (a) evaluate research about the structure of personality in childhood and in adulthood, with special attention to possible developmental changes in the lower-order components of broad traits; (b) summarize new directions in behavioral genetic studies of personality; (c) synthesize evidence from longitudinal studies to pinpoint where and when in the life course personality change is most likely to occur; and (d) document which personality traits influence social relationships, status attainment, and health, and the mechanisms by which these personality effects come about. In each of these four areas, we note gaps and identify priorities for further research.


title: UWB Bandpass Filter Using Cascaded Miniature High-Pass and Low-Pass Filters With Multilayer Liquid Crystal Polymer Technology

abstract: This paper presents a new ultra-wideband (UWB) bandpass filter that is formed by cascading miniature high-pass and low-pass filters implemented with multilayer liquid crystal polymer technology. The miniature high-pass and low-pass filters can be designed independently and the design procedures are described. Experiments are carried out to validate the designs. Small sizes for the fabricated high-pass filter, low-pass filter, and UWB bandpass filter are achieved, which are 4.0 mm × 4.4 mm (0.162 ?g × 0.178 ?g), 4.56 mm × 4.9 mm (0.185 ?g × 0.198 ?g) and 5.1 mm × 8.86 mm (0.207 ?g X 0.359 ?g), respectively, where ?g is the guided wavelength at 6.85 GHz. Excellent performance is obtained for all the measured filters, including low insertion losses and high selectivity. Due to its simple structure and excellent performance, the proposed UWB bandpass filter is favorable for practical UWB communication and radar systems.


title: A high bandwidth radar target simulator for automotive radar sensors

abstract: In this paper, the construction of a modular target simulator for automotive radar sensors is described. Using a fully analog design, the simultaneous covering of all E-band automobile radar bands is possible. Static scenarios and moving targets with realistic parameters can be simulated.


title: Ontology Research and Development Part 1 – A Review of Ontology Generation

abstract: Ontology is an important emerging discipline that has the huge potential to improve information organization, management and understanding. It has a crucial role to play in enabling content-based access, interoperability, communications, and providing qualitatively new levels of services on the next wave of web transformation in the form of the Semantic Web. The issues pertaining to ontology generation, mapping and maintenance are critical key areas that need to be understood and addressed. This survey is presented in two parts. The first part reviews the state-of-the-art techniques and work done on semi-automatic and automatic ontology generation, as well as the problems facing such research. The second complementary survey is dedicated to ontology mapping and ontology ‘evolving’. Through this survey, we have identified that shallow information extraction and natural language processing techniques are deployed to extract concepts or classes from free-text or semi-structured data. However, relation extrac...


title: Fully Context-Aware Video Prediction

abstract: This paper proposes a new neural network design for unsupervised learning through video prediction. Current video prediction models based on convolutional networks, recurrent networks, and their combinations often result in blurry predictions. Recent work has attempted to address this issue with techniques like separation of background and foreground modeling, motion flow learning, or adversarial training. We highlight that a contributing factor for this problem is the failure of current architectures to fully capture relevant past information for accurately predicting the future. To address this shortcoming we introduce a fully context-aware architecture, which captures the entire available past context for each pixel using Parallel Multi-Dimensional LSTM units and aggregates it using context blending blocks. Our model is simple, efficient and directly applicable to high resolution video frames. It yields state-of-the-art performance for next step prediction on three challenging real-world video datasets: Human 3.6M, Caltech Pedestrian, and UCF-101 and produces sharp predictions of high visual quality.


title: Progressive Reasoning by Module Composition

abstract: Humans learn to solve tasks of increasing complexity by building on top of previously acquired knowledge. Typically, there exists a natural progression in the tasks that we learn - most do not require completely independent solutions, but can be broken down into simpler subtasks. We propose to represent a solver for each task as a neural module that calls existing modules (solvers for simpler tasks) in a program-like manner. Lower modules are a black box to the calling module, and communicate only via a query and an output. Thus, a module for a new task learns to query existing modules and composes their outputs in order to produce its own output. Each module also contains a residual component that learns to solve aspects of the new task that lower modules cannot solve. Our model effectively combines previous skill-sets, does not suffer from forgetting, and is fully differentiable. We test our model in learning a set of visual reasoning tasks, and demonstrate state-of-the-art performance in Visual Question Answering, the highest-level task in our task set. By evaluating the reasoning process using non-expert human judges, we show that our model is more interpretable than an attention-based baseline.


title: Value-Aware Item Weighting for Long-Tail Recommendation

abstract: Many recommender systems suffer from the popularity bias problem: popular items are being recommended frequently while less popular, niche products, are recommended rarely if not at all. However, those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial. In this paper, we examine an item weighting approach to improve long-tail recommendation. Our approach works as a simple yet powerful add-on to existing recommendation algorithms for making a tunable trade-off between accuracy and long-tail coverage.