title: Hysteresis of submicron permalloy particulate arrays

abstract: We have prepared controlled arrays of submicron permalloy particles as part of a program to determine the magnetic properties of verifiable single‐domain particles (SDP) and the effects of interactions between SDPs. We have measured the hysteresis loops of these arrays as a function of size, aspect ratio, and spacing. The arrays are fabricated utilizing electron beam lithography and liftoff techniques. Each array consists of approximately 106 identical particles with identical spacing between the particles. Particle size and spacing [center‐to‐center distance (ccd)], vary from 5 μm×15 μm (width by length) with ccd of 25 μm to 0.07 μm×0.1 μm with ccd of 0.25 μm. The thickness of the films varied from 0.072 to 0.049 μm. Hysteresis loops are obtained utilizing a SQUID magnetometer. For well‐separated particles we observe an order of magnitude increase in Hc with decreasing particle size, and then a subsequent decrease in Hc as the ccd is reduced. However, even for the smallest particles, the ratio of the rem...


title: Fault Detection and Protection of Induction Motors Using Sensors

abstract: Protection of an induction motor (IM) against possible problems, such as overvoltage, overcurrent, overload, overtemperature, and undervoltage, occurring in the course of its operation is very important, because it is used intensively in industry as an actuator. IMs can be protected using some components, such as timers, contactors, voltage, and current relays. This method is known as the classical method that is very basic and involves mechanical dynamic parts. Computer and programmable integrated circuit (PIC) based protection methods have eliminated most of the mechanical components. However, the computer-based protection method requires an analog-to-digital conversion (ADC) card, and the PIC-based protection method does not visualize the electrical parameters measured. In this study, for IMs, a new protection method based on a programmable logic controller (PLC) has been introduced. In this method, all contactors, timers, relays, and the conversion card are eliminated. Moreover, the voltages, the currents, the speed, and the temperature values of the motor, and the problems occurred in the system, are monitored and warning messages are shown on the computer screen. Experimental results show that the PLC-based protection method developed costs less, provides higher accuracy as well as safe and visual environment compared with the classical, the computer, and the PIC-based protection systems.


title: An 8.8–9.8 GHz 100W hybrid solid state power amplifier for high power applications

abstract: This paper presents an 8.8–9.8 GHz hybrid power amplifier (PA) designed with GaN HEMT bare die transistors. Generally, hybrid X-band high power amplifier (HPA) suffers from relatively low bandwidth. However, this paper introduces wideband high gain power amplifier, by using low loss Wilkinson combiner/divider, a modified form of taper-based divider, and wideband matching structures. The optimum values of all elements are determined by using Random and Gradient algorithms. The microstrip matching structures are realized on Rogers4003 and RT/druid5880 substrates. Large-signal simulation results show peak efficiency of 40%, an output power of 100 W and power gain of 50 dB at 9.5GHz. The input and output return losses are respectively less than −12 dB and −10 dB.


title: An Action Research Report from a Multi-Year Approach to Teaching Artificial Intelligence at the K-6 Level

abstract: In Australia, the Scientists-in-Schools program partners professional scientists with teachers from K-12 schools to improve early engagement and educational outcomes in the sciences and mathematics. An overview of the developing syllabus of a K-6 course resulting from the pairing of a senior AI researcher with teachers from a K-6 (primary) school is presented. Now entering its third year, the course introduces the basic concepts, vocabulary and history of science generally and AI specifically in a manner that emphasises student engagement and provides a challenging but age appropriate syllabus. Reflecting on the course at this time provides an action research basis for ongoing maturation of the syllabus, and the paper is presented in that light.


title: Customizing an Adversarial Example Generator with Class-Conditional GANs

abstract: Adversarial examples are intentionally crafted data with the purpose of deceiving neural networks into misclassification. When we talk about strategies to create such examples, we usually refer to perturbation-based methods that fabricate adversarial examples by applying invisible perturbations onto normal data. The resulting data reserve their visual appearance to human observers, yet can be totally unrecognizable to DNN models, which in turn leads to completely misleading predictions. In this paper, however, we consider crafting adversarial examples from existing data as a limitation to example diversity. We propose a non-perturbation-based framework that generates native adversarial examples from class-conditional generative adversarial networks.As such, the generated data will not resemble any existing data and thus expand example diversity, raising the difficulty in adversarial defense. We then extend this framework to pre-trained conditional GANs, in which we turn an existing generator into an"adversarial-example generator". We conduct experiments on our approach for MNIST and CIFAR10 datasets and have satisfactory results, showing that this approach can be a potential alternative to previous attack strategies.


title: A case study-based comparison of web testing techniques applied to AJAX web applications

abstract: Asynchronous Javascript And XML (AJAX) is a recent technology used to develop rich and dynamic Web applications. Different from traditional Web applications, AJAX applications consist of a single page whose elements are updated dynamically in response to callbacks activated asynchronously by the user or by a server message. On the one hand, AJAX improves the responsiveness and usability of a Web application, but on the other hand, it makes the testing phase more difficult. In this paper, our state-based testing technique, developed to test AJAX-based applications, is compared to existing Web testing techniques, such as white-box and black-box ones. To this aim, an experiment based on two case studies has been conducted to evaluate effectiveness and test effort involved in the compared Web testing techniques. In particular, the capability of each technique to reveal injected faults of different fault categories is analyzed in detail. The associated effort was also measured. The results show that state-based testing is complementary to the existing Web testing techniques and can reveal faults otherwise unnoticed or hard to reveal with the other techniques.


Improving Company Recognition from Unstructured Text by using Dictionaries.


title: Childhood asymmetric labium majus enlargement: mimicking a neoplasm.

abstract: We report a distinctive lesion of the labium majus resected in 14 girls from 3.9 to 13.2 years of age. All presented with enlargement of 1 or occasionally both labia majora. Radiographic imaging and surgical exploration showed expansion of the labium majus without definable borders. Grossly, specimens consisted of fibro-fatty tissue from 2 to 8 cm in greatest dimension. Microscopic examination revealed the usual constituents of vulvar soft tissue, with expansion of the fibrous component. Sparsely to moderately cellular interconnected bands encircled lobules of fat, blood vessels, and nerves. The bands consisted of plump and occasionally stellate or round fibroblasts immersed in an abundant pale myxoid matrix containing thin collagen fibers. These fibrous bands merged with thinner denser fibrous septa simlar to those seen in the vulva from age-matched controls. Elastic stains showed variably abundant thin parallel elastic fibers. Fibroblasts were immunohistochemically positive for estrogen and progesterone receptors. Electron microscopy showed fibroblasts with dilated rough endoplasmic reticulum cisternae and prominent nuclear fibrous laminae; extracellular matrix contained precollagen, collagen, elastic fibers, and numerous proteoglycan granules. Cytogenetic analysis of 3 lesions revealed a normal karyotype. Recurrence was observed in 7 (50%) patients, and regression was observed in 1 whose recurrence was not reexcised. Over an 11-year period at Children's Hospital (Boston, MA), these lesions represented 22% of all pediatric vulvar soft tissue masses and 3% of all vulvar lesions biopsied. We conclude that "childhood asymmetric labium majus enlargement" is a distinctive clinicopathologic entity of pre- and early puberty. Recognition of this fairly common lesion is important, since it may clinically, radiographically, and histologically mimic an infiltrative neoplasm. Its occurrence at an age roughly coincident with the time of breast budding, capacity for spontaneous regression, histologic architecture and composition of elements native to the vulva, expression of hormone receptors, and normal karyotype suggest that it is an asymmetric physiologic enlargement in response to hormonal surges of pre- and early puberty.


title: Semi-supervised Aspect-level Sentiment Classification Model based on Variational Autoencoder

abstract: Abstract Aspect-level sentiment classification aims to predict the sentiment of a text in different aspects and it is a fine-grained sentiment analysis task. Recent work exploits an Attention-based Long Short-Term Memory Network to perform aspect-level sentiment classification. Most previous work are based on supervised learning that needs a large number of labeled samples, but the problem is that only a limited subset of data samples are labeled in practical applications. To solve this problem, we propose a novel Semi-supervised Aspect Level Sentiment Classification Model based on Variational Autoencoder (AL-SSVAE) for semi-supervised learning in the aspect-level sentiment classification. The AL-SSVAE model inputs a given aspect to an encoder a decoder based on a variational autoencoder (VAE), and it also has an aspect level sentiment classifier. It enables the attention mechanism to deal with different parts of a text when different aspects are taken as input as previous methods. Due to that the sentiment polarity of a word is usually sensitive to the given aspect, a single vector for a word is problematic. Therefore, we propose the aspect-specific word embedding learning from a topical word embeddings model to express a word and also append the corresponding sentiment vector into the word input vector. We compare our AL-SSVAE model with several recent aspect-level sentiment classification models on the SemEval 2016 dataset. The experimental results indicate that the proposed model is able to capture more accurate semantics and sentiment for the given aspect and obtain better performance on the task of the aspect level sentiment classification. Moreover, the AL-SSVAE model is able to learn with the semi-supervised mode in the aspect level sentiment classification, which enables it to learn efficiently using less labeled data.


title: Control systems in omni-directional robotic vehicle with mecanum wheels

abstract: Mobile robotic systems are mechatronic devices that are currently becoming more and more complex. To design such a system, a combination of expertise from the fields of mechanical, electrical and computer engineering is required. This paper describes our custom omni-directional robotic platform designed for both indoor and outdoor use that contains a lot of prototypic hardware. All the designed components are interesting as they permit control of the robot as a complex mechatronic system. The main objective here was to use a larger amount of smaller control subsystems, rather than a central one. This is more advantageous from the control point of view. The electronics of the robot consists of microcontroller controlled distributed subsystems that are able to communicate with the master system. The following sections offer an insight into the control structures of the mobile robot.


title: Relational Similarity Machines

abstract: This paper proposes Relational Similarity Machines (RSM): a fast, accurate, and flexible relational learning framework for supervised and semi-supervised learning tasks. Despite the importance of relational learning, most existing methods are hard to adapt to different settings, due to issues with efficiency, scalability, accuracy, and flexibility for handling a wide variety of classification problems, data, constraints, and tasks. For instance, many existing methods perform poorly for multi-class classification problems, graphs that are sparsely labeled or network data with low relational autocorrelation. In contrast, the proposed relational learning framework is designed to be (i) fast for learning and inference at real-time interactive rates, and (ii) flexible for a variety of learning settings (multi-class problems), constraints (few labeled instances), and application domains. The experiments demonstrate the effectiveness of RSM for a variety of tasks and data.


title: Impact Evaluation of In-Space Additive Manufacturing and Recycling Technologies for On-Orbit Servicing

abstract: This paper proposes an integrated simulation tool to evaluate the impact of future in-space additive manufacturing (ISAM) and recycling technologies on the responsiveness of an on-orbit servicing (...


title: Clickstream User Behavior Models

abstract: The next generation of Internet services is driven by users and user-generated content. The complex nature of user behavior makes it highly challenging to manage and secure online services. On one hand, service providers cannot effectively prevent attackers from creating large numbers of fake identities to disseminate unwanted content (e.g., spam). On the other hand, abusive behavior from real users also poses significant threats (e.g., cyberbullying). ::: In this article, we propose clickstream models to characterize user behavior in large online services. By analyzing clickstream traces (i.e., sequences of click events from users), we seek to achieve two goals: (1) detection: to capture distinct user groups for the detection of malicious accounts, and (2) understanding: to extract semantic information from user groups to understand the captured behavior. To achieve these goals, we build two related systems. The first one is a semisupervised system to detect malicious user accounts (Sybils). The core idea is to build a clickstream similarity graph where each node is a user and an edge captures the similarity of two users’ clickstreams. Based on this graph, we propose a coloring scheme to identify groups of malicious accounts without relying on a large labeled dataset. We validate the system using ground-truth clickstream traces of 16,000 real and Sybil users from Renren, a large Chinese social network. The second system is an unsupervised system that aims to capture and understand the fine-grained user behavior. Instead of binary classification (malicious or benign), this model identifies the natural groups of user behavior and automatically extracts features to interpret their semantic meanings. Applying this system to Renren and another online social network, Whisper (100K users), we help service providers identify unexpected user behaviors and even predict users’ future actions. Both systems received positive feedback from our industrial collaborators including Renren, LinkedIn, and Whisper after testing on their internal clickstream data.


title: MEASURING THE SIMILARITY OF TWO IMAGE SEQUENCES

abstract: We propose a novel similarity measure of two image sequences based on shapeme histograms. The idea of shapeme histogram has been used for single image/texture recognition, but is used here to solve the sequence-tosequence matching problem. We develop techniques to represent each sequence as a set of shapeme histograms, which captures different variations of the object appearances within the sequence. These shapeme histograms are computed from the set of 2D invariant features that are stable across multiple images in the sequence, and therefore minimizes the effect of both background clutter, and 2D pose variations. We define sequence similarity measure as the similarity of the most similar pair of images from both sequences. This definition maximizes the chance of matching between two sequences of the same object, because it requires only part of the sequences being similar. We also introduce a weighting scheme to conduct an implicit feature selection process during the matching of two shapeme histograms. Experiments on clustering image sequences of tracked objects demonstrate the efficacy of the proposed method.


title: Turing Test Considered Harmful

abstract: Passing the Turing Test is not a sensible goal for Artificial Intelligence. Adherence to Turing's vision from 1950 is now actively harmful to our field. We review problems with Turing's idea, and suggest that, ironically, the very cognitive science that he tried to create must reject his research goal.


A Threshold Selection Method from Gray-Level Histograms


title: Scheduling the Chilean soccer league by integer programming

abstract: Since 2005, Chile's professional soccer league has used a game-scheduling system that is based on an integer linear programming model. The Chilean league managers considered several operational, economic, and sporting criteria for the final tournaments' scheduling. Thus, they created a highly constrained problem that had been, in practice, unsolvable using their previous methodology. This led to the adoption of a model that used some techniques that were new in soccer-league sports scheduling. The schedules they generated provided the teams with benefits such as lower costs, higher incomes, and fairer seasons. In addition, the tournaments were more attractive to sports fans. The success of the new scheduling system has completely fulfilled the expectations of the Asociacion Nacional de Futbol Profesional (ANFP), the organization for Chilean professional soccer.


title: Affective Video Summarization and Story Board Generation Using Pupillary Dilation and Eye Gaze

abstract: We propose a semi-automated, eye-gaze based method for affective analysis of videos. Pupillary Dilation (PD) is introduced as a valuable behavioural signal for assessment of subject arousal and engagement. We use PD information for computationally inexpensive, arousal based composition of video summaries and descriptive story-boards. Video summarization and story-board generation is done offline, subsequent to a subject viewing the video. The method also includes novel eye-gaze analysis and fusion with content based features to discover affective segments of videos and Regions of interest (ROIs) contained therein. Effectiveness of the framework is evaluated using experiments over a diverse set of clips, significant pool of subjects and comparison with a fully automated state-of-art affective video summarization algorithm. Acquisition and analysis of PD information is demonstrated and used as a proxy for human visual attention and arousal based video summarization and story-board generation. An important contribution is to demonstrate usefulness of PD information in identifying affective video segments with abstract semantics or affective elements of discourse and story-telling, that are likely to be missed by automated methods. Another contribution is the use of eye-fixations in the close temporal proximity of PD based events for key frame extraction and subsequent story board generation. We also show how PD based video summarization can to generate either a personalized video summary or to represent a consensus over affective preferences of a larger group or community.


title: Self-organized computation with unreliable, memristive nanodevices

abstract: Nanodevices have terrible properties for building Boolean logic systems: high defect rates, high variability, high death rates, drift, and (for the most part) only two terminals. Economical assembly requires that they be dynamical. We argue that strategies aimed at mitigating these limitations, such as defect avoidance/reconfiguration, or applying coding theory to circuit design, present severe scalability and reliability challenges. We instead propose to mitigate device shortcomings and exploit their dynamical character by building self-organizing, self-healing networks that implement massively parallel computations. The key idea is to exploit memristive nanodevice behavior to cheaply implement adaptive, recurrent networks, useful for complex pattern recognition problems. Pulse-based communication allows the designer to make trade-offs between power consumption and processing speed. Self-organization sidesteps the scalability issues of characterization, compilation and configuration. Network dynamics supplies a graceful response to device death. We present simulation results of such a network—a self-organized spatial filter array—that demonstrate its performance as a function of defects and device variation.


title: Bayesian super-resolution of text image sequences from low resolution observations

abstract: This paper deals with the problem of reconstructing high-resolution text images from an incomplete set of under-sampled, blurred, and noisy images shifted with subpixel displacement. We derive mathematical expressions for the calculation of the maximum a posteriori estimate of the high resolution image and the estimation of the parameters involved in the model. The method is tested on real text images and car plates, examining the impact of blurring and the number of available low resolution images on the final estimate.


title: Minimax Regret Bounds for Reinforcement Learning

abstract: We consider the problem of provably optimal exploration in reinforcement learning for finite horizon MDPs. We show that an optimistic modification to value iteration achieves a regret bound of $\tilde{O}( \sqrt{HSAT} + H^2S^2A+H\sqrt{T})$ where $H$ is the time horizon, $S$ the number of states, $A$ the number of actions and $T$ the number of time-steps. This result improves over the best previous known bound $\tilde{O}(HS \sqrt{AT})$ achieved by the UCRL2 algorithm of Jaksch et al., 2010. The key significance of our new results is that when $T\geq H^3S^3A$ and $SA\geq H$, it leads to a regret of $\tilde{O}(\sqrt{HSAT})$ that matches the established lower bound of $\Omega(\sqrt{HSAT})$ up to a logarithmic factor. Our analysis contains two key insights. We use careful application of concentration inequalities to the optimal value function as a whole, rather than to the transitions probabilities (to improve scaling in $S$), and we define Bernstein-based "exploration bonuses" that use the empirical variance of the estimated values at the next states (to improve scaling in $H$).


title: A hybrid clustering algorithm for data mining

abstract: Data clustering is a process of arranging similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is better than among groups. In this paper a hybrid clustering algorithm based on K-mean and K-harmonic mean (KHM) is described. The proposed algorithm is tested on five different datasets. The research is focused on fast and accurate clustering. Its performance is compared with the traditional K-means & KHM algorithm. The result obtained from proposed hybrid algorithm is much better than the traditional K-mean & KHM algorithm.


title: Decentralized self-adaptation for elastic Data Stream Processing

abstract: Abstract Data Stream Processing (DSP) applications are widely used to develop new pervasive services, which require to seamlessly process huge amounts of data in a near real-time fashion. To keep up with the high volume of daily produced data, these applications need to dynamically scale their execution on multiple computing nodes, so to process the incoming data flow in parallel. In this paper, we present a hierarchical distributed architecture for the autonomous control of elastic DSP applications. It consists of a two-layered hierarchical solution, where a centralized per-application component coordinates the run-time adaptation of subordinated distributed components, which, in turn, locally control the adaptation of single DSP operators. Thanks to its features, the proposed solution can efficiently run in large-scale Fog computing environments. Exploiting this framework, we design several distributed self-adaptation policies, including a popular threshold-based approach and two reinforcement learning solutions. We integrate the hierarchical architecture and the devised self-adaptation policies in Apache Storm, a popular open-source DSP framework. Relying on the DEBS 2015 Grand Challenge as a benchmark application, we show the benefits of the presented self-adaptation policies, and discuss the strengths of reinforcement learning based approaches, which autonomously learn from experience how to optimize the application performance.


title: Ocular symptom detection using smartphones

abstract: It is often said that "the eyes are the windows to the soul". Although this usually metaphorically refers to a person's emotional state, there is literal truth in this statement with reference to a person's biological state. The eyes are dynamic and sensitive organs that are connected to the rest of the body via the circulatory and nervous systems. This means that diseases originating from an organ within the body can indirectly affect the eyes. Although the eyes may not be the origin for many such diseases, they are visible to external observers, making them an accessible point of diagnosis. For instance, broken blood vessels in the sclera could indicate a blood clotting disorder or a vitamin K deficiency. Instead of "windows to the soul", the eyes are "sensors to the body" that can be monitored to screen for certain diseases.


title: Preference, context and communities: a multi-faceted approach to predicting smartphone app usage patterns

abstract: Reliable smartphone app prediction can strongly benefit both users and phone system performance alike. However, real-world smartphone app usage behavior is a complex phenomena driven by a number of competing factors. In this pa- per, we develop an app usage prediction model that leverages three key everyday factors that affect app usage decisions -- (1) intrinsic user app preferences and user historical patterns; (2) user activities and the environment as observed through sensor-based contextual signals; and, (3) the shared aggregate patterns of app behavior that appear in various user communities. While rapid progress has been made recently in smartphone app prediction, existing prediction models tend to focus on only one of these factors. We evaluate a multi-faceted approach to prediction using (1) a 3-week 35-user field trial, along with (2) analysis of app usage logs of 4,606 smartphone users worldwide. We find our app usage model can not only produce more robust app predictions than conventional techniques, but it can also enable significant smartphone system optimizations.


title: Non-invasive optical detection of hand gestures

abstract: In this paper we present a novel type of sensing technology for hand and finger gesture recognition that utilizes light in the invisible spectrum to detect changes in position and form of body tissue like tendons and muscles. The proposed system can be easily integrated with existing wearable devices. Our approach not only enables gesture recognition but it could potentially double to perform a variety of health related monitoring tasks (e.g. heart rate, stress).


title: Visual Detection, Tracking and Pose Estimation of a Robotic Arm End Effector

abstract: In this work we present a robust tracking and pose estimation of an end effector of a 7 DoF robotic arm under a binocular stand-alone configuration. The proposed method is composed of sequence of three processes. The first one is a fast segmentation in HSV color space of a planar patch placed at the end effector. This step considers the segmentation using pixels classification and Euclidean distance a similarity measure. The second step is the selection of a region of interest, feature point extraction and tracking over that region. Finally, the feature points can be used to estimate the homography between world reference frame and image frame. The proposed methodology runs in real time and it is applicable for visual servoing and the grasping.


title: Lexico-syntactic text simplification and compression with typed dependencies

abstract: We describe two systems for text simplification using typed dependency structures, one that performs lexical and syntactic simplification, and another that performs sentence compression optimised to satisfy global text constraints such as lexical density, the ratio of difficult words, and text length. We report a substantial evaluation that demonstrates the superiority of our systems, individually and in combination, over the state of the art, and also report a comprehension based evaluation of contemporary automatic text simplification systems with target non-native readers.


title: Query Processing, Resource Management, and Approximation in a Data Stream Management System –

abstract: This paper describes our ongoing work developing the Stanford Stream Data Manager (STREAM), a system for executing continuous queries over multiple continuous data streams. The STREAM system supports a declarative query language, and it copes with high data rates and query workloads by providing approximate answers when resources are limited. This paper describes specific contributions made so far and enumerates our next steps in developing a general-purpose Data Stream Management System.


title: Performance analysis of accelerated biophysically-meaningful neuron simulations

abstract: In-vivo and in-vitro experiments are routinely used in neuroscience to unravel brain functionality. Although they are a powerful experimentation tool, they are also time-consuming and, often, restrictive. Computational neuroscience attempts to solve this by using biologically-plausible and biophysically-meaningful neuron models, most prominent among which are the conductance-based models. Their computational complexity calls for accelerator-based computing to mount large-scale or real-time neuroscientific experiments. In this paper, we analyze and draw conclusions on the class of conductance models by using a representative modeling application of the inferior olive (InfOli), an important part of the olivocerebellar brain circuit. We conduct an extensive profiling session to identify the computational and data-transfer requirements of the application under various realistic use cases. The application is, then, ported onto two acceleration nodes, an Intel Xeon Phi and a Maxeler Vectis Data Flow Engine (DFE). We evaluate the performance scalability and resource requirements of the InfOli application on the two target platforms. The analysis of InfOli, which is a real-life neuroscientific application, can serve as a useful guide for porting a wide range of similar workloads on platforms like the Xeon Phi or the Maxeler DFEs. As accelerators are increasingly populating High-Performance Computing (HPC) infrastructure, the current paper provides useful insight on how to optimally use such nodes to run complex and relevant neuron modeling workloads.


title: InterAxis: Steering Scatterplot Axes via Observation-Level Interaction

abstract: Scatterplots are effective visualization techniques for multidimensional data that use two (or three) axes to visualize data items as a point at its corresponding x and y Cartesian coordinates. Typically, each axis is bound to a single data attribute. Interactive exploration occurs by changing the data attributes bound to each of these axes. In the case of using scatterplots to visualize the outputs of dimension reduction techniques, the x and y axes are combinations of the true, high-dimensional data. For these spatializations, the axes present usability challenges in terms of interpretability and interactivity. That is, understanding the axes and interacting with them to make adjustments can be challenging. In this paper, we present InterAxis, a visual analytics technique to properly interpret, define, and change an axis in a user-driven manner. Users are given the ability to define and modify axes by dragging data items to either side of the x or y axes. from which the system computes a linear combination of data attributes and binds it to the axis. Further, users can directly tune the positive and negative contribution to these complex axes by using the visualization of data attributes that correspond to each axis. We describe the details of our technique and demonstrate the intended usage through two scenarios.


title: brat: a Web-based Tool for NLP-Assisted Text Annotation

abstract: We introduce the brat rapid annotation tool (BRAT), an intuitive web-based tool for text annotation supported by Natural Language Processing (NLP) technology. BRAT has been developed for rich structured annotation for a variety of NLP tasks and aims to support manual curation efforts and increase annotator productivity using NLP techniques. We discuss several case studies of real-world annotation projects using pre-release versions of BRAT and present an evaluation of annotation assisted by semantic class disambiguation on a multicategory entity mention annotation task, showing a 15% decrease in total annotation time. BRAT is available under an open-source license from: http://brat.nlplab.org


title: Measuring the critical thickness of thin metalorganic precursor films

abstract: Successful application of sol-gel, metalorganic decomposition, or hydrothermal routes to ceramic thin films depends on the mechanical integrity of the precursor film. Above a critical thickness, a precursor film will crack or decohere from the substrate during drying. The cracking and thickness of thin metalorganic precursor films were simultaneously observed during drying using a standard optical microscope. Isochromatic color fringes produced by interference of reflected white light were used to monitor film thickness. The critical film thickness was determined by the color fringe corresponding to the thickness at which propagating cracks terminated. As a demonstration of the technique, the critical thickness of titanium di(isopropoxide) bis(ethyl acetoacetate) films was measured, showing increased critical thickness with the addition of small amounts of an elastomeric polymer.


title: Psychosocial Health Among Young Victims and Offenders of Direct and Indirect Bullying

abstract: OBJECTIVE ::: To assess the association between bullying (both directly and indirectly) and indicators of psychosocial health for boys and girls separately. ::: ::: ::: STUDY DESIGN ::: A school-based questionnaire survey of bullying, depression, suicidal ideation, and delinquent behavior. ::: ::: ::: SETTING ::: Primary schools in Amsterdam, The Netherlands. ::: ::: ::: PARTICIPANTS ::: A total of 4811 children aged 9 to 13. ::: ::: ::: RESULTS ::: Depression and suicidal ideation are common outcomes of being bullied in both boys and girls. These associations are stronger for indirect than direct bullying. After correction, direct bullying had a significant effect on depression and suicidal ideation in girls, but not in boys. Boy and girl offenders of bullying far more often reported delinquent behavior. Bullying others directly is a much greater risk factor for delinquent behavior than bullying others indirectly. This was true for both boys and girls. Boy and girl offenders of bullying also more often reported depressive symptoms and suicidal ideation. However, after correction for both sexes only a significant association still existed between bullying others directly and suicidal ideation. ::: ::: ::: CONCLUSIONS ::: The association between bullying and psychosocial health differs notably between girls and boys as well as between direct and indirect forms of bullying. Interventions to stop bullying must pay attention to these differences to enhance effectiveness.


abstract: This paper explores and discusses the use of creative movement in the normal classroom setting as a means of stimulating the brain to be active in learning and eliciting emotional responses in children. Creative methods of pedagogy are explored through research about the involvement of the Multiple Intelligences by Howard Gardner, right and left brain functions while learning, 21 st Century skills, and movement as a mode of learning. The benefits of arts integration in the classroom are examined as they relate to pedagogy and ways to include all learners regardless of mental, physical or emotional capabilities is discussed. The research presented provides a rationale for the inclusion of creative movement and other arts in the classroom. This paper also serves as an introduction for the kindergarten unit plan designed to demonstrate how educators can use creative movement to effectively teach the content areas while yielding evidence of learning. The unit plan includes a description and outline of the subject area and creative movement content students will encounter. Important information for teachers regarding the implementation of movement in the classroom is included with suggestions for classroom interactions, materials for teacher and student preparation, and methods of fostering a creative, academically stimulating, and safe learning environment. STIMULATING THE BRAIN WITH CREATIVE MOVEMENT 3 Stimulating the Brain with Creative Movement in the Classroom Discussion When teaching for understanding in subject areas in a normal classroom setting, educators often find themselves looking for ways to engage students with effective strategies that stimulate the brain to process and retain information. In order to do so, it is important for educators to have a basic understanding of the development of the left and right hemispheres of the brain in children. There must then be knowledge about strategies that help children to learn more efficiently, understand more effectively, and retain and recall information. The arts are recognized as important to include in education, but many teachers are unaware of the tools available to engage their children in subject areas while including dynamic aesthetic experiences. Creative movement is integration of the mind, body, emotions, and spirit which stimulates learning in students with different intelligences and brain hemisphere dominances. It is a method of communication through movement that entails using elements of dance which involve the human body, movement, space, time, energy/dynamics, and visual relationships (Joyce, 1980, pp. 1, 2). The research presented in this paper shall examine functions of the left and right hemispheres of the brain, how to teach to both left and right brain learners, the importance and impacts of creative movement in the classroom, and how all educators can use creative movement as a strategic tool for teaching the content areas. Intelligences All human beings generally have a natural preference for the side of the brain they use the most frequently and comfortably, and the eye, ear, hand or leg they prefer to use for completing tasks (Hannaford, 1997). In children, it is very easy to identify their dominant intelligence of those distinguished by Howard Gardner (2006) in what he termed the “Multiple STIMULATING THE BRAIN WITH CREATIVE MOVEMENT 4 Intelligences” since children often work, act, and react based on their natural intelligence tendency. The eight intelligences include: linguistic; logical-mathematical; spatial; bodilykinesthetic; musical; interpersonal; intrapersonal; and naturalist (Gardner, 2006). The intelligence(s) that an individual uses most naturally can tell educators a great deal about which side of the brain they also prefer using and therefore, how they best receive and process


title: Global RDF Vector Space Embeddings

abstract: Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks. Existing approaches, such as RDF2Vec, use local information, i.e., they rely on local sequences generated for nodes in the RDF graph. For word embeddings, global techniques, such as GloVe, have been proposed as an alternative. In this paper, we show how the idea of global embeddings can be transferred to RDF embeddings, and show that the results are competitive with traditional local techniques like RDF2Vec.


title: Unsupervised Entity-Relation Analysis in IBM Watson

abstract: Text paraphrasing algorithms play a fundamental role in several NLP applications such as automated question answering (QA), summarization and machine translation. We propose a novel paraphrasing approach based on an entity-relation (ER) analysis of text. The algorithm uses a combination of deep linguistic analysis (part of speech, dependency parse information) and background resources (NGram, PRISMATIC KB, domain dictionaries) to detect and match entities and relations. We evaluate the ER approach in a QA setting by adding it to the suite of passage scoring algorithms in IBM Watson, a state-of-the-art question answering system. We show a statistically significant improvement in the ability of IBM Watson to identify justifying passages.


title: Extreme efficiency power electronics

abstract: In this paper a generalized description and an overview of degrees of freedom and selected measures for efficiency improvement of power electronics converters is given. The background of all considerations is formed by single-phase PFC rectifier systems, but the concepts shown are fundamental and fully applicable for other converter systems. First, the influence of the main components of the losses of a converter on the efficiency characteristic over the output power is discussed. Subsequently, a detailed analysis of the possibilities, of minimizing the semiconductor losses, the losses of the passive components including the EMI filter, and the power requirements of auxiliary systems in the course of the design process are given. In this context also the technological boundaries that limit the maximum efficiency of a converter are clarified and the compromise that always has to be made between efficiency and power density is highlighted. Furthermore, a control procedure is discussed to maximize the efficiency in the partial load range and a resonant transition mode ZVS converter system is presented that allows to attain efficiencies significantly over 99% without the use of SiC semiconductors. In addition the accuracy of the input and output power measurements required for measuring highest efficiencies is clarified, whereby the advantage of a direct loss measurement by means of a calorimeter becomes immediately clear. Finally, results of measurements on a demonstrator of a CCM single-phase PFC rectifier system with 99.1% max. efficiency and η > 99% above half rated power, and on a resonant transition mode PFC rectifier system with ηmax = 99.3% and η > 99% above 15% rated power are presented.


title: Cancerous stem cells can arise from pediatric brain tumors.

abstract: Pediatric brain tumors are significant causes of morbidity and mortality. It has been hypothesized that they derive from self-renewing multipotent neural stem cells. Here, we tested whether different pediatric brain tumors, including medulloblastomas and gliomas, contain cells with properties similar to neural stem cells. We find that tumor-derived progenitors form neurospheres that can be passaged at clonal density and are able to self-renew. Under conditions promoting differentiation, individual cells are multipotent, giving rise to both neurons and glia, in proportions that reflect the tumor of origin. Unlike normal neural stem cells, however, tumor-derived progenitors have an unusual capacity to proliferate and sometimes differentiate into abnormal cells with multiple differentiation markers. Gene expression analysis reveals that both whole tumors and tumor-derived neurospheres express many genes characteristic of neural and other stem cells, including CD133, Sox2, musashi-1, bmi-1, maternal embryonic leucine zipper kinase, and phosphoserine phosphatase, with variation from tumor to tumor. After grafting to neonatal rat brains, tumor-derived neurosphere cells migrate, produce neurons and glia, and continue to proliferate for more than 4 weeks. The results show that pediatric brain tumors contain neural stem-like cells with altered characteristics that may contribute to tumorigenesis. This finding may have important implications for treatment by means of specific targeting of stem-like cells within brain tumors.


title: Using augmented reality to support observations about trees during summer camp

abstract: This research examines how augmented reality (AR) learning experiences supported children's engagement in science. We conducted a video-based study of seven sessions over two weeks at a summer camp program. We investigated how scientific talk related to observational practices could be supported by a mobile app incorporating AR. Researchers coded videos of youth (n=35) during an outdoor program on the tree life cycle to understand science talk related to observations of trees. Findings suggested that the use of AR to support tree identification led to learner-initiated talk and observations as demonstrated by high levels of perceptual (describing) talk. Learners relied on the AR technology and peers in order to engage in this observational work.


title: An origami-inspired multicell biobattery stack

abstract: We created a 9-cell biobattery stack on paper, which was capable of generating bio-power from microbial metabolism, delivering on-board energy to the next generation of paper-based systems. The developed biobattery stack has competitive advantages over other paper-based batteries in resource-limited and remote regions because water in every environment generally hosts various microorganisms that can operate such a battery. Bacterial cells were contained in patterned hydrophilic paper reservoirs and produced a targeted performance through three-dimensionally stacked batteries. Using origami, a compact, stackable 3-D battery stack was created from 2-D sheets through high degrees of folding along pre-defined creases. The battery stack generated the maximum output voltage of 1.8V and current of 2.5μΛ under 100kΩ.


title: Graph Embedding and Dimensionality Reduction- A Survey

abstract: Dimension reduction is defined as the process of mapping high-dimensional data to a lower- dimensional vector space. Most machine learning and data mining techniques may not be effective for high-dimensional data. In order to handle this data adequately, its dimensionality needs to be reduced. Dimensionality reduction is also needed for visualization, graph embedding, image retrieval and a variety of applications. This paper discuss the most popular linear dimensionality reduction method Principal Component Analysis and the various non linear dimensionality reduction methods such as Multidimensional scaling, Isomap, Locally Linear Embedding, Laplacian Eigen Map, Semidefinite embedding, Minimum Volume Embedding and Structure Preserving Embedding .


title: A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

abstract: Preference-based recommendation systems have transformed how we consume media. By analyzing usage data, these methods uncover our latent preferences for items (such as articles or movies) and form recommendations based on the behavior of others with similar tastes. But traditional preference-based recommendations do not account for the social aspect of consumption, where a trusted friend might point us to an interesting item that does not match our typical preferences. In this work, we aim to bridge the gap between preference- and social-based recommendations. We develop social Poisson factorization (SPF), a probabilistic model that incorporates social network information into a traditional factorization method; SPF introduces the social aspect to algorithmic recommendation. We develop a scalable algorithm for analyzing data with SPF, and demonstrate that it outperforms competing methods on six real-world datasets; data sources include a social reader and Etsy.


title: The generic model query language GMQL - Conceptual specification, implementation, and runtime evaluation

abstract: The generic model query language GMQL is designed to query collections of conceptual models created in arbitrary graph-based modelling languages. Querying conceptual models means searching for particular model subgraphs that comply with a predefined pattern query. Such a query specifies the structural and semantic properties of the model fragment to be returned. In this paper, we derive requirements for a generic model query language from the literature and formally specify the language's syntax and semantics. We conduct an analysis of GMQL's theoretical and practical runtime performance concluding that it returns query results within satisfactory time. Given its generic nature, GMQL contributes to a broad range of different model analysis scenarios ranging from business process compliance management to model translation and business process weakness detection. As GMQL returns results with acceptable runtime performance, it can be used to query large collections of hundreds or thousands of conceptual models containing not only process models, but also data models or organizational charts. In this paper, we furthermore evaluate GMQL against the backdrop of existing query approaches thereby carving out its advantages and limitations as well as pointing toward future research. We describe the language GMQL for querying any graph-based conceptual model.GMQL is applicable to a broad range of model analysis scenarios.GMQL returns query results within (fractions of) milliseconds.The model size and the number of query results influence its runtime performance.GMQL provides features that no other existing query language provides.


title: An Infrared Location System for Relative Pose Estimation of Robots

abstract: In this work we present an infrared location system for relative pose (position and orientation) estimation in a multi-robot system. Pose estimates are essential for tasks like cooperative simultaneous localization and mapping (C-SLAM), and formation control. In simultaneous localization and mapping (SLAM) relative pose estimates enable more accurate and less time-consuming map building. Respectively, formation control requires accurate pose estimates of other robots to enable robot cooperation in required formation. To address these challenging tasks for small-sized robots, we present a small-sized infrared location system with low current consumption. In the location system, robots use intensity and bearing measurements of received infrared signals to estimate the positions of other robots in polar coordinates. In addition, each robot has a unique modulation frequency from which they are recognized. The location system performs position estimation by rotating a beam collector at constant rotation speed and by measuring the bearing and intensity of the received signal. Infrared signals are received through a small aperture in the beam collector enabling accurate bearing measurements. In order to maximize the measurement range, infrared radiation is reflected sideways into a uniform zone using a conical mirror. Experiments were performed in a group of three robots with a measurement range of up to 3 m while the maximum number of robots was eight. The location system implemented enables relative position estimation among a group of small-sized robots without exchanging position estimates. This is advantageous since the robots are able to maintain formation also in the absence of a radio link.


title: A rating prediction method for e-commerce application using ordinal regression based on LDA with multi-modal features

abstract: This paper presents a new method for rating prediction in e-commerce, which uses ordinal regression based on linear discriminant analysis (LDA) with multi-modal features. In order to realize accurate recommendation in e-commerce, the proposed method estimates each user's rating for target items. Note that we define the rating as “the degree of preference for each item by a user.” For estimating the target user's preference of each item from the past ratings of other items, the proposed method performs training from pairs of “ratings of items” and their feature vectors using ordinal regression based on LDA. Furthermore, in this approach, new features are obtained by applying canonical correlation analysis (CCA) to textual and visual features extracted from review's texts and images on the Web, respectively. Therefore, higher performance of the rating prediction can be realized by our method than that when using single kind of features. Experimental results obtained by applying the proposed method to an actual movie data set, which has been provided by SNAP, show the effectiveness of the proposed method.


title: Social Media Technologies’ Use for the Competitive Information and Knowledge Sharing, and Its Effects on Industrial SMEs’ Innovation

abstract: The effective use of technologies supporting decision making is essential to companies’ survival. Recent studies analyzed social media technologies SMT in the context of small-and medium-sized enterprises SMEs, contributing to the discussion on SMT benefits from the marketing perspective. This article focuses on the effects of SMT use on innovation. Our findings provide empirical evidence on the positive effects of SMT use for acquiring external information and for sharing knowledge and innovation performance.


title: Interactive Data Analytics for the Humanities

abstract: In this vision paper, we argue that current solutions to data analytics are not suitable for complex tasks from the humanities, as they are agnostic of the user and focused on static, predefined tasks with large-scale benchmarks. Instead, we believe that the human must be put into the loop to address small data scenarios that require expert domain knowledge and fluid, incrementally defined tasks, which are common for many humanities use cases. Besides the main challenges, we discuss existing and urgently required solutions to interactive data acquisition, model development, model interpretation, and system support for interactive data analytics. In the envisioned interactive systems, human users not only provide annotations to a machine learner, but train a model by using the system and demonstrating the task. The learning system will actively query the user for feedback, refine its model in real-time, and is able to explain its decisions. Our vision links natural language processing research with recent advances in machine learning, computer vision, and data management systems, as realizing this vision relies on combining expertise from all of these scientific fields.


title: CloudBI: Practical Privacy-Preserving Outsourcing of Biometric Identification in the Cloud

abstract: Biometric identification has been incredibly useful in the law enforcement to authenticate an individual’s identity and/or to figure out who someone is, typically by scanning a database of records for a close enough match. In this work, we investigate the privacy-preserving biometric identification outsourcing problem, where the database owner outsources both the large-scale encrypted database and the computationally intensive identification job to the semi-honest cloud, relieving itself from data storage and computation burden. We present new privacy-preserving biometric identification protocols, which substantially reduce the computation burden on the database owner. Our protocols build on new biometric data encryption, distance-computation and matching algorithms that novelly exploit inherent structures of biometric data and properties of identification operations. A thorough security analysis shows that our solutions are practically-secure, and the ultimate solution offers a higher level of privacy protection than the-state-of-the-art on biometric identification outsourcing. We evaluate our protocols by implementing an efficient privacy-preserving fingerprint-identification system, showing that our protocols meet both the security and efficiency needs well, and they are appropriate for use in various privacy-preserving biometric identification applications.


title: Designing the assessment of the collaborative learning process in LMS courses

abstract: The main purpose of this paper is to present the made improvements to an innovative tool called Assessment Manager. These improvements are related with the implementation of analytic rubrics to help the instructors in the design of the assessment from the collaborative learning process completed by the students into Learning Management System courses. The assessment tool is part of a Teaching Assistant Service embedded into the Learning Management System Moodle. This assistant together with the assessment tool are being used and tested in several courses to support Computer Science Engineering Education. The tests are being performed as innovative educational practices with instructors and students members from the Faculty of Computer Science Engineering at the Universidad Pontificia Bolivariana-UPB Monteria and members from the School of Computer Science Engineering at the Universidad del Valle-UNIVALLE Santiago de Cali (Colombia).


title: VISALOGY: Answering Visual Analogy Questions

abstract: In this paper, we study the problem of answering visual analogy questions. These questions take the form of image A is to image B as image C is to what. Answering these questions entails discovering the mapping from image A to image B and then extending the mapping to image C and searching for the image D such that the relation from A to B holds for C to D. We pose this problem as learning an embedding that encourages pairs of analogous images with similar transformations to be close together using convolutional neural networks with a quadruple siamese architecture. We introduce a dataset of visual analogy questions in natural images, and show first results of its kind on solving analogy questions on natural images.


title: Learning Deep Object Detectors from 3D Models

abstract: Crowdsourced 3D CAD models are becoming easily accessible online, and can potentially generate an infinite number of training images for almost any object category.We show that augmenting the training data of contemporary Deep Convolutional Neural Net (DCNN) models with such synthetic data can be effective, especially when real training data is limited or not well matched to the target domain. Most freely available CAD models capture 3D shape but are often missing other low level cues, such as realistic object texture, pose, or background. In a detailed analysis, we use synthetic CAD-rendered images to probe the ability of DCNN to learn without these cues, with surprising findings. In particular, we show that when the DCNN is fine-tuned on the target detection task, it exhibits a large degree of invariance to missing low-level cues, but, when pretrained on generic ImageNet classification, it learns better when the low-level cues are simulated. We show that our synthetic DCNN training approach significantly outperforms previous methods on the PASCAL VOC2007 dataset when learning in the few-shot scenario and improves performance in a domain shift scenario on the Office benchmark.


title: Lucid touch: a see-through mobile device

abstract: Touch is a compelling input modality for interactive devices; however, touch input on the small screen of a mobile device is problematic because a user's fingers occlude the graphical elements he wishes to work with. In this paper, we present LucidTouch, a mobile device that addresses this limitation by allowing the user to control the application by touching the back of the device. The key to making this usable is what we call pseudo-transparency: by overlaying an image of the user's hands onto the screen, we create the illusion of the mobile device itself being semi-transparent. This pseudo-transparency allows users to accurately acquire targets while not occluding the screen with their fingers and hand. Lucid Touch also supports multi-touch input, allowing users to operate the device simultaneously with all 10 fingers. We present initial study results that indicate that many users found touching on the back to be preferable to touching on the front, due to reduced occlusion, higher precision, and the ability to make multi-finger input.


title: Modelling and control of a variable speed wind energy conversion turbine driven synchronous generator connected to the grid

abstract: This paper presents the modelling and control design for a wind energy conversion scheme using synchronous generator. The wind turbine is coupled to a synchronous generator connected to grid trough a static converter. The objective of the proposed control strategy is to maximize energy captured from the wind turbine. The adapting control law used for extracting maximum power from the wind is based on the utilisation of anemometer sensor. The developed maximum wind power extraction algorithm has the capability of searching the maximum wind turbine power at variable wind speed, constructing an intelligent system to control the inverter for maximum power extraction, with the need and knowledge of wind characteristics and the measurements of mechanical variables such as wind speed and turbine rotor speed. First, the dynamic modelling and control design for 800kW horizontal axis wind turbine synchronous generator is presented. Second, the control of the proposed structure is achieved using proportional integrator controllers which are based on the linear model and tested under large electrical disturbances of wind speed. The system has been validated by numerical simulation using data from a wind farm turbine situated in the north of Tunisia (SIDI DAOUED). The simulation results have shown good performances of the system and a better grid integration of the wind energy with the proposed converter control strategy.


title: Statistical power analyses using G*Power 3.1: Tests for correlation and regression analyses

abstract: G*Power is a free power analysis program for a variety of statistical tests. We present extensions and improvements of the version introduced by Faul, Erdfelder, Lang, and Buchner (2007) in the domain of correlation and regression analyses. In the new version, we have added procedures to analyze the power of tests based on (1) single-sample tetrachoric correlations, (2) comparisons of dependent correlations, (3) bivariate linear regression, (4) multiple linear regression based on the random predictor model, (5) logistic regression, and (6) Poisson regression. We describe these new features and provide a brief introduction to their scope and handling.


title: Phylodiversity to inform conservation policy: An Australian example.

abstract: Phylodiversity measures summarise the phylogenetic diversity patterns of groups of organisms. By using branches of the tree of life, rather than its tips (e.g., species), phylodiversity measures provide important additional information about biodiversity that can improve conservation policy and outcomes. As a biodiverse nation with a strong legislative and policy framework, Australia provides an opportunity to use phylogenetic information to inform conservation decision-making. We explored the application of phylodiversity measures across Australia with a focus on two highly biodiverse regions, the south west of Western Australia (SWWA) and the South East Queensland bioregion (SEQ). We analysed seven diverse groups of organisms spanning five separate phyla on the evolutionary tree of life, the plant genera Acacia and Daviesia, mammals, hylid frogs, myobatrachid frogs, passerine birds, and camaenid land snails. We measured species richness, weighted species endemism (WE) and two phylodiversity measures, phylogenetic diversity (PD) and phylogenetic endemism (PE), as well as their respective complementarity scores (a measure of gains and losses) at 20 km resolution. Higher PD was identified within SEQ for all fauna groups, whereas more PD was found in SWWA for both plant groups. PD and PD complementarity were strongly correlated with species richness and species complementarity for most groups but less so for plants. PD and PE were found to complement traditional species-based measures for all groups studied: PD and PE follow similar spatial patterns to richness and WE, but highlighted different areas that would not be identified by conventional species-based biodiversity analyses alone. The application of phylodiversity measures, particularly the novel weighted complementary measures considered here, in conservation can enhance protection of the evolutionary history that contributes to present day biodiversity values of areas. Phylogenetic measures in conservation can include important elements of biodiversity in conservation planning, such as evolutionary potential and feature diversity that will improve decision-making and lead to better biodiversity conservation outcomes.


title: PixelNet: Representation of the pixels, by the pixels, and for the pixels

abstract: We explore design principles for general pixel-level prediction problems, from low-level edge detection to mid-level surface normal estimation to high-level semantic segmentation. Convolutional predictors, such as the fully-convolutional network (FCN), have achieved remarkable success by exploiting the spatial redundancy of neighboring pixels through convolutional processing. Though computationally efficient, we point out that such approaches are not statistically efficient during learning precisely because spatial redundancy limits the information learned from neighboring pixels. We demonstrate that stratified sampling of pixels allows one to (1) add diversity during batch updates, speeding up learning; (2) explore complex nonlinear predictors, improving accuracy; and (3) efficiently train state-of-the-art models tabula rasa (i.e., "from scratch") for diverse pixel-labeling tasks. Our single architecture produces state-of-the-art results for semantic segmentation on PASCAL-Context dataset, surface normal estimation on NYUDv2 depth dataset, and edge detection on BSDS.


title: Characteristics of a novel switched reluctance motor having permanent magnets between the stator pole-tips

abstract: This paper presents a novel switched reluctance (SR) motor which has permanent magnets between the stator pole-tips. The proposed SR motor has large torque and high efficiency in comparison with the conventional one. Neither induced voltage nor cogging torque is produced in spite of the existence of the permanent magnets. First, the effect of the permanent magnets is explained by finite element method (FEM). It is shown that the working area of the proposed SR motor is expanded by the permanent magnets. Next, the availability of the proposed motor is clarified experimentally. The maximum torque is increased by approximately 20% as compared with the conventional SR motor, and the efficiency under a full-load condition is improved remarkably. These mean that the proposed SR motor is suitable for driving an electric vehicle (EV) and hybrid electric vehicle (HEV) because a smooth acceleration under a full-load or overload conditions is always required.


title: TiSGeD: a database for tissue-specific genes

abstract: UNLABELLED ::: The tissue-specific genes are a group of genes whose function and expression are preferred in one or several tissues/cell types. Identification of these genes helps better understanding of tissue-gene relationship, etiology and discovery of novel tissue-specific drug targets. In this study, a statistical method is introduced to detect tissue-specific genes from more than 123 125 gene expression profiles over 107 human tissues, 67 mouse tissues and 30 rat tissues. As a result, a novel subject-specialized repository, namely the tissue-specific genes database (TiSGeD), is developed to represent the analyzed results. Auxiliary information of tissue-specific genes was also collected from biomedical literatures. ::: ::: ::: AVAILABILITY ::: http://bioinf.xmu.edu.cn/databases/TiSGeD/index.html.


title: General Automatic Human Shape and Motion Capture Using Volumetric Contour Cues

abstract: Markerless motion capture algorithms require a 3D body with properly personalized skeleton dimension and/or body shape and appearance to successfully track a person. Unfortunately, many tracking methods consider model personalization a different problem and use manual or semi-automatic model initialization, which greatly reduces applicability. In this paper, we propose a fully automatic algorithm that jointly creates a rigged actor model commonly used for animation – skeleton, volumetric shape, appearance, and optionally a body surface – and estimates the actor’s motion from multi-view video input only. The approach is rigorously designed to work on footage of general outdoor scenes recorded with very few cameras and without background subtraction. Our method uses a new image formation model with analytic visibility and analytically differentiable alignment energy. For reconstruction, 3D body shape is approximated as a Gaussian density field. For pose and shape estimation, we minimize a new edge-based alignment energy inspired by volume ray casting in an absorbing medium. We further propose a new statistical human body model that represents the body surface, volumetric Gaussian density, and variability in skeleton shape. Given any multi-view sequence, our method jointly optimizes the pose and shape parameters of this model fully automatically in a spatiotemporal way.


title: Comparison of LSB Steganography in GIF and BMP Images

abstract: Data encryption is not the only safe way to protect data from penetration given the tremendous development in the information age particularly in the field of speed of the processors and the cryptanalysis. In some cases, we need new te chnologies or methods to protect our secret data. There has emerg ed a set of new technologies that provides protection with or without data encryption technology such as data hiding. The word steganography literally means 'covered writing'. It is derived from the Greek words "stegos" meaning "cover", and "grafia" meaning "writing". Steganography functions to hide a secret message embedded in media such as text, image, audio and video. There are a lot of algorithms used in the field of information hiding in the media; the simplest and best known te chnique is Least Significant Bit (LSB). In this paper we have made a comparison and of the (LSB) algorithm using the cover object as an image. From this image, we have chosen two types: Gif and BMP. The comparison and is based on a number of criteria to find out the stren gths and


title: Investigating Voice Quality as a Speaker-Independent Indicator of Depression and PTSD

abstract: We seek to investigate voice quality characteristics, in particular on a breathy to tense dimension, as an indicator for psychological distress, i.e. depression and post-traumatic stress disorder (PTSD), within semi-structured virtual human interviews. Our evaluation identifies significant differences between the voice quality of psychologically distressed participants and not-distressed participants within this limited corpus. We investigate the capability of automatic algorithms to classify psychologically distressed speech in speaker-independent experiments. Additionally, we examine the impact of the posed questions’ affective polarity, as motivated by findings in the literature on positive stimulus attenuation and negative stimulus potentiation in emotional reactivity of psychologically distressed participants. The experiments yield promising results using standard machine learning algorithms and solely four distinct features capturing the tenseness of the speaker’s voice.


title: New Developments in Culinary Computational Creativity

abstract: In this paper, we report developments in the evaluation and generation processes in culinary computational creativity. In particular, we explore the personalization aspect of the quality and novelty assessment of newly created recipes. In addition, we argue that evaluation should be a part of the generation process and propose an optimization-based approach for the recipe creation problem. The experimental results show a more than 41% lift in the objective evaluation metrics when compared to a sampling approach to recipe creation.


title: Boosting Image Retrieval

abstract: We present an approach for image retrieval using a very large number of highly selective features and efficient learning of queries. Our approach is predicated on the assumption that each image is generated by a sparse set of visual “causes” and that images which are visually similar share causes. We propose a mechanism for computing a very large number of highly selective features which capture some aspects of this causal structure (in our implementation there are over 46,000 highly selective features). At query time a user selects a few example images, and the AdaBoost algorithm is used to learn a classification function which depends on a small number of the most appropriate features. This yields a highly efficient classification function. In addition we show that the AdaBoost framework provides a natural mechanism for the incorporation of relevance feedback. Finally we show results on a wide variety of image queries.


title: The capacity of low-density parity-check codes under message passing decoding

abstract: We present a general method for determining the capacity of low-density parity-check (LDPC) codes under message-passing decoding when used over any binary-input memoryless channel with discrete or continuous output alphabets. Transmitting at rates below this capacity, a randomly chosen element of the given ensemble will achieve an arbitrarily small target probability of error with a probability that approaches one exponentially fast in the length of the code. (By concatenating with an appropriate outer code one can achieve a probability of error that approaches zero exponentially fast in the length of the code with arbitrarily small loss in rate.) Conversely, transmitting at rates above this capacity the probability of error is bounded away from zero by a strictly positive constant which is independent of the length of the code and of the number of iterations performed. Our results are based on the observation that the concentration of the performance of the decoder around its average performance, as observed by Luby et al. in the case of a binary-symmetric channel and a binary message-passing algorithm, is a general phenomenon. For the particularly important case of belief-propagation decoders, we provide an effective algorithm to determine the corresponding capacity to any desired degree of accuracy. The ideas presented in this paper are broadly applicable and extensions of the general method to low-density parity-check codes over larger alphabets, turbo codes, and other concatenated coding schemes are outlined.


title: A Component-Based Dual Decomposition Method for the OPF Problem

abstract: This paper proposes a component-based dual decomposition of the nonconvex AC optimal power flow (OPF) problem, where the modified dual function is solved in a distributed fashion. The main contribution of this work is that is demonstrates that a distributed method with carefully tuned parameters can converge to globally optimal solutions despite the inherent nonconvexity of the problem and the absence of theoretical guarantees of convergence. This paper is the first to conduct extensive numerical analysis resulting in the identification and tabulation of the algorithmic parameter settings that are crucial for the convergence of the method on 72 AC OPF test instances. Moreover, this work provides a deeper insight into the geometry of the modified Lagrange dual function of the OPF problem and highlights the conditions that make this function differentiable. This numerical demonstration of convergence coupled with the scalability and the privacy preserving nature of the proposed method makes it well suited for smart grid applications such as multi-period OPF with demand response (DR) and security constrained unit commitment (SCUC) with contingency constraints and multiple transmission system operators (TSOs).


title: Atypical speech lateralization in adults with developmental coordination disorder demonstrated using functional transcranial Doppler ultrasound

abstract: Research using clinical populations to explore the relationship between hemispheric speech lateralization and handedness has focused on individuals with speech and language disorders, such as dyslexia or specific language impairment (SLI). Such work reveals atypical patterns of cerebral lateralization and handedness in these groups compared to controls. There are few studies that examine this relationship in people with motor coordination impairments but without speech or reading deficits, which is a surprising omission given the prevalence of theories suggesting a common neural network underlying both functions. We use an emerging imaging technique in cognitive neuroscience; functional transcranial Doppler (fTCD) ultrasound, to assess whether individuals with developmental coordination disorder (DCD) display reduced left-hemisphere lateralization for speech production compared to control participants. Twelve adult control participants and 12 adults with DCD, but no other developmental/cognitive impairments, performed a word-generation task whilst undergoing fTCD imaging to establish a hemispheric lateralization index for speech production. All participants also completed an electronic peg-moving task to determine hand skill. As predicted, the DCD group showed a significantly reduced left lateralization pattern for the speech production task compared to controls. Performance on the motor skill task showed a clear preference for the dominant hand across both groups; however, the DCD group mean movement times were significantly higher for the non-dominant hand. This is the first study of its kind to assess hand skill and speech lateralization in DCD. The results reveal a reduced leftwards asymmetry for speech and a slower motor performance. This fits alongside previous work showing atypical cerebral lateralization in DCD for other cognitive processes (e.g., executive function and short-term memory) and thus speaks to debates on theories of the links between motor control and language production.


Attention Networks for Aspect-Level Sentiment Classification


title: Test anxiety in elementary and secondary school students

abstract: Disclosed are (1) nucleic acid and amino acid sequences for a novel morphogenic protein; (2) methods for producing and expressing the protein in a biologically active form; and (3) methods for utilizing the protein to induce tissue morphogenesis in a mammal, including methods for increasing a progenitor cell population in a mammal, methods for stimulating progenitor cells to differentiate and maintain their differentiated phenotype in vivo or in vitro, methods for inducing tissue-specific growth in vivo and methods for the replacement of diseased or damaged tissue in vivo.


title: Support Vector Machine Approach for Protein Subcellular Localization Prediction

abstract: Motivation: Subcellular localization is a key functional characteristic of proteins. A fully automatic and reliable prediction system for protein subcellular localization is needed, especially for the analysis of large-scale genome sequences. Results: In this paper, Support Vector Machine has been introduced to predict the subcellular localization of proteins from their amino acid compositions. The total prediction accuracies reach 91.4% for three subcellular locations in prokaryotic organisms and 79.4% for four locations in eukaryotic organisms. Predictions by our approach are robust to errors in the protein N-terminal sequences. This new approach provides superior prediction performance compared with existing algorithms based on amino acid composition and can be a complementary method to other existing methods based on sorting signals. Availability: A web server implementing the prediction method is available at http://www.bioinfo.tsinghua.edu.cn/ SubLoc/.


title: CDD: NCBI's conserved domain database

abstract: NCBI's CDD, the Conserved Domain Database, enters its 15(th) year as a public resource for the annotation of proteins with the location of conserved domain footprints. Going forward, we strive to improve the coverage and consistency of domain annotation provided by CDD. We maintain a live search system as well as an archive of pre-computed domain annotation for sequences tracked in NCBI's Entrez protein database, which can be retrieved for single sequences or in bulk. We also maintain import procedures so that CDD contains domain models and domain definitions provided by several collections available in the public domain, as well as those produced by an in-house curation effort. The curation effort aims at increasing coverage and providing finer-grained classifications of common protein domains, for which a wealth of functional and structural data has become available. CDD curation generates alignment models of representative sequence fragments, which are in agreement with domain boundaries as observed in protein 3D structure, and which model the structurally conserved cores of domain families as well as annotate conserved features. CDD can be accessed at http://www.ncbi.nlm.nih.gov/Structure/cdd/cdd.shtml.


title: Robot Learning from Demonstration: A Task-level Planning Approach

abstract: In this paper, we deal with the problem of learning by demonstration, task level learning and planning for robotic applications that involve object manipulation. Preprogramming robots for execution of complex domestic tasks such as setting a dinner table is of little use, since the same order of subtasks may not be conceivable in the run time due to the changed state of the world. In our approach, we aim to learn the goal of the task and use a task planner to reach the goal given different initial states of the world. For some tasks, there are underlying constraints that must be fulfille, and knowing just the final goal is not sufficient. We propose two techniques for constraint identification. In the first case, the teacher can directly instruct the system about the underlying constraints. In the second case, the constraints are identified by the robot itself based on multiple observations. The constraints are then considered in the planning phase, allowing the task to be executed without violating any of them. We evaluate our work on a real robot performing pick-and-place tasks.


title: Projective invariant multiscale analysis

abstract: Thanks to the use of a 3D homogeneous representation of a picture, we present a multiscale analysis (T/sub t//sup P/), t/spl isin/R/sup +/, P/spl isin//spl sime/S/sup 2/, which is invariant under the projective group: let g be a picture in the plane; for every projective transformation A, there exist t'=t'(A, t), Q=Q(A, P) such that A(T/sub t'//sup Q/g)=T/sub t//sup P/(Ag). Moreover, this study allows us to propose simplified multiscale analysis, which are given by a unique PDE, for subgroups of the projective group: the subgroups of the projective transformations which leave invariant a line in the plane; the subgroup of the projective transformations associated, up to a non-zero scalar factor, to an orthogonal 3,3 matrix.


title: Ambient Backscatter Communications: A Contemporary Survey

abstract: Recently, ambient backscatter communications has been introduced as a cutting-edge technology which enables smart devices to communicate by utilizing ambient radio frequency (RF) signals without requiring active RF transmission. This technology is especially effective in addressing communication and energy efficiency problems for low-power communications systems such as sensor networks. It is expected to realize numerous Internet-of-Things (IoT) applications. Therefore, this paper aims to provide a contemporary and comprehensive literature review on fundamentals, applications, challenges, and research efforts/progress of ambient backscatter communications. In particular, we first present fundamentals of backscatter communications and briefly review bistatic backscatter communications systems. Then, the general architecture, advantages, and solutions to address existing issues and limitations of ambient backscatter communications systems are discussed. Additionally, emerging applications of ambient backscatter communications are highlighted. Finally, we outline some open issues and future research directions.


Research on Software Security Testing


title: Towards smart phone based monitoring of bipolar disorder

abstract: We describe a real life study of the use of smart phone based sensors for state monitoring of bipolar disorder (manic-depressive disorder). The study was conducted with 10 patients in a rural area of Austria under the supervision of a local psychiatric hospital. It shows initial evidence that relatively simple features derived from location, motion and phone call patterns are a good indication of state transitions.


title: FOIL it! Find One mismatch between Image and Language caption

abstract: In this paper, we aim to understand whether current language and vision (LaVi) models truly grasp the interaction between the two modalities. To this end, we propose an extension of the MSCOCO dataset, FOIL-COCO, which associates images with both correct and "foil" captions, that is, descriptions of the image that are highly similar to the original ones, but contain one single mistake ("foil word"). We show that current LaVi models fall into the traps of this data and perform badly on three tasks: a) caption classification (correct vs. foil); b) foil word detection; c) foil word correction. Humans, in contrast, have near-perfect performance on those tasks. We demonstrate that merely utilising language cues is not enough to model FOIL-COCO and that it challenges the state-of-the-art by requiring a fine-grained understanding of the relation between text and image.


title: A review of methods for spike sorting : the detection and classification of neural action potentials

abstract: The detection of neural spike activity is a technical challenge that is a prerequisite for studying many types of brain function. Measuring the activity of individual neurons accurately can be difficult due to large amounts of background noise and the difficulty in distinguishing the action potentials of one neuron from those of others in the local area. This article reviews algorithms and methods for detecting and classifying action potentials, a problem commonly referred to as spike sorting. The article first discusses the challenges of measuring neural activity and the basic issues of signal detection and classification. It reviews and illustrates algorithms and techniques that have been applied to many of the problems in spike sorting and discusses the advantages and limitations of each and the applicability of these methods for different types of experimental demands. The article is written both for the physiologist wanting to use simple methods that will improve experimental yield and minimize the selection biases of traditional techniques and for those who want to apply or extend more sophisticated algorithms to meet new experimental challenges.


title: Power- and area-efficient Approximate Wallace Tree Multiplier for error-resilient systems

abstract: Today in sub-nanometer regime, chip/system designers add accuracy as a new constraint to optimize Latency-Power-Area (LPA) metrics. In this paper, we present a new power and area-efficient Approximate Wallace Tree Multiplier (AWTM) for error-tolerant applications. We propose a bit-width aware approximate multiplication algorithm for optimal design of our multiplier. We employ a carry-in prediction method to reduce the critical path. It is further augmented with hardware efficient precomputation of carry-in. We also optimize our multiplier design for latency, power and area using Wallace trees. Accuracy as well as LPA design metrics are used to evaluate our approximate multiplier designs of different bit-widths, i.e. 4 × 4, 8 × 8 and 16 × 16. The simulation results show that we obtain a mean accuracy of 99.85% to 99.965%. Single cycle implementation of AWTM gives almost 24% reduction in latency. We achieve significant reduction in power and area, i.e. up to 41.96% and 34.49% respectively that clearly demonstrates the merits of our proposed AWTM design. Finally, AWTM is used to perform a real time application on a benchmark image. We obtain up to 39% reduction in power and 30% reduction in area without any loss in image quality.


title: Tools to support systematic reviews in software engineering: a cross-domain survey using semi-structured interviews

abstract: Background: A number of software tools are being developed to support systematic reviewers within the software engineering domain. However, at present, we are not sure which aspects of the review process can most usefully be supported by such tools or what characteristics of the tools are most important to reviewers. Aim: The aim of the study is to explore the scope and practice of tool support for systematic reviewers in other disciplines. Method: Researchers with experience of performing systematic reviews in Healthcare and the Social Sciences were surveyed. Qualitative data was collected through semi-structured interviews and data analysis followed an inductive approach. Results: 13 interviews were carried out. 21 software tools categorised into one of seven types were identified. Reference managers were the most commonly mentioned tools. Features considered particularly important by participants were support for multiple users, support for data extraction and support for tool maintenance. The features and importance levels identified by participants were compared with those proposed for tools to support systematic reviews in software engineering. Conclusions: Many problems faced by systematic reviewers in other disciplines are similar to those faced in software engineering. There is general consensus across domains that improved tools are needed.


title: On the Use of Soft-Decision Error-Correction Codes in nand Flash Memory

abstract: As technology continues to scale down, NAND Flash memory has been increasingly relying on error-correction codes (ECCs) to ensure the overall data storage integrity. Although advanced ECCs such as low-density parity-check (LDPC) codes can provide significantly stronger error-correction capability over BCH codes being used in current practice, their decoding requires soft-decision log-likelihood ratio (LLR) information. This results in two critical issues. First, accurate calculation of LLR demands fine-grained memory-cell sensing, which nevertheless tends to incur implementation overhead and access latency penalty. Hence, it is critical to minimize the fine-grained memory sensing precision. Second, accurate calculation of LLR also demands the availability of a memory-cell threshold-voltage distribution model. As the major source for memory-cell threshold-voltage distribution distortion, cell-to-cell interference must be carefully incorporated into the model. However, these two critical issues have not been ever addressed in the open literature. This paper attempts to address these open issues. We derive mathematical formulations to approximately model the threshold-voltage distribution of memory cells in the presence of cell-to-cell interference, based on which the calculation of LLRs is mathematically formulated. This paper also proposes a nonuniform memory sensing strategy to reduce the memory sensing precision and, thus, sensing latency while still maintaining good error-correction performance. In addition, we investigate these design issues under the scenario when we can also sense interfering cells and hence explicitly estimate cell-to-cell interference strength. We carry out extensive computer simulations to demonstrate the effectiveness and involved tradeoffs, assuming the use of LDPC codes in 2-bits/cell NAND Flash memory.


title: Deep neural networks for anatomical brain segmentation

abstract: We present a novel approach to automatically segment magnetic resonance (MR) images of the human brain into anatomical regions. Our methodology is based on a deep artificial neural network that assigns each voxel in an MR image of the brain to its corresponding anatomical region. The inputs of the network capture information at different scales around the voxel of interest: 3D and orthogonal 2D intensity patches capture a local spatial context while downscaled large 2D orthogonal patches and distances to the regional centroids enforce global spatial consistency. Contrary to commonly used segmentation methods, our technique does not require any non-linear registration of the MR images. To benchmark our model, we used the dataset provided for the MICCAI 2012 challenge on multi-atlas labelling, which consists of 35 manually segmented MR images of the brain. We obtained competitive results (mean dice coefficient 0.725, error rate 0.163) showing the potential of our approach. To our knowledge, our technique is the first to tackle the anatomical segmentation of the whole brain using deep neural networks.


A Production Scheduling Strategy for an Assembly Plant based on Reinforcement Learning


title: SGM-based dense disparity estimation using adaptive Census transform

abstract: Generating an accurate and dense disparity image is one of the important requirements for many applications such as 3D video and stereo vision-based advanced driver assistance systems (ADAS). Depth estimation is the process of obtaining a depth map based on two or more reference images. Recently, several techniques that use semi-global optimization for estimating depth maps have been suggested. Although robustness against illumination changes is a vital factor in applications like ADAS, semi-global matching (SGM) based on mutual information achieves limited performance under illumination changes. In this paper, a modified SGM algorithm is proposed which is based on adaptive window patterns of census transform. The goal of the proposed method is to improve the quality of the estimated depth map while reducing the processing time and making it applicable for depth-based pedestrian detection techniques. To enhance the quality of the estimated depth map, a spatial Gaussian weighted averaging filter along with a color-aware filter is implemented. To demonstrate the efficiency of the proposed method, the Middlebury stereo dataset and the KITTI vision benchmark have been used on the experiments. Experimental results show that the proposed method reduces the percentage of bad pixels by 0.7-1.2% for the test sequences compared to the original SGM algorithm with reduced processing time.


title: Linked knowledge sources for topic classification of microposts: a semantic graph-based approach

abstract: Short text messages, a.k.a microposts (e.g., tweets), have proven to be an effective channel for revealing information about trends and events, ranging from those related to disaster (e.g., Hurricane Sandy) to those related to violence (e.g., Egyptian revolution). Being informed about such events as they occur could be extremely important to authorities and emergency professionals by allowing such parties to immediately respond. In this work we study the problem of topic classification (TC) of microposts, which aims to automatically classify short messages based on the subject(s) discussed in them. The accurate TC of microposts however is a challenging task since the limited number of tokens in a post often implies a lack of sufficient contextual information. In order to provide contextual information to microposts, we present and evaluate several graph structures surrounding concepts present in linked knowledge sources (KSs). Traditional TC techniques enrich the content of microposts with features extracted only from the microposts content. In contrast our approach relies on the generation of different weighted semantic meta-graphs extracted from linked KSs. We introduce a new semantic graph, called category meta-graph. This novel meta-graph provides a more fine grained categorisation of concepts providing a set of novel semantic features. Our findings show that such category meta-graph features effectively improve the performance of a topic classifier of microposts. Furthermore our goal is also to understand which semantic feature contributes to the performance of a topic classifier. For this reason we propose an approach for automatic estimation of accuracy loss of a topic classifier on new, unseen microposts. We introduce and evaluate novel topic similarity measures, which capture the similarity between the KS documents and microposts at a conceptual level, considering the enriched representation of these documents. Extensive evaluation in the context of Emergency Response (ER) and Violence Detection (VD) revealed that our approach outperforms previous approaches using single KS without linked data and Twitter data only up to 31.4% in terms of F1 measure. Our main findings indicate that the new category graph contains useful information for TC and achieves comparable results to previously used semantic graphs. Furthermore our results also indicate that the accuracy of a topic classifier can be accurately predicted using the enhanced text representation, outperforming previous approaches considering content-based similarity measures.