title: A survey of Context-aware Recommender System and services

abstract: The advancement of the Internet, mobile and wireless technologies have produced a rapid change of information in terms of volume and accessibility. The enormous volume of information can be devastating especially to mobile users exceeding human ability to differentiate information which is relevant and that which is irrelevant. For many yzears now, recommender system have become well-known, and have been studied in various domains such as e-learning, online shopping, tourism to help overcome information overload. Recommendations are produced based on users who have interests in a particular thing or item. This recommendation process was further enhanced by incorporating context such as time, weather, and location to make recommendations more accurate and efficient. However, these systems have introduced context-aware recommender systems. This paper presents a survey of Context-aware recommender systems, the background and algorithms of Context-aware Recommender System, and also discusses the open issues of context-aware recommender systems.


title: State of the art of graph-based data mining

abstract: The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well.


title: An Optimized FFT-Based Direct Poisson Solver on CUDA GPUs

abstract: A highly multithreaded FFT-based direct Poisson solver that makes effective use of the capabilities of the current NVIDIA graphics processing units (GPUs) is presented. Our algorithms carefully manage the multiple layers of the memory hierarchy of the GPUs such that almost all the global memory accesses are coalesced into 128-byte device memory transactions, and all computations are carried out directly on the registers. A new strategy to interleave the FFT computation along each dimension with other computations is used to minimize the total number of accesses to the 3D grid. We illustrate the performance of our algorithms on the NVIDIA Tesla and Fermi architectures for a wide range of grid sizes, up to the largest size that can fit on the device memory ($(512\times 512\times 512)$ on the Tesla C1060/C2050 and $(512\times 256\times 256)$ on the GeForce GTX 280/480). We achieve up to 140 GFLOPS and a bandwidth of 70 GB/s on the Tesla C1060, and up to 375 GFLOPS with a bandwidth of 120GB/s on the GTX 480. The performance of our algorithms is superior to what can be achieved using the CUDA FFT library in combination with well-known parallel algorithms for solving tridiagonal linear systems of equations.


title: Transfer Sparse Coding for Robust Image Representation

abstract: Sparse coding learns a set of basis functions such that each input signal can be well approximated by a linear combination of just a few of the bases. It has attracted increasing interest due to its state-of-the-art performance in BoW based image representation. However, when labeled and unlabeled images are sampled from different distributions, they may be quantized into different visual words of the codebook and encoded with different representations, which may severely degrade classification performance. In this paper, we propose a Transfer Sparse Coding (TSC) approach to construct robust sparse representations for classifying cross-distribution images accurately. Specifically, we aim to minimize the distribution divergence between the labeled and unlabeled images, and incorporate this criterion into the objective function of sparse coding to make the new representations robust to the distribution difference. Experiments show that TSC can significantly outperform state-of-the-art methods on three types of computer vision datasets.


title: Hybrid Transformer ZVS/ZCS DC–DC Converter With Optimized Magnetics and Improved Power Devices Utilization for Photovoltaic Module Applications

abstract: This paper presents a nonisolated, high boost ratio dc-dc converter with the application for photovoltaic (PV) modules. The proposed converter utilizes a hybrid transformer to incorporate the resonant operation mode into a traditional high boost ratio active-clamp coupled-inductor pulse-width-modulation dc-dc converter, achieving zero-voltage-switching (ZVS) turn-on of active switches and zero-current-switching turn-off of diodes. As a result of the inductive and capacitive energy being transferred simultaneously within the whole switching period, power device utilization (PDU) is improved and magnetic utilization (MU) is optimized. The improved PDU allows reduction of the silicon area required to realize the power devices of the converter. The optimized MU reduces the dc-bias of magnetizing current in the magnetic core, leading to smaller sized magnetics. Since the magnetizing current has low dc-bias, the ripple magnetizing current can be utilized to assist ZVS of main switch, while maintaining low root-mean-square (RMS) conduction loss. The voltage stresses on the active switches and diodes are maintained at a low level and are independent of the wide changing PV voltages as a result of the resonant capacitor in series in the energy transfer loop. The experimental results based on 250 W prototype circuit show 97.7% peak efficiency and system CEC efficiencies greater than 96.7% over 20 to 45 V input voltages. Due to the high efficiency over wide power range, the ability to operate with a wide variable input voltage and compact size, the proposed converter is an attractive design for PV module applications.


title: Cognitive behavioral therapy in anxiety disorders: current state of the evidence

abstract: A plethora of studies have examined the efficacy and effectiveness of cognitive-behavioral therapy (CBT) for adult anxiety disorders. In recent years, several meta-analyses have been conducted to quantitatively review the evidence of CBT for anxiety disorders, each using different inclusion criteria for studies, such as use of control conditions or type of study environment. This review aims to summarize and to discuss the current state of the evidence regarding CBT treatment for panic disorder, generalized anxiety disorder, social anxiety disorder, obsessive-compulsive disorder, and post-traumatic stress disorder. Overall, CBT demonstrates both efficacy in randomized controlled trials and effectiveness in naturalistic settings in the treatment of adult anxiety disorders. However, due to methodological issues, the magnitude of effect is currently difficult to estimate. In conclusion, CBT appears to be both efficacious and effective in the treatment of anxiety disorders, but more high-quality studies are needed to better estimate the magnitude of the effect.


title: Metaphor Identification Using Verb and Noun Clustering

abstract: We present a novel approach to automatic metaphor identification in unrestricted text. Starting from a small seed set of manually annotated metaphorical expressions, the system is capable of harvesting a large number of metaphors of similar syntactic structure from a corpus. Our method is distinguished from previous work in that it does not employ any hand-crafted knowledge, other than the initial seed set, but, in contrast, captures metaphoricity by means of verb and noun clustering. Being the first to employ unsupervised methods for metaphor identification, our system operates with the precision of 0.79.


title: Hippocampal Replay Is Not a Simple Function of Experience

abstract: Summary Replay of behavioral sequences in the hippocampus during sharp wave ripple complexes (SWRs) provides a potential mechanism for memory consolidation and the learning of knowledge structures. Current hypotheses imply that replay should straightforwardly reflect recent experience. However, we find these hypotheses to be incompatible with the content of replay on a task with two distinct behavioral sequences (A and B). We observed forward and backward replay of B even when rats had been performing A for >10 min. Furthermore, replay of nonlocal sequence B occurred more often when B was infrequently experienced. Neither forward nor backward sequences preferentially represented highly experienced trajectories within a session. Additionally, we observed the construction of never-experienced novel-path sequences. These observations challenge the idea that sequence activation during SWRs is a simple replay of recent experience. Instead, replay reflected all physically available trajectories within the environment, suggesting a potential role in active learning and maintenance of the cognitive map.


title: Multi-view 3D Models from Single Images with a Convolutional Network

abstract: We present a convolutional network capable of inferring a 3D representation of a previously unseen object given a single image of this object. Concretely, the network can predict an RGB image and a depth map of the object as seen from an arbitrary view. Several of these depth maps fused together give a full point cloud of the object. The point cloud can in turn be transformed into a surface mesh. The network is trained on renderings of synthetic 3D models of cars and chairs. It successfully deals with objects on cluttered background and generates reasonable predictions for real images of cars.


title: An Experimental Evaluation of a Monte-Carlo Algorithm for Singular Value Decomposition

abstract: We demonstrate that an algorithm proposed by Drineas et. al. in [7] to approximate the singular vectors/values ofa matrix A, is not only oft heoretical interest but also a fast, viable alternative to traditional algorithms. The algorithm samples a small number ofro ws (or columns) oft he matrix, scales them appropriately to form a small matrix S and computes the singular value decomposition (SVD) of S, which is a good approximation to the SVD ofthe original matrix. We experimentally evaluate the accuracy and speed oft his randomized algorithm using image matrices and three different sampling schemes. Our results show that our approximations oft he singular vectors of A span almost the same space as the corresponding exact singular vectors of A.


title: 3D object proposals for accurate object class detection

abstract: The goal of this paper is to generate high-quality 3D object proposals in the context of autonomous driving. Our method exploits stereo imagery to place proposals in the form of 3D bounding boxes. We formulate the problem as minimizing an energy function encoding object size priors, ground plane as well as several depth informed features that reason about free space, point cloud densities and distance to the ground. Our experiments show significant performance gains over existing RGB and RGB-D object proposal methods on the challenging KITTI benchmark. Combined with convolutional neural net (CNN) scoring, our approach outperforms all existing results on all three KITTI object classes.


title: An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data

abstract: Due to the intermittency and randomness of solar photovoltaic (PV) power, it is difficult for system operators to dispatch PV power stations. In order to find a precise expectation for day-ahead PV power generation, conventional models have taken into consideration the temperature, humidity, and wind speed data for forecasting, but these predictions were always not accurate enough under extreme weather conditions. Aerosol index (AI), which indicates the particulate matter in the atmosphere, has been found to have strong linear correlation with solar radiation attenuation, and might have potential influence on the power generated by PV panels. A novel PV power forecasting model is proposed in this paper, considering AI data as an additional input parameter. Based on seasonal weather classification, the back propagation (BP) artificial neural network (ANN) approach is utilized to forecast the next 24-h PV power outputs. The estimated results of the proposed PV power forecasting model coincide well with measurement data, and the proposed model has shown the ability of improving prediction accuracy, compared with conventional methods using ANN.


title: Symbiotic Cognitive Computing

abstract: IBM Research is engaged in a research program in symbiotic cognitive computing to investigate how to embed cognitive computing in physical spaces. This article proposes 5 key principles of symbiotic cognitive computing. We describe how these principles are applied in a particular symbiotic cognitive computing environment and in an illustrative application.


title: Large Scale Graph Learning from Smooth Signals

abstract: Graphs are a prevalent tool in data science, as they model the inherent structure of the data. They have been used successfully in unsupervised and semi-supervised learning. Typically they are constructed either by connecting nearest samples, or by learning them from data, solving an optimization problem. While graph learning does achieve a better quality, it also comes with a higher computational cost. In particular, the current state-of-the-art model cost is $\mathcal{O}(n^2)$ for $n$ samples. In this paper, we show how to scale it, obtaining an approximation with leading cost of $\mathcal{O}(n\log(n))$, with quality that approaches the exact graph learning model. Our algorithm uses known approximate nearest neighbor techniques to reduce the number of variables, and automatically selects the correct parameters of the model, requiring a single intuitive input: the desired edge density.


Identification of Talent in Soccer – What Do Coaches Look For ?


The Karlsruhe ARMAR Humanoid Robot Family


title: Comparison of winding topologies in a pot core rotating transformer

abstract: This paper discusses the comparison of two winding topologies in a contactless energy transfer system from the stationary to the rotating part of a device. A rotating transformer, based on a pot core geometry, is proposed as a replacement for wires and slip rings. An electromagnetic and a thermal model of the rotating transformer are derived. The models are combined and used in a multi-objective optimization. A Pareto front, in terms of minimal volume and power losses, is derived to compare both winding topologies. Finally, the optimization algorithm is used to design a prototype transformer for each winding topology, which are manufactured using a commercially available pot core.


title: Nanostructured materials for advanced energy conversion and storage devices

abstract: New materials hold the key to fundamental advances in energy conversion and storage, both of which are vital in order to meet the challenge of global warming and the fi nite nature of fossil fuels. Nanomaterials in particular offer unique properties or combinations of properties as electrodes and electrolytes in a range of energy devices. This review describes some recent developments in the discovery of nanoelectrolytes and nanoelectrodes for lithium batteries, fuel cells and supercapacitors. The advantages and disadvantages of the nanoscale in materials design for such devices are highlighted.


title: Exponential function and its derivative revisited

abstract: Most of the available proofs for rely on results involving either power series, uniform convergence or a round-about definition of the natural logarithm function ln(x) by the definite integral , and are thus not readily accessible by high school teachers and students. Even instructors of calculus courses avoid showing the complete proof to their undergraduate students because a direct and elementary approach is missing. This short article fills in this gap by supplying a simple proof of the aforementioned basic calculus fact.


title: Minimally supervised question classification on fine-grained taxonomies

abstract: This article presents a minimally supervised approach to question classification on fine-grained taxonomies. We have defined an algorithm that automatically obtains lists of weighted terms for each class in the taxonomy, thus identifying which terms are highly related to the classes and are highly discriminative between them. These lists have then been applied to the task of question classification. Our approach is based on the divergence of probability distributions of terms in plain text retrieved from the Web. A corpus of questions with which to train the classifier is not therefore necessary. As the system is based purely on statistical information, it does not require additional linguistic resources or tools. The experiments were performed on English questions and their Spanish translations. The results reveal that our system surpasses current supervised approaches in this task, obtaining a significant improvement in the experiments carried out.


title: Representation of perceived object shape by the human lateral occipital complex.

abstract: The human lateral occipital complex (LOC) has been implicated in object recognition, but it is unknown whether this region represents low-level image features or perceived object shape. We used an event-related functional magnetic resonance imaging adaptation paradigm in which the response to pairs of successively presented stimuli is lower when they are identical than when they are different. Adaptation across a change between the two stimuli in a pair provides evidence for a common neural representation invariant to that change. We found adaptation in the LOC when perceived shape was identical but contours differed, but not when contours were identical but perceived shape differed. These data indicate that the LOC represents not simple image features, but rather higher level shape information.


title: Religion and Morality

abstract: The relationship between religion and morality has long been hotly debated. Does religion make us more moral? Is it necessary for morality? Do moral inclinations emerge independently of religious intuitions? These debates, which nowadays rumble on in scientific journals as well as in public life, have frequently been marred by a series of conceptual confusions and limitations. Many scientific investigations have failed to decompose "religion" and "morality" into theoretically grounded elements; have adopted parochial conceptions of key concepts-in particular, sanitized conceptions of "prosocial" behavior; and have neglected to consider the complex interplay between cognition and culture. We argue that to make progress, the categories "religion" and "morality" must be fractionated into a set of biologically and psychologically cogent traits, revealing the cognitive foundations that shape and constrain relevant cultural variants. We adopt this fractionating strategy, setting out an encompassing evolutionary framework within which to situate and evaluate relevant evidence. Our goals are twofold: to produce a detailed picture of the current state of the field, and to provide a road map for future research on the relationship between religion and morality.


title: An Approximation of the Universal Intelligence Measure

abstract: The Universal Intelligence Measure is a recently proposed formal definition of intelligence. It is mathematically specified, extremely general, and captures the essence of many informal definitions of intelligence. It is based on Hutter’s Universal Artificial Intelligence theory, an extension of Ray Solomonoff’s pioneering work on universal induction. Since the Universal Intelligence Measure is only asymptotically computable, building a practical intelligence test from it is not straightforward. This paper studies the practical issues involved in developing a real-world UIM-based performance metric. Based on our investigation, we develop a prototype implementation which we use to evaluate a number of different artificial agents.


title: Language Models and Topic Models for Personalizing Tag Recommendation

abstract: More and more content on the Web is generated by users. To organize this information and make it accessible via current search technology, tagging systems have gained tremendous popularity. Especially for multimedia content they allow to annotate resources with keywords (tags) which opens the door for classic text-based information retrieval. To support the user in choosing the right keywords, tag recommendation algorithms have emerged. In this setting, not only the content is decisive for recommending relevant tags but also the user's preferences. In this paper we introduce an approach to personalized tag recommendation that combines a probabilistic model of tags from the resource with tags from the user. As models we investigate simple language models as well as Latent Dirichlet Allocation. Extensive experiments on a real world dataset crawled from a big tagging system show that personalization improves tag recommendation, and our approach significantly outperforms state-of-the-art approaches.


title: Smart Rehabilitation Garment for posture monitoring

abstract: Posture monitoring and correction technologies can support prevention and treatment of spinal pain or can help detect and avoid compensatory movements during the neurological rehabilitation of upper extremities, which can be very important to ensure their effectiveness. We describe the design and development of Smart Rehabilitation Garment (SRG) a wearable system designed to support posture correction. The SRG combines a number of inertial measurement units (IMUs), controlled by an Arduino processor. It provides feedback with vibration on the garment, audible alarm signals and visual instruction through a Bluetooth connected smartphone. We discuss the placement of sensing modules, the garment design, the feedback design and the integration of smart textiles and wearable electronics which aimed at achieving wearability and ease of use. We report on the system's accuracy as compared to optical tracker method.


title: A hybrid technique for License Plate Recognition based on feature selection of wavelet transform and artificial neural network

abstract: A number of vehicles may be controlled and supervised by traffic security and its management. The License Plate Recognition is broadly employed in traffic management to recognize a vehicle whose owner has despoiled traffic laws or to find stolen vehicles. Vehicle License Plate Detection and Recognition is a key technique in most of the traffic related applications such as searching of stolen vehicles, road traffic monitoring, airport gate monitoring, speed monitoring and automatic parking lots access control. It is simply the ability of automatically extract and recognition of the vehicle license number plate's character from a captured image. Number Plate Recognition method suffered from problem of feature selection process. The current method of number plate recognition system only focus on local, global and Neural Network process of Feature Extraction and process for detection. The Optimized Feature Selection process improves the detection ratio of number plate recognition. In this paper, it is proposed a new methodology for `License Plate Recognition' based on wavelet transform function. This proposed methodology compare with Correlation based method for detection of number plate. Empirical result shows that better performance in comparison of correlation based technique for number plate recognition. Here, it is modified the Matching Technique for numberplate recognition by using Multi-Class RBF Neural Network Optimization.


title: Storage-less and converter-less maximum power point tracking of photovoltaic cells for a nonvolatile microprocessor

abstract: This paper pioneers the maximum power point tracking (MPPT) of photovoltaic (PV) cells that directly supply power to a microprocessor without an energy storage element (a battery or a large-size capacitor) nor power converters. The maximum power point tracking is conventionally performed by an MPPT charger that stores in the energy storage element, and a voltage regulator (typically a DC-DC converter) produces a proper voltage level for the microprocessor. The energy storage element is an energy buffer and makes it possible to perform MPPT of the PV cells and power management of the microprocessor independently. However, the energy storage element, MPPT charger and DC-DC converter cause seriously limited lifetime (when a typical battery is adopted), significant energy loss (typically over 20%), increased weight/volume and high cost, etc. The proposed method enables extremely fine-grain dynamic power management (DPM) in every a few hundred microseconds and performs the MPPT without using an MPPT charger and a DC-DC converter as well as an energy storage element. We achieve 84.5% of energy harvesting efficiency using the proposed setup with huge reduction in cost, weight and volume, and extended lifetime, which is not even numerically comparable with conventional MPPT methods.


title: Analysis of Emotion Recognition System through Speech Signal Using KNN & GMM Classifier

abstract: In machine interaction with human being is yet challenging task that machine should be able to identify and react to human non-verbal communication such as emotions which makes the human computer interaction become more natural. In present research area automatic emotion recognition using speech is an essential task which paid close attention. Speech signal is a rich source of information and it is an attractive and efficient medium due to its numerous features of expressing approach & extracting emotions through speech is possible. In this paper emotions is recognized through speech using spectral features such as Mel frequency cepstrum coefficient prosodic features like pitch , energy and were utilized & study is carried out using K- Nearest Neighbor classifiers and Gaussian mixture model classifier which is used for detection of six basic emotional states of speaker's such as anger ,happiness , sadness , fear , disgust and neutral using Berlin emotional speech database.


title: Cartography of complex networks: modules and universal roles

abstract: Integrative approaches to the study of complex systems demand that one knows the manner in which the parts comprising the system are connected. The structure of the complex network defining the interactions provides insight into the function and evolution of the components of the system. Unfortunately, the large size and intricacy of these networks implies that such insight is usually difficult to extract. Here, we propose a method that allows one to systematically extract and display information contained in complex networks. Specifically, we demonstrate that one can (i) find modules in complex networks and (ii) classify nodes into universal roles according to their pattern of within- and between-module connections. The method thus yields a 'cartographic representation' of complex networks.


title: Real Time Operation of Smart Grids via FCN Networks and Optimal Power Flow

abstract: This paper proposes an Energy Management System for the optimal operation of Smart Grids and Microgrids, using Fully Connected Neuron Networks combined with Optimal Power Flow. An adaptive training algorithm based on Genetic Algorithms, Fuzzy Clustering and Neuron-by-Neuron Algorithms is used for generating new clusters and new neural networks. The proposed approach, integrating Demand Side Management and Active Management Schemes, allows significant enhancements in energy saving, customers' active participation in the open market and exploitation of renewable energy resources. The effectiveness of the proposed Energy Management System and adaptive training algorithm is verified on a 23-bus 11 kV microgrid.


title: Cognitive Level of Academic Challenges Provided to College Students.

abstract: A study assessed the cognitive level of academic challenges incorporated into courses offered in the College of Agricultural Sciences at the Pennsylvania State University. Eleven faculty members from nine departments provided copies of all academic challenges used in their courses. They were categorized by type: activities, problem sets, written reports, presentations, laboratory tests, quizzes, midterms, and finals. A number corresponding to one of the six levels of Bloom's taxonomy (knowledge, comprehension, application, analysis, synthesis, evaluation) was assigned to each individual task, question, problem, or action. The value of the academic challenge to students' course grade was calculated based on the grade weighting information provided in each course's syllabus. Findings indicated that the mean cognitive distribution for all the courses' academic challenges was fairly even across the six cognitive levels with an emphasis on the higher cognitive levels. On average, 31.4 percent of the student's grade came from work at the lower cognitive levels and 66.6 from work at the higher levels. Individual presentations, quizzes, midterm exams, and final exams tended to provide lower cognitive challenges for students. Generally, students were rewarded more for completing academic challenges written at the higher cognitive levels and less for lower cognitive work. (Contains 12 references) (YLB) ******************************************************************************** * Reproductions supplied by EDRS are the best that can be made * * from the original document. * ******************************************************************************** COGNITIVE LEVEL OF ACADEMIC CHALLENGES PROVIDED TO COLLEGE STUDENTS Dr. M. Susie Whittington Assistant Professor The Pennsylvania State University 101 Agricultural Administration University Park, PA 16802


title: Linear and Nonlinear Programming

abstract: Linear programs (LPs) and nonlinear programs (NLPs) are mathematical problems in which data are used to find the values of variables that minimize or maximize an objective function while simultaneously satisfying several imposed constraints on the values of the variables. Such problems arise frequently in computer science, mathematics, business, economics, statistics, engineering, operations research, and the sciences. An overview of the similarities and differences between LPs and NLPs is presented here. For example, although the art of building LP and NLP models involves identifying the variables, objective function, and constraints, methods for solving such problems differ greatly. A finite procedure has been developed to solve all LPs; however, no such procedure is available for solving NLPs. ::: ::: ::: Keywords: ::: ::: linear programming; ::: nonlinear programming; ::: optimization; ::: linear optimization; ::: nonlinear optimization; ::: mathematical models; ::: operations research


title: Mobile Vehicle-to-Vehicle Narrow-Band Channel Measurement and Characterization of the 5.9 GHz Dedicated Short Range Communication (DSRC) Frequency Band

abstract: This study presents narrow-band measurements of the mobile vehicle-to-vehicle propagation channel at 5.9 GHz, under realistic suburban driving conditions in Pittsburgh, Pennsylvania. Our system includes differential Global Positioning System (DGPS) receivers, thereby enabling dynamic measurements of how large-scale path loss, Doppler spectrum, and coherence time depend on vehicle location and separation. A Nakagami distribution is used for describing the fading statistics. The speed-separation diagram is introduced as a new tool for analyzing and understanding the vehicle-to-vehicle propagation environment. We show that this diagram can be used to model and predict channel Doppler spread and coherence time using vehicle speed and separation.


title: A Multi-Domain SDN Scalability Architecture Implementation Based on the Coordinate Controller

abstract: Software-defined Networking, known as SDN, is a novel architecture that decouples the control plane from the data plane, and opens a programmable network. However, it is common practice for network operators to divide the large-scale networks into multiple domains and manage the networks by using a group of controllers. Designing a scalable SDN control plane becomes a critical problem. In this paper, we propose a SDN scalability architecture for multi-domain, multi-vendor networking. We design and implement the Coordinator Controller to enable different SDN administrative domains to cooperate by unifying the northbound APIs of the controllers which are working for different SDN domains. To validate the proposed architecture, we build a multi-domain experiment environment consisting of three vendors. The results show great ability in the network state view consistency and end-to-end provisioning services.


title: Particle Filter Networks: End-to-End Probabilistic Localization From Visual Observations

abstract: Particle filters sequentially approximate posterior distributions by sampling representative points and updating them independently. The idea is applied in various domains, e.g. reasoning with uncertainty in robotics. A remaining challenge is constructing probabilistic models of the system, which can be especially hard for complex sensors, e.g. a camera. We introduce the Particle Filter Networks (PF-nets) that encode both a learned probabilistic system model and the particle filter algorithm in a single neural network architecture. The unified representation allows learning models end-to-end, circumventing the difficulties of conventional model-based methods. We applied PF-nets to a challenging visual localization task that requires matching visual features from camera images with the geometry encoded in a 2-D floor map. In preliminary experiments end-to-end PF-nets consistently outperformed alternative learning architectures, as well as conventional model-based methods.


title: Convex incremental extreme learning machine

abstract: Unlike the conventional neural network theories and implementations, Huang et al. [Universal approximation using incremental constructive feedforward networks with random hidden nodes, IEEE Transactions on Neural Networks 17(4) (2006) 879-892] have recently proposed a new theory to show that single-hidden-layer feedforward networks (SLFNs) with randomly generated additive or radial basis function (RBF) hidden nodes (according to any continuous sampling distribution) can work as universal approximators and the resulting incremental extreme learning machine (I-ELM) outperforms many popular learning algorithms. I-ELM randomly generates the hidden nodes and analytically calculates the output weights of SLFNs, however, I-ELM does not recalculate the output weights of all the existing nodes when a new node is added. This paper shows that while retaining the same simplicity, the convergence rate of I-ELM can be further improved by recalculating the output weights of the existing nodes based on a convex optimization method when a new hidden node is randomly added. Furthermore, we show that given a type of piecewise continuous computational hidden nodes (possibly not neural alike nodes), if SLFNs f"n(x)=@?i=1n@b"iG(x,a"i,b"i) can work as universal approximators with adjustable hidden node parameters, from a function approximation point of view the hidden node parameters of such ''generalized'' SLFNs (including sigmoid networks, RBF networks, trigonometric networks, threshold networks, fuzzy inference systems, fully complex neural networks, high-order networks, ridge polynomial networks, wavelet networks, etc.) can actually be randomly generated according to any continuous sampling distribution. In theory, the parameters of these SLFNs can be analytically determined by ELM instead of being tuned.


title: Visualization of Brain Microstructure Through Spherical Harmonics Illumination of High Fidelity Spatio-Angular Fields

abstract: Diffusion kurtosis imaging (DKI) is gaining rapid adoption in the medical imaging community due to its ability to measure the non-Gaussian property of water diffusion in biological tissues. Compared to traditional diffusion tensor imaging (DTI), DKI can provide additional details about the underlying microstructural characteristics of the neural tissues. It has shown promising results in studies on changes in gray matter and mild traumatic brain injury where DTI is often found to be inadequate. The DKI dataset, which has high-fidelity spatio-angular fields, is difficult to visualize. Glyph-based visualization techniques are commonly used for visualization of DTI datasets; however, due to the rapid changes in orientation, lighting, and occlusion, visually analyzing the much more higher fidelity DKI data is a challenge. In this paper, we provide a systematic way to manage, analyze, and visualize high-fidelity spatio-angular fields from DKI datasets, by using spherical harmonics lighting functions to facilitate insights into the brain microstructure.


title: Creating ROS launch files using a visual programming interface

abstract: Visual programming is widely used to allow users to create programs by manipulating program elements graphically rather than by specifying them textually. It is intuitive and effective. However, it has only just begun to be used in robotic programming. For example, in the Robot Operating System (ROS) — a popular framework used for developing robotic applications — in order to run multiple modules together, a ROS launch file needs to be created and used. The files are in XML format and are difficult to write and understand for nontechnical users. To address this problem, in this paper, we propose a visual programming software tool that helps in the creation and visualization of these ROS launch files. This tool enables non-experienced operators to program a robot at a modular level. The tool is one among a set of software tools in the Robot Application Development and Operating Environment (RADOE), which aims to ease the development of robot applications in ROS.


Semi-Supervised Learning with Generative Adversarial Networks


title: DeepDeblur: Fast one-step blurry face images restoration

abstract: We propose a very fast and effective one-step restoring method for blurry face images. In the last decades, many blind deblurring algorithms have been proposed to restore latent sharp images. However, these algorithms run slowly because of involving two steps: kernel estimation and following non-blind deconvolution or latent image estimation. Also they cannot handle face images in small size. Our proposed method restores sharp face images directly in one step using Convolutional Neural Network. Unlike previous deep learning involved methods that can only handle a single blur kernel at one time, our network is trained on totally random and numerous training sample pairs to deal with the variances due to different blur kernels in practice. A smoothness regularization as well as a facial regularization are added to keep facial identity information which is the key to face image applications. Comprehensive experiments demonstrate that our proposed method can handle various blur kenels and achieve state-of-the-art results for small size blurry face images restoration. Moreover, the proposed method shows significant improvement in face recognition accuracy along with increasing running speed by more than 100 times.


title: The Bright Side of Political Uncertainty: The Case of R&D

abstract: We examine the relationship between political uncertainty and R&D investment by exploiting the timing of U.S. gubernatorial elections as a source of plausibly exogenous variation in uncertainty. In contrast to the literature documenting negative effects of political uncertainty on real investment, we find that uncertainty over government policy encourages firm-level R&D. Firms increase R&D investments by an average of 4.6% in election years relative to non-election years. The uncertainty effect is stronger in hotly contested elections, in politically sensitive and hard-to-innovate industries, and in firms subject to higher growth options and greater product market competition. Our findings suggest that, as predicted by models of investment under uncertainty, the real effects of political uncertainty depend on the properties of the investment and the degree of product market competition and therefore the total effect of political uncertainty on the long-run growth of an economy is unclear.


Surface Area, and Oxidation Effects on Nitridation Kinetics of Silicon Powder Compacts


title: Symbolic Dynamics in Text: Application to Automated Construction of Concept Hierarchies

abstract: Following a symbolic encoding of selected terms used in text, we determine symmetries that are furnished by local hierarchical structure. We develop this study so that hierarchical fragments can be used in a concept hierarchy, or ontology. By “letting the data speakrd in this way, we avoid the arbitrariness of approximately fitting a model to the data.


title: Effects of mirror therapy on motor and sensory recovery in chronic stroke: a randomized controlled trial.

abstract: OBJECTIVE ::: To compare the effects of mirror therapy (MT) versus control treatment (CT) on movement performance, motor control, sensory recovery, and performance of activities of daily living in people with chronic stroke. ::: ::: ::: DESIGN ::: Single-blinded, randomized controlled trial. ::: ::: ::: SETTING ::: Four hospitals. ::: ::: ::: PARTICIPANTS ::: Outpatients with chronic stroke (N=33) with mild to moderate motor impairment. ::: ::: ::: INTERVENTIONS ::: The MT group (n=16) received upper extremity training involving repetitive bimanual, symmetrical movement practice, in which the individual moves the affected limb while watching the reflective illusion of the unaffected limb's movements from a mirror. The CT group received task-oriented upper extremity training. The intensity for both groups was 1.5 hours/day, 5 days/week, for 4 weeks. ::: ::: ::: MAIN OUTCOME MEASUREMENTS ::: The Fugl-Meyer Assessment; kinematic variables, including reaction time, normalized movement time, normalized total displacement, joint recruitment, and maximum shoulder-elbow cross-correlation; the Revised Nottingham Sensory Assessment; the Motor Activity Log; and the ABILHAND questionnaire. ::: ::: ::: RESULTS ::: The MT group performed better in the overall (P=.01) and distal part (P=.04) Fugl-Meyer Assessment scores and demonstrated shorter reaction time (P=.04), shorter normalized total displacement (P=.04), and greater maximum shoulder-elbow cross-correlation (P=.03). The Revised Nottingham Sensory Assessment temperature scores improved significantly more in the MT group than in the CT group. No significant differences on the Motor Activity Log and the ABILHAND questionnaire were found immediately after MT or at follow-up. ::: ::: ::: CONCLUSIONS ::: The application of MT after stroke might result in beneficial effects on movement performance, motor control, and temperature sense, but may not translate into daily functions in the population with chronic stroke.


title: The theory of planned behaviour: Reactions and reflections

abstract: The seven articles in this issue, and the accompanying meta-analysis in Health Psychology Review [McEachan, R.R.C., Conner, M., Taylor, N., & Lawton, R.J. (2011). Prospective prediction of health-related behaviors with the theory of planned behavior: A meta-analysis. Health Psychology Review, 5, 97–144], illustrate the wide application of the theory of planned behaviour [Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211] in the health domain. In this editorial, Ajzen reflects on some of the issues raised by the different authors. Among the topics addressed are the nature of intentions and the limits of predictive validity; rationality, affect and emotions; past behaviour and habit; the prototype/willingness model; and the role of such background factors as the big five personality traits and social comparison tendency.


title: An evaluation of image denoising techniques applied to CT baggage screening imagery

abstract: This paper investigates the efficacy of several popular denoising methods in the previously unconsidered context of Computed Tomography (CT) baggage imagery. The performance of a dedicated CT baggage denoising approach (alpha-weighted mean separation and histogram equalisation) is compared to the following popular denoising techniques: anisotropic diffusion; total variation denoising; bilateral filtering; translation invariant wavelet shrinkage and non-local means filtering. In addition to a standard qualitative performance analysis (visual comparisons), denoising performance is evaluated with a recently developed 3D SIFT-based analysis technique that quantifies the impact of denoising on the implementation of automated 3D object recognition. The study yields encouraging results in both the qualitative and quantitative analyses, with wavelet thresholding producing the most satisfactory results. The results serve as a strong indication that simple denoising will aid human and computerised analyses of 3D CT baggage imagery for transport security screening.


title: Area efficient parallel decoder architecture for long BCH codes

abstract: Long BCH codes achieve additional coding gain of around 0.6 dB compared to Reed-Solomon codes with similar code rate used for long-haul optical communication systems. For our considered parallel decoder architecture, a novel group matching scheme is proposed to reduce the overall hardware complexity of both Chien search and syndrome generator units by 46% for BCH(2047, 1926, 23) code as opposed to only 22% if directly applying the iterative matching algorithm. The proposed scheme exploits the substructure sharing within a finite field multiplier (FFM) and among groups of FFMs.


title: Ethics in Rehabilitation: Access to Prosthetics and Quality Care Following Amputation

abstract: Health outcomes for people who have had amputations are affected not only by barriers to access, such as race, socioeconomic status and cost, but also by the type of facility where they receive treatment and rehabilitative services.


title: Knowledge Modeling - State of the Art

abstract: A major characteristic of developments in the broad field of Artificial Intelligence (AI) during the 1990s has been an increasing integration of AI with other disciplines. A number of other computer science fields and technologies have been used in developing intelligent systems, starting from traditional information systems and databases, to modern distributed systems and the Internet. This paper surveys knowledge modeling techniques that have received most attention in recent years among developers of intelligent systems, AI practitioners and researchers. The techniques are described from two perspectives, theoretical and practical. Hence the first part of the paper presents major theoretical and architectural concepts, design approaches, and research issues. The second part discusses several practical systems, applications, and ongoing projects that use and implement the techniques described in the first part. Finally, the paper briefly covers some of the most recent results in the fields of intelligent manufacturing systems, intelligent tutoring systems, and ontologies.


title: Sort vs. Hash Revisited: Fast Join Implementation on Modern Multi-Core CPUs

abstract: Join is an important database operation. As computer architectures evolve, the best join algorithm may change hand. This paper re-examines two popular join algorithms -- hash join and sort-merge join -- to determine if the latest computer architecture trends shift the tide that has favored hash join for many years. For a fair comparison, we implemented the most optimized parallel version of both algorithms on the latest Intel Core i7 platform. Both implementations scale well with the number of cores in the system and take advantages of latest processor features for performance. Our hash-based implementation achieves more than 100M tuples per second which is 17X faster than the best reported performance on CPUs and 8X faster than that reported for GPUs. Moreover, the performance of our hash join implementation is consistent over a wide range of input data sizes from 64K to 128M tuples and is not affected by data skew. We compare this implementation to our highly optimized sort-based implementation that achieves 47M to 80M tuples per second. We developed analytical models to study how both algorithms would scale with upcoming processor architecture trends. Our analysis projects that current architectural trends of wider SIMD, more cores, and smaller memory bandwidth per core imply better scalability potential for sort-merge join. Consequently, sort-merge join is likely to outperform hash join on upcoming chip multiprocessors. In summary, we offer multicore implementations of hash join and sort-merge join which consistently outperform all previously reported results. We further conclude that the tide that favors the hash join algorithm has not changed yet, but the change is just around the corner.


title: Fashion Apparel Detection: The Role of Deep Convolutional Neural Network and Pose-dependent Priors

abstract: In this work, we propose and address a new computer vision task, which we call fashion item detection, where the aim is to detect various fashion items a person in the image is wearing or carrying. The types of fashion items we consider in this work include hat, glasses, bag, pants, shoes and so on. The detection of fashion items can be an important first step of various e-commerce applications for fashion industry. Our method is based on state-of-the-art object detection method pipeline which combines object proposal methods with a Deep Convolutional Neural Network. Since the locations of fashion items are in strong correlation with the locations of body joints positions, we incorporate contextual information from body poses in order to improve the detection performance. Through the experiments, we demonstrate the effectiveness of the proposed method.


title: Characterization and modeling of PIDX parallel I/O for performance optimization

abstract: Parallel I/O library performance can vary greatly in response to user-tunable parameter values such as aggregator count, file count, and aggregation strategy. Unfortunately, manual selection of these values is time consuming and dependent on characteristics of the target machine, the underlying file system, and the dataset itself. Some characteristics, such as the amount of memory per core, can also impose hard constraints on the range of viable parameter values. In this work we address these problems by using machine learning techniques to model the performance of the PIDX parallel I/O library and select appropriate tunable parameter values. We characterize both the network and I/O phases of PIDX on a Cray XE6 as well as an IBM Blue Gene/P system. We use the results of this study to develop a machine learning model for parameter space exploration and performance prediction.


title: Executive Function The Search for an Integrated Account

abstract: In general, executive function can be thought of as the set of abilities required to effortfully guide behavior toward a goal, especially in nonroutine situations. Psychologists are interested in expanding the understanding of executive function because it is thought to be a key process in intelligent behavior, it is compromised in a variety of psychiatric and neurological disorders, it varies across the life span, and it affects performance in complicated environments, such as the cockpits of advanced aircraft. This article provides a brief introduction to the concept of executive function and discusses how it is assessed and the conditions under which it is compromised. A short overview of the diverse theoretical viewpoints regarding its psychological and biological underpinnings is also provided. The article concludes with a consideration of how a multilevel approach may provide a more integrated account of executive function than has been previously available.


3 D Stereo Reconstruction Using Multiple Spherical Views


title: Fully Connected Deep Structured Networks

abstract: Convolutional neural networks with many layers have recently been shown to achieve excellent results on many high-level tasks such as image classification, object detection and more recently also semantic segmentation. Particularly for semantic segmentation, a two-stage procedure is often employed. Hereby, convolutional networks are trained to provide good local pixel-wise features for the second step being traditionally a more global graphical model. In this work we unify this two-stage process into a single joint training algorithm. We demonstrate our method on the semantic image segmentation task and show encouraging results on the challenging PASCAL VOC 2012 dataset.


title: Frenchay Aphasia Screening Test: validity and comparability

abstract: The Frenchay Aphasia Screening Test1 is a reliable test which can be used by non-specialists to discriminate between aphasia and normal language. Preliminary studies have shown good test—retest reliability, and the test itself is quick and simple to use. The present investigation examines the validity of the test by comparing the results of this test with those on more structured, detailed and frequently used assessments of aphasia the Minnesota Test for Differential Diagnosis of Aphasia (MTDDA) and general performance in communication as reflected by the Functional Communication Profile. The results demonstrate a positive correlation between the tests, and this allows confidence in the use of this test by non-specialists as a screening instrument.


title: VOWEL DURATION IN THREE AMERICAN ENGLISH DIALECTS

abstract: Given the massive body of research on regional and social variation in American English vowels, it is surprising to find a large gap in research on an issue as basic as durational differences. Indeed, duration is the normal starting point for work on vowels in phonetics and speech science, yet in sociolinguistics and dialectology this factor is often still not treated directly in even the most encyclopedic and recent research (see, e.g., Labov, Ash, and Boberg 2006, 36). The present article reports acoustic results from a detailed study of three regions of contemporary American English: westernmost North Carolina (at the heart of the Southern Shift area), central Ohio (Midlands), and south-central and southeastern Wisconsin (Inland North). The results show consistent differences in the length of five vowels within each of these dialects. ::: ::: In the first section, we review the most relevant work done to date on durational differences across American dialects. The second section presents the method and design of our study, while the third presents the results and analysis. The discussion situates our findings in the context of previous research on the topic. We then note how the regional sampling design employed here complements other current approaches, in particular broader but shallower surveys, and we close with a word on the position of the Midlands among American dialects.


title: Generalized velocity obstacles

abstract: We address the problem of real-time navigation in dynamic environments for car-like robots. We present an approach to identify controls that will lead to a collision with a moving obstacle at some point in the future. Our approach generalizes the concept of velocity obstacles, which have been used for navigation among dynamic obstacles, and takes into account the constraints of a car-like robot. We use this formulation to find controls that will allow collision free navigation in dynamic environments. Finally, we demonstrate the performance of our algorithm on a simulated car-like robot among moving obstacles.


title: Coupling Distributed and Symbolic Execution for Natural Language Queries

abstract: Building neural networks to query a knowledge base (a table) with natural language is an emerging research topic in deep learning. An executor for table querying typically requires multiple steps of execution because queries may have complicated structures. In previous studies, researchers have developed either fully distributed executors or symbolic executors for table querying. A distributed executor can be trained in an end-to-end fashion, but is weak in terms of execution efficiency and explicit interpretability. A symbolic executor is efficient in execution, but is very difficult to train especially at initial stages. In this paper, we propose to couple distributed and symbolic execution for natural language queries, where the symbolic executor is pretrained with the distributed executor's intermediate execution results in a step-by-step fashion. Experiments show that our approach significantly outperforms both distributed and symbolic executors, exhibiting high accuracy, high learning efficiency, high execution efficiency, and high interpretability.


title: A theory of changes for higher-order languages: incrementalizing λ-calculi by static differentiation

abstract: If the result of an expensive computation is invalidated by a small change to the input, the old result should be updated incrementally instead of reexecuting the whole computation. We incrementalize programs through their derivative. A derivative maps changes in the program's input directly to changes in the program's output, without reexecuting the original program. We present a program transformation taking programs to their derivatives, which is fully static and automatic, supports first-class functions, and produces derivatives amenable to standard optimization. We prove the program transformation correct in Agda for a family of simply-typed λ-calculi, parameterized by base types and primitives. A precise interface specifies what is required to incrementalize the chosen primitives. We investigate performance by a case study: We implement in Scala the program transformation, a plugin and improve performance of a nontrivial program by orders of magnitude.


title: Next-generation sequencing and its applications in molecular diagnostics.

abstract: DNA sequencing is a powerful approach for decoding a number of human diseases, including cancers. The advent of next-generation sequencing (NGS) technologies has reduced sequencing cost by orders of magnitude and significantly increased the throughput, making whole-genome sequencing a possible way for obtaining global genomic information about patients on whom clinical actions may be taken. However, the benefits offered by NGS technologies come with a number of challenges that must be adequately addressed before they can be transformed from research tools to routine clinical practices. This article provides an overview of four commonly used NGS technologies from Roche Applied Science//454 Life Sciences, Illumina, Life Technologies and Helicos Biosciences. The challenges in the analysis of NGS data and their potential applications in clinical diagnosis are also discussed.


title: Advanced vehicle dynamic model for EV emulation considering environment conditions

abstract: This paper presents an improved electric vehicle (EV) dynamic model by considering the actual environmental conditions for EV powertrain components testing. The environmental conditions considered include the road gradient, road surface roughness, weather conditions, and wind speed. In comparison to the simplified vehicle dynamics model, the proposed enhanced model aims to simulate the vehicle dynamics in a more realistic condition to provide a simulated EV environment close to the actual EV environment for EV-specific electric machine testing. Moreover, the rotational inertia of each component is also taken into account in the electric machine testing using the standard drive cycle. Simulation model is developed in Matlab/ Simulink and the empirical platform is our laboratory equipment.


title: Extracting hypernym relations from Wikipedia disambiguation pages: comparing symbolic and machine learning approaches

abstract: Extracting hypernym relations from text is one of the key steps in the construction and enrichment of semantic resources. Several methods have been exploited in a variety of propositions in the literature. However, the strengths of each approach on a same corpus are still poorly identified in order to better take advantage of their complementarity. In this paper, we study how complementary two approaches of different nature are when identifying hypernym relations on a structured corpus containing both well-written text and syntactically poor formulations, together with a rich formatting. A symbolic approach based on lexico-syntactic patterns and a statistical approach using a supervised learning method are applied to a sub-corpus of Wikipedia in French, composed of disambiguation pages. These pages, particularly rich in hypernym relations, contain both kinks of formulations. We compared the results of each approach independently of each other and compared the performance when combining together their individual results. We obtain the best results in the latter case, with an F-measure of 0.75. In addition, 55% of the relations identified by our approach, with respect to a reference corpus, are not expressed in the French DBPedia and could be used to enrich this resource.


title: Exploiting different word clusterings for class-based RNN language modeling in speech recognition

abstract: We propose to exploit the potential of multiple word clusterings in class-based recurrent neural network (RNN) language models for ensemble RNN language modeling. By varying the clustering criteria and the space of word embedding, different word clusterings are obtained to define different word/class factorizations. For each such word/class factorization, several base RNNLMs are learned, and the word prediction probabilities of the base RNNLMs are then combined to form an ensemble prediction. We use a greedy backward model selection procedure to select a subset of models and combine these models for word prediction. The proposed ensemble language modeling method has been evaluated on Penn Treebank test set as well as Wall Street Journal (WSJ) Eval 92 and 93 test sets, where it improved test set perplexity and word error rate over the state-of-the-art single RNNLMs as well as multiple RNNLMs produced by varying RNN learning conditions.


title: Pedestrian Motion Tracking and Crowd Abnormal Behavior Detection Based on Intelligent Video Surveillance

abstract: Pedestrian tracking and detection of crowd abnormal activity under dynamic and complex background us ing I ntelligent V ideo S urveillance (IVS) system are beneficial for security in public places. T his paper present s a pedestrian tracking method combing Histogram of Oriented Gradients (HOG) detection and particle filter . This method regards the particle filter as the tracking framework, identifies the target area according to the result of HOG detection and modifies particle sampling constantly. Our method can track pedestrians in dynamic backgrounds more accurately compared with the traditional particle filter algorithms. Meanwhile, a method to detect crowd abnormal activity is also proposed based on a model of crowd features using Mixture of Gaussian (MOG). This method calculates features of crowd-interest points, then establishes the crowd features model using MOG, conducts self-adaptive updating and detect s a bnormal activity by matching the input feature with model distribution. Experiments show o ur algorithm can efficiently detect abnormal velocity and escape panic in crowds with a high detection rate and a relatively low false alarm rate


title: smartPrediction: a real-time smartphone-based fall risk prediction and prevention system

abstract: The high risk of falls and the substantial increase in the elderly population have recently stimulated scientific research on Smartphone-based fall detection systems. Even though these systems are helpful for fall detection, the best way to reduce the number of falls and their consequences is to predict and prevent them from happening in the first place. To address the issue of fall prevention, in this paper, we propose a fall prediction system by integrating the sensor data of Smartphones and a Smartshoe. We designed and implemented a Smartshoe that contains four pressure sensors with a Wi-Fi communication module to unobtrusively collect data in any environment. By assimilating the Smartshoe and Smartphone sensors data, we performed an extensive set of experiments to evaluate normal and abnormal walking patterns. The system can generate an alert message in the Smartphone to warn the user about the high-risk gait patterns and potentially save them from an imminent fall. We validated our approach using a decision tree with 10-fold cross validation and found 97.2% accuracy in gait abnormality detection.


title: Extraction of Historical Events from Wikipedia

abstract: The DBpedia project extracts structured information from Wikipedia and makes it available on the web. Information is gathered mainly with the help of infoboxes that contain structured information of the Wikipedia article. A lot of information is only contained in the article body and is not yet included in DBpedia. In this paper we focus on the extraction of historical events from Wikipedia articles that are available for about 2,500 years for different languages. We have extracted about 121,000 events with more than 325,000 links to DBpedia entities and provide access to this data via a Web API, SPARQL endpoint, Linked Data Interface and in a timeline application.


title: Classification of Aesthetic Curves and Surfaces for Industrial Designs

abstract: This paper aims to figure out difference of our impressions on curves that are used in form designs, and also contribute industrial designers by implementing a smart computer aided design (CAD) system that have as same feeling on curves as human designers have. The proposing K-vector is a mathematical form of classifying such curves by designers’ impressions.


title: A Comparison of Visualisation Methods for Disambiguating Verbal Requests in Human-Robot Interaction

abstract: Picking up objects requested by a human user is a common task in human-robot interaction. When multiple objects match the user's verbal description, the robot needs to clarify which object the user is referring to before executing the action. Previous research has focused on perceiving user's multimodal behaviour to complement verbal commands or minimising the number of follow up questions to reduce task time. In this paper, we propose a system for reference disambiguation based on visualisation and compare three methods to disambiguate natural language instructions. In a controlled experiment with a YuMi robot, we investigated realtime augmentations of the workspace in three conditions - head-mounted display, projector, and a monitor as the baseline - using objective measures such as time and accuracy, and subjective measures like engagement, immersion, and display interference. Significant differences were found in accuracy and engagement between the conditions, but no differences were found in task time. Despite the higher error rates in the head-mounted display condition, participants found that modality more engaging than the other two, but overall showed preference for the projector condition over the monitor and head-mounted display conditions.


title: The total synthesis of hyperpapuanone, hyperibone L, epi-clusianone and oblongifolin A.

abstract: Polyprenylated polycyclic acylphloroglucines (PPAPs) are a family of natural products that possess a wide range of different important biological activities because of the relative position and configuration of four substituents that decorate one common central bicyclo[3.3.1]nonane-2,4,9-trione core. The rigid bicyclic framework with its lipophilic side chains and its hydrophilic trione moiety represents a nature-derived lead structure that arranges the substituents (R(1) to R(4)) into a defined topographical orientation. As the substituents are responsible for the biological activities, the seven-step synthetic approach presented here sets the stage for an iterative introduction of R(1) to R(4) and thus generates structurally diverse trans-type B PPAPs. Four natural and one non-natural trans-type B PPAPs were prepared starting from acetylacetone with overall yields that ranged from 6 to 22%. The concept of separating framework construction from decorating transformations plus the minimization of protecting-group operations are the key issues for the realization of our synthetic approach.


title: Image Manipulation on Facebook for Forensics Evidence

abstract: The growth of popularity of Social Network Services (SNSs) opened new perspectives in many research fields, including the emerging area of Multimedia Forensics. In particular, the huge amount of images uploaded to the social networks can represent a significant source of evidence for investigations, if properly processed. This work aims to exploit the algorithms and techniques behind the uploading process of a picture on Facebook, in order to find out if any of the involved steps (resizing, compression, renaming, etc.) leaves a trail on the picture itself, so to infer proper hypotheses about the authenticity and other forensic aspects of the pipeline.


title: DsUniPi: An SVM-based Approach for Sentiment Analysis of Figurative Language on Twitter

abstract: The DsUniPi team participated in the SemEval 2015 Task#11: Sentiment Analysis of Figurative Language in Twitter. The proposed approach employs syntactical and morphological features, which indicate sentiment polarity in both figurative and non-figurative tweets. These features were combined with others that indicate presence of figurative language in order to predict a fine-grained sentiment score. The method is supervised and makes use of structured knowledge resources, such as SentiWordNet sentiment lexicon for assigning sentiment score to words and WordNet for calculating word similarity. We have experimented with different classification algorithms (Naive Bayes, Decision trees, and SVM), and the best results were achieved by an SVM classifier with linear kernel.


title: Certificate Transparency with Enhancements and Short Proofs

abstract: Browsers can detect malicious websites that are provisioned with forged or fake TLS/SSL certificates. However, they are not so good at detecting these websites if they are provisioned with mistakenly (or maliciously) issued certificates. Google proposed certificate transparency which is an open framework to monitor and audit certificates in real time. Thereafter, a few other certificate transparency schemes have been proposed which can even handle revocation. All currently known constructions use Merkle hash trees and have proof size logarithmic in the number of certificates/domain owners. We present a new certificate transparency scheme with short (constant size) proofs. Our construction makes use of dynamic bilinear-map accumulators. The scheme has many desirable properties like efficient revocation, low verification cost and update costs comparable to the existing schemes. We provide proofs of security and evaluate the performance of our scheme.


title: Gradient descent in hyperbolic space

abstract: Gradient descent generalises naturally to Riemannian manifolds, and to hyperbolic $n$-space, in particular. Namely, having calculated the gradient at the point on the manifold representing the model parameters, the updated point is obtained by travelling along the geodesic passing in the direction of the gradient. Some recent works employing optimisation in hyperbolic space have not attempted this procedure, however, employing instead various approximations to avoid a calculation that was considered to be too complicated. In this tutorial, we demonstrate that in the hyperboloid model of hyperbolic space, the necessary calculations to perform gradient descent are in fact straight-forward. The advantages of the approach are then both illustrated and quantified for the optimisation problem of computing the Fr\'echet mean (i.e. barycentre) of points in hyperbolic space.


Linking Accounts across Social Networks: the Case of StackOverflow, Github and Twitter


title: Connecting Generative Adversarial Networks and Actor-Critic Methods

abstract: Both generative adversarial networks (GAN) in unsupervised learning and actor-critic methods in reinforcement learning (RL) have gained a reputation for being difficult to optimize. Practitioners in both fields have amassed a large number of strategies to mitigate these instabilities and improve training. Here we show that GANs can be viewed as actor-critic methods in an environment where the actor cannot affect the reward. We review the strategies for stabilizing training for each class of models, both those that generalize between the two and those that are particular to that model. We also review a number of extensions to GANs and RL algorithms with even more complicated information flow. We hope that by highlighting this formal connection we will encourage both GAN and RL communities to develop general, scalable, and stable algorithms for multilevel optimization with deep networks, and to draw inspiration across communities.


Predicting Success in Goal-Driven Human-Human Dialogues


title: Health, Inequality, and Economic Development

abstract: I explore the connection between income inequality and health in both poor and rich countries. I discuss a range of mechanisms, including nonlinear income effects, credit restrictions, nutritional traps, public goods provision, and relative deprivation. I review the evidence on the effects of income inequality on the rate of decline of mortality over time, on geographical pattens of mortality, and on individual-level mortality. Much of the literature needs to be treated skeptically, if only because of the low quality of much of the data on income inequality. Although there are many puzzles that remain, I conclude that there is no direct link from income inequality to ill-health; individuals are no more likely to die if they live in more unequal places. The raw correlations that are sometimes found are likely the result of factors other than income inequality, some of which are intimately linked to broader notions of inequality and unfairness. That income inequality itself is not a health risk does not deny the importance for health of other inequalities, nor of the social environment. Whether income redistribution can improve population health does not depend on a direct effect of income inequality and remains an open question.


title: Amusic does not mean unmusical: Beat perception and synchronization ability despite pitch deafness

abstract: Pitch deafness, the most commonly known form of congenital amusia, refers to a severe deficit in musical pitch processing (i.e., melody discrimination and recognition) that can leave time processing—including rhythm, metre, and “feeling the beat”—preserved. In Experiment 1, we show that by presenting musical excerpts in nonpitched drum timbres, rather than pitched piano tones, amusics show normal metre recognition. Experiment 2 reveals that body movement influences amusics’ interpretation of the beat of an ambiguous drum rhythm. Experiment 3 and a subsequent exploratory study show an ability to synchronize movement to the beat of popular dance music and potential for improvement when given a modest amount of practice. Together the present results are consistent with the idea that rhythm and beat processing are spared in pitch deafness—that is, being pitch-deaf does not mean one is beat-deaf. In the context of drum music especially, amusics can be musical.


title: Active Semi-Supervised Classification Based on Multiple Clustering Hierarchies

abstract: Active semi-supervised learning can play an important role in classification scenarios in which labeled data are difficult to obtain, while unlabeled data can be easily acquired. This paper focuses on an active semi-supervised algorithm that can be driven by multiple clustering hierarchies. If there is one or more hierarchies that can reasonably align clusters with class labels, then a few queries are needed to label with high quality all the unlabeled data. We take as a starting point the well-known Hierarchical Sampling (HS) algorithm and perform changes in different aspects of the original algorithm in order to tackle its main drawbacks, including its sensitivity to the choice of a single particular hierarchy. Experimental results over many real datasets show that the proposed algorithm performs superior or competitive when compared to a number of state-of-the-art algorithms for active semi-supervised classification.


title: Face Recognition Based on SVM and 2DPCA

abstract: The paper will present a novel approach for solving face recognition problem. Our method combines 2D Principal Component Analysis (2DPCA), one of the prominent methods for extracting feature vectors, and Support Vector Machine (SVM), the most powerful discriminative method for classification. Experiments based on proposed method have been conducted on two public data sets FERET and ATT the results show that the proposed method could improve the classification rates.


title: On the in-controller performance of an open source EtherCAT master using open platforms

abstract: Because of the rapid development of EtherCAT as the standard fieldbus in control and automation and the increasing demands of low cost portable main controllers, we aim to assess the performance of an EtherCAT network in handling real-time demands using open source EtherCAT master protocol on open hardware platforms. In comparison to that of commercial products, the performance of open platforms is still indefinite and may depend on different indicators such as the kernel version, real-time architecture, and the protocol of EtherCAT. This paper provides implementation guidelines and procedures to develop the embedded platforms-a task which is difficult due to lack of systematic documents and technical support. The real-time evaluation of an open source EtherCAT master with a dual-kernel approach of real-time embedded Linux, Xenomai, is performed to validate that even low-cost open embedded hardware platforms are also capable in performing real-time EtherCAT control applications.


title: Depth camera based indoor mobile robot localization and navigation

abstract: The sheer volume of data generated by depth cameras provides a challenge to process in real time, in particular when used for indoor mobile robot localization and navigation. We introduce the Fast Sampling Plane Filtering (FSPF) algorithm to reduce the volume of the 3D point cloud by sampling points from the depth image, and classifying local grouped sets of points as belonging to planes in 3D (the “plane filtered” points) or points that do not correspond to planes within a specified error margin (the “outlier” points). We then introduce a localization algorithm based on an observation model that down-projects the plane filtered points on to 2D, and assigns correspondences for each point to lines in the 2D map. The full sampled point cloud (consisting of both plane filtered as well as outlier points) is processed for obstacle avoidance for autonomous navigation. All our algorithms process only the depth information, and do not require additional RGB data. The FSPF, localization and obstacle avoidance algorithms run in real time at full camera frame rates (30Hz) with low CPU requirements (16%). We provide experimental results demonstrating the effectiveness of our approach for indoor mobile robot localization and navigation. We further compare the accuracy and robustness in localization using depth cameras with FSPF vs. alternative approaches that simulate laser rangefinder scans from the 3D data.


title: Vibration control of a multi-flexible-link robot arm under gravity

abstract: This paper proposes a novel independent joint control concept on a 3 DOF flexible link robot subject to deflections caused by gravity. The scheme dampens induced link oscillations in the presence of configuration dependant plant frequency and damping variations by integrating link strain feedback and impulse based input shaping. The approach is robust and the control does not depend on a dynamic model at runtime. The damping efficiency is evaluated experimentally in terms of strain measurement as well as end-effector position across the entire workspace.


title: Languages as libraries

abstract: Programming language design benefits from constructs for extending the syntax and semantics of a host language. While C's string-based macros empower programmers to introduce notational shorthands, the parser-level macros of Lisp encourage experimentation with domain-specific languages. The Scheme programming language improves on Lisp with macros that respect lexical scope. The design of Racket---a descendant of Scheme---goes even further with the introduction of a full-fledged interface to the static semantics of the language. A Racket extension programmer can thus add constructs that are indistinguishable from "native" notation, large and complex embedded domain-specific languages, and even optimizing transformations for the compiler backend. This power to experiment with language design has been used to create a series of sub-languages for programming with first-class classes and modules, numerous languages for implementing the Racket system, and the creation of a complete and fully integrated typed sister language to Racket's untyped base language. This paper explains Racket's language extension API via an implementation of a small typed sister language. The new language provides a rich type system that accommodates the idioms of untyped Racket. Furthermore, modules in this typed language can safely exchange values with untyped modules. Last but not least, the implementation includes a type-based optimizer that achieves promising speedups. Although these extensions are complex, their Racket implementation is just a library, like any other library, requiring no changes to the Racket implementation.


title: Meta-Analysis of Early Intensive Behavioral Intervention for Children With Autism

abstract: A systematic literature search for studies reporting effects of Early Intensive Behavioral Intervention identified 34 studies, 9 of which were controlled designs having either a comparison or a control group. We completed a meta-analysis yielding a standardized mean difference effect size for two available outcome measures: change in full-scale intelligence and/or adaptive behavior composite. Effect sizes were computed using Hedges's g. The average effect size was 1.10 for change in full-scale intelligence (95% confidence interval = .87, 1.34) and .66 (95% confidence interval = .41, .90) for change in adaptive behavior composite. These effect sizes are generally considered to be large and moderate, respectively. Our results support the clinical implication that at present, and in the absence of other interventions with established efficacy, Early Intensive Behavioral Intervention should be an intervention of choice for children with autism.


title: Particle Filter technique for position estimation in GNSS-based localisation systems

abstract: The usage of filter techniques for position estimation for safety-relevant purposes is an extensive field of research. Depending on the needed application for the Global Navigation Satellite System (GNSS) the state estimation can be achieved by several techniques. State estimation by means of Kalman Filter (KF), as well as Extended Kalman Filter (EKF) and Particle Filter (PF) have been developed and tested. Map-matching algorithms integrate the localisation data provided by GNSS with spatial road network data (also called “digital map”) to identify the correct line (or track) on which a vehicle is traveling and to determine the location of a vehicle within the line (or track). The goal of map-matching techniques is to exploit prior information contained in road or railway networks. However, incorporating digital map information within the conventional KF framework is not easy, because this constraint leads to highly non-Gaussian posterior densities that are difficult to represent accurately using conventional techniques. Since the PF approach presents no restrictions regarding non-linearity of models and noise distribution the velocity and heading measurement errors can be accurately modelled. The most significant advantages of the PF approach for map-matching application are: 1) PF approach provides a natural way for road map information to be incorporated into vehicle position estimation. 2) PF approach is capable of capturing multi-modal distributions. The selected PF-based location estimators presented here is oriented to work within an intelligent GNSS-based localisation system. The PF-based map matching techniques are presented in a mathematical ground and tests are performed, as part of a developed satellite localisation system based on artificial intelligence (AI) tools. In the railway domain the same integration can be performed by means of a “digital trap map” to identify the location within the tracks. A map-matching algorithm can be the key component of the data fusion to improve the performance of localisation systems that support the navigation function of intelligent transport systems (ITS).


Negative Urgency and Gender Moderate the Association between Anxiety Sensitivity and Alcohol-Related Problems


title: 1.2 Tb/s symmetric WDM-OFDMA-PON over 90km straight SSMF and 1∶32 passive split with digitally-selective ONUs and coherent receiver OLT

abstract: Record 1.2-Tb/s (25λ×48Gb/s/λ) symmetric WDM-OFDMA-PON is demonstrated over 90km SSMF with a 1∶32 passive split, featuring multi-band-OFDMA, digitally-selective ONUs and a coherent receiver OLT. The architecture supports 800 ONUs with 1.25/10-Gb/s guaranteed/peak rates.


title: 60 GHz slot antenna array based on ridge gap waveguide technology enhanced with dielectric superstrate

abstract: A 2 × 2 planar slot antenna array is designed at 60 GHz. The slots are fed by a printed feeding network based on the ridge gap waveguide (RGW) technology. Such a feeding network requires the slots to be more than a full wavelength apart, which causes excitation of undesired grating lobes. In order to reduce the grating lobes level, a low-cost dielectric superstrate at a distance of half wavelength in the air above the slot antenna array is used. The presence of the superstrate acts as a planar lens that increases the slot array gain by 7 dB along the broadside direction as well as greatly reduces the grating lobes level. The single element and the array characteristics are provided.


title: Learning to Optimize: Training Deep Neural Networks for Interference Management

abstract: Numerical optimization has played a central role in addressing key signal processing (SP) problems. Highly effective methods have been developed for a large variety of SP applications such as communications, radar, filter design, and speech and image analytics, just to name a few. However, optimization algorithms often entail considerable complexity, which creates a serious gap between theoretical design/analysis and real-time processing. In this paper, we aim at providing a new learning-based perspective to address this challenging issue. The key idea is to treat the input and output of an SP algorithm as an unknown nonlinear mapping and use a deep neural network (DNN) to approximate it. If the nonlinear mapping can be learned accurately by a DNN of moderate size, then SP tasks can be performed effectively—since passing the input through a DNN only requires a small number of simple operations. In our paper, we first identify a class of optimization algorithms that can be accurately approximated by a fully connected DNN. Second, to demonstrate the effectiveness of the proposed approach, we apply it to approximate a popular interference management algorithm, namely, the WMMSE algorithm. Extensive experiments using both synthetically generated wireless channel data and real DSL channel data have been conducted. It is shown that, in practice, only a small network is sufficient to obtain high approximation accuracy, and DNNs can achieve orders of magnitude speedup in computational time compared to the state-of-the-art interference management algorithm.


title: Low-Drift Visual Odometry in Structured Environments by Decoupling Rotational and Translational Motion

abstract: We present a low-drift visual odometry algorithm that separately estimates rotational and translational motion from lines, planes, and points found in RGB-D images. Previous methods estimate drift-free rotational motion from structural regularities to reduce drift in the rotation estimate, which is the primary source of positioning inaccuracy in visual odometry. However, multiple orthogonal planes are required to be visible throughout the entire motion estimation process; otherwise, these VO approaches fail. We propose a new approach to estimate drift-free rotational motion jointly from both lines and planes by exploiting environmental regularities. We track the spatial regularities with an efficient SO(3)-manifold constrained mean shift algorithm. Once the drift-free rotation is found, we recover the translational motion from all tracked points with and without depth by minimizing the de-rotated reprojection error. We compare the proposed algorithm to other state-of-the-art visual odometry methods on a variety of RGB-D datasets (including especially challenging pure rotations) and demonstrate improved accuracy and lower drift error.


title: Fast Algorithms for the Maximum Clique Problem on Massive Graphs with Applications to Overlapping Community Detection

abstract: The maximum clique problem is a well known NP-Hard problem with applications in data mining, network analysis, information retrieval and many other areas related to the World Wide Web. There exist several algorithms for the problem with acceptable runtimes for certain classes of graphs, but many of them are infeasible for massive graphs. We present a new exact algorithm that employs novel pruning techniques and is able to find maximum cliques in very large, sparse graphs quickly. Extensive experiments on different kinds of synthetic and real-world graphs show that our new algorithm can be orders of magnitude faster than existing algorithms. We also present a heuristic that runs orders of magnitude faster than the exact algorithm while providing optimal or near-optimal solutions. We illustrate a simple application of the algorithms in developing methods for detection of overlapping communities in networks.


title: Sorting out symptoms: design and evaluation of the 'babylon check' automated triage system

abstract: Prior to seeking professional medical care it is increasingly common for patients to use online resources such as automated symptom checkers. Many such systems attempt to provide a differential diagnosis based on the symptoms elucidated from the user, which may lead to anxiety if life or limb-threatening conditions are part of the list, a phenomenon termed 'cyberchondria' [1]. Systems that provide advice on where to seek help, rather than a diagnosis, are equally popular, and in our view provide the most useful information. In this technical report we describe how such a triage system can be modelled computationally, how medical insights can be translated into triage flows, and how such systems can be validated and tested. We present babylon check, our commercially deployed automated triage system, as a case study, and illustrate its performance in a large, semi-naturalistic deployment study.


title: Colorization Using Neural Network Ensemble

abstract: This paper investigates into the colorization problem, which converts a grayscale image to a colorful version. This is a difficult problem and normally requires manual adjustment to achieve artifact-free quality. For instance, it normally requires human-labeled color scribbles on the grayscale target image or a careful selection of colorful reference images. The recent learning-based colorization techniques automatically colorize a grayscale image using a single neural network. Since different scenes usually have distinct color styles, it is difficult to accurately capture the color characteristics using a single neural network. We propose a mixture learning model representing the presence of sub-color-style within an overall image data set. We, therefore, ensemble multiple neural networks to obtain better color estimation performance than could be obtained from any of the constituent neural network alone. A two-step colorization strategy is utilized as an adaptive color style clustering followed by a neural network ensemble. To ensure artifact-free quality, a joint bilateral filtering-based post-processing step is proposed. Numerous experiments demonstrate that our method generates high-quality results comparable with state-of-the-art algorithms.


title: Elemental: An Insight into the Development and Evaluation of a Secondary Education Chemistry Game Using XNA

abstract: This article discusses the creation of an educational game intended for UK GCSE-level content, called Elemental. Elemental, developed using Microsoft's XNA studio and deployed both on the PC and Xbox 360 platforms, addresses the periodic table of elements, a subject with extensions in chemistry, physics and engineering. Through the development process of the game but also the eventual pilot user study with 15 subjects (using a pre and post test method to measure learning using the medium and self-report questions), examples are given on how an educator can, without expert knowledge, utilize modern programming tools to create and test custom-made content for delivering part of a secondary education curriculum.


title: Evaluation of energy consumption in virtualization environments : Proof of concept using containers

abstract: The demand for cloud services offering virtualization increases with a continual interest for different types of applications. Regardless of the resource demand of the application, some supplier is ...


title: A Study on Watermarking Schemes for Image Authentication

abstract: The digital revolution in digital image processing has made it possible to create, manipulate and transmit digital images in a simple and fast manner. The adverse affect of this is that the same image processing techniques can be used by hackers to tamper with any image and use it illegally. This has made digital image safety and integrity the top prioritized issue in today’s information explosion. Watermarking is a popular technique that is used for copyright protection and authentication. This paper presents an overview of the various concepts and research works in the field of image watermark authentication. In particular, the concept of content-based image watermarking is reviewed in details. KeywordsWatermark Lifecycle, Robust Watermarking Schemes, Fragile Watermarking Schemes, HVS, ICA.


title: Knowledge harvesting from text and Web sources

abstract: The proliferation of knowledge-sharing communities such as Wikipedia and the progress in scalable information extraction from Web and text sources has enabled the automatic construction of very large knowledge bases. Recent endeavors of this kind include academic research projects such as DBpedia, KnowItAll, Probase, ReadTheWeb, and YAGO, as well as industrial ones such as Freebase and Trueknowledge. These projects provide automatically constructed knowledge bases of facts about named entities, their semantic classes, and their mutual relationships. Such world knowledge in turn enables cognitive applications and knowledge-centric services like disambiguating natural-language text, deep question answering, and semantic search for entities and relations in Web and enterprise data. Prominent examples of how knowledge bases can be harnessed include the Google Knowledge Graph and the IBM Watson question answering system. This tutorial presents state-of-the-art methods, recent advances, research opportunities, and open challenges along this avenue of knowledge harvesting and its applications.


title: PRISM 4.0: Verification of Probabilistic Real-time Systems

abstract: This paper describes a major new release of the PRISMprobabilistic model checker, adding, in particular, quantitative verification of (priced) probabilistic timed automata. These model systems exhibiting probabilistic, nondeterministic and real-time characteristics. In many application domains, all three aspects are essential; this includes, for example, embedded controllers in automotive or avionic systems, wireless communication protocols such as Bluetooth or Zigbee, and randomised security protocols. PRISM, which is open-source, also contains several new components that are of independent use. These include: an extensible toolkit for building, verifying and refining abstractions of probabilistic models; an explicit-state probabilistic model checking library; a discrete-event simulation engine for statistical model checking; support for generation of optimal adversaries/strategies; and a benchmark suite.


title: Analysis of a Magnetic Screw for High Force Density Linear Electromagnetic Actuators

abstract: This paper describes a high force density linear electromagnetic actuator based on the concept of magnetic screw-nut, in which helically disposed, radially magnetised permanent magnets are placed on both the screw and the nut. Magnetic force and torque can be developed between the two parts without direct mechanical contact. Analytical and numerical analysis has been carried to predict the performance of the magnetic screw-nut assembly. It has been shown that a thrust force density in excess of 10 MN/m3 can be achieved for airgap lengths varying from 0.4 mm to 0.8 mm and a lead λ greater than 7 mm. When combined with a naturally cooled permanent magnet brushless machine having a torque density of ~15 kNm/m3, the resulting thrust force density of the combined system, i.e. electrical machine and magnetic screw-nut assembly, is an order of magnitude higher than that of a liquid-cooled tubular permanent magnet brushless machine.