title: Call Me Back!: Attacks on System Server and System Apps in Android through Synchronous Callback

abstract: Android is the most commonly used mobile device operation system. The core of Android, the System Server (SS), is a multi-threaded process that provides most of the system services. Based on a new understanding of the security risks introduced by the callback mechanism in system services, we have discovered a general type of design flaw. A vulnerability detection tool has been designed and implemented based on static taint analysis. We applied the tool on all the 80 system services in the SS of Android 5.1.0. With its help, we have discovered six previously unknown vulnerabilities, which are further confirmed on Android 2.3.7-6.0.1. According to our analysis, about 97.3% of the entire 1.4 billion real-world Android devices are vulnerable. Our proof-of-concept attack proves that the vulnerabilities can enable a malicious app to freeze critical system functionalities or soft-reboot the system immediately. It is a neat type of denial-of-service at-tack. We also proved that the attacks can be conducted at mission critical moments to achieve meaningful goals, such as anti anti-virus, anti process-killer, hindering app updates or system patching. After being informed, Google confirmed our findings promptly. Several suggestions on how to use callbacks safely are also proposed to Google.


title: Searchable Symmetric Encryption: Designs and Challenges

abstract: Searchable Symmetric Encryption (SSE) when deployed in the cloud allows one to query encrypted data without the risk of data leakage. Despite the widespread interest, existing surveys do not examine in detail how SSE’s underlying structures are designed and how these result in the many properties of a SSE scheme. This is the gap we seek to address, as well as presenting recent state-of-the-art advances on SSE. Specifically, we present a general framework and believe the discussions may lead to insights for potential new designs. We draw a few observations. First, most schemes use index table, where optimal index size and sublinear search can be achieved using an inverted index. Straightforward updating can only be achieved using direct index, but search time would be linear. A recent trend is the combinations of index table, and tree, deployed for efficient updating and storage. Secondly, mechanisms from related fields such as Oblivious RAM (ORAM) have been integrated to reduce leakages. However, using these mechanisms to minimise leakages in schemes with richer functionalities (e.g., ranked, range) is relatively unexplored. Thirdly, a new approach (e.g., multiple servers) is required to mitigate new and emerging attacks on leakage. Lastly, we observe that a proposed index may not be practically efficient when implemented, where I/O access must be taken into consideration.


title: Structured Sparse Method for Hyperspectral Unmixing

abstract: Hyperspectral Unmixing (HU) has received increasing attention in the past decades due to its ability of unveiling information latent in hyperspectral data. Unfortunately, most existing methods fail to take advantage of the spatial information in data. To overcome this limitation, we propose a Structured Sparse regularized Nonnegative Matrix Factorization (SS-NMF) method from the following two aspects. First, we incorporate a graph Laplacian to encode the manifold structures embedded in the hyperspectral data space. In this way, the highly similar neighboring pixels can be grouped together. Second, the lasso penalty is employed in SS-NMF for the fact that pixels in the same manifold structure are sparsely mixed by a common set of relevant bases. These two factors act as a new structured sparse constraint. With this constraint, our method can learn a compact space, where highly similar pixels are grouped to share correlated sparse representations. Experiments on real hyperspectral data sets with different noise levels demonstrate that our method outperforms the state-of-the-art methods significantly.


title: Counterfactual potency.

abstract: Counterfactual thoughts typically take the form of implied or explicit if-then statements. We propose that the multiplicative combination of "if likelihood" (the degree to which the antecedent condition of the counterfactual is perceived to be likely) and "then likelihood" (the perceived conditional likelihood of the outcome of the counterfactual, given the antecedent condition) determine the strength and impact of counterfactuals. This construct, termed counterfactual potency, is a reliable predictor of the degree of influence of counterfactual thinking upon judgments of regret, causation, and responsibility. Through 4 studies, we demonstrate the predictive power of this construct in a variety of contexts and show that it plays a causal role in determining the strength of the effects of counterfactual thought. Implications of counterfactual potency as a central factor of counterfactual influence are discussed.


title: Background Recovery by Fixed-Rank Robust Principal Component Analysis

abstract: Background recovery is a very important theme in computer vision applications. Recent research shows that robust principal component analysis RPCA is a promising approach for solving problems such as noise removal, video background modeling, and removal of shadows and specularity. RPCA utilizes the fact that the background is common in multiple views of a scene, and attempts to decompose the data matrix constructed from input images into a low-rank matrix and a sparse matrix. This is possible if the sparse matrix is sufficiently sparse, which may not be true in computer vision applications. Moreover, algorithmic parameters need to be fine tuned to yield accurate results. This paper proposes a fixed-rank RPCA algorithm for solving background recovering problems whose low-rank matrices have known ranks. Comprehensive tests show that, by fixing the rank of the low-rank matrix to a known value, the fixed-rank algorithm produces more reliable and accurate results than existing low-rank RPCA algorithm.


title: A Capacitance-Minimized, Doubly Grounded Transformer less Photovoltaic Inverter With Inherent Active-Power Decoupling

abstract: Two major challenges in the transformerless photovoltaic (PV) inverters are the presence of common-mode leakage currents, and as in most single-phase converters the need for reliable and compact double-line-frequency power decoupling. In the proposed doubly grounded inverter topology with innovative active-power-decoupling approach, both of these issues are simultaneously addressed. The topology allows the PV negative terminal to be directly connected to the neutral, thereby eliminating the capacitive-coupled common-mode ground currents. The decoupling capacitance requirement is minimized by a dynamically variable dc-link with large voltage swing, allowing an all-film-capacitor implementation. Furthermore, the use of wide bandgap devices enables the converter operation at higher switching frequency, resulting in smaller magnetic components. The topology uses only four switches and potentially enables a high power density solution. The operating principles, design and optimization, and control methods are explained in detail, and compared with other transformer-less, active-decoupling topologies. A 3 kVA, 100/75 kHz single-phase hardware prototype at 400 V dc nominal input and 240 V ac output with a wide range of power factor has been developed using SiC MOSFETs with only 45 μF/1100 V dc-link capacitance. Extensive experimental results from the prototype are presented to validate the concept, design, and superior performance of the proposed topology.


title: Evaluating and predicting user engagement change with degraded search relevance

abstract: User engagement in search refers to the frequency for users (re-)using the search engine to accomplish their tasks. Among factors that affected users' visit frequency, relevance of search results is believed to play a pivotal role. While multiple work in the past has demonstrated the correlation between search success and user engagement based on longitudinal analysis, we examine this problem from a different perspective in this work. Specifically, we carefully designed a large-scale controlled experiment on users of a large commercial Web search engine, in which users were separated into control and treatment groups, where users in treatment group were presented with search results which are deliberate degraded in relevance. We studied users' responses to the relevance degradation through tracking several behavioral metrics (such as query per user, click per session) over an extended period of time both during and following the experiment. By quantifying the relationship between user engagement and search relevance, we observe significant differences between user's short-term search behavior and long-term engagement change. By leveraging some of the key findings from the experiment, we developed a machine learning model to predict the long term impact of relevance degradation on user engagement. Overall, our model achieves over 67% of accuracy in predicting user engagement drop. Besides, our model is also capable of predicting engagement change for low-frequency users with very few user signals. We believe that insights from this study can be leveraged by search engine companies to detect and intervene search relevance degradation and to prevent long term user engagement drop.


title: Contextual Sequence Modeling for Recommendation with Recurrent Neural Networks

abstract: Recommendations can greatly benefit from good representations of the user state at recommendation time. Recent approaches that leverage Recurrent Neural Networks (RNNs) for session-based recommendations have shown that Deep Learning models can provide useful user representations for recommendation. However, current RNN modeling approaches summarize the user state by only taking into account the sequence of items that the user has interacted with in the past, without taking into account other essential types of context information such as the associated types of user-item interactions, the time gaps between events and the time of day for each interaction. To address this, we propose a new class of Contextual Recurrent Neural Networks for Recommendation (CRNNs) that can take into account the contextual information both in the input and output layers and modifying the behavior of the RNN by combining the context embedding with the item embedding and more explicitly, in the model dynamics, by parametrizing the hidden unit transitions as a function of context information. We compare our CRNNs approach with RNNs and non-sequential baselines and show good improvements on the next event prediction task.


title: A 0.3-THz SiGe-based frequency doubler chip with 3-dB 50 GHz bandwidth and 17 dB peak conversion gain

abstract: This paper presents a broadband frequency doubler chip working in the WR-03 band (220–325 GHz). The chip is implemented in a 130-nm SiGe BiCMOS technology with an of 250/300 GHz. It consists of an integrated high-gain wideband amplifier to drive the frequency doubler. The doubler is based on a cascode push-push topology. Conversion loss of the doubler is reduced by utilizing an inductive feedback in the common-base stage. A very wideband operation of the doubler is achieved using optimally sized transistors and 4-reactance based input matching network. On-wafer measurement of the chip shows a state-of-the-art 17.4 dB peak conversion gain at 270 GHz. It delivers a maximum output power of almost 1 mW with a 3-dB bandwidth ranging from 257 GHz to 307 GHz, which is the highest bandwidth for Si-based frequency doublers working entirely in the WR-03 band. The chip consumes around 429 mW from a supply voltage of 3.3 V.


title: Proposed Security Model and Threat Taxonomy for the Internet of Things (IoT)

abstract: IoT is an intelligent collaboration of tiny sensors and devices giving new challenges to security and privacy in end to end communication of things. Protection of data and privacy of things is one of the key challenges in the IoT. Lack of security measures will result in decreased adoption among users and therefore is one of the driving factors in the success of the IoT. This paper gives an overview, analysis and taxonomy of security and privacy challenges in IoT. Finally, Security Model for IoT has been proposed.


title: Extended Cooperative Task Space for manipulation tasks of humanoid robots

abstract: A humanoid robot can be viewed as a constrained dynamic system with constraints imposed by manipulation tasks, locomotion tasks, and the environment. This paper focuses on dealing with constraints in the upper-body of humanoid robots for manipulation tasks that involve coordinated motion of two arms. Inspired by research on human bimanual actions in the biomechanics area, we have developed the Extended-Cooperative-Task-Space (ECTS) representation that efficiently describes various coordinated motion tasks performed by a humanoid robot. Furthermore, we present a general whole-body control framework as an optimal controller based on Gauss's principle of least constraint. We show that all the constraints imposed on a humanoid system can be handled in a unified manner. The proposed framework is verified by numerical simulations on a Hubo II+ humanoid robot model.


FOREST FIRE RISK ZONE MAPPING FROM SATELLITE IMAGERY AND GIS A CASE STUDY


title: A generalized criterion for rubber toughening: The critical matrix ligament thickness

abstract: The thickness of matrix ligament is shown to be the single parameter determining whether a polymer/rubber blend will be tough or brittle. The matrix ligament is defined as the region of the matrix between two neighboring rubber particles. Specifically, the ligament thickness is the surface-to-surface interparticle distance. When the average ligament thickness is smaller than a critical value, a blend will be tough; when greater, it will be brittle. In other words, a sharp brittle–tough transition occurs at the critical ligament thickness. This critical parameter is independent of rubber volume fraction and particle size, and is characteristic of the matrix for a given mode, temperature and rate of deformation. What is important is the matrix ligament, not rubber particles. The single matrix parameter explains the effects of phase morphology, rubber volume fraction, particle size, particle-size polydispersity, and particle flocculation on toughness.


Named Entity Linking in #Tweets with KEA


title: Learning Meta-Embeddings by Using Ensembles of Embedding Sets

abstract: Word embeddings -- distributed representations of words -- in deep learning are beneficial for many tasks in natural language processing (NLP). However, different embedding sets vary greatly in quality and characteristics of the captured semantics. Instead of relying on a more advanced algorithm for embedding learning, this paper proposes an ensemble approach of combining different public embedding sets with the aim of learning meta-embeddings. Experiments on word similarity and analogy tasks and on part-of-speech tagging show better performance of meta-embeddings compared to individual embedding sets. One advantage of meta-embeddings is the increased vocabulary coverage. We will release our meta-embeddings publicly.


title: Spreading processes in Multilayer Networks

abstract: Several systems can be modeled as sets of interconnected networks or networks with multiple types of connections, here generally called multilayer networks. Spreading processes such as information propagation among users of online social networks, or the diffusion of pathogens among individuals through their contact network, are fundamental phenomena occurring in these networks. However, while information diffusion in single networks has received considerable attention from various disciplines for over a decade, spreading processes in multilayer networks is still a young research area presenting many challenging research issues. In this paper, we review the main models, results and applications of multilayer spreading processes and discuss some promising research directions.


title: Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN

abstract: The recently proposed distributional approach to reinforcement learning (DiRL) is centered on learning the distribution of the reward-to-go, often referred to as the value distribution. In this work, we show that the distributional Bellman equation, which drives DiRL methods, is equivalent to a generative adversarial network (GAN) model. In this formulation, DiRL can be seen as learning a deep generative model of the value distribution, driven by the discrepancy between the distribution of the current value, and the distribution of the sum of current reward and next value. We use this insight to propose a GAN-based approach to DiRL, which leverages the strengths of GANs in learning distributions of high-dimensional data. In particular, we show that our GAN approach can be used for DiRL with multivariate rewards, an important setting which cannot be tackled with prior methods. The multivariate setting also allows us to unify learning the distribution of values and state transitions, and we exploit this idea to devise a novel exploration method that is driven by the discrepancy in estimating both values and states.


title: Automatic Evaluation of Topic Coherence

abstract: This paper introduces the novel task of topic coherence evaluation, whereby a set of words, as generated by a topic model, is rated for coherence or interpretability. We apply a range of topic scoring models to the evaluation task, drawing on WordNet, Wikipedia and the Google search engine, and existing research on lexical similarity/relatedness. In comparison with human scores for a set of learned topics over two distinct datasets, we show a simple co-occurrence measure based on pointwise mutual information over Wikipedia data is able to achieve results for the task at or nearing the level of inter-annotator correlation, and that other Wikipedia-based lexical relatedness methods also achieve strong results. Google produces strong, if less consistent, results, while our results over WordNet are patchy at best.


title: Fall prediction based on biomechanics equilibrium using Kinect

abstract: The fall is one of the most important research fields of solitary elder healthcare at home based on Internet of Things technology. Current studies mainly focus on the fall detection, which helps me...


title: Pay-per-Question: Towards Targeted Q&A with Payments

abstract: Online question and answer (Q&A) services are facing key challenges to motivate domain experts to provide quick and high-quality answers. Recent systems seek to engage real-world experts by allowing them to set a price on their answers. This leads to a "targeted" Q&A model where users to ask questions to a target expert by paying the price. In this paper, we perform a case study on two emerging targeted Q&A systems Fenda (China) and Whale (US) to understand how monetary incentives affect user behavior. By analyzing a large dataset of 220K questions (worth 1 million USD), we find that payments indeed enable quick answers from experts, but also drive certain users to game the system for profits. In addition, this model requires users (experts) to proactively adjust their price to make profits. People who are unwilling to lower their prices are likely to hurt their income and engagement over time.


2 MS / s S PLIT SAR ADC U SING 0 . 18 um CMOS T ECHNOLOGY


title: Designing simple nonlinear filters using hysteresis of single recurrent neurons for acoustic signal recognition in robots

abstract: In this article we exploit the discrete-time dynamics of a single neuron with self-connection to systematically design simple signal filters. Due to hysteresis effects and transient dynamics, this single neuron behaves as an adjustable low-pass filter for specific parameter configurations. Extending this neuro-module by two more recurrent neurons leads to versatile high- and band-pass filters. The approach presented here helps to understand how the dynamical properties of recurrent neural networks can be used for filter design. Furthermore, it gives guidance to a new way of implementing sensory preprocessing for acoustic signal recognition in autonomous robots.


title: Photosynthesis, photorespiration, and light signalling in defence responses.

abstract: Visible light is the basic energetic driver of plant biomass production through photosynthesis. The constantly fluctuating availability of light and other environmental factors means that the photosynthetic apparatus must be able to operate in a dynamic fashion appropriate to the prevailing conditions. Dynamic regulation is achieved through an array of homeostatic control mechanisms that both respond to and influence cellular energy and reductant status. In addition, light availability and quality are continuously monitored by plants through photoreceptors. Outside the laboratory growth room, it is within the context of complex changes in energy and signalling status that plants must regulate pathways to deal with biotic challenges, and this can be influenced by changes in the highly energetic photosynthetic pathways and in the turnover of the photosynthetic machinery. Because of this, defence responses are neither simple nor easily predictable, but rather conditioned by the nutritional and signalling status of the plant cell. This review discusses recent data and emerging concepts of how recognized defence pathways interact with and are influenced by light-dependent processes. Particular emphasis is placed on the potential roles of the chloroplast, photorespiration, and photoreceptor-associated pathways in regulating the outcome of interactions between plants and pathogenic organisms.


title: A Neural Conversational Model

abstract: Conversational modeling is an important task in natural language understanding and machine intelligence. Although previous approaches exist, they are often restricted to specific domains (e.g., booking an airline ticket) and require hand-crafted rules. In this paper, we present a simple approach for this task which uses the recently proposed sequence to sequence framework. Our model converses by predicting the next sentence given the previous sentence or sentences in a conversation. The strength of our model is that it can be trained end-to-end and thus requires much fewer hand-crafted rules. We find that this straightforward model can generate simple conversations given a large conversational training dataset. Our preliminary results suggest that, despite optimizing the wrong objective function, the model is able to converse well. It is able extract knowledge from both a domain specific dataset, and from a large, noisy, and general domain dataset of movie subtitles. On a domain-specific IT helpdesk dataset, the model can find a solution to a technical problem via conversations. On a noisy open-domain movie transcript dataset, the model can perform simple forms of common sense reasoning. As expected, we also find that the lack of consistency is a common failure mode of our model.


title: Integrating effective rules to improve arabic text stemming

abstract: Nowadays, with the growth in the use of search engines, the extension of spying programs and anti -terrorism prevention, several researches focused on text analysis. In this sense, lemmatization and stemming are two common requirements of these researches. They include reducing different grammatical forms of a word and bring them to a common base form. In what follows, we will discuss these treatment methods on arabic text, especially the Khoja Stemmer, show their limits and provide new tools to improve it.


title: On the effectiveness of isolation-based anomaly detection in cloud data centers

abstract: Summary ::: The high volume of monitoring information generated by large-scale cloud infrastructures poses a challenge to the capacity of cloud providers in detecting anomalies in the infrastructure. Traditional anomaly detection methods are resource-intensive and computationally complex for training and/or detection, what is undesirable in very dynamic and large-scale environment such as clouds. Isolation-based methods have the advantage of low complexity for training and detection and are optimized for detecting failures. In this work, we explore the feasibility of Isolation Forest, an isolation-based anomaly detection method, to detect anomalies in large-scale cloud data centers. We propose a method to code time-series information as extra attributes that enable temporal anomaly detection and establish its feasibility to adapt to seasonality and trends in the time-series and to be applied online and in real-time.


title: Evolutionary Bayesian Rose Trees

abstract: We present an evolutionary multi-branch tree clustering method to model hierarchical topics and their evolutionary patterns over time. The method builds evolutionary trees in a Bayesian online filtering framework. The tree construction is formulated as an online posterior estimation problem, which well balances both the fitness of the current tree and the smoothness between trees. The state-of-the-art multi-branch clustering method, Bayesian rose trees, is employed to generate a topic tree with a high fitness value. A constraint model is also introduced to preserve the smoothness between trees. A set of comprehensive experiments on real world news data demonstrates that the proposed method better incorporates historical tree information and is more efficient and effective than the traditional evolutionary hierarchical clustering algorithm. In contrast to our previous method [31] , we implement two additional baseline algorithms to compare them with our algorithm. We also evaluate the performance of the clustering algorithm based on multiple constraint trees. Furthermore, two case studies are conducted to demonstrate the effectiveness and usefulness of our algorithm in helping users understand the major hierarchical topic evolutionary patterns in text data.


title: Performance evaluation of OPC UA

abstract: The widespread use of standard, worldwide and vendor-independent OPC UA specifications in industrial environment introduces many benefits as it allows to keep open the market of the industrial applications. On the other hand, OPC UA adopts a very complex software infrastructure to realise the data exchange between industrial applications and devices (e.g. mainframes, PLC, microcontroller); this complexity may impact on the overall performance of the communication between industrial applications. The aim of this paper is to deal with the performance evaluation of OPC UA, pointing out all the main features which could influence performance in the client/server exchange of information.


title: SwellShark: A Generative Model for Biomedical Named Entity Recognition without Labeled Data

abstract: We present SwellShark, a framework for building biomedical named entity recognition (NER) systems quickly and without hand-labeled data. Our approach views biomedical resources like lexicons as function primitives for autogenerating weak supervision. We then use a generative model to unify and denoise this supervision and construct large-scale, probabilistically labeled datasets for training high-accuracy NER taggers. In three biomedical NER tasks, SwellShark achieves competitive scores with state-of-the-art supervised benchmarks using no hand-labeled training data. In a drug name extraction task using patient medical records, one domain expert using SwellShark achieved within 5.1% of a crowdsourced annotation approach -- which originally utilized 20 teams over the course of several weeks -- in 24 hours.


title: Geometric Feature-Based Facial Expression Recognition in Image Sequences Using Multi-Class AdaBoost and Support Vector Machines

abstract: Facial expressions are widely used in the behavioral interpretation of emotions, cognitive science, and social interactions. In this paper, we present a novel method for fully automatic facial expression recognition in facial image sequences. As the facial expression evolves over time facial landmarks are automatically tracked in consecutive video frames, using displacements based on elastic bunch graph matching displacement estimation. Feature vectors from individual landmarks, as well as pairs of landmarks tracking results are extracted, and normalized, with respect to the first frame in the sequence. The prototypical expression sequence for each class of facial expression is formed, by taking the median of the landmark tracking results from the training facial expression sequences. Multi-class AdaBoost with dynamic time warping similarity distance between the feature vector of input facial expression and prototypical facial expression, is used as a weak classifier to select the subset of discriminative feature vectors. Finally, two methods for facial expression recognition are presented, either by using multi-class AdaBoost with dynamic time warping, or by using support vector machine on the boosted feature vectors. The results on the Cohn-Kanade (CK+) facial expression database show a recognition accuracy of 95.17% and 97.35% using multi-class AdaBoost and support vector machines, respectively.


title: Towards Analytics Aware Ontology Based Access to Static and Streaming Data (Extended Version)

abstract: Real-time analytics that requires integration and aggregation of heterogeneous and distributed streaming and static data is a typical task in many industrial scenarios such as diagnostics of turbines in Siemens. OBDA approach has a great potential to facilitate such tasks; however, it has a number of limitations in dealing with analytics that restrict its use in important industrial applications. Based on our experience with Siemens, we argue that in order to overcome those limitations OBDA should be extended and become analytics, source, and cost aware. In this work we propose such an extension. In particular, we propose an ontology, mapping, and query language for OBDA, where aggregate and other analytical functions are first class citizens. Moreover, we develop query optimisation techniques that allow to efficiently process analytical tasks over static and streaming data. We implement our approach in a system and evaluate our system with Siemens turbine data.


title: Zero Shot Learning via Multi-scale Manifold Regularization

abstract: We address zero-shot learning using a new manifold alignment framework based on a localized multi-scale transform on graphs. Our inference approach includes a smoothness criterion for a function mapping nodes on a graph (visual representation) onto a linear space (semantic representation), which we optimize using multi-scale graph wavelets. The robustness of the ensuing scheme allows us to operate with automatically generated semantic annotations, resulting in an algorithm that is entirely free of manual supervision, and yet improves the state-of-the-art as measured on benchmark datasets.


title: Mechanical sensorless control of PMSM with on-line estimation of stator resistance

abstract: This paper provides an improvement in sensorless control performance of nonsalient-pole permanent-magnet synchronous machines. To ensure sensorless operation, most of the existing methods require that the initial position error as well as the parameters uncertainties must be limited. In order to overcome these drawbacks, we study them analytically and present a solution using an online identification method which is easy to implement and is highly stable. A stability analysis based on Lyapunov's linearization method shows the stability of the closed-loop system with the proposed estimator combined with the sensorless algorithm. This approach does not need a well-known initial rotor position and makes the sensorless control more robust with respect to the stator resistance variations at low speed. The simulation and experimental results illustrate the validity of the analytical approach and the efficiency of the proposed method.


title: Chaotic family with smooth Lyapunov dependence

abstract: Simple one-dimensional maps are often the key for understanding the behavior of complicated physical systems @1#. This, for example, is the case for the different routes by which systems are driven from simple to chaotic behavior @2#. In many cases simple maps already provide a good model for the physical process @3#. It is widely believed that in the parameter region where chaotic behavior is possible, the Lyapunov exponent @4# shows a nonsmooth dependence on the control parameter. As a typical example of such a behavior we mention the logistic map beyond the accumulation point of period doubling @5#. More recently, similar examples have been discovered in connection with the diffusional behavior @6#. There it has been shown that similar effects for tentlike maps on grids of unit cells lead rather directly to a fractal, Weierstrass-like, dependence of the diffusion coefficient D on the family parameter ~which, in this case, is the slope of the map!. In our contribution we show that the parameter dependence can be of smooth nature if the family is appropriately chosen. The work explains implicitly why the parameter dependence is nonsmooth for differently chosen families. As a consequence of our approach it also emerges that it may not be necessary to simulate chaotic systems by a large number of numerical orbits, once the internal ~topological! structure of the map is known. Powerful analytical tools allow a thorough investigation of the system without simulations. The example for which we apply our analytical approach is the bungalow-tent map. This map is of special importance on its own because of the fact that it is the simplest nontrivial linearization of the quadratic parabola ~see Fig. 1!. Using appropriate parameters, the nonhyperbolic character of the parabola can be carried over to this linearized model, in spite of the linearization. In our contribution, for a specifically chosen one-parameter family of bungalow-tent maps, the Lyapunov exponents are analytically calculated. A smooth dependence of the Lyapunov exponent on the control parameter is found. The analytical results are compared with numerical orbit simulations that yield identical results, from which, however, no statement of smoothness can be derived. The bungalow-tent maps constitute a two-parameter family of fully developed maps that have a four-piece linearly increasing graph, with a symmetry along a vertical line through x50.5. A two-parameter representation is obtained


title: A UML-based Methodology for Hypermedia Design

abstract: We propose a methodology for hypermedia design which is based on a UML profile for the hypermedia domain. Starting with a use case analysis and a conceptual model of the application we first provide guidelines for modeling the navigation space. From the navigation space model we can derive, in a next step, a navigational structure model which shows how to navigate through the navigation space using access elements like indexes, guided tours, queries and menus. Finally, a presentation model is constructed that can be directly implemented by HTML frames. The different models of the design process are represented by using a hypermedia extension of UML. ::: ::: The strength of the presented methodology is given by the fact that most steps can be performed in an automatic way thus providing the basis for a generation mechanism for hypermedia design.


title: Crime places in crime theory

abstract: Criminologists and crime prevention practitioners are increasingly aware of the importance of places of crime. A place is a very small area, usually a street corner, address, building, or street segment. A focus on crime places contrasts with a focus on neighborhoods. Neighborhood theories usually highlight the development of offenders. while place level explanations emphasize crime events. Three perspectives suggest the importance of places for understanding crime: rational choice; routine activity theory; and crime pattern theory. Though these perspectives are mutually supportive, routine activity theory and crime pattern theory provide different explanations for crime occurring at different places. Five areas of research help us understand the importance of places: crime concentration about particular facilities (e.g., bars); the high concentration of crime at some addresses and the absence of crime at others; the preventive effects of various place features; the mobility of offenders; and studies of how offenders select targets. Concern has been expressed that efforts to prevent crime at specific locations will only move it to other, unprotected locations. Recent research suggests that these fears may be exaggerated, and that under some circumstances the opposite effect occurs: instead of crime displacing, the benefits of the prevention efforts diffuse to unprotected locations. This paper concludes with a review of the 14 original articles in this volume.


title: Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks

abstract: In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model selection attempts to find the most likely (MAP) model, and uses its structure to answer these questions. However, when the amount of available data is modest, there might be many models that have non-negligible posterior. Thus, we want compute the Bayesian posterior of a feature, i.e., the total posterior probability of all models that contain it. In this paper, we propose a new approach for this task. We first show how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables. This allows us to compute, for a given order, both the marginal probability of the data and the posterior of a feature. We then use this result as the basis for an algorithm that approximates the Bayesian posterior of a feature. Our approach uses a Markov Chain Monte Carlo (MCMC) method, but over orders rather than over network structures. The space of orders is smaller and more regular than the space of structures, and has much a smoother posterior “landscape”. We present empirical results on synthetic and real-life datasets that compare our approach to full model averaging (when possible), to MCMC over network structures, and to a non-Bayesian bootstrap approach.


The interaction between genotype and site: its influence on tree selection programmes in Britain.


title: Big data, big data quality problem

abstract: A USAF sponsored MITRE research team undertook four separate, domain-specific case studies about Big Data applications. Those case studies were initial investigations into the question of whether or not data quality issues encountered in Big Data collections are substantially different in cause, manifestation, or detection than those data quality issues encountered in more traditionally sized data collections. The study addresses several factors affecting Big Data Quality at multiple levels, including collection, processing, and storage. Though not unexpected, the key findings of this study reinforce that the primary factors affecting Big Data reside in the limitations and complexities involved with handling Big Data while maintaining its integrity. These concerns are of a higher magnitude than the provenance of the data, the processing, and the tools used to prepare, manipulate, and store the data. Data quality is extremely important for all data analytics problems. From the study's findings, the "truth about Big Data" is there are no fundamentally new DQ issues in Big Data analytics projects. Some DQ issues exhibit return-s-to-scale effects, and become more or less pronounced in Big Data analytics, though. Big Data Quality varies from one type of Big Data to another and from one Big Data technology to another.


title: A low-power 2.4-GHz receiver for wireless implantable neural stimulators

abstract: This paper presents a 2.4 GHz low-power CMOS On-Off Keying receiver front-end which contains a low noise amplifier with a novel down conversion mixer that is designed for wireless forward telemetry link in neural stimulation applications. The transceiver operates between 2.4 and 2.5 GHz to support the full industrial, scientific and medical band. The post-layout simulation results show that the fully integrated low-noise amplifier exhibits a gain of 15 dB, a noise figure of 1 dB at 2.4 GHz, and the input matching (S11) is -16 dB. The proposed mixer is resistor-less and designed based on current starved delay elements in a Gilbert topology. The transceiver implemented in a TSMC 0.18 μm CMOS technology uses a supply voltage of 1.2 V, supports a data rate of up to 100 Mbps, and consumes 7 mW. The total size of the proposed receiver front-end equals 0.38 mm 2 .


title: What are health professionals’ intentions toward using research and products of research in clinical practice? A systematic review and narrative synthesis

abstract: AIM ::: To explore health professionals' intentional behaviour and what determines their intention to use products of research in clinical practice. ::: ::: ::: BACKGROUND ::: Trying to get research and products of research into clinical practice is an enduring problem. A clearer picture is emerging as to how individual practitioners respond toward practical problems of changing clinical practice, but this does not include health professionals' intentions to use products of research and what influences their intentions. ::: ::: ::: DESIGN ::: Systematic Review and Narrative Synthesis. ::: ::: ::: DATA SOURCES ::: Five databases were searched systematically. This included BNI, HMIC, Psych INFO, CINHAL and MEDLINE; articles published in the English language only were included. ::: ::: ::: REVIEW METHODS ::: PRISMA guidelines were used as a framework for structuring the review and methods of narrative synthesis to analyse study outcomes. ::: ::: ::: RESULTS ::: Eighteen studies matched the final inclusion criteria. All studies used questionnaires to measure intention. Most studies involved Nurses or Physicians. Nurses' intentions were mostly influenced by their perceived ability to use guidelines in their practice. Physicians' intentions were often influenced by their perceptions of the usefulness and relevance of the guideline and peer pressure amongst the professional group. Practice habits, when added to intentional models were also predictive of intentional behaviour. In studies that compared intentions with behaviour, the level of intention often did not match self-report or actual behaviour.


title: Emulating self-reconfigurable robots - design of the SMORES system

abstract: Self-reconfigurable robots are capable of changing their shape to suit a task. The design of one system called SMORES (Self-assembling MOdular Robot for Extreme Shape-shifting) is introduced. This system is capable of rearranging its modules in all three classes of reconfiguration; lattice style, chain style and mobile reconfiguration. This system is capable of emulating many of the other existing systems and promises to be a step towards a universal modular robot.


title: GWAS for BMI: a treasure trove of fundamental insights into the genetic basis of obesity

abstract: Muller et al. [1] have provided a strong critique of the Genome-Wide Association Studies (GWAS) of body-mass index (BMI), arguing that the GWAS approach for the study of BMI is flawed, and has provided us with few biological insights. They suggest that what is needed instead is a new start, involving GWAS for more complex energy balance related traits. In this invited counter-point, we highlight the substantial advances that have occurred in the obesity field, directly stimulated by the GWAS of BMI. We agree that GWAS for BMI is not perfect, but consider that the best route forward for additional discoveries will likely be to expand the search for common and rare variants linked to BMI and other easily obtained measures of obesity, rather than attempting to perform new, much smaller GWAS for energy balance traits that are complex and expensive to measure. For GWAS in general, we emphasise that the power from increasing the sample size of a crude but easily measured phenotype outweighs the benefits of better phenotyping.


title: Scale robust multi view stereo

abstract: We present a Multi View Stereo approach for huge unstructured image datasets that can deal with large variations in surface sampling rate of single images. Our method reconstructs surface parts always in the best available resolution. It considers scaling not only for large scale differences, but also between arbitrary small ones for a weighted merging of the best partial reconstructions. We create depth maps with our GPU based depth map algorithm, that also performs normal optimization. It matches several images that are found with a heuristic image selection method, to a reference image. We remove outliers by comparing depth maps against each other with a fast but reliable GPU approach. Then, we merge the different reconstructions from depth maps in 3D space by selecting the best points and optimizing them with not selected points. Finally, we create the surface by using a Delaunay graph cut.


title: WireCAP: a novel packet capture engine for commodity NICs in high-speed networks

abstract: Packet capture is an essential function for many network applications. However, packet drop is a major problem with packet capture in high-speed networks. This paper presents WireCAP, a novel packet capture engine for commodity network interface cards (NICs) in high-speed networks. WireCAP provides lossless zero-copy packet capture and delivery services by exploiting multi-queue NICs and multicore architectures. WireCAP introduces two new mechanisms-the ring-buffer-pool mechanism and the buddy-group-based offloading mechanism-to address the packet drop problem of packet capture in high-speed network. WireCAP is efficient. It also facilitates the design and operation of a user-space packet-processing application. Experiments have demonstrated that WireCAP achieves better packet capture performance when compared to existing packet capture engines. In addition, WireCAP implements a packet transmit function that allows captured packets to be forwarded, potentially after the packets are modified or inspected in flight. Therefore, WireCAP can be used to support middlebox-type applications. Thus, at a high level, WireCAP provides a new packet I/O framework for commodity NICs in high-speed networks.


title: Emotion analysis of Arabic articles and its impact on identifying the author's gender

abstract: The Gender Identification (GI) problem is concerned with determining the gender of the author of a given text based on its contents. The GI problem is one of the authorship profiling problems which have a wide range of applications in various fields such as marketing and security. Due to its importance, extensive research efforts have been invested in the GI problem for different languages. Unfortunately, the same cannot be said about the Arabic language despite its strategic importance and widespread. In this work, we explore the GI problem for Arabic text as a supervised learning problem. Specifically, we consider and compare two approaches for feature extraction. The first one is the Bag-Of-Words (BOW) approach while the second one is based on computing features related to sentiments and emotions. One goal of this work is to confirm the validity of the common stereotype that female authors tend to write in a more emotional way than male authors. Our results show that there is no conclusive evidence that this is true for our dataset.


title: A 77–81-GHz 16-Element Phased-Array Receiver With $\pm {\hbox{50}}^{\circ}$ Beam Scanning for Advanced Automotive Radars

abstract: A 16-element phased-array receiver has been developed for advanced W-band automotive radars. The phased-array receiver is based on a single SiGe chip with RF beamforming capabilities, which is packaged using low-cost bond-wire techniques and attached to a 16-element linear microstrip array. The antenna results in a directivity of 29.3 dB and a gain of 28.0 dB at 77-81 GHz, and can be scanned to ±50 ::: ° ::: in the azimuth plane in ~ 1 ::: ° ::: steps. The packaging details are presented together with the steps taken to ensure a wideband impedance match and low coupling between the phased-array channels. Gain measurements done at 79 GHz agree well with simulations. The 16-element phased array receiver was used with a 2-element frequency-modulated continuous-wave transmitter at 76.5-77 GHz and high-resolution millimeter-wave images were obtained. The work shows that complex millimeter-wave phased arrays can be packaged using traditional bond-wire techniques, and can be a powerful solution for advanced automotive radars.


title: 2D and 3D face recognition: A survey

abstract: Government agencies are investing a considerable amount of resources into improving security systems as result of recent terrorist events that dangerously exposed flaws and weaknesses in today's safety mechanisms. Badge or password-based authentication procedures are too easy to hack. Biometrics represents a valid alternative but they suffer of drawbacks as well. Iris scanning, for example, is very reliable but too intrusive; fingerprints are socially accepted, but not applicable to non-consentient people. On the other hand, face recognition represents a good compromise between what's socially acceptable and what's reliable, even when operating under controlled conditions. In last decade, many algorithms based on linear/nonlinear methods, neural networks, wavelets, etc. have been proposed. Nevertheless, Face Recognition Vendor Test 2002 shown that most of these approaches encountered problems in outdoor conditions. This lowered their reliability compared to state of the art biometrics. This paper provides an ''ex cursus'' of recent face recognition research trends in 2D imagery and 3D model based algorithms. To simplify comparisons across different approaches, tables containing different collection of parameters (such as input size, recognition rate, number of addressed problems) are provided. This paper concludes by proposing possible future directions.


title: SVM based context awareness using body area sensor network for pervasive healthcare monitoring

abstract: In the present growing era advancement of computer processing power, data communication capabilities, low power micro electronics devices and micro sensors increases the popularity of wireless sensor network in real life. Body area sensor network is a group of sensor nodes inside and outside the human body for continuous monitoring of health conditions, behavior and activities. Context awareness in pervasive health care is a proactive approach which is different from a conventional event-driven model (for example: visiting doctor when sick) and here we are continuously monitoring a patient health conditions through the use of Body area sensor network. This paper presents a layered architecture of Wide Area Wireless Sensor Body Area Network (WA-WSBAN) along with data fusion techniques, standards and sensor network hardware requirement for context awareness. A BodyMedia sensor dataset collected from 9 different sensor nodes has been used to classify the user activities with reference to different sensor readings. The context information derived from the proposed Wide Area Wireless Sensor Body Area Network (WA-WSBAN) architecture may be used in pervasive healthcare monitoring to detect various events and accurate episodes and unusual patterns and activities obtained from the study can be marked for later review. In this research work patient activity and gender classification has been done by using one to all and multi kernel based support vector data classification. The similar practices may be utilized for the study of various observations in real time health care applications and proactive measures may be initiated based on results obtained from data classification.


title: Adaptive systems for foreign exchange trading

abstract: Foreign exchange markets are notoriously difficult to predict. For many years academics and practitioners alike have tried to build trading models, but history has not been kind to their efforts. Consistently predicting FX markets has seemed like an impossible goal but recent advances in financial research now suggest otherwise. With newly developed computational techniques and newly available data, the development of successful trading models is looking possible. The Centre for Financial Research (CFR) at Cambridge University’s Judge Institute of Management has been researching trading techniques in foreign exchange markets for a number of years. Over the last 18 months a joint project with HSBC Global Markets has looked at how the bank’s proprietary information on customer order flow and on the customer limit order book can be used to enhance the profitability of technical trading systems in FX markets. Here we give an overview of that research and report our results.


Mininet Clustering


title: Separation of sound sources by convolutive sparse coding

abstract: An algorithm for the separation of sound sources is presented. Each source is parametrized as a convolution between a time-frequency magnitude spectrogam and an onset vector. The source model is able to represent several types of sounds, for example repetitive drum sounds and harmonic sounds with modulations. An iterative algorithm is proposed for the estimation the parameters. The algorithm is based on minimizing the reconstruction error and the number of onsets. The number of onsets is minimized by applying the sparse coding scheme for onset vectors. A way of modeling the loudness perception of the human auditory system is proposed. The method compresses high-energy sources, and enables the separation of lowenergy sources which are perceptually significant. The algorithm is able to separate meaningful sources from real-world signals. Simulation experiments were carried out using mixtures of harmonic instruments. Demonstration signals are available at http://www.cs.tut.fi/~tuomasv/demopage.html.


title: User identification across multiple social networks

abstract: Today, more and more people have their virtual identities on the web. It is common that people are users of more than one social network and also their friends may be registered on multiple websites. A facility to aggregate our online friends into a single integrated environment would enable the user to keep up-to-date with their virtual contacts more easily, as well as to provide improved facility to search for people across different websites. In this paper, we propose a method to identify users based on profile matching. We use data from two popular social networks to study the similarity of profile definition. We evaluate the importance of fields in the web profile and develop a profile comparison tool. We demonstrate the effectiveness and efficiency of our tool in identifying and consolidating duplicated users on different websites.


title: 3D Human Motion Tracking with a Coordinated Mixture of Factor Analyzers

abstract: A major challenge in applying Bayesian tracking methods for tracking 3D human body pose is the high dimensionality of the pose state space. It has been observed that the 3D human body pose parameters typically can be assumed to lie on a low-dimensional manifold embedded in the high-dimensional space. The goal of this work is to approximate the low-dimensional manifold so that a low-dimensional state vector can be obtained for efficient and effective Bayesian tracking. To achieve this goal, a globally coordinated mixture of factor analyzers is learned from motion capture data. Each factor analyzer in the mixture is a "locally linear dimensionality reducer" that approximates a part of the manifold. The global parametrization of the manifold is obtained by aligning these locally linear pieces in a global coordinate system. To enable automatic and optimal selection of the number of factor analyzers and the dimensionality of the manifold, a variational Bayesian formulation of the globally coordinated mixture of factor analyzers is proposed. The advantages of the proposed model are demonstrated in a multiple hypothesis tracker for tracking 3D human body pose. Quantitative comparisons on benchmark datasets show that the proposed method produces more accurate 3D pose estimates over time than those obtained from two previously proposed Bayesian tracking methods.


title: Going Native: Using a Large-Scale Analysis of Android Apps to Create a Practical Native-Code Sandboxing Policy

abstract: Current static analysis techniques for Android applications operate at the Java level—that is, they analyze either the Java source code or the Dalvik bytecode. However, Android allows developers to write code in C or C++ that is cross-compiled to multiple binary architectures. Furthermore, the Java-written components and the native code components (C or C++) can interact. Native code can access all of the Android APIs that the Java code can access, as well as alter the Dalvik Virtual Machine, thus rendering static analysis techniques for Java unsound or misleading. In addition, malicious apps frequently hide their malicious functionality in native code or use native code to launch kernel exploits. It is because of these security concerns that previous research has proposed native code sandboxing, as well as mechanisms to enforce security policies in the sandbox. However, it is not clear whether the large-scale adoption of these mechanisms is practical: is it possible to define a meaningful security policy that can be imposed by a native code sandbox without breaking app functionality? In this paper, we perform an extensive analysis of the native code usage in 1.2 million Android apps. We first used static analysis to identify a set of 446k apps potentially using native code, and we then analyzed this set using dynamic analysis. This analysis demonstrates that sandboxing native code with no permissions is not ideal, as apps’ native code components perform activities that require Android permissions. However, our analysis provided very encouraging insights that make us believe that sandboxing native code can be feasible and useful in practice. In fact, it was possible to automatically generate a native code sandboxing policy, which is derived from our analysis, that limits many malicious behaviors while still allowing the correct execution of the behavior witnessed during dynamic analysis for 99.77% of the benign apps in our dataset. The usage of our system to generate policies would reduce the attack surface available to native code and, as a further benefit, it would also enable more reliable static analysis of Java code.


title: A General Natural-language Text Processor for Clinical Radiology

abstract: Objective : Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms. ::: ::: Design : The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain. ::: ::: Measurements : The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. ::: ::: Results : Without training specific to the four diseases, recall and precision of the system(combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.


title: Functional specialization of areas along the anterior–posterior axis of the primate prefrontal cortex

abstract: Functional specialization of areas along the anterior-posterior axis of the lateral prefrontal cortex has been speculated but little evidence exists about distinct neurophysiological properties between prefrontal sub-regions. To address this issue we divided the lateral prefrontal cortex into a posterior-dorsal, a mid-dorsal, an anterior-dorsal, a posterior-ventral, and an anterior ventral region. Selectivity for spatial locations, shapes, and colors was evaluated in six monkeys never trained in working memory tasks, while they viewed the stimuli passively. Recordings from over two thousand neurons revealed systematic differences between anterior and posterior regions. In the dorsal prefrontal cortex, anterior regions exhibited the largest receptive fields, longest response latencies, and lowest amount of information for stimuli. In the ventral prefrontal cortex, posterior regions were characterized by a low percentage of responsive neurons to any stimuli we used, consistent with high specialization for stimulus features. Additionally, spatial information was more prominent in the dorsal and color in ventral regions. Our results provide neurophysiological evidence for a rostral-caudal gradient of stimulus selectivity through the prefrontal cortex, suggesting that posterior areas are selective for stimuli even when these are not releant for execution of a task, and that anterior areas are likely engaged in more abstract operations.


title: An Attention-Based Approach for Single Image Super Resolution

abstract: The main challenge of single image super resolution (SISR) is the recovery of high frequency details such as tiny textures. However, most of the state-of-the-art methods lack specific modules to identify high frequency areas, causing the output image to be blurred. We propose an attention-based approach to give a discrimination between texture areas and smooth areas. After the positions of high frequency details are located, high frequency compensation is carried out. This approach can incorporate with previously proposed SISR networks. By providing high frequency enhancement, better performance and visual effect are achieved. We also propose our own SISR network composed of DenseRes blocks. The block provides an effective way to combine the low level features and high level features. Extensive benchmark evaluation shows that our proposed method achieves significant improvement over the state-of-the-art works in SISR.


title: A Survey on Essential Components of a Self-Sovereign Identity

abstract: This paper provides an overview of the Self-Sovereign Identity (SSI) concept, focusing on four different components that we identified as essential to the architecture. Self-Sovereign Identity is enabled by the new development of blockchain technology. Through the trustless, decentralised database that is provided by a blockchain, classic Identity Management registration processes can be replaced. We start off by giving a simple overview of blockchain based SSI, introducing an architecture overview as well as relevant actors in such a system. We further distinguish two major approaches, namely the Identifier Registry Model and its extension the Claim Registry Model. Subsequently we discuss identifiers in such a system, presenting past research in the area and current approaches in SSI in the context of Zooko's Triangle. As the user of an SSI has to be linked with his digital identifier we also discuss authentication solutions. Most central to the concept of an SSI are the verifiable claims that are presented to relying parties. Resources in the field are only losely connected. We will provide a more coherent view of verifiable claims in regards to blockchain based SSI and clarify differences in the used terminology. Storage solutions for the verifiable claims, both on- and off-chain, are presented with their advantages and disadvantages.


title: End-to-End Learning of Latent Deformable Part-Based Representations for Object Detection

abstract: Object detection methods usually represent objects through rectangular bounding boxes from which they extract features, regardless of their actual shapes. In this paper, we apply deformations to regions in order to learn representations better fitted to objects. We introduce DP-FCN, a deep model implementing this idea by learning to align parts to discriminative elements of objects in a latent way, i.e. without part annotation. This approach has two main assets: it builds invariance to local transformations, thus improving recognition, and brings geometric information to describe objects more finely, leading to a more accurate localization. We further develop both features in a new model named DP-FCN2.0 by explicitly learning interactions between parts. Alignment is done with an in-network joint optimization of all parts based on a CRF with custom potentials, and deformations are influencing localization through a bilinear product. We validate our models on PASCAL VOC and MS COCO datasets and show significant gains. DP-FCN2.0 achieves state-of-the-art results of 83.3 and 81.2% on VOC 2007 and 2012 with VOC data only.


title: Optimization Techniques to Improve Training Speed of Deep Neural Networks for Large Speech Tasks

abstract: While Deep Neural Networks (DNNs) have achieved tremendous success for large vocabulary continuous speech recognition (LVCSR) tasks, training these networks is slow. Even to date, the most common approach to train DNNs is via stochastic gradient descent, serially on one machine. Serial training, coupled with the large number of training parameters (i.e., 10-50 million) and speech data set sizes (i.e., 20-100 million training points) makes DNN training very slow for LVCSR tasks. In this work, we explore a variety of different optimization techniques to improve DNN training speed. This includes parallelization of the gradient computation during cross-entropy and sequence training, as well as reducing the number of parameters in the network using a low-rank matrix factorization. Applying the proposed optimization techniques, we show that DNN training can be sped up by a factor of 3 on a 50-hour English Broadcast News (BN) task with no loss in accuracy. Furthermore, using the proposed techniques, we are able to train DNNs on a 300-hr Switchboard (SWB) task and a 400-hr English BN task, showing improvements between 9-30% relative over a state-of-the art GMM/HMM system while the number of parameters of the DNN is smaller than the GMM/HMM system.


title: CRLH metamaterial leaky-wave and resonant antennas

abstract: Composite right-/left-handed (CRLH) transmission-line (TL) metamaterials, with their rich dispersion and fundamental right-/left-hand duality, represent a paradigm shift in electromagnetics engineering and, in particular, for antennas. This paper presents an overview of the most practical leaky-wave and resonant CRLH antennas, which all exhibit functionalities or/and performance superior to prior state of the art. The leaky-wave antennas provide full-space dynamic scanning capability, with fan beams, conical beams in uni-planar configurations, pencil beams without any complex feeding network, and actively shaped beams based on the concept of aperture digitization. The resonant antennas offer alternative properties and a solution to beam-squinting when no scanning is required, including multi-band (dual/tri-band) operation, zeroth-order high efficiency, high directivity, and planar electric and magnetic monopole radiators.


title: A Hierarchical Aggregation Framework for Efficient Multilevel Visual Exploration and Analysis

abstract: Data exploration and visualization systems are of great importance in the Big Data era, in which the volume and heterogeneity of available information make it difficult for humans to manually explore and analyse data. Most traditional systems operate in an offline way, limited to accessing preprocessed (static) sets of data. They also restrict themselves to dealing with small dataset sizes, which can be easily handled with conventional techniques. However, the Big Data era has realized the availability of a great amount and variety of big datasets that are dynamic in nature; most of them offer API or query endpoints for online access, or the data is received in a stream fashion. Therefore, modern systems must address the challenge of on-the-fly scalable visualizations over large dynamic sets of data, offering efficient exploration techniques, as well as mechanisms for information abstraction and summarization. In this work, we present a generic model for personalized multilevel exploration and analysis over large dynamic sets of numeric and temporal data. Our model is built on top of a lightweight tree-based structure which can be efficiently constructed on-the-fly for a given set of data. This tree structure aggregates input objects into a hierarchical multiscale model. Considering different exploration scenarios over large datasets, the proposed model enables efficient multilevel exploration, offering incremental construction and prefetching via user interaction, and dynamic adaptation of the hierarchies based on user preferences. A thorough theoretical analysis is presented, illustrating the efficiency of the proposed model. The proposed model is realized in a web-based prototype tool, called SynopsViz that offers multilevel visual exploration and analysis over Linked Data datasets.


title: Spatial-Winograd Pruning Enabling Sparse Winograd Convolution

abstract: Deep convolutional neural networks (CNNs) are deployed in various applications but demand immense computational requirements. Pruning techniques and Winograd convolution are two typical methods to reduce the CNN computation. However, they cannot be directly combined because Winograd transformation fills in the sparsity resulting from pruning. Li et al. (2017) propose sparse Winograd convolution in which weights are directly pruned in the Winograd domain, but this technique is not very practical because Winograd-domain retraining requires low learning rates and hence significantly longer training time. Besides, Liu et al. (2018) move the ReLU function into the Winograd domain, which can help increase the weight sparsity but requires changes in the network structure. To achieve a high Winograd-domain weight sparsity without changing network structures, we propose a new pruning method, spatial-Winograd pruning. As the first step, spatial-domain weights are pruned in a structured way, which efficiently transfers the spatial-domain sparsity into the Winograd domain and avoids Winograd-domain retraining. For the next step, we also perform pruning and retraining directly in the Winograd domain but propose to use an importance factor matrix to adjust weight importance and weight gradients. This adjustment makes it possible to effectively retrain the pruned Winograd-domain network without changing the network structure. For the three models on the datasets of CIFAR10, CIFAR-100, and ImageNet, our proposed method can achieve the Winograd domain sparsities of 63%, 50%, and 74%, respectively.


title: Time series sales forecasting for short shelf-life food products based on artificial neural networks and evolutionary computing

abstract: Due to the strong competition that exists today, most manufacturing organizations are in a continuous effort for increasing their profits and reducing their costs. Accurate sales forecasting is certainly an inexpensive way to meet the aforementioned goals, since this leads to improved customer service, reduced lost sales and product returns and more efficient production planning. Especially for the food industry, successful sales forecasting systems can be very beneficial, due to the short shelf-life of many food products and the importance of the product quality which is closely related to human health. In this paper we present a complete framework that can be used for developing nonlinear time series sales forecasting models. The method is a combination of two artificial intelligence technologies, namely the radial basis function (RBF) neural network architecture and a specially designed genetic algorithm (GA). The methodology is applied successfully to sales data of fresh milk provided by a major manufacturing company of dairy products.


title: Neuromorphic Learning and Recognition With One-Transistor-One-Resistor Synapses and Bistable Metal Oxide RRAM

abstract: Resistive switching memory (RRAM) has been proposed as an artificial synapse in neuromorphic circuits due to its tunable resistance, low power operation, and scalability. For the development of high-density neuromorphic circuits, it is essential to validate the state-of-the-art bistable RRAM and to introduce small-area building blocks serving as artificial synapses. This paper introduces a new synaptic circuit consisting of a one-transistor/one-resistor structure, where the resistive element is a HfO2 RRAM with bipolar switching. The spike-timing-dependent plasticity is demonstrated in both the deterministic and stochastic regimes of the RRAM. Finally, a fully connected neuromorphic network is simulated showing online unsupervised pattern learning and recognition for various voltages of the POST spike. The results support bistable RRAM for high-performance artificial synapses in neuromorphic circuits.


title: Consolidate IoT Edge Computing with Lightweight Virtualization

abstract: Lightweight virtualization (LV) technologies have refashioned the world of software development by introducing flexibility and new ways of managing and distributing software. Edge computing complements today's powerful centralized data centers with a large number of distributed nodes that provide virtualization close to the data source and end users. This emerging paradigm offers ubiquitous processing capabilities on a wide range of heterogeneous hardware characterized by different processing power and energy availability. The scope of this article is to present an in-depth analysis on the requirements of edge computing from the perspective of three selected use cases that are particularly interesting for harnessing the power of the Internet of Things. We discuss and compare the applicability of two LV technologies, containers and unikernels, as platforms for enabling the scalability, security, and manageability required by such pervasive applications that soon may be part of our everyday lives. To inspire further research, we identify open problems and highlight future directions to serve as a road map for both industry and academia.


title: A prevention strategy to reduce the incidence of injury in high school basketball: a cluster randomized controlled trial.

abstract: OBJECTIVE: To examine the effectiveness of a sport-specific balance training program in reducing injury in adolescent basketball. DESIGN: Cluster randomized controlled trial. SETTING: Twenty-five high schools in Calgary and surrounding area. SUBJECTS: Nine hundred and twenty high school basketball players (ages 12-18). INTERVENTION: Subjects were randomly allocated by school to the control (n = 426) and training group (n = 494). Both groups were taught a standardized warm-up program. The training group was also taught an additional warm-up component and a home-based balance training program using a wobble board. MAIN OUTCOME MEASURES: All injuries occurring during basketball that required medical attention and/or caused a player to be removed from that current session and/or miss a subsequent session were then recorded and assessed by a team therapist who was blinded to training group allocation. RESULTS: A basketball-specific balance training program was protective of acute-onset injuries in high school basketball [RR = 0.71 (95% CI; 0.5-0.99)]. The protective effect found with respect to all injury [RR = 0.8 (95% CI; 0.57-1.11)], lower-extremity injury [RR = 0.83 (95% CI; 0.57-1.19)], and ankle sprain injury [RR = 0.71 (95% CI; 0.45-1.13)] were not statistically significant. Self-reported compliance to the intended home-based training program was poor (298/494 or 60.3%). CONCLUSIONS: A basketball-specific balance training program was effective in reducing acute-onset injuries in high school basketball. There was also a clinically relevant trend found with respect to the reduction of all, lower-extremity, and ankle sprain injury. Future research should include further development of neuromuscular prevention strategies in addition to further evaluation of methods to increase compliance to an injury-prevention training program in adolescents. Language: en


title: Edge detection of optimal wavelet scale space image

abstract: Edge detection is one of most important fields in computer vision,and lots of algorithms have been proposed by former scholars.Based on properties of image and detector,and taking relevances as criterion,this paper proposes an algorithm which use genetic algorithm to select optimal wavelet transform scales to form an adaptive Gaussian wavelet scale space.By fusing image's edges detected at different scales in this scale space,image will be improved in edge details and noise immunity.Experiments show that this adaptive scale wavelet algorithm has advantage in terms of noise removing and corner points positioning over Canny algorithm,single-scale wavelet algorithm,binary scale wavelet algorithm or multi-scale wavelet a algorithm.


title: Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time

abstract: Various deep learning models have recently been applied to predictive modeling of Electronic Health Records (EHR). In medical claims data, which is a particular type of EHR data, each patient is represented as a sequence of temporally ordered irregularly sampled visits to health providers, where each visit is recorded as an unordered set of medical codes specifying patient's diagnosis and treatment provided during the visit. Based on the observation that different patient conditions have different temporal progression patterns, in this paper we propose a novel interpretable deep learning model, called Timeline. The main novelty of Timeline is that it has a mechanism that learns time decay factors for every medical code. This allows the Timeline to learn that chronic conditions have a longer lasting impact on future visits than acute conditions. Timeline also has an attention mechanism that improves vector embeddings of visits. By analyzing the attention weights and disease progression functions of Timeline, it is possible to interpret the predictions and understand how risks of future visits change over time. We evaluated Timeline on two large-scale real world data sets. The specific task was to predict what is the primary diagnosis category for the next hospital visit given previous visits. Our results show that Timeline has higher accuracy than the state of the art deep learning models based on RNN. In addition, we demonstrate that time decay factors and attentions learned by Timeline are in accord with the medical knowledge and that Timeline can provide a useful insight into its predictions.


DCGANs for image super-resolution , denoising and debluring


title: Voronoi Grid-Shell Structures

abstract: We introduce a framework for the generation of grid-shell structures that is based on Voronoi diagrams and allows us to design tessellations that achieve excellent static performances. We start from an analysis of stress on the input surface and we use the resulting tensor field to induce an anisotropic non-Euclidean metric over it. Then we compute a Centroidal Voronoi Tessellation under the same metric. The resulting mesh is hex-dominant and made of cells with a variable density, which depends on the amount of stress, and anisotropic shape, which depends on the direction of maximum stress. This mesh is further optimized taking into account symmetry and regularity of cells to improve aesthetics. We demonstrate that our grid-shells achieve better static performances with respect to quad-based grid shells, while offering an innovative and aesthetically pleasing look.


title: Implementation and performance comparison of five DSP-based control methods for three-phase six-switch boost PFC rectifier

abstract: In this paper, implementation and performance comparison of five control methods for the average-current-controlled three-phase six-switch boost PFC rectifier are presented. The considered average-current-control methods include implementations with three independent controllers, with six-step PWM, and with three-step PWM all in stationary (a,b,c) reference frame, as well as average-current control in rotating (d,q) reference frame with zero-sequence-signal (ZSS) injection-based PWM and with space vector modulation (SVM). It is shown that the average-current control with three independent controllers exhibits the lowest total harmonic distortion (THD) of input currents, whereas, the average-current control in the rotating (d,q) reference frame with ZSS injection-based PWM and SVM exhibit the highest power factor. It is also shown that the average-current control with three independent controllers requires the shortest calculation time. The measurements were performed on a 3-kW, three-phase, six-switch, boost PFC rectifier prototype controlled by the TMS320F2808 DSP from TI.


title: Proposing stochastic probability-based math model and algorithms utilizing social networking and academic data for good fit students prediction

abstract: The research progress presented in this paper comes under the areas of data science. The authors propose enhanced machine learning (supervised learning) framework for the prediction of the students through stochastic probability-based math constructs/model and an algorithm [Good Fit Student (GFS)], along with the enhanced quantification of target variables and algorithmic metrics. Academia in today’s modern world sees the problem of dropouts, low retention, poor student performances, lack of motivation, and unnecessary change of study majors and re-admissions. The authors consider this challenge as a research problem and attempt to solve it by utilizing social networking-based personality traits, relevant data and features to improve the predictive modeling approach. The authors recognize that admission choices are often governed by family trends, affordability, basic motivation, market trends, and natural instincts. However, natural gifts and talents are minimally used to select such directions in the academics. The authors based on literature review identify this a research gap and improves with a unique blend of algorithms/methods, an improved modeling of performance metrics, built upon cross-validation to improve fitness, and enhance the process of feature engineering and tuning for reduced errors and optimum fitness, at the end. The authors present the latest progress of their research in this paper. The included results show the progress of the work and ongoing improvements. The authors use machine learning techniques, Microsoft SQL Server, Excel data mining, R and Python to develop and test their model. The authors provide related work and conclude with final remarks and future work.


title: CERAMICS IN RESTORATIVE AND PROSTHETIC DENTISTRY

abstract: This review is intended to provide the ceramic engineer with information about the history and current use of ceramics in dentistry, contemporary research topics, and potential research agenda. Background material includes intra-oral design considerations, descriptions of ceramic dental components, and the origin, composition, and microstructure of current dental ceramics. Attention is paid to efforts involving net-shape processing, machining as a forming method, and the analysis of clinical failure. A rationale is presented for the further development of all-ceramic restorative systems. Current research topics receiving attention include microstructure/processing/property relationships, clinical failure mechanisms and in vitro testing, wear damage and wear testing, surface treatments, and microstructural modifications. The status of the field is critically reviewed with an eye toward future work. Significant improvements seem possible in the clinical use of ceramics based on engineering solutions derived from the study of clinically failed restorations, on the incorporation of higher levels of “biomimicry” in new systems, and on the synergistic developments in dental cements and adhesive dentin bonding.


title: Active Learning with Multiple Views

abstract: Active learners alleviate the burden of labeling large amounts of data by detecting and asking the user to label only the most informative examples in the domain. We focus here on active learning for multi-view domains, in which there are several disjoint subsets of features (views), each of which is sufficient to learn the target concept. In this paper we make several contributions. First, we introduce Co-Testing, which is the first approach to multi-view active learning. Second, we extend the multi-view learning framework by also exploiting weak views, which are adequate only for learning a concept that is more general/specific than the target concept. Finally, we empirically show that Co-Testing outperforms existing active learners on a variety of real world domains such as wrapper induction, Web page classification, advertisement removal, and discourse tree parsing.


title: Identifying hallmarks of consciousness in non-mammalian species. Conscious Cogn

abstract: Abstract Most early studies of consciousness have focused on human subjects. This is understandable, given that humans are capable of reporting accurately the events they experience through language or by way of other kinds of voluntary response. As researchers turn their attention to other animals, “accurate report” methodologies become increasingly difficult to apply. Alternative strategies for amassing evidence for consciousness in non-human species include searching for evolutionary homologies in anatomical substrates and measurement of physiological correlates of conscious states. In addition, creative means must be developed for eliciting behaviors consistent with consciousness. In this paper, we explore whether necessary conditions for consciousness can be established for species as disparate as birds and cephalopods. We conclude that a strong case can be made for avian species and that the case for cephalopods remains open. Nonetheless, a consistent effort should yield new means for interpreting animal behavior.


title: Sex differences in childhood sexual abuse characteristics and victims’ emotional and behavioral problems: Findings from a national sample of youth

abstract: OBJECTIVE: The first objective of this study was to test for sex differences in four childhood sexual abuse characteristics-penetration, substantiation, perpetrator familial status, and multi-maltreatment-in a national sample of youth. The second objective was to test for sex differences in how these abuse characteristics were associated with victims' emotional and behavioral problems. METHODS: The sample was drawn from the National Survey of Child and Adolescent Well-Being, a sample of children investigated by United States child welfare services. Youth in the current study (n=573, including 234 adolescents) were investigated for alleged sexual abuse. Logistic regression and multivariate analysis of covariance were used to test for sex differences in abuse characteristics, and to determine whether sex moderated associations between abuse characteristics and emotional and behavioral problems. RESULTS: Girls were more likely than boys to have their abuse substantiated and to experience penetrative abuse (although differences in penetration status did not emerge among adolescents). Substantiation status and child age were positively associated with caregiver-reported internalizing and externalizing symptoms. Sex did not moderate the relationship between abuse characteristics and youth emotional and behavioral problems. CONCLUSIONS: Sexual abuse characteristics might not be highly predictive factors when making decisions about services needs. Furthermore, there may not be a strong empirical basis for operating on the assumption that one sex is more vulnerable to negative consequences of abuse than the other, or that abuse affects girls and boys differently. The processes explaining why some victims exhibit more impairment than others are likely complex. Language: en


title: Laser treatment of pigmented lesions.

abstract: Several pigment-specific lasers can effectively treat epidermal and dermal pigmented lesions without complications using the basic principles of selective photothermolysis. Although such pigmented lesions as solar lentigines and nevi of Ota are relatively easy to treat using pigment-specific laser technology, café-au-lait macules and melasma show variable responses to treatment. New, long-pulsed pigment-specific lasers may prove to further enhance the clinical results obtained in resistant pigmented lesions and other conditions.


title: What is the relationship between mental workload factors and cognitive load types

abstract: The present study tested the hypothesis of an additive interaction between intrinsic, extraneous and germane cognitive load, by manipulating factors of mental workload assumed to have a specific effect on either type of cognitive load. The study of cognitive load factors and their interaction is essential if we are to improve workers' wellbeing and safety at work. High cognitive load requires the individual to allocate extra resources to entering information. It is thought that this demand for extra resources may reduce processing efficiency and performance. The present study tested the effects of three factors thought to act on either cognitive load type, i.e. task difficulty, time pressure and alertness in a working memory task. Results revealed additive effects of task difficulty and time pressure, and a modulation by alertness on behavioral, subjective and psychophysiological workload measures. Mental overload can be the result of a combination of task-related components, but its occurrence may also depend on subject-related characteristics, including alertness. Solutions designed to reduce incidents and accidents at work should consider work organization in addition to task constraints in so far that both these factors may interfere with mental workload. Language: en


title: Clinical experimental stress studies: methods and assessment

abstract: Abstract Stress is a state of threatened homeostasis during which a variety of adaptive processes are activated to produce physiological and behavioral changes. Stress induction methods are pivotal for understanding these physiological or pathophysiological changes in the body in response to stress. Furthermore, these methods are also important for the development of novel pharmacological agents for stress management. The well-described methods to induce stress in humans include the cold pressor test, Trier Social Stress Test, Montreal Imaging Stress Task, Maastricht Acute Stress Test, CO2 challenge test, Stroop test, Paced Auditory Serial Addition Task, noise stress, and Mannheim Multicomponent Stress Test. Stress assessment in humans is done by measuring biochemical markers such as cortisol, cortisol awakening response, dexamethasone suppression test, salivary α-amylase, plasma/urinary norepinephrine, norepinephrine spillover rate, and interleukins. Physiological and behavioral changes such as galvanic skin response, heart rate variability, pupil size, and muscle and/or skin sympathetic nerve activity (microneurography) and cardiovascular parameters such as heart rate, blood pressure, and self-reported anxiety are also monitored to assess stress response. This present review describes these commonly employed methods to induce stress in humans along with stress assessment methods.


title: Document re-ranking using cluster validation and label propagation

abstract: This paper proposes a novel document re-ranking approach in information retrieval, which is done by a label propagation-based semi-supervised learning algorithm to utilize the intrinsic structure underlying in the large document data. Since no labeled relevant or irrelevant documents are generally available in IR, our approach tries to extract some pseudo labeled documents from the ranking list of the initial retrieval. For pseudo relevant documents, we determine a cluster of documents from the top ones via cluster validation-based k-means clustering; for pseudo irrelevant ones, we pick a set of documents from the bottom ones. Then the ranking of the documents can be conducted via label propagation. Evaluation on benchmark corpora shows that the approach can achieve significant improvement over standard baselines and performs better than other related approaches.


title: An Ant Colony Optimization Algorithm for Knight's Tour Problem on the Chessboard with Holes

abstract: Knight’s tour problem (KTP) on the chessboard with holes is an especial but interest tour problem. We improve the basic ant colony optimization,and introduce more better pheromone resource update and sharing mechanism,, and find its tour path solutions on the chessboard with holes with the help of colonies sharing the information resource by their intercommunion. We have used the algorithm to encumerate the knight’s tours on different chessboard with holes, and made the particular analysis for produce. Experiment results show that the algorithm not only produce the path solutions quickly on the chessboard with holes, but also is easy to control the path starting square and path style.


title: Depression, somatization and the “new cross-cultural psychiatry”

abstract: Abstract Contrary to Singer's contention that the features of depressive disorders do not exhibit significant cross-cultural differences, the author uses material from field research in Taiwan and data from recent anthropological and clinical investigations to support the opposite view that such differences exist and are a function of the cultural shaping of normative and deviant behavior. Somatization amongst Chinese depressives is used as an illustration. This discrepancy reflects substantial changes in the nature of more recent cross-cultural studies by anthropologists and psychiatrists, changes which are giving rise to a new cross-cultural approach to psychiatric issues. Some features and implications of that approach are described.


title: Sentiment user profile analysis based on forgetting curve in mobile environments

abstract: The user profile is a way of describing user characteristics using collected information about what the user prefers. However, it cannot be used for other applications with a general purpose, because it is generally created for personal recommendation and search. Furthermore, there are restrictions to analyze the user profile in terms of temporal influence in mobile environments. In this paper, in order to overcome these limitations, we propose a sentiment user profile analysis, which can analyze individual user characteristics based on the forgetting curve in mobile environments. In order to evaluate the effectiveness and feasibility of the proposed analysis, it has been implemented and applied to clothing domain. Experimental results show that the proposed analysis extracts the individual user characteristics with better accuracy than the existing method.


title: An empirical task analysis of warehouse order picking using head-mounted displays

abstract: Evaluations of task guidance systems often focus on evaluations of new technologies rather than comparing the nuances of interaction across the various systems. One common domain for task guidance systems is warehouse order picking. We present a method involving an easily reproducible ecologically motivated order picking environment for quantitative user studies designed to reveal differences in interactions. Using this environment, we perform a 12 participant within-subjects experiment demonstrating the advantages of a head-mounted display based picking chart over a traditional text-based pick list, a paper-based graphical pick chart, and a mobile pick-by-voice system. The test environment proved sufficiently sensitive, showing statistically significant results along several metrics with the head-mounted display system performing the best. We also provide a detailed analysis of the strategies adopted by our participants.