title: Full-Bridge+SRT Hybrid DC/DC Converter for a 6.6-kW EV On-Board Charger

abstract: This paper suggests a hybrid pulsewidth-modulation (PWM) dc/dc converter for an electric vehicle (EV) on-board charger (OBC). It is composed of a full-bridge (FB) converter and a resonant converter with secondary resonance tank (SRT). They are coupled in a single transformer so that any power distribution problem that causes a size increase in the transformer can be solved. Dual power flow of the proposed charger provides an increase in charger gain and a reduction of turn-off voltage spikes of the rectifying diodes caused by transformer leakage inductance and diode junction capacitance. These characteristics help reduce diode loss and remove dissipative snubber circuitry. In addition, this structure has the additional advantage of output inductor size due to the reduction of voltage variation applied to the inductor. The feasibility of the proposed charger is verified with a 6.6-kW prototype implemented by a design procedure.


title: Anomaly Detection Using Forecasting Methods ARIMA and HWDS

abstract: Understanding the normal operation of IP networks is a common step in building a solution for automatic detection of network anomalies. Toward this end, we analyze the usage of two different approaches: the AutoRegressive Integrated Moving Average (ARIMA) model and an improvement of the traditional Holt-winters method. We use both models for traffic characterization, called Digital Signature of Network Segment using Flow analysis (DSNSF), and volume anomaly or outliers detection. The DSNSFs obtained by the presented models are compared to the actual traffic of bits and packets of a real network environment and then subjected to specific evaluations in order to measure its accuracy. The presented models are capable of providing feedback through its predictive capabilities and hence provide an early warning system.


title: Development of a Clinical Score (A2DS2) to Predict Pneumonia in Acute Ischemic Stroke

abstract: Background and Purpose—Poststroke pneumonia is a potentially preventable complication after stroke associated with poor outcome. We developed and externally validated a prognostic score for predicting risk of pneumonia after ischemic stroke. Methods—The prognostic score was developed based on clinical data routinely collected after admission from the Berlin Stroke Register, Germany. The association of demographics, comorbidities, and clinical characteristics with poststroke pneumonia was investigated using multivariable logistic regression analyses. Independent predictors of poststroke pneumonia were translated into a point scoring system based on the corresponding regression coefficients. The predictive properties of the developed prognostic score were externally validated using an independent data set from the Stroke Register Northwest-Germany. Results—Between 2007 and 2009, 15 335 patients with ischemic stroke were registered within the Berlin Stroke Register. The observed rate of pneumonia in hospital...


title: Method and simulation program informed decisions in the early stages of building design

abstract: Abstract The early stages of building design include a number of decisions which have a strong influence on the performance of the building throughout the rest of the process. It is therefore important that designers are aware of the consequences of these design decisions. This paper presents a method for making informed decisions in the early stages of building design to fulfil performance requirements with regard to energy consumption and indoor environment. The method is operationalised in a program that utilises a simple simulation program to make performance predictions of user-defined parameter variations. The program then presents the output in a way that enables designers to make informed decisions. The method and the program reduce the need for design iterations, reducing time consumption and construction costs, to obtain the intended energy performance and indoor environment.


title: Optimal transition maneuvers for a class of V/STOL aircraft

abstract: This paper focuses on the problem of computing optimal transition maneuvers for a particular class of tail-sitter aircraft able to switch their flight configuration from hover to level flight and vice versa. Both minimum-time and minimum-energy optimal transition problems are formulated and solved numerically in order to compute reference maneuvers to be employed by the onboard flight control system to change the current flight condition. In order to guide the numerical computation and to validate its results, in a first stage approximated solutions are obtained as a combination of a finite number of motion primitives corresponding to analytical trajectories of approximated dynamic models. The approximated solution is then employed to generate an initial guess for the numerical computation applied to a more accurate dynamic model. Numerical trajectories computed for a small scale prototype of tail-sitter aircraft are finally presented, showing the effectiveness of the proposed methodology to deal with the complex dynamics governing this kind of systems.


title: An image scrambling encryption using chaos-controlled Poker shuffle operation

abstract: Image scrambling is an important technique in digital image encryption and digital image watermarking. This paper presents a novel image scrambling method using Poker shuffle, which is controlled dynamically by chaotic system. Compared with other scrambling techniques such as algebraic permutations and chaotic permutations, the proposed method has properties of nonlinearity, non-analytic formula and large key space. Moreover, its scrambling performance is satisfied and can deal with non-square image. All these features show that the proposed method is more secure and efficient for image scrambling encryption.


title: Origami tubes assembled into stiff, yet reconfigurable structures and metamaterials

abstract: Thin sheets have long been known to experience an increase in stiffness when they are bent, buckled, or assembled into smaller interlocking structures. We introduce a unique orientation for coupling rigidly foldable origami tubes in a "zipper" fashion that substantially increases the system stiffness and permits only one flexible deformation mode through which the structure can deploy. The flexible deployment of the tubular structures is permitted by localized bending of the origami along prescribed fold lines. All other deformation modes, such as global bending and twisting of the structural system, are substantially stiffer because the tubular assemblages are overconstrained and the thin sheets become engaged in tension and compression. The zipper-coupled tubes yield an unusually large eigenvalue bandgap that represents the unique difference in stiffness between deformation modes. Furthermore, we couple compatible origami tubes into a variety of cellular assemblages that can enhance mechanical characteristics and geometric versatility, leading to a potential design paradigm for structures and metamaterials that can be deployed, stiffened, and tuned. The enhanced mechanical properties, versatility, and adaptivity of these thin sheet systems can provide practical solutions of varying geometric scales in science and engineering.


title: Graph drawing by classical multidimensional scaling: new perspectives

abstract: With shortest-path distances as input, classical multidimensional scaling can be regarded as a spectral graph drawing algorithm, and recent approximation techniques make it scale to very large graphs. In comparison with other methods, however, it is considered inflexible and prone to degenerate layouts for some classes of graphs. ::: ::: We want to challenge this belief by demonstrating that the method can be flexibly adapted to provide focus+context layouts. Moreover, we propose an alternative instantiation that appears to be more suitable for graph drawing and prevents certain degeneracies.


title: Identification of separable cognitive factors in schizophrenia

abstract: One of the primary goals in the NIMH initiative to encourage development of new interventions for cognitive deficits in schizophrenia, Measurement and Treatment Research to Improve Cognition in Schizophrenia (MATRICS), has been to develop a reliable and valid consensus cognitive battery for use in clinical trials. Absence of such a battery has hampered standardized evaluation of new treatments and, in the case of pharmacological agents, has been an obstacle to FDA approval of medications targeting cognitive deficits in schizophrenia. A fundamental step in developing such a battery was to identify the major separable cognitive impairments in schizophrenia. As part of this effort, we evaluated the empirical evidence for cognitive performance dimensions in schizophrenia, emphasizing factor analytic studies. We concluded that seven separable cognitive factors were replicable across studies and represent fundamental dimensions of cognitive deficit in schizophrenia: Speed of Processing, Attention/Vigilance, Working Memory, Verbal Learning and Memory, Visual Learning and Memory, Reasoning and Problem Solving, and Verbal Comprehension. An eighth domain, Social Cognition, was added due to recent increased interest in this area and other evidence of its relevance for clinical trials aiming to evaluate the impact of potential cognitive enhancers on cognitive performance and functional outcome. Verbal Comprehension was not considered appropriate for a cognitive battery intended to be sensitive to cognitive change, due to its resistance to change. The remaining seven domains were recommended for inclusion in the MATRICS-NIMH consensus cognitive battery and will serve as the basic structure for that battery. These separable cognitive dimensions also have broader relevance to future research aimed at understanding the nature and structure of core cognitive deficits in schizophrenia.


title: The impact TQM System on Supply Chain Performance through Supply Chain Integration and Employee Satisfaction

abstract: Indonesia has entered the Southeast Asian free trade market or better known as ASEAN Economic Community (AEC). The economic growth and the increasing number of companies annually will bring some impacts on the competitions for many companies in Indonesia, so they have find their competitive advantage. The biggest competitive advantage of Indonesia is in the primary sector companies which is producing consumer goods from natural raw materials and semi-finished goods. To win the competition, the primary sectors require product standards as the company output by implementing TQM, which usually adopts ISO as the main requirement for exporting their products. This is also the criteria for the research samples. The questionnaires are distributed to 100 companies that have over 100 employees, and the returned questionnaires are 64, with four incomplete questionnaires, making the response rate of 60%. The data analysis is using PLS and proves that all hypotheses are accepted. First, TQM implementation has a positive and significant impact on supply chain integration. Second, TQM implementations in companies are able to create a proper documentation system for each department to facilitate good communication among departments. Third, TQM implementation has a positive impact on supply chain performance and employee satisfaction. Fourth, supply chain integration has a positive impact on employee satisfaction and supply chain performance. Employee satisfaction has a positive effect on supply chain performance. The overall impact of TQM implementation on supply chain performance is 0.4680. TQM implementation brings a positive impact on supply chain integration and supply chain performance, thus increases the total impact of supply chain performance


title: How should I explain? A comparison of different explanation types for recommender systems

abstract: Recommender systems help users locate possible items of interest more quickly by filtering and ranking them in a personalized way. Some of these systems provide the end user not only with such a personalized item list but also with an explanation which describes why a specific item is recommended and why the system supposes that the user will like it. Besides helping the user understand the output and rationale of the system, the provision of such explanations can also improve the general acceptance, perceived quality, or effectiveness of the system. In recent years, the question of how to automatically generate and present system-side explanations has attracted increased interest in research. Today some basic explanation facilities are already incorporated in e-commerce Web sites such as Amazon.com. In this work, we continue this line of recent research and address the question of how explanations can be communicated to the user in a more effective way. In particular, we present the results of a user study in which users of a recommender system were provided with different types of explanation. We experimented with 10 different explanation types and measured their effects in different dimensions. The explanation types used in the study include both known visualizations from the literature as well as two novel interfaces based on tag clouds. Our study reveals that the content-based tag cloud explanations are particularly helpful to increase the user-perceived level of transparency and to increase user satisfaction even though they demand higher cognitive effort from the user. Based on these insights and observations, we derive a set of possible guidelines for designing or selecting suitable explanations for recommender systems.


title: A minimum spanning tree algorithm with inverse-Ackermann type complexity

abstract: A deterministic algorithm for computing a minimum spanning tree of a connected graph is presented. Its running time is 0 ( m α( m, n )), where α is the classical functional inverse of Ackermann's function and n (respectively, m ) is the number of vertices (respectively, edges). The algorithm is comparison-based : it uses pointers, not arrays, and it makes no numeric assumptions on the edge costs.


title: Large-Scale Sparse Principal Component Analysis with Application to Text Data

abstract: Sparse PCA provides a linear combination of small number of features that maximizes variance across data. Although Sparse PCA has apparent advantages compared to PCA, such as better interpretability, it is generally thought to be computationally much more expensive. In this paper, we demonstrate the surprising fact that sparse PCA can be easier than PCA in practice, and that it can be reliably applied to very large data sets. This comes from a rigorous feature elimination pre-processing result, coupled with the favorable fact that features in real-life data typically have exponentially decreasing variances, which allows for many features to be eliminated. We introduce a fast block coordinate ascent algorithm with much better computational complexity than the existing first-order ones. We provide experimental results obtained on text corpora involving millions of documents and hundreds of thousands of features. These results illustrate how Sparse PCA can help organize a large corpus of text data in a user-interpretable way, providing an attractive alternative approach to topic models.


title: GitGraph - Architecture Search Space Creation through Frequent Computational Subgraph Mining

abstract: The dramatic success of deep neural networks across multiple application areas often relies on experts painstakingly designing a network architecture specific to each task. To simplify this process and make it more accessible, an emerging research effort seeks to automate the design of neural network architectures, using e.g. evolutionary algorithms or reinforcement learning or simple search in a constrained space of neural modules. Considering the typical size of the search space (e.g. $10^{10}$ candidates for a $10$-layer network) and the cost of evaluating a single candidate, current architecture search methods are very restricted. They either rely on static pre-built modules to be recombined for the task at hand, or they define a static hand-crafted framework within which they can generate new architectures from the simplest possible operations. In this paper, we relax these restrictions, by capitalizing on the collective wisdom contained in the plethora of neural networks published in online code repositories. Concretely, we (a) extract and publish GitGraph, a corpus of neural architectures and their descriptions; (b) we create problem-specific neural architecture search spaces, implemented as a textual search mechanism over GitGraph; (c) we propose a method of identifying unique common subgraphs within the architectures solving each problem (e.g., image processing, reinforcement learning), that can then serve as modules in the newly created problem specific neural search space.


title: The stressed hippocampus, synaptic plasticity and lost memories

abstract: Stress is a biologically significant factor that, by altering brain cell properties, can disturb cognitive processes such as learning and memory, and consequently limit the quality of human life. Extensive rodent and human research has shown that the hippocampus is not only crucially involved in memory formation, but is also highly sensitive to stress. So, the study of stress-induced cognitive and neurobiological sequelae in animal models might provide valuable insight into the mnemonic mechanisms that are vulnerable to stress. Here, we provide an overview of the neurobiology of stress–memory interactions, and present a neural–endocrine model to explain how stress modifies hippocampal functioning.


title: Utilization of 3-D computer tomographs in oral surgery.

abstract: Imaging is essential in the diagnosis and treatment of maxillofacial abnormalities. CT scans are but one type of imaging studies available to the practitioner. Computers have recently enhanced the traditional CT scan so that it can now be viewed in a three-dimensional reconstruction. Essentials of 3-D imaging and its uses will be presented by the authors, with various case reports to illustrate their value in the dental office.


title: A unified control method for quadrotor tail-sitter UAVs in all flight modes: Hover, transition, and level flight

abstract: This paper presents a unified control framework for controlling a quadrotor tail-sitter UAV. The most salient feature of this framework is its capability of uniformly treating the hovering and forward flight, and enabling continuous transition between these two modes, depending on the commanded velocity. The key part of this framework is a nonlinear solver that solves for the proper attitude and thrust that produces the required acceleration set by the position controller in an online fashion. The planned attitude and thrust are then achieved by an inner attitude controller that is global asymptotically stable. To characterize the aircraft aerodynamics, a full envelope wind tunnel test is performed on the full-scale quadrotor tail-sitter UAV. In addition to planning the attitude and thrust required by the position controller, this framework can also be used to analyze the UAV's equilibrium state (trimmed condition), especially when wind gust is present. Finally, simulation results are presented to verify the controller's capacity, and experiments are conducted to show the attitude controller's performance.


title: Markov Chain Monte Carlo Simulation Methods in Econometrics

abstract: We present several Markov chain Monte Carlo simulation methods that have been widely used in recent years in econometrics and statistics. Among these is the Gibbs sampler, which has been of particular interest to econometricians. Although the paper summarizes some of the relevant theoretical literature, its emphasis is on the presentation and explanation of applications to important models that are studied in econometrics. We include a discussion of some implementation issues, the use of the methods in connection with the EM algorithm, and how the methods can be helpful in model specification questions. Many of the applications of these methods are of particular interest to Bayesians, but we also point out ways in which frequentist statisticians may find the techniques useful.


title: A boost PFC rectifier with a passive lossless snubber circuit using coupled inductors methods

abstract: In this paper, a boost PFC rectifier with a novel passive lossless snubber circuit is proposed to minimize switching loss in high power applications. A new turn-on mechanism of the lossless snubber using coupled inductor is applied to reduce the reverse recovery loss of the output diode rectifier. The turn-off operation of the lossless snubber is composed of a single capacitor and two diodes to reduce the voltage stress and the power loss at the turn-off of the switch. Due to the lossless clamping operation, the proposed PFC operates under soft switching conditions with high power conversion efficiency. The design and performance of the boost PFC rectifier using the proposed lossless snubber are verified by experimental results using a 3.3kW prototype rectifier.


title: A cell-level photovoltaic model for high-granularity simulations

abstract: This paper presents a cell-level simulation model of photovoltaic (PV) systems that is capable of reproducing partial-shading and other mismatch effects at high granularity. The nonlinear diode voltage-current characteristic is stored in a piecewise-linear model, and the stored values are employed to compute the Jacobian matrices very efficiently. As a result, the model can be solved with low computational effort, so that simulations at cell-level granularity and over long time spans can be carried out efficiently. The model is well suited for evaluations of energy capture, including the effects of partial shading or other mismatches, in large PV systems over long periods of time. Comparisons of simulation and experimental results are provided for three representative PV systems.


title: Deciding where to attend: Large‐scale network mechanisms underlying attention and intention revealed by graph‐theoretic analysis

abstract: Abstract The neural mechanisms by which intentions are transformed into actions remain poorly understood. We investigated the network mechanisms underlying spontaneous voluntary decisions about where to focus visual-spatial attention (willed attention). Graph-theoretic analysis of two independent datasets revealed that regions activated during willed attention form a set of functionally-distinct networks corresponding to the frontoparietal network, the cingulo-opercular network, and the dorsal attention network. Contrasting willed attention with instructed attention (where attention is directed by external cues), we observed that the dorsal anterior cingulate cortex was allied with the dorsal attention network in instructed attention, but shifted connectivity during willed attention to interact with the cingulo-opercular network, which then mediated communications between the frontoparietal network and the dorsal attention network. Behaviorally, greater connectivity in network hubs, including the dorsolateral prefrontal cortex, the dorsal anterior cingulate cortex, and the inferior parietal lobule, was associated with faster reaction times. These results, shown to be consistent across the two independent datasets, uncover the dynamic organization of functionally-distinct networks engaged to support intentional acts.


title: A coordinate gradient descent method for nonsmooth separable minimization

abstract: We consider the problem of minimizing the sum of a smooth function and a separable convex function. This problem includes as special cases bound-constrained optimization and smooth optimization with l1-regularization. We propose a (block) coordinate gradient descent method for solving this class of nonsmooth separable problems. We establish global convergence and, under a local Lipschitzian error bound assumption, linear convergence for this method. The local Lipschitzian error bound holds under assumptions analogous to those for constrained smooth optimization, e.g., the convex function is polyhedral and the smooth function is (nonconvex) quadratic or is the composition of a strongly convex function with a linear mapping. We report numerical experience with solving the l1-regularization of unconstrained optimization problems from More et al. in ACM Trans. Math. Softw. 7, 17–41, 1981 and from the CUTEr set (Gould and Orban in ACM Trans. Math. Softw. 29, 373–394, 2003). Comparison with L-BFGS-B and MINOS, applied to a reformulation of the l1-regularized problem as a bound-constrained optimization problem, is also reported.


title: Situated Anonymity: Impacts of Anonymity, Ephemerality, and Hyper-Locality on Social Media

abstract: Anonymity, ephemerality, and hyper-locality are an uncommon set of features in the design of online communities. However, these features were key to Yik Yak's initial success and popularity. In an interview-based study, we found that these three features deeply affected the identity of the community as a whole, the patterns of use, and the ways users committed to this community. We conducted interviews with 18 Yik Yak users on an urban American university campus and found that these three focal design features contributed to casual commitment, transitory use, and emergent community identity. We describe situated anonymity, which is the result of anonymity, ephemerality, and hyper-locality coexisting as focal design features of an online community. This work extends our understanding of use and identity-versus-bond based commitment, which has implications for the design and study of other atypical online communities.


title: Advances in single-beacon one-way-travel-time acoustic navigation for underwater vehicles

abstract: This paper reports the formulation and evaluation of a centralized extended Kalman filter designed for a novel navigation system for underwater vehicles. The navigation system employs Doppler sonar, depth sensors, synchronous clocks, and acoustic modems to achieve simultaneous acoustic communication and navigation. The use of a single moving reference beacon eliminates the requirement for the underwater vehicle to remain in a bounded navigable area; the use of underwater modems and synchronous clocks enables range measurements based on one-way time-of-flight information from acoustic data-packet broadcasts. The acoustic data packets are broadcast from a single, moving reference beacon and can be received simultaneously by multiple vehicles within acoustic range. We report results from a simulated deep-water survey and real field data collected from an autonomous underwater vehicle survey in 4000 m of water on the southern Mid-Atlantic Ridge with an independent long-baseline navigation system for ground truth.


title: A Novel Shadow-Free Feature Extractor for Real-Time Road Detection

abstract: Road detection is one of the most important research areas in driver assistance and automated driving field. However, the performance of existing methods is still unsatisfactory, especially in severe shadow conditions. To overcome those difficulties, first we propose a novel shadow-free feature extractor based on the color distribution of road surface pixels. Then we present a road detection framework based on the extractor, whose performance is more accurate and robust than that of existing extractors. Also, the proposed framework has much low-complexity, which is suitable for usage in practical systems.


title: Bayes Meets Bellman: The Gaussian Process Approach to Temporal Difference Learning

abstract: We present a novel Bayesian approach to the problem of value function estimation in continuous state spaces. We define a probabilistic generative model for the value function by imposing a Gaussian prior over value functions and assuming a Gaussian noise model. Due to the Gaussian nature of the random processes involved, the posterior distribution of the value function is also Gaussian and is therefore described entirely by its mean and covariance. We derive exact expressions for the posterior process moments, and utilizing an efficient sequential sparsification method, we describe an on-line algorithm for learning them. We demonstrate the operation of the algorithm on a 2-dimensional continuous spatial navigation domain.


title: Machine Learning for Wireless Communications in the Internet of Things: A Comprehensive Survey

abstract: The Internet of Things (IoT) is expected to require more effective and efficient wireless communications than ever before. For this reason, techniques such as spectrum sharing and dynamic spectrum access will soon become essential components of the IoT wireless communication process. In this vision, IoT devices must be able to not only learn to autonomously extract spectrum knowledge on-the-fly from the network but also leverage such knowledge to dynamically change appropriate wireless parameters (e.g., frequency band, symbol modulation, coding rate, route selection etc) to reach the network's optimal operating point. To address the above challenges, much research has been devoted to exploring the use of machine learning to address problems in the IoT wireless communications domain. The reason behind machine learning's popularity is that it provides a general framework to solve very complex problems where a model of the phenomenon being learned is too complex to derive or too dynamic to be summarized in mathematical terms. This work provides a comprehensive survey of the state of the art in the application of machine learning techniques to address key problems in IoT wireless communications with an emphasis on its ad hoc networking aspect. First, we present extensive background notions on machine learning techniques. Then, by adopting a bottom-up approach, we examine existing work on machine learning for the IoT at the physical, data-link and network layer of the protocol stack. Thereafter, we discuss directions taken by the community towards hardware implementation to ensure the feasibility of these techniques. Finally, we provide a series of research challenges associated with the applications of machine learning techniques for IoT wireless communications.


Fluid Balance during Labour: A Review 1


title: Learning to recommend with trust and distrust relationships

abstract: With the exponential growth of Web contents, Recommender System has become indispensable for discovering new information that might interest Web users. Despite their success in the industry, traditional recommender systems suffer from several problems. First, the sparseness of the user-item matrix seriously affects the recommendation quality. Second, traditional recommender systems ignore the connections among users, which loses the opportunity to provide more accurate and personalized recommendations. In this paper, aiming at providing more realistic and accurate recommendations, we propose a factor analysis-based optimization framework to incorporate the user trust and distrust relationships into the recommender systems. The contributions of this paper are three-fold: (1) We elaborate how user distrust information can benefit the recommender systems. (2) In terms of the trust relations, distinct from previous trust-aware recommender systems which are based on some heuristics, we systematically interpret how to constrain the objective function with trust regularization. (3) The experimental results show that the distrust relations among users are as important as the trust relations. The complexity analysis shows our method scales linearly with the number of observations, while the empirical analysis on a large Epinions dataset proves that our approaches perform better than the state-of-the-art approaches.


title: Combining marker-based mocap and RGB-D camera for acquiring high-fidelity hand motion data

abstract: Motion capture data has been pivotal to the success of creating realistic animation for human characters. There are a number of public full-body motion databases available, but large and heterogeneous databases for hand articulations are not available. In this paper, we introduce a novel acquisition framework for acquiring a wide range of high-fidelity hand motion data. Our key idea is to leverage marker position data recorded by a twelve-camera optical motion capture system and RGB/Depth data obtained from a single Microsoft Kinect camera. We formulate the hand motion capture problem in a nonlinear optimization framework by maximizing consistency between the reconstructed motion and observed measurement. We introduce an efficient optimization technique to estimate the optimal hand pose that best matches observed data. We have demonstrated the power and effectiveness of our system by capturing a wide variety of delicate hand articulations, even in case of significant self-occlusion.


title: A robust humanoid robot navigation algorithm with ZUPT

abstract: This paper discusses algorithmic concepts, design and testing of a pedestrian dead reckoning (PDR) navigation system based on a low-cost inertial measurement unit (IMU) attached to a user's shoe. The algorithm uses the technique known as “Zero Velocity Update” (ZUPT) and Kalman Filter consists of 24 error states to reduce IMU errors. We propose a novel dynamic and more robust algorithm to detect the stance phases during walking. The system works well in both 2D (2-dimensional) and 3D environments. Test results show that its horizontal positioning errors are always below 0.3% of the total travelled distance, and the vertical errors are below 0.7%, even on 3D terrain. These results reach the highest position accuracy in available literature.


title: Can complexity decrease in Congestive Heart failure?

abstract: The complexity of a signal can be measured by the Recurrence period density entropy (RPDE) from the reconstructed phase space. We have chosen a window based RPDE method for the classification of signals, as RPDE is an average entropic measure of the whole phase space. We have observed the changes in the complexity in cardiac signals of normal healthy person (NHP) and congestive heart failure patients (CHFP). The results show that the cardiac dynamics of a healthy subject is more complex and random compare to the same for a heart failure patient, whose dynamics is more deterministic. We have constructed a general threshold to distinguish the border line between a healthy and a congestive heart failure dynamics. The results may be useful for wide range for physiological and biomedical analysis.


title: A Robust Decision Tree Algorithm for Imbalanced Data Sets

abstract: We propose a new decision tree algorithm, Class Confidence Proportion Decision Tree (CCPDT), which is robust and insensitive to size of classes and generates rules which are statistically significant. In order to make decision trees robust, we begin by expressing Information Gain, the metric used in C4.5, in terms of confidence of a rule. This allows us to immediately explain why Information Gain, like confidence, results in rules which are biased towards the majority class. To overcome this bias, we introduce a new measure, Class Confidence Proportion (CCP), which forms the basis of CCPDT. To generate rules which are statistically significant we design a novel and efficient top-down and bottom-up approach which uses Fisher’s exact test to prune branches of the tree which are not statistically significant. Together these two changes yield a classifier that performs statistically better than not only traditional decision trees but also trees learned from data that has been balanced by well known sampling techniques. Our claims are confirmed through extensive experiments and comparisons against C4.5, CART, HDDT and SPARCCC.


title: Beyond Computation : Information Technology , Organizational Transformation and Business Performance

abstract: To understand the economic value of computers, one must broaden the traditional definition of both the technology and its effects. Case studies and firm-level econometric evidence suggest that: 1) organizational "investments" have a large influence on the value of IT investments; and 2) the benefits of IT investment are often intangible and disproportionately difficult to measure. Our analysis suggests that the link between IT and increased productivity emerged well before the recent surge in the aggregate productivity statistics and that the current macroeconomic productivity revival may in part reflect the contributions of intangible capital accumulated in the past.


title: P.: Generic and real-time structure from motion

abstract: We introduce a generic and incremental Structure from Motion method. By generic, we mean that the proposed method is independent of any specific camera model. During the incremental 3D reconstruction, parameters of 3D points and camera poses are refined simultaneously by a generic local bundle adjustment that minimizes an angular error between rays. This method has three main advantages: it is generic, fast and accurate. The proposed method is evaluated by experiments on real data with three kinds of calibrated cameras: stereo rig, perspective and catadioptric cameras.


title: Pilot safety study of intrabronchial instillation of bone marrow-derived mononuclear cells in patients with silicosis

abstract: Silicosis is an occupational disease for which no effective treatment is currently known. Systemic administration of bone marrow-derived mononuclear cells (BMDMCs) has shown to be safe in lung diseases. However, so far, no studies have analyzed whether bronchoscopic instillation of autologous BMDMCs is a safe route of administration in patients with silicosis. We conducted a prospective, non-randomized, single-center longitudinal study in five patients. Inclusion criteria were age 18–50 years, chronic and accelerated silicosis, forced expiratory volume in 1 s <60 % and >40 %, forced vital capacity ≥60 % and arterial oxygen saturation >90 %. The exclusion criteria were smoking, active tuberculosis, neoplasms, autoimmune disorders, heart, liver or renal diseases, or inability to undergo bronchoscopy. BMDMCs were administered through bronchoscopy (2 × 107 cells) into both lungs. Physical examination, laboratory evaluations, quality of life questionnaires, computed tomography of the chest, lung function tests, and perfusion scans were performed before the start of treatment and up to 360 days after BMDMC therapy. Additionally, whole-body and planar scans were evaluated 2 and 24 h after instillation. No adverse events were observed during and after BMDMC administration. Lung function, quality of life and radiologic features remained stable throughout follow-up. Furthermore, an early increase of perfusion in the base of both lungs was observed and sustained after BMDMC administration. Administration of BMDMCs through bronchoscopy appears to be feasible and safe in accelerated and chronic silicosis. This pilot study provides a basis for prospective randomized trials to assess the efficacy of this treatment approach. NCT01239862 Date of Registration: November 10, 2010


title: Facial expression recognition using a dynamic model and motion energy

abstract: Previous efforts at facial expression recognition have been based on the Facial Action Coding System (FACS), a representation developed in order to allow human psychologists to code expression from static facial "mugshots." We develop new more accurate representations for facial expression by building a video database of facial expressions and then probabilistically characterizing the facial muscle activation associated with each expression using a detailed physical model of the skin and muscles. This produces a muscle based representation of facial motion, which is then used to recognize facial expressions in two different ways. The first method uses the physics based model directly, by recognizing expressions through comparison of estimated muscle activations. The second method uses the physics based model to generate spatio temporal motion energy templates of the whole face for each different expression. These simple, biologically plausible motion energy "templates" are then used for recognition. Both methods show substantially greater accuracy at expression recognition than has been previously achieved. >


title: Faking Good and Faking Bad Among Military Conscripts

abstract: A question that continues to worry practitioners and researchers is how much recruiters can trust self-reported measures of personality. Several models of faking assume that applicants differ in their motivation to fake, but field evidence regarding these differences is still rare. For the current field study, we exploited a unique setting: The examination for compulsory military service in Switzerland. We were able to show that differences in the military service motivation of Swiss conscripts (N = 918) were associated with faking good or faking bad, respectively. In particular, military service motivation was related to self-admitted faking, mean personality scores, and increased correlations between personality dimensions, which supports faking models that stress the importance of motivational differences.


title: Eliminating the Blind Spot: Adapting 3D Object Detection and Monocular Depth Estimation to 360{\deg} Panoramic Imagery

abstract: Recent automotive vision work has focused almost exclusively on processing forward-facing cameras. However, future autonomous vehicles will not be viable without a more comprehensive surround sensing, akin to a human driver, as can be provided by 360{\deg} panoramic cameras. We present an approach to adapt contemporary deep network architectures developed on conventional rectilinear imagery to work on equirectangular 360{\deg} panoramic imagery. To address the lack of annotated panoramic automotive datasets availability, we adapt a contemporary automotive dataset, via style and projection transformations, to facilitate the cross-domain retraining of contemporary algorithms for panoramic imagery. Following this approach we retrain and adapt existing architectures to recover scene depth and 3D pose of vehicles from monocular panoramic imagery without any panoramic training labels or calibration parameters. Our approach is evaluated qualitatively on crowd-sourced panoramic images and quantitatively using an automotive environment simulator to provide the first benchmark for such techniques within panoramic imagery.


title: Dynamic Facial Expression Recognition Based on Convolutional Neural Networks with Dense Connections

abstract: Facial expression recognition (FER) is a challenging problem with important applications. Applying deep learning techniques for dynamic FER is advantageous in terms of automatically generating discriminative expression features. However, inadequate training data in small expression databases aggravates overfitting and hinders the performance of deep networks. Recent studies have shown that dense connectivity in convolutional neural networks can encourage feature sharing and alleviate overfitting when training with small datasets. Still, traditional dense structures are too deep for FER with insufficient training samples. In this paper, we propose a relatively shallow CNN structure with densely connected short paths for FER. Instead of using transition layers to down-sample feature maps between dense blocks, we introduce dense connectivity across pooling to enforce feature sharing in the shallow CNN structure. Extensive experiments show that our method achieves competitive performance on benchmark datasets CK+ and Oulu-CASIA.


title: Digital audio forensics: a first practical evaluation on microphone and environment classification

abstract: In this paper a first approach for digital media forensics is presented to determine the used microphones and the environments of recorded digital audio samples by using known audio steganalysis features. Our first evaluation is based on a limited exemplary test set of 10 different audio reference signals recorded as mono audio data by four microphones in 10 different rooms with 44.1 kHz sampling rate and 16 bit quantisation. Note that, of course, a generalisation of the results cannot be achieved. Motivated by the syntactical and semantical analysis of information and in particular by known audio steganalysis approaches, a first set of specific features are selected for classification to evaluate, whether this first feature set can support correct classifications. The idea was mainly driven by the existing steganalysis features and the question of applicability within a first and limited test set. In the tests presented in this paper, an inter-device analysis with different device characteristics is performed while intra-device evaluations (identical microphone models of the same manufacturer) are not considered. For classification the data mining tool WEKA with K-means as a clustering and Naive Bayes as a classification technique are applied with the goal to evaluate their classification in regard to the classification accuracy on known audio steganalysis features. Our results show, that for our test set, the used classification techniques and selected steganalysis features, microphones can be better classified than environments. These first tests show promising results but of course are based on a limited test and training set as well a specific test set generation. Therefore additional and enhanced features with different test set generation strategies are necessary to generalise the findings.


title: Development and Validation of Extrinsic Motivation Scale for Crowdsourcing Micro-task Platforms

abstract: In this paper, we introduce a scale for measuring the extrinsic motivation of crowd workers. The new questionnaire is strongly based on the Work Extrinsic Intrinsic Motivation Scale (WEIMS) [17] and theoretically follows the Self-Determination Theory (SDT) of motivation. The questionnaire has been applied and validated in a crowdsourcing micro-task platform. This instrument can be used for studying the dynamics of extrinsic motivation by taking into account individual differences and provide meaningful insights which will help to design a proper incentives framework for each crowd worker that eventually leads to a better performance, an increased well-being, and higher overall quality.


title: Relations, objects, and the . . .

abstract: This research addresses the kinds of matching elements that determine analogical relatedness and literal similarity. Despite theoretical agreement on the importance of relational match, the empirical evidence is neither systematic nor definitive. In 3 studies, participants performed online evaluations of relatedness of sentence pairs that varied in either the object or relational match. Results show a consistent focus on relational matches as the main determinant of analogical acceptance. In addition, analogy does not require strict overall identity of relational concepts. Semantically overlapping but nonsynonymous relations were commonly accepted, but required more processing time. Finally, performance in a similarity rating task partly paralleled analogical acceptance; however, relatively more weight was given to object matches. Implications for psychological theories of analogy and similarity are addressed.


title: Orthogonal vs. uncorrelated least squares discriminant analysis for feature extraction

abstract: In this paper, a new discriminant analysis for feature extraction is derived from the perspective of least squares regression. To obtain great discriminative power between classes, all the data points in each class are expected to be regressed to a single vector, and the basic task is to find a transformation matrix such that the squared regression error is minimized. To this end, two least squares discriminant analysis methods are developed under the orthogonal or the uncorrelated constraint. We show that the orthogonal least squares discriminant analysis is an extension to the null space linear discriminant analysis, and the uncorrelated least squares discriminant analysis is exactly equivalent to the traditional linear discriminant analysis. Comparative experiments show that the orthogonal one is more preferable for real world applications.


title: Model predictive control of a multi-rotor with a slung load for avoiding obstacles

abstract: This paper addresses the control problem of transportation of a slung load by a multi-rotor. Control of the slung load system cannot be achieved with usual controllers for multi-rotors because motion of the load is highly coupled with the multi-rotor. Although many controllers have been developed for the system, there still exist several problems. For instance, in trajectory-following control, many previous works make trajectory beforehand, and perform trajectory-following control afterward. Since the time spent to generate trajectories is too long for real-time control applications, alternative efficient algorithms are required. With this in mind, we adopt a fast nonlinear Model Predictive Control (MPC) algorithm. This control framework solves a nonlinear optimal control problem with a finite time horizon using Sequential Linear Quadratic (SLQ) solver, in MPC. The originally proposed work optimizes the implementation time and successfully conducts real-time waypoint flight of general multi-rotor. Here, we apply the framework to the slung load system. Also, an obstacle-avoidance algorithm suitable for the slung load system is developed for cluttered environments. Numerical simulations are conducted to validate the developed method.


title: Three-dimensional assessment of hand outcome

abstract: INTRODUCTION ::: Patient reported outcome measures are central to National Health Service quality of care assessments. This study investigated the benefit of elective hand surgery by the simultaneous analysis of pain, function and appearance, using a three-dimensional (3D) graphical model for evaluating and presenting outcome. ::: ::: ::: METHODS ::: A total of 188 patients scheduled for surgery completed pre- and postoperative questionnaires grading the severity of their pain, dysfunction and deformity of their hand(s). Scores were plotted on a 3D graph to demonstrate the degree of 'normalisation' following surgery. ::: ::: ::: RESULTS ::: Surgical groups included: nerve compression (n=53), Dupuytren's disease (n=51), trigger finger (n=20), ganglion (n=17) or other lump (n=21), trapeziometacarpal joint osteoarthritis (n=10), rheumatoid disease (n=5) and other pathology (n=13). A significant improvement towards normality was seen after surgery in each group except for patients with rheumatoid disease. ::: ::: ::: CONCLUSIONS ::: This study provides a simple, visual representation of hand surgery outcome by plotting patient scores for pain, function and appearance simultaneously on a 3D graph.


title: I feel how you feel but not always: the empathic brain and its modulation

abstract: The ability to share the other's feelings, known as empathy, has recently become the focus of social neuroscience studies. We review converging evidence that empathy with, for example, the pain of another person, activates part of the neural pain network of the empathizer, without first hand pain stimulation to the empathizer's body. The amplitude of empathic brain responses is modulated by the intensity of the displayed emotion, the appraisal of the situation, characteristics of the suffering person such as perceived fairness, and features of the empathizer such as gender or previous experience with pain-inflicting situations. Future studies in the field should address inter-individual differences in empathy, development and plasticity of the empathic brain over the life span, and the link between empathy, compassionate motivation, and prosocial behavior.


E-Banking Security Issues – Is There A Solution in Biometrics?


title: Evaluation of Group Cognitive Behavioral Therapy for Adults With ADHD

abstract: Objective: A brief cognitive behavioral therapy (CBT) group intervention was designed to treat comorbid anxiety, depression, and low self-esteem and self-efficacy in adults with ADHD. It was hypothesised that participants would gain knowledge about ADHD, experience a reduction in comorbid symptoms, and benefit from the supportive aspect of group treatment. Method: Participants in the study formed a CBT treatment group that attended six workshops and a waiting list control group. The intervention was evaluated with measures assessing knowledge about ADHD, psychological symptoms, and support received. The groups were compared using repeated measures ANOVAs. Results: The CBT group had significantly greater improvement on measures of knowledge about ADHD, self-efficacy, and self-esteem than the control group. Participants' evaluations of the sessions suggested that sharing personal experiences with other adults with ADHD was an important aspect of the intervention. Conclusion: Brief CBT group treatments may b...


title: YOUStatAnalyzer: a tool for analysing the dynamics of YouTube content popularity

abstract: Understanding the dynamics of on-line content popularity is an active research field with application in sectors as diverse as media advertising, content replication and caching and on-line marketing. In most cases, scientists have focused on user-generated contents, which are freely accessible through different on-line services. Among such services, the incumbent one is indeed YouTube. This online platform was launched in 2005 and it currently features more than 6 billions hours of video watched every month (almost one hour per person on Earth), with more than 100 hours of videos uploaded every minute and 1 billion unique users per month. In order to analyze or predict content popularity, statistics about viewers, watch time and shares must be retrieved. The YouTube APIs, however, do not allow third parties to retrieve such an information in an open and accessible way. In order to overcome this problem, we have developed a framework, based on Web scraping techniques and big data tools, for the collection and analysis of YouTube video content popularity at scale. Our framework, called YOUStatAnalyzer, enables researchers to create their own dataset, according to a number of different search criteria and analyse them to extract relevant features and significant statistics.


title: S band broadband RF LDMOS with excellent performance

abstract: In this paper, RF LDMOS devices with gain of 16.5dB, high efficiency (~60%) and high power density (1.26W/mm) at 2.5 GHz is demonstrated. The basic device structure is described and the load pull test setup was put up to evaluate its RF performance. Moreover, the Hot Carrier Injection (HCI) issue is suppressed to a negligible level (less than 5% over the extrapolated 20 years). The broadband property is investigated by characterizing the device's RF performance at 1GHz and also at a range among 2.3 to 2.5 GHz. The transducer gain and efficiency are greatly improved which is consistent with trend indicated by the small signal S parameter test result. The RF test results show us a broadband S band RF LDMOS transistor with excellent performance.


title: Artificial calf weaning strategies and the role of machine learning: A review

abstract: Research in Machine Learning has increased dramatically over the past couple of decades, with applications greatly benefitting industry. These applications vary widely in the fields of space exploration technologies, transportation, robotics, agriculture, animal husbandry, medicine and finance. However, the agricultural and animal husbandry related applications are relatively few when compared to other fields. This paper reviews the past and present calf weaning strategies mainly focussing on the incorporation of Machine Learning techniques in classifying the behavioural responses in cattle and makes an attempt to extend the scope of machine learning classification applications related to calf weaning. This review further discusses the integration of relevant sensors (sensor fusion), deep learning and the improvement of prediction accuracy.


Metrics for Gerrit code review


title: A Fairness-aware Hybrid Recommender System

abstract: Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inherent in recommender systems: observation bias and bias stemming from imbalanced data. Observation bias exists due to a feedback loop which causes the model to learn to only predict recommendations similar to previous ones. Imbalance in data occurs when systematic societal, historical, or other ambient bias is present in the data. In this paper, we address both biases by proposing a hybrid fairness-aware recommender system. Our model provides efficient and accurate recommendations by incorporating multiple user-user and item-item similarity measures, content, and demographic information, while addressing recommendation biases. We implement our model using a powerful and expressive probabilistic programming language called probabilistic soft logic. We experimentally evaluate our approach on a popular movie recommendation dataset, showing that our proposed model can provide more accurate and fairer recommendations, compared to a state-of-the art fair recommender system.


Targeted Storyfying: Creating Stories About Particular Events


End-to-End Training of Hybrid CNN-CRF Models for Semantic Segmentation using Structured Learning


title: Still Happy After All These Years: Research Frontiers on Subjective Well-being in Later Life

abstract: Objectives. Understanding the factors that promote quality of life in old age has been a staple of social gerontology since its inception and remains a significant theme in aging research. The purpose of this article was to review the state of the science with regard to subjective well-being (SWB) in later life and to identify promising directions for future research. Methods. This article is based on a review of literature on SWB in aging, sociological, and psychological journals. Although the materials reviewed date back to the early 1960s, the emphasis is on publications in the past decade. Results. Research to date paints an effective portrait of the epidemiology of SWB in late life and the factors associated with it. Although the research base is large, causal inferences about the determinants of SWB remain problematic. Two recent contributions to the research base are highlighted as emerging issues: studies of secular trends in SWB and crossnational studies.


title: Performance Optimization of a High Current Dual Active Bridge with a Wide Operating Voltage Range

abstract: The main aim of this paper is to improve the performance of high current dual active bridge converters when operated over a wide voltage range. A typical application is for fuel cell vehicles where a bi-directional interface between a 12V battery and a high voltage DC bus is required. The battery side voltage ranges from 11V to 16V while the fuel cell is operated between 220V and 447V and the required power is typically 1kW. Careful analysis shows that the high currents on the battery side cause significant design issues in order to obtain a high efficiency. The standard phase shift modulation method can result in high conduction and switching losses. This paper proposes a combined triangular and trapezoidal modulation method to reduce losses over the wide operating range. Approximately, a 2% improvement in efficiency can be expected. An experimental system is used to verify the improved performance of the dual active bridge using the proposed advanced modulation method.


title: Compact stepped-impedance resonator bandpass filter with two wide spread passbands

abstract: This paper presents the design method for a compact dual-band bandpass filter with a large ratio of center frequencies. Emphasis is placed on circuit synthesis for simultaneously matching the in-band responses at the two designated passbands. In an interdigital configuration, a 0.9/5.8 GHz bandpass filter is designed and fabricated. It is believed that this is the filter has the largest ratio of the center frequencies in comparison with those in open literature. The measured responses show good agreement with the simulation results.


title: The lived experiences of general student nurses on their first clinical placement: A phenomenological study

abstract: Summary This phenomenological study explores and describes the ‘lived' experiences of general student nurses on their first clinical placement in an Irish School of Nursing. The research question: ‘What are the experiences of general student nurses on their first clinical placement?' provided the focus for the study. In-depth interviews were conducted with nine female student nurses and one male student nurse. Data from the interviews were collected, recorded and analysed using Coliazzi's (1978) framework (Coliazzi, P., 1978 . Psychological research as the phenomenologist views it. In: Valle, R.S., King, M. (Eds.), Existential Phenomenological Alternatives for Psychology, Oxford University Press, New York). Five core themes emerged: self-awareness, confidence, anxiety, facilitation and professional issues. The findings revealed that the standard of preparation for the placement was viewed positively though some aspects of preparation require a critical shift in thinking towards meeting the students' needs in future curriculum planning. The conclusions of the study indicate that the presence of mutual respect and regard for others had a positive impact on the students' self-esteem. The acquisition of knowledge led to an increase in confidence levels that subsequently reduced anxiety. This enhanced the learning process that was dependant upon the collaborative support and facilitation in the clinical learning environment. The findings prepare the way for further research that could continue to develop and maximise the educational value and clinical experience for undergraduate nursing students.


"Reinstatement" of the communicator in delayed measurement of opinion change.


title: Developing theories of change for social programmes: co-producing evidence-supported quality improvement

abstract: For much of the past two decades, expensive and often imported evidence-based programmes (EBPs) developed by clinician-researchers have been much in vogue in the family and parenting support field, as in many other areas of social provision. With their elaborate infrastructures, voluminous research bases and strict licensing criteria, they have seemed to offer certainty of success over less packaged, less well-evidenced locally developed approaches. Yet recently, evaluation research is showing that success is not assured. EBPs can and regularly do fail, at substantial cost to the public purse. In times of severe resource pressure, a pressing question is, therefore, whether lower cost, home-grown, practitioner-developed programmes—the sort often overlooked by policy-makers —can deliver socially significant and scientifically convincing outcomes at lower cost and at least on a par with their better resourced cousins. This paper shows how the application of techniques increasingly used in implementation science (the science of effective delivery) could help level the playing field. Processes for doing this including co-produced theory of change development and validation are illustrated with reference to the Family Links Ten Week Nurturing Programme (FLNP-10), a popular manualised group-based parenting support programme, designed and disseminated since the 1990s by a UK-based purveyor organisation. The paper draws out general principles for formulating and structuring strong theories of change for practice improvement projects. The work shows that novel application of implementation science-informed techniques can help home-grown programmes to compete scientifically by strengthening their design and delivery, and preparing the ground for better and fairer evaluation.


title: Deep Learning for IoT Big Data and Streaming Analytics: A Survey

abstract: In the era of the Internet of Things (IoT), an enormous amount of sensing devices collect and/or generate various sensory data over time for a wide range of fields and applications. Based on the nature of the application, these devices will result in big or fast/real-time data streams. Applying analytics over such data streams to discover new information, predict future insights, and make control decisions is a crucial process that makes IoT a worthy paradigm for businesses and a quality-of-life improving technology. In this paper, we provide a thorough overview on using a class of advanced machine learning techniques, namely deep learning (DL), to facilitate the analytics and learning in the IoT domain. We start by articulating IoT data characteristics and identifying two major treatments for IoT data from a machine learning perspective, namely IoT big data analytics and IoT streaming data analytics. We also discuss why DL is a promising approach to achieve the desired analytics in these types of data and applications. The potential of using emerging DL techniques for IoT data analytics are then discussed, and its promises and challenges are introduced. We present a comprehensive background on different DL architectures and algorithms. We also analyze and summarize major reported research attempts that leveraged DL in the IoT domain. The smart IoT devices that have incorporated DL in their intelligence background are also discussed. DL implementation approaches on the fog and cloud centers in support of IoT applications are also surveyed. Finally, we shed light on some challenges and potential directions for future research. At the end of each section, we highlight the lessons learned based on our experiments and review of the recent literature.


title: StalemateBreaker: A Proactive Content-Introducing Approach to Automatic Human-Computer Conversation

abstract: Existing open-domain human-computer conversation systems are typically passive: they either synthesize or retrieve a reply provided a human-issued utterance. It is generally presumed that humans should take the role to lead the conversation and introduce new content when a stalemate occurs, and that the computer only needs to "respond." In this paper, we propose StalemateBreaker, a conversation system that can proactively introduce new content when appropriate. We design a pipeline to determine when, what, and how to introduce new content during human-computer conversation. We further propose a novel reranking algorithm Bi-PageRank-HITS to enable rich interaction between conversation context and candidate replies. Experiments show that both the content-introducing approach and the reranking algorithm are effective. Our full StalemateBreaker model outperforms a state-of-the-practice conversation system by +14.4% p@1 when a stalemate occurs.


title: Side-Channel Inference Attacks on Mobile Keypads Using Smartwatches

abstract: Smartwatches enable many novel applications and are fast gaining popularity. However, the presence of a diverse set of on-board sensors provides an additional attack surface to malicious software and services on these devices. In this paper, we investigate the feasibility of key press inference attacks on handheld numeric touchpads by using smartwatch motion sensors as a side-channel. We consider different typing scenarios, and propose multiple attack approaches to exploit the characteristics of the observed wrist movements for inferring individual key presses. Experimental evaluation using commercial off-the-shelf smartwatches and smartphones show that key press inference using smartwatch motion sensors is not only fairly accurate, but also comparable with similar attacks using smartphone motion sensors. Additionally, hand movements captured by a combination of both smartwatch and smartphone motion sensors yields better inference accuracy than either device considered individually.


title: Energy Detection Technique for Spectrum Sensing in Cognitive Radio: A Survey

abstract: Spectrum sensing is the basic and essential mechanisms of Cognitive Radio (CR) to find the unused spectrum. This paper presents an overview of CR architecture, discusses the characteristics and benefits of a CR. Energy detectionbased spectrum sensing has beenproposed and used widely because it doesn’t require transmitted signal properties, channel information, or even the type of modulation. In this paper, a surveyof energy detector over Additive White Gaussian Noise (AWGN), different fading channels for spectrum sensing methodologies in cognitive radio is presented.Theoretical analysis of time domain energy detection and threshold setting is investigated. Cooperative spectrum sensing and a multiple antenna processing based energy detector receptions are also discussed.


title: Argument Extraction from News

abstract: Argument extraction is the task of identifying arguments, along with their components in text. Arguments can be usually decomposed into a claim and one or more premises justifying it. The proposed approach tries to identify segments that represent argument elements (claims and premises) on social Web texts (mainly news and blogs) in the Greek language, for a small set of thematic domains, including articles on politics, economics, culture, various social issues, and sports. The proposed approach exploits distributed representations of words, extracted from a large non-annotated corpus. Among the novel aspects of this work is the thematic domain itself which relates to social Web, in contrast to traditional research in the area, which concentrates mainly on law documents and scientific publications. The huge increase of socialwebcommunities, alongwiththeirusertendency to debate, makes the identification of arguments in these texts a necessity. In addition, a new manually annotated corpus has been constructedthatcanbeusedfreelyforresearchpurposes. Evaluation results are quite promising, suggesting that distributed representations can contributepositivelyto thetaskofargumentextraction.


title: CapAuth: Identifying and Differentiating User Handprints on Commodity Capacitive Touchscreens

abstract: User identification and differentiation have implications in many application domains, including security, personalization, and co-located multiuser systems. In response, dozens of approaches have been developed, from fingerprint and retinal scans, to hand gestures and RFID tags. In this work, we propose CapAuth, a technique that uses existing, low-level touchscreen data, combined with machine learning classifiers, to provide real-time authentication and even identification of users. As a proof-of-concept, we ran our software on an off-the-shelf Nexus 5 smartphone. Our user study demonstrates twenty-participant authentication accuracies of 99.6%. For twenty-user identification, our software achieved 94.0% accuracy and 98.2% on groups of four, simulating family use.


title: Dirofilaria repens microfilariae from a human node fine-needle aspirate: a case report

abstract: Background ::: Human dirofilariosis is still a little known infection even in endemic areas. Dirofilariosis is zoonotic infection usually abortive in humans; instead, we report a very rare case (the 4th in the world), the first in Italy, in which at least two infective larvae became mature adults that mated and produced active microfilariae even though they did not reach peripheral blood.


title: Discovery of Novel Pim-1 Kinase Inhibitors with a Flexible-Receptor Docking Protocol

abstract: A flexible-receptor docking protocol was designed for treating binding-site side-chain flexibility by integrating essential aspects of “Conformational Selection” and “Induced Fit” in a hierarchical fashion. Assessed in a diverse set of pharmaceutically relevant targets, this protocol showed improved performance in reproducing binding poses and ligand enrichment studies compared to rigid-receptor docking. Moreover, it has also exhibited encouraging efficiency in prospective ligand discovery for Pim-1 kinase, which led to novel Pim-1 inhibitors with single-digit nanomolar potencies.


title: CommTrust: Computing Multi-Dimensional Trust by Mining E-Commerce Feedback Comments

abstract: Reputation-based trust models are widely used in e-commerce applications, and feedback ratings are aggregated to compute sellers' reputation trust scores. The "all good reputation" problem however is prevalent in current reputation systems -- reputation scores are universally high for sellers and it is difficult for potential buyers to select trustworthy sellers. In this paper, based on the observation that buyers often express opinions openly in free text feedback comments, we propose CommTrust for trust evaluation by mining feedback comments. Our main contributions include: (1) We propose a multidimensional trust model for computing reputation scores from user feedback comments; (2) We propose an algorithm for mining feedback comments for dimension ratings and weights, combining techniques of natural language processing, opinion mining and topic modelling. Extensive experiments on eBay and Amazon data demonstrate that CommTrust can effectively address the "all good reputation" issue and rank sellers effectively. To the best of our knowledge, our research is the first piece of work on trust evaluation by mining feedback comments.


title: Internationally recruited nurses in London: a survey of career paths and plans

abstract: BackgroundThe paper reports on a survey of recently arrived international nurses working in London, to assess their demographic profile, motivations, experiences and career plans.MethodsA postal survey was conducted in October-December 2004 on a sample of 1000 nurses who were London-based international members of the Royal College of Nursing (RCN). The usable response rate was 40%. Registration data from the Nurses and Midwives Council (NMC) were also analysed.ResultsThe Philippines, Nigeria and South Africa were the three most commonly reported countries of training (in total, more than 30 countries of training were reported). Sixty per cent of the nurses from sub-Saharan Africa and more than 40% from South Africa and India/Pakistan/Mauritius were aged 40 or older; the youngest age profile was reported by the Australia/New Zealand/USA nurses. Two thirds of all the respondents indicated that a recruitment agency had been involved in their move to the United Kingdom (UK). Three quarters of the respondents (76%) reported that they were required to complete a supervised practice course/period of adaptation in the UK in order to be eligible to practice as a nurse in the UK. Two thirds (69%) of respondents were working in NHS hospitals in London, 13% were working in the private sector hospitals and 10% were working in private sector nursing homes. Most of the nurses reported they were the major or sole wage-earner contributing to household income. More than half of the respondents (57%) reported that they regularly sent remittances to their home country. The majority of respondents (60%) indicated that they planned to stay for at least five years, but just under half (43%) also reported that they were considering a move to another country.ConclusionOne critical issue for UK policy-makers is to determine if internationally recruited nurses will stay on in the UK, move back to their home country, or move on to another. That these nurses have made at least one international move means they are likely to have the propensity to move again. As such, retention efforts in the UK will have to take account of their career aspirations.


title: Analysis, Design, and Implementation of Multiple Parallel Ozone Chambers for High Flow Rate

abstract: It is well known that ozone concentration depends on air/oxygen input flow rate and power consumed by the ozone chamber. For every chamber, there exists a unique optimum flow rate that results in maximum ozone concentration. If the flow rate is increased (beyond) or decreased (below) from this optimum value, the ozone concentration drops. This paper proposes a technique whereby the concentration can be maintained even if the flow rate increases. The idea is to connect n number of ozone chambers in parallel, with each chamber designed to operate at its optimum point. Aside from delivering high ozone concentration at high flow rate, the proposed system requires only one power supply to drive all these (multiple) chambers simultaneously. In addition, due to its modularity, the system is very flexible, i.e., the number of chambers can be added or removed as demanded by the (output) ozone requirements. This paper outlines the chamber design using mica as dielectric and the determination of its parameters. To verify the concept, three chambers are connected in parallel and driven by a single transformer-less LCL resonant power supply. Moreover, a closed-loop feedback controller is implemented to ensure that the voltage gain remains at the designated value even if the number of chambers is changed or there is a variation in the components. It is shown that the flow rate can be increased linearly with the number of chambers while maintaining a constant ozone concentration.


title: Three-dimensional analysis of facial morphology: growth, development and aging of the orolabial region.

abstract: Soft tissue analysis plays an increasing, strategic role in the recognition of facial alterations, but there are scanty three-dimensional reference data during normal growth, development and aging. In the current study, 532 male and 386 female healthy subjects aged 4 to 73 years were analyzed using a non-invasive, computerized electromagnetic digitizer, and normal dimensions of mouth and lips were obtained in the three-dimensional space. Labial thickness and curvature were also assessed in a selected group of 40 men and women, equally divided into young (age 21-30 yr, mean 25 yr) and old persons (age 45-65 yr, mean 55 yr). Lip vermilion area to volume, and vermilion height to cutaneous lip height ratios decreased with age in both sexes. On average, the lips were thicker in men and in young persons than in women and in old persons. Mean labial curvature was larger in young men than in the other groups, and it was more variable in young than in old persons. Data collected in the present investigation can be used as a data base for the quantitative description of human lip morphology during normal growth, development and aging.


title: The microbiome-gut-brain axis: from bowel to behavior

abstract: The ability of gut microbiota to communicate with the brain and thus modulate behavior is emerging as an exciting concept in health and disease. The enteric microbiota interacts with the host to form essential relationships that govern homeostasis. Despite the unique enteric bacterial fingerprint of each individual, there appears to be a certain balance that confers health benefits. It is, therefore, reasonable to note that a decrease in the desirable gastrointestinal bacteria will lead to deterioration in gastrointestinal, neuroendocrine or immune relationships and ultimately disease. Therefore, studies focusing on the impact of enteric microbiota on the host and in particular on the central nervous system are essential to our understanding of the influence of this system. Recent studies published in this Journal demonstrate that germ-free mice display alterations in stress-responsivity, central neurochemistry and behavior indicative of a reduction in anxiety in comparison to conventional mice. Such data offer the enticing proposition that specific modulation of the enteric microbiota may be a useful strategy for stress-related disorders and for modulating the co-morbid aspects of gastrointestinal disorders such as irritable bowel syndrome and inflammatory bowel disease.


title: Race Is Not Neutral: A National Investigation of African American and Latino Disproportionality in School Discipline

abstract: Discipline practices in schools affect the social quality of each educa- tional environment, and the ability of children to achieve the academic and social gains essential for success in a 21st century society. We review the documented patterns of office discipline referrals in 364 elementary and middle schools during the 2005-2006 academic year. Data were reported by school personnel through daily or weekly uploading of office discipline referrals using the Web-based School-wide Information System. Descriptive and logistic regression analyses indicate that students from African American families are 2.19 (elementary) to 3.78 (middle) times as likely to be referred to the office for problem behavior as their White peers. In addition, the results indicate that students from African American and Latino families are more likely than their White peers to receive expulsion or out of school suspension as consequences for the same or similar problem behavior. These results extend and are consistent with a long history of similar findings, and argue for direct efforts in policy, practice, and research to address ubiquitous racial and ethnic disparities in school discipline.


title: Schedulability-driven frame packing for multi-cluster distributed embedded systems

abstract: We present an approach to frame packing for multi-cluster distributed embedded systems consisting of time-triggered and event-triggered clusters, interconnected via gateways. In our approach, the application messages are packed into frames such that the application is schedulable. Thus, we have also proposed a schedulability analysis for applications consisting of mixed event-triggered and time-triggered processes and messages, and a worst case queuing delay analysis for the gateways, responsible for routing inter-cluster traffic. Optimization heuristics for frame packing aiming at producing a schedulable system have been proposed. Extensive experiments and a real-life example show the efficiency of our frame-packing approach.


title: Social Media Data Mining: A Social Network Analysis Of Tweets During The 2010-2011 Australian Floods

abstract: Using tweets extracted from Twitter during the Australian 2010-2011 floods, social network analysis techniques were used to generate and analyse the online networks that emerged at that time. The aim was to develop an understanding of the online communities for the Queensland, New South Wales and Victorian floods in order to identify active players and their effectiveness in disseminating critical information. A secondary goal was to identify important online resources disseminated by these communities. Important and effective players during the Queensland floods were found to be: local authorities (mainly the Queensland Police Services), political personalities (Queensland Premier, Prime Minister, Opposition Leader, Member of Parliament), social media volunteers, traditional media reporters, and people from not-for-profit, humanitarian, and community associations. A range of important resources were identified during the Queensland flood; however, they appeared to be of a more general information nature rather than vital information and updates on the disaster. Unlike Queensland, there was no evidence of Twitter activity from the part of local authorities and the government in the New South Wales and Victorian floods. Furthermore, the level of Twitter activity during the NSW floods was almost nil. Most of the active players during the NSW and Victorian floods were volunteers who were active during the Queensland floods. Given the positive results obtained by the active involvement of the local authorities and government officials in Queensland, and the increasing adoption of Twitter in other parts of the world for emergency situations, it seems reasonable to push for greater adoption of Twitter from local and federal authorities Australia-wide during periods of mass emergencies.


title: IT outsourcing research from 1992 to 2013: A literature review based on main path analysis

abstract: We used main path analysis to review the IT outsourcing literature from 1992 to 2013.We reviewed 280 IT outsourcing papers based on 120,000 main paths among them.We identified the global main path in IT outsourcing citation networks.We identified 12 major research themes based on multiple main paths.Future directions of IT outsourcing research are predicted. Applying main path analysis, this paper investigates the IT outsourcing (ITO) field by identifying a set of papers that have played a central role in the development of the field and the major research themes emerging from the citation patterns. We selected the top 120,000 main paths from 6.45 million main paths among 798 ITO papers, resulting in a data set of 280 papers that represent the most important nodes supporting ITO knowledge flow. Based on our analysis of the multiple main paths, twelve major research themes emerged: ITO motivations, ITO decisions, ITO risks, debate around transaction cost theory, client-vendor relationship, the vendor's perspective, psychological and formal contracts, ASP, BPO, opensourcing and crowdsourcing, offshore outsourcing, and multisourcing. Finally, we discussed future directions of ITO research based on our findings. Our study is among the few studies that have used bibliometric analysis methods to analyze and visualize the citation network characterizing the rich body of ITO literature. The main path analysis precisely identified and visualized the major knowledge flow in the evolution of ITO research and major ITO research streams, thus providing an in-depth understanding of ITO research in the last 20 years.


title: A Smart Antenna Module Using OMNeT++ for Wireless Sensor Network Simulation

abstract: We introduce a smart antenna (SA) module to be used in OMNeT++ for wireless sensor networks (WSN). This module is a collection of tools in OMNeT++ for adding smart antenna capability to a central node in WSNs. It is based on sectoral sweeper (SS) scheme which was shown to provide efficient task management and easy localization scheme. The number of sensing nodes, energy consumption of nodes, and data traffic carried in the network are reduced with the SS. OMNeT++ is an open source object-oriented modular discrete event network simulator consisting of hierarchically nested modules. With the SA module, desired task region can be specified by a task beam with changing beam width and beam direction parameters in the OMNeT++ configuration file. In addition, each node in the network model that uses SA module has capability of Mobility framework which is intended to support wireless and mobile simulations within OMNeT++. The simulation results provide performance evaluation using the developed module in OMNeT++ for WSNs.


title: Single and multi switch fault tolerant topology of multi level inverter

abstract: In applications using multilevel inverter topology, low reliability is one of the biggest concerns since a large number of semiconductor devices are used. Semiconductor devices are the weakest link in the power inverter system. Therefore, fault tolerance control becomes necessary where human intervention is least possible. In this paper, a novel topology is proposed which is fault tolerant to open switch faults and delivers power uninterruptedly. The fault tolerance has been achieved by adding additional but a minimum number of switches required. The proposed topology shows better results when compared with the recently proposed topologies. The switching scheme employed exploits the redundant paths in such a way that leads to the self-voltage balancing of the capacitors under healthy and faulty conditions. The validity of the proposed open switch fault tolerance is verified through simulation results.


title: Toward a Deeper Understanding of the Role of Interaction in Information Visualization

abstract: Even though interaction is an important part of information visualization (Infovis), it has garnered a relatively low level of attention from the Infovis community. A few frameworks and taxonomies of Infovis interaction techniques exist, but they typically focus on low-level operations and do not address the variety of benefits interaction provides. After conducting an extensive review of Infovis systems and their interactive capabilities, we propose seven general categories of interaction techniques widely used in Infovis: 1) Select, 2) Explore, 3) Reconfigure, 4) Encode, 5) Abstract/Elaborate, 6) Filter, and 7) Connect. These categories are organized around a user's intent while interacting with a system rather than the low-level interaction techniques provided by a system. The categories can act as a framework to help discuss and evaluate interaction techniques and hopefully lay an initial foundation toward a deeper understanding and a science of interaction.


title: Evolution of an innovative role: the clinical nurse leader

abstract: Aims ::: This study describes the evolution of the clinical nurse leader (CNL®) role and its utility in a tertiary care and community hospital. ::: ::: Background ::: In the US, quality and safety metrics are being publically reported and healthcare organizations are just beginning to experience pay-for-performance and its impact. The American Association of the Colleges of Nursing (AACN) developed the role of the CNL to address the complexities and challenges of providing high-quality care in the current environment. ::: ::: Evaluation ::: Since 2007, a cohort of CNLs in practice has evaluated the effectiveness of the role with measures of clinical outcomes, financial savings and case studies. ::: ::: Key issues ::: Having CNLs with a strategic perspective acting as facilitators and integrators of care has proven invaluable. Leadership support has been critical and commitment to maintaining the integrity of the role has ensured its success and sustainability. ::: ::: Conclusions ::: This role has established its value in risk assessment, strategic quality improvement, interdisciplinary collaboration and the implementation of evidence-based solutions. ::: ::: Implications for nursing management ::: The flexibility and broad scope of this role allows for its use across practice settings and represents an exciting opportunity for nursing to drive quality of care to new levels while managing costs.


title: Inferring the Root Cause in Road Traffic Anomalies

abstract: We propose a novel two-step mining and optimization framework for inferring the root cause of anomalies that appear in road traffic data. We model road traffic as a time-dependent flow on a network formed by partitioning a city into regions bounded by major roads. In the first step we identify link anomalies based on their deviation from their historical traffic profile. However, link anomalies on their own shed very little light on what caused them to be anomalous. In the second step we take a generative approach by modeling the flow in a network in terms of the origin-destination (OD) matrix which physically relates the latent flow between origin and destination and the observable flow on the links. The key insight is that instead of using all of link traffic as the observable vector we only use the link anomaly vector. By solving an L1 inverse problem we infer the routes (the origin-destination pairs) which gave rise to the link anomalies. Experiments on a very large GPS data set consisting on nearly eight hundred million data points demonstrate that we can discover routes which can clearly explain the appearance of link anomalies. The use of optimization techniques to explain observable anomalies in a generative fashion is, to the best of our knowledge, entirely novel.


title: Correlation Alignment by Riemannian Metric for Domain Adaptation

abstract: Domain adaptation techniques address the problem of reducing the sensitivity of machine learning methods to the so-called domain shift, namely the difference between source (training) and target (test) data distributions. In particular, unsupervised domain adaptation assumes no labels are available in the target domain. To this end, aligning second order statistics (covariances) of target and source domains have proven to be an effective approach ti fill the gap between the domains. However, covariance matrices do not form a subspace of the Euclidean space, but live in a Riemannian manifold with non-positive curvature, making the usual Euclidean metric suboptimal to measure distances. In this paper, we extend the idea of training a neural network with a constraint on the covariances of the hidden layer features, by rigorously accounting for the curved structure of the manifold of symmetric positive definite matrices. The resulting loss function exploits a theoretically sound geodesic distance on such manifold. Results show indeed the suboptimal nature of the Euclidean distance. This makes us able to perform better than previous approaches on the standard Office dataset, a benchmark for domain adaptation techniques.


title: DB 2 Design Advisor : Integrated Automatic Physical Database Design

abstract: The DB2 Design Advisor in IBM® DB2® Universal DatabaseTM (DB2 UDB) Version 8.2 for Linux®, UNIX® and Windows® is a tool that, for a given workload, automatically recommends physical design features that are any subset of indexes, materialized query tables (also called materialized views), shared-nothing database partitionings, and multidimensional clustering of tables. Our work is the very first industrial-strength tool that covers the design of as many as four different features, a significant advance to existing tools, which support no more than just indexes and materialized views. Building such a tool is challenging, because of not only the large search space introduced by the interactions among features, but also the extensibility needed by the tool to support additional features in the future. We adopt a novel "hybrid" approach in the Design Advisor that allows us to take important interdependencies into account as well as to encapsulate design features as separate components to lower the reengineering cost. The Design Advisor also features a built-in module that automatically reduces the given workload, and therefore provides great scalability for the tool. Our experimental results demonstrate that our tool can quickly provide good physical design recommendations that satisfy users' requirements.


title: Job-shop scheduling with multi-purpose machines

abstract: Consider the following generalization of the classical job-shop scheduling problem in which a set of machines is associated with each operation of a job. The operation can be processed on any of the machines in this set. For each assignment μ of operations to machines letP(μ) be the corresponding job-shop problem andf(μ) be the minimum makespan ofP(μ). How to find an assignment which minimizesf(μ)? For problems with two jobs a polynomial algorithm is derived.


title: Wavelets and signal processing

abstract: A simple, nonrigorous, synthetic view of wavelet theory is presented for both review and tutorial purposes. The discussion includes nonstationary signal analysis, scale versus frequency, wavelet analysis and synthesis, scalograms, wavelet frames and orthonormal bases, the discrete-time case, and applications of wavelets in signal processing. The main definitions and properties of wavelet transforms are covered, and connections among the various fields where results have been developed are shown. >


ABSTRACT Security of Biometric Authentication Systems


title: Saliency based algorithm for ship detection in infrared images

abstract: In this paper we have worked on the problem of automatic ship detection in IR images. Segmentation of IR ship images is always a challenging task because of the intensity inhomogeneity, sea clutters and noise. In our proposed approach, we have shown that efficiency and accuracy of IR ship detection algorithms can be enhanced by only searching around the salient parts of the input image. In order to identify most salient regions, at first we computed the saliency map of the input image using the Graph-Based Visual Saliency (GBVS) algorithm. Next a multilevel thresholding of the saliency map is performed to get the ranked salient regions of the input image. By using the ship size as prior information, top-k regions are further processed to get the fine segmentation of the target. For this purpose we have used spatial constraint based fuzzy c-mean (FCM) segmentation algorithm along with a strategy to choose the cluster selection threshold. Experiments are performed on a data set of 18 diverse and challenging IR ship images, collected from different sources. Results show that our proposed framework is very effective and perform better compare to the methods which directly search the target in entire image.


title: School Engagement: Potential of the Concept, State of the Evidence

abstract: The concept of school engagement has attracted increasing attention as representing a possible antidote to declining academic motivation and achievement. Engagement is presumed to be malleable, responsive to contextual features, and amenable to environmental change. Researchers describe behavioral, emotional, and cognitive engagement and recommend studying engagement as a multifaceted construct. This article reviews definitions, measures, precursors, and outcomes of engagement; discusses limitations in the existing research; and suggests improvements. The authors conclude that, although much has been learned, the potential contribution of the concept of school engagement to research on student experience has yet to be realized. They call for richer characterizations of how students behave, feel, and think—research that could aid in the development of finely tuned interventions


title: Fabrication and Modeling of a Capacitor Microfluidically Tuned by Water

abstract: This letter presents the fabrication and the modeling of a continuously tuned microfluidic capacitor. On one hand its electrodes are realized by classical electrodeposition techniques and on the other hand, the lamination of SU-8 films allows superposing some microfluidic channels. According to the experimental results, the capacitor value increases continuously following the deionized (DI) water penetration in microchannels. The capacitance variations are comprised between Cmin = 0.52 pF and Cmax = 18.5 pF, allowing a wide tuning range that reaches 3460% at 500 MHz. The quality factor decreases from Qmax = 69 when the capacitor is empty to Qmin = 5.3 when it is fully filled with DI water. We have also investigated the theoretical aspects of our device by modeling the electric field and the current distributions inside the channels: when they are entirely filled with the DI water, the electric field is cancelled.


Graph-Based Translation Via Graph Segmentation