title: Enactive Cognitive Science From Cybernetics to Second-Order Cybernetics: A Comparative Analysis of Their Central Ideas

abstract: > Context • The enactive paradigm in the cognitive sciences is establishing itself as a strong and comprehensive alternative to the computationalist mainstream. However, its own particular historical roots have so far been largely ignored in the historical analyses of the cognitive sciences. > Problem • In order to properly assess the enactive paradigm’s theoretical foundations in terms of their validity, novelty and potential future directions of development, it is essential for us to know more about the history of ideas that has led to the current state of affairs. > Method • The meaning of the disappearance of the field of cybernetics and the rise of second-order cybernetics is analyzed by taking a closer look at the work of representative figures for each of the phases – Rosenblueth, Wiener and Bigelow for the early wave of cybernetics, Ashby for its culmination, and von Foerster for the development of the second-order approach. > Results • It is argued that the disintegration of cybernetics eventually resulted in two distinct scientific traditions, one going from symbolic AI to modern cognitive science on the one hand, and the other leading from second-order cybernetics to the current enactive paradigm. > Implications • We can now understand that the extent to which the cognitive sciences have neglected their cybernetic parent is precisely the extent to which cybernetics had already carried the tendencies that would later find fuller expression in second-order cybernetics. >


title: Engineering change management in individual and mass production

abstract: Abstract The ability to manage engineering changes (ECs) efficiently reflects the agility of an enterprise. A large majority of products become gradually improved and perfected through the developmental-design process, during which the set design requirements are met or even upgraded, thus prolonging the product life cycle. The concept of product improvement was based on the activation and tracking of (ECs) through the developmental-design phase and the manufacturing phase. A special method was used to recognize activities within the process and the degree of involvement of individual participants. The individuals involved in the process were provided with appropriate information and the required communication channels with others were ensured. The EC process was generalized and applied to different types of production. A product's complexity and design level were analyzed first, and key factors such as CE methods, process definition, information system, communication and organization were used as a tool for optimizing the EC process. The method was tested and successfully applied into industrial practice.


title: Risk assessment method for cyber security of cyber physical systems

abstract: Cyber security is one of the most important risks for all types of cyber-physical systems (CPS). To evaluate the cyber security risk of CPS, a quantitative hierarchized assessment model consists of attack severity, attack success probability and attack consequence is proposed, which can assess the risk caused by an ongoing attack at host level and system level. Then the definitions and calculation methods of the three indexes are discussed in detail. Finally, this paper gives the risk assessment algorithm which describes the steps of implementation. Numerical example shows that the model can response to the attack timely and obtain the system security risk change curve. So that it can help users response to the risk timely. The risk change curve can also be used to predict the risk for the future time.


title: Predestination: Inferring Destinations from Partial Trajectories

abstract: We describe a method called Predestination that uses a history of a driver's destinations, along with data about driving behaviors, to predict where a driver is going as a trip progresses. Driving behaviors include types of destinations, driving efficiency, and trip times. Beyond considering previously visited destinations, Predestination leverages an open-world modeling methodology that considers the likelihood of users visiting previously unobserved locations based on trends in the data and on the background properties of locations. This allows our algorithm to smoothly transition between “out of the box” with no training data to more fully trained with increasing numbers of observations. Multiple components of the analysis are fused via Bayesian inference to produce a probabilistic map of destinations. Our algorithm was trained and tested on hold-out data drawn from a database of GPS driving data gathered from 169 different subjects who drove 7,335 different trips.


title: Implicit and explicit camera calibration: theory and experiments

abstract: By implicit camera calibration, we mean the process of calibrating a camera without explicitly computing its physical parameters. Implicit calibration can be used for both three-dimensional (3-D) measurement and generation of image coordinates. In this paper, we present a new implicit model based on the generalized projective mappings between the image plane and two calibration planes. The back-projection and projection processes are modelled separately to ease the computation of distorted image coordinates from known world points. A set of constraints of perspectivity is derived to relate the transformation parameters of the two calibration planes. Under the assumption of the radial distortion model, we present a computationally efficient method for explicitly correcting the distortion of image coordinates in frame buffer without involving the computation of camera position and orientation. By combining with any linear calibration techniques, this method makes explicit the camera physical parameters. Extensive experimental comparison of our methods with the classic photogrammetric method and Tsai's (1986) method in the aspects of 3-D measurement (both absolute and relative errors), the prediction of image coordinates, and the effect of the number of calibration points, is made using real images from 15 different depth values. >


title: Predictions of default probabilities in structural models of debt

abstract: This paper examines default probabilities predicted by alternative "structural" models of risky corporate debt. We focus on default probabilities rather than credit spreads because (i) they are not affected by additional market factors such as liquidity and tax differences; and (ii) prediction of the relative likelihood of default is often stated as the objective of bond ratings. We have three objectives: (1) To distinguish "exogenous default" from "endogenous default" models (2) To compare these models' predictions of default probabilities, given common inputs (3) To examine how well these models capture actual average default frequencies, as reflected in Moody's (2001) corporate bond default data 1970-2000. We find that endogenous and exogenous default boundary models fit observed default frequencies very well for horizons 5 years and longer, for both investment grade and non-investment grade ratings. Shorter-term default frequencies tend to be underestimated. This suggests that a jump component should be included in asset value dynamics. Both types of structural models fit available default data equally well. But the models make different predictions about how default probabilities and recovery rates change with changes in debt maturity or asset volatility. Further data and testing will be needed to test these differences. Finally, we compare and contrast these structural models' default predictions with a simplified version of the widely-used Moody's-KMV "distance to default" model described in Crosbie and Bohn (2002).


title: A Multilingual, Multi-style and Multi-granularity Dataset for Cross-language Textual Similarity Detection.

abstract: In this paper we describe our effort to create a dataset for the evaluation of cross-language textual similarity detection. We present pre-existing corpora and their limits and we explain the various gathered resources to overcome these limits and build our enriched dataset. The proposed dataset is multilingual, includes cross-language alignment for different granularities (from chunk to document), is based on both parallel and comparable corpora and contains human and machine translated texts. Moreover, it includes texts written by multiple types of authors (from average to professionals). With the obtained dataset, we conduct a systematic and rigorous evaluation of several state-of-the-art cross-language textual similarity detection methods. The evaluation results are reviewed and discussed. Finally, dataset and scripts are made publicly available on GitHub: http://github.com/FerreroJeremy/Cross-Language-Dataset.


title: CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions

abstract: Spectrograms have been widely used in Convolutional Neural Networks based schemes for acoustic scene classification, such as the STFT spectrogram and the MFCC spectrogram, etc. They have different time-frequency characteristics, contributing to their own advantages and disadvantages in recognizing acoustic scenes. In this letter, a novel multi-spectrogram fusion framework is proposed, making the spectrograms complement each other. In the framework, a single CNN architecture is applied onto multiple spectrograms for feature extraction. The deep features extracted from multiple spectrograms are then fused to discriminate the acoustic scenes. Moreover, motivated by the inter-class similarities in acoustic scene datasets, a label expansion method is further proposed in which super-class labels are constructed upon the original classes. On the help of the expanded labels, the CNN models are transformed into the multitask learning form to improve the acoustic scene classification by appending the auxiliary task of super-class classification. To verify the effectiveness of the proposed methods, intensive experiments have been performed on the DCASE2017 and the LITIS Rouen datasets. Experimental results show that the proposed method can achieve promising accuracies on both datasets. Specifically, accuracies of 0.9744, 0.8865 and 0.7778 are obtained for the LITIS Rouen dataset, the DCASE Development set and Evaluation set respectively.


title: Software Engineering at Google

abstract: We catalog and describe Google's key software engineering practices.


title: Processing fluency and aesthetic pleasure: Is beauty in the perceiver’s processing experience? Personality and

abstract: We propose that aesthetic pleasure is a function of the perceiver’s processing dynamics: The more fluently perceivers can process an object, the more positive their aesthetic response. We review variables known to influence aesthetic judgments, such as figural goodness, figure–ground contrast, stimulus repetition, symmetry, and prototypicality, and trace their effects to changes in processing fluency. Other variables that influence processing fluency, like visual or semantic priming, similarly increase judgments of aesthetic pleasure. Our proposal provides an integrative framework for the study of aesthetic pleasure and sheds light on the interplay between early preferences versus cultural influences on taste, preferences for both prototypical and abstracted forms, and the relation between beauty and truth. In contrast to theories that trace aesthetic pleasure to objective stimulus features per se, we propose that beauty is grounded in the processing experiences of the perceiver, which are in part a function of stimulus properties.


VSearch Colour: Full-Colour Visual Search Experi-ments on the Macintosh II


An automatic voice conversion evaluation strategy based on perceptual background noise distortion and speaker similarity


title: MULTI-SCALE STRUCTURAL SIMILARITY FOR IMAGE QUALITY ASSESSMENT

abstract: The structural similarity image quality paradigm is based on the assumption that the human visual system is highly adapted for extracting structural information from the scene, and therefore a measure of structural similarity can provide a good approximation to perceived image quality. This paper proposes a multi-scale structural similarity method, which supplies more flexibility than previous single-scale methods in incorporating the variations of viewing conditions. We develop an image synthesis method to calibrate the parameters that define the relative importance of different scales. Experimental comparisons demonstrate the effectiveness of the proposed method.


title: A 300-GHz Fundamental Oscillator in 65-nm CMOS Technology

abstract: Fundamental oscillators prove the existence of gain at high frequencies, revealing the speed limitations of other circuits in a given technology. This paper presents an oscillator topology that employs feedback from an output stage to the core, thus achieving a high speed. The behavior of the proposed oscillator is formulated and simulations are used to compare it with the conventional cross-coupled pair circuit. Three prototypes realized in 65-nm CMOS technology operate at 205 GHz, 240 GHz, and 300 GHz, each drawing 3.7 mW from a 0.8-V supply.


title: Fingertip friction modulation due to electrostatic attraction

abstract: The human fingertip is extremely sensitive to lateral (shear) forces that arise in surface exploration. We and others have developed haptic displays that work by modulating surface friction via electrostatic attraction. Despite the demonstrated ability of these displays to render haptic environments, an understanding of the fingertip-surface interface is lacking. We have developed a tribometer for measuring the lateral frictional forces on a fingertip under well-controlled conditions. We show an expected square law dependence of frictional force (and inferred electrostatic normal force) on actuation voltage, although we observe a large person to person variability. We model an expected dependence of the frictional force on the frequency of the actuation voltage, predicting a first order cut off below about 500Hz. However, our measurements are unambiguously at odds with the model's predictions.


title: Particle filter theory and practice with positioning applications

abstract: The particle filter (PF) was introduced in 1993 as a numerical approximation to the nonlinear Bayesian filtering problem, and there is today a rather mature theory as well as a number of successful applications described in literature. This tutorial serves two purposes: to survey the part of the theory that is most important for applications and to survey a number of illustrative positioning applications from which conclusions relevant for the theory can be drawn. The theory part first surveys the nonlinear filtering problem and then describes the general PF algorithm in relation to classical solutions based on the extended Kalman filter (EKF) and the point mass filter (PMF). Tuning options, design alternatives, and user guidelines are described, and potential computational bottlenecks are identified and remedies suggested. Finally, the marginalized (or Rao-Blackwellized) PF is overviewed as a general framework for applying the PF to complex systems. The application part is more or less a stand-alone tutorial without equations that does not require any background knowledge in statistics or nonlinear filtering. It describes a number of related positioning applications where geographical information systems provide a nonlinear measurement and where it should be obvious that classical approaches based on Kalman filters (KFs) would have poor performance. All applications are based on real data and several of them come from real-time implementations. This part also provides complete code examples.


Young consumers online shopping : an empirical study


title: StreamQRE: modular specification and efficient evaluation of quantitative queries over streaming data

abstract: Real-time decision making in emerging IoT applications typically relies on computing quantitative summaries of large data streams in an efficient and incremental manner. To simplify the task of programming the desired logic, we propose StreamQRE, which provides natural and high-level constructs for processing streaming data. Our language has a novel integration of linguistic constructs from two distinct programming paradigms: streaming extensions of relational query languages and quantitative extensions of regular expressions. The former allows the programmer to employ relational constructs to partition the input data by keys and to integrate data streams from different sources, while the latter can be used to exploit the logical hierarchy in the input stream for modular specifications. We first present the core language with a small set of combinators, formal semantics, and a decidable type system. We then show how to express a number of common patterns with illustrative examples. Our compilation algorithm translates the high-level query into a streaming algorithm with precise complexity bounds on per-item processing time and total memory footprint. We also show how to integrate approximation algorithms into our framework. We report on an implementation in Java, and evaluate it with respect to existing high-performance engines for processing streaming data. Our experimental evaluation shows that (1) StreamQRE allows more natural and succinct specification of queries compared to existing frameworks, (2) the throughput of our implementation is higher than comparable systems (for example, two-to-four times greater than RxJava), and (3) the approximation algorithms supported by our implementation can lead to substantial memory savings.


title: Rational Design of Self-Supported Ni3S2Nanosheets Array for Advanced Asymmetric Supercapacitor with a Superior Energy Density

abstract: We report a rationally designed two-step method to fabricate self-supported Ni3S2 nanosheet arrays. We first used 2-methylimidazole (2-MI), an organic molecule commonly served as organic linkers in metal–organic frameworks (MOFs), to synthesize an α-Ni(OH)2 nanosheet array as a precursor, followed by its hydrothermal sulfidization into Ni3S2. The resulting Ni3S2 nanosheet array demonstrated superior supercapacitance properties, with a very high capacitance of about 1,000 F g–1 being delivered at a high current density of 50 A g–1 for 20,000 charge–discharge cycles. This performance is unparalleled by other reported nickel sulfide-based supercapacitors and is also advantageous compared to other nickel-based materials such as NiO and Ni(OH)2. An asymmetric supercapacitor was then established, exhibiting a very stable capacitance of about 200 F g–1 at a high current density of 10 A g–1 for 10,000 cycles and a surprisingly high energy density of 202 W h kg–1. This value is comparable to that of the lithium-io...


title: Compact Modeling Source-to-Drain Tunneling in Sub-10-nm GAA FinFET With Industry Standard Model

abstract: We present a compact model for source-to-drain tunneling current in sub-10-nm gate-all-around FinFET, where tunneling current becomes nonnegligible. Wentzel–Kramers–Brillouin method with a quadratic potential energy profile is used to analytically capture the dependence on biases in the tunneling probability expression and simplify the equation. The calculated tunneling probability increases with smaller effective mass and with increasing bias. We at first use the Gaussian quadrature method to integrate Landauer’s equation for tunneling current computation without further approximations. To boost simulation speed, some approximations are made. The simplified equation shows a good accuracy and has more flexibility for compact model purpose. The model is implemented into industry standard Berkeley Short-channel IGFET Model-common multi-gate model for future technology node, and is validated by the full-band atomistic quantum transport simulation data.


title: MOOC Dropout Prediction: Lessons Learned from Making Pipelines Interpretable

abstract: Dropout prediction in MOOCs is a well-researched problem where we classify which students are likely to persist or drop out of a course. Most research into creating models which can predict outcomes is based on student engagement data. Why these students might be dropping out has only been studied through retroactive exit surveys. This helps identify an important extension area to dropout prediction- how can we interpret dropout predictions at the student and model level? We demonstrate how existing MOOC dropout prediction pipelines can be made interpretable, all while having predictive performance close to existing techniques. We explore each stage of the pipeline as design components in the context of interpretability. Our end result is a layer which longitudinally interprets both predictions and entire classification models of MOOC dropout to provide researchers with in-depth insights of why a student is likely to dropout.


title: The ketogenic diet : From molecular mechanisms to clinical effects

abstract: Recent years have witnessed an increased interest from pediatric neurologists, neuropediatricians, epileptologists and general neurologists in the use of the ketogenic diet (KD) for the management of refractory epilepsies, particularly in children and adolescents. This article summarizes current knowledge on various issues related to its use, as discussed at a recent international workshop. Aspects discussed in some detail include (i) the putative mechanisms responsible for the diet’s anticonvulsant effects, based on results of biochemical and neurophysiological studies in experimental models; (ii) consensus and controversies on the modalities of initiation of the diet, and different protocols of implementation; (iii) indications and contraindications; (iv) efficacy data, also in relation to seizure type, syndromic form and patients age; (v) adverse effects; (vi) methodological aspects related to assessment of the diet’s clinical effects, and perspectives for future research. Overall, the data reviewed indicate that considerable advances have been made in understanding the modes of action of the diet, its efficacy and tolerability profiles and its potential role in different types of epilepsy. Although clinical studies performed to date have important methodological limitations, including suboptimally characterized patients’ populations and an uncontrolled design, a number of innovative, prospective randomized study protocols have been recently proposed and are being implemented. The results of these will hopefully provide much needed high-quality information to better define the role of the diet in the treatment algorithms in different epilepsy syndromes.


Implied volatility measures


title: Recent advances in document summarization

abstract: The task of automatic document summarization aims at generating short summaries for originally long documents. A good summary should cover the most important information of the original document or a cluster of documents, while being coherent, non-redundant and grammatically readable. Numerous approaches for automatic summarization have been developed to date. In this paper we give a self-contained, broad overview of recent progress made for document summarization within the last 5 years. Specifically, we emphasize on significant contributions made in recent years that represent the state-of-the-art of document summarization, including progress on modern sentence extraction approaches that improve concept coverage, information diversity and content coherence, as well as attempts from summarization frameworks that integrate sentence compression, and more abstractive systems that are able to produce completely new sentences. In addition, we review progress made for document summarization in domains, genres and applications that are different from traditional settings. We also point out some of the latest trends and highlight a few possible future directions.


title: Social Engineering Attack Detection Model: SEADMv2

abstract: Information security is a fast-growing discipline, and therefore the effectiveness of security measures to protect sensitive information needs to be increased. Since people are generally susceptible to manipulation, humans often prove to be the weak link in the security chain. A social engineering attack targets this weakness by using various manipulation techniques to elicit individuals to perform sensitive requests. The field of social engineering is still in its infancy as far as formal definitions, attack frameworks, examples of attacks and detection models are concerned. This paper therefore proposes a revised version of the Social Engineering Attack Detection Model. The previous model was designed with a call centre environment in mind and is only able to cater for social engineering attacks that use bidirectional communication. Previous research discovered that social engineering attacks can be classified into three different categories, namely attacks that utilise bidirectional communication, unidirectional communication or indirect communication. The proposed (and revised) Social Engineering Attack Detection Model addresses this problem by extending the model to cater for social engineering attacks that use bidirectional communication, unidirectional communication or indirect communication. The revised Social Engineering Attack Detection Model is further verified using published generalised social engineering attack examples from each of the three categories mentioned.


title: A two-step system for direct bank telemarketing outcome classification

abstract: A two-step system is presented to improve prediction of telemarketing outcomes and to help the marketing management team effectively manage customer relationships in the banking industry. In the first step, several neural networks are trained with different categories of information to make initial predictions. In the second step, all initial predictions are combined by a single neural network to make a final prediction. Particle swarm optimization is employed to optimize the initial weights of each neural network in the ensemble system. Empirical results indicate that the two-step system presented performs better than all its individual components. In addition, the two-step system outperforms a baseline one where all categories of marketing information are used to train a single neural network. As a neural networks ensemble model, the proposed two-step system is robust to noisy and nonlinear data, easy to interpret, suitable for large and heterogeneous marketing databases, fast and easy to implement.


title: Nutrition Status of Junior Elite Canadian Female Soccer Athletes

abstract: Context: Adolescent female team-sport athletes are faced with the challenge of meeting nutrition requirements for growth and development, as well as sport performance. There is a paucity of evidence describing the dietary adequacy of this population in respect to these physiological demands. Therefore, the aim of this study was to comprehensively evaluate the nutrition status of junior elite female soccer athletes. Method: A total of 33 athletes (15.7 ± 0.7 yr) completed anthropometric assessment, 4-day food records analyzed for macro- and micronutrient intake, and hematological analysis. Energy expenditure was estimated using predictive equations. Results: Mean sum of 7 skinfolds was 103.1 ± 35.2 mm, and body-mass index was 22.7 ± 2.7. Mean energy intake was 2,079 ± 460 kcal/day, and estimated energy expenditure was 2,546 ± 190 kcal/day. Of the athletes, 51.5% consumed <5g/kg carbohydrate, 27.3% consumed <1.2g/kg protein, and 21.2% consumed <25% of energy intake from fat. A large proportion of athletes did not meet Dietary Reference Intakes for pantothenic acid (54.5%), vitamin D (100%), folate (69.7%), vitamin E (100%), and calcium (66.7%). Compared with recommendations for athletes, 89.3% and 50.0% of participants had depleted iron and 25-hydroxyvitamin D, respectively. Conclusion: A high proportion of players were not in energy balance, failed to meet carbohydrate and micronutrient recommendations, and presented with depleted iron and vitamin D status. Suboptimal nutrition status may affect soccer performance and physiological growth and development. More research is needed to understand the unique nutrition needs of this population and inform sport nutrition practice and research.


title: Microhydraulic Electrowetting Actuators

abstract: The conversion of electrical to mechanical power on a sub-centimeter scale is a key technology in many microsystems and energy harvesting devices. In this paper, we present a type of a capacitive energy conversion device that uses capillary pressure and electrowetting to reversibly convert electrical power to hydraulic power. These microhydraulic actuators use a high surface-to-volume ratio to deliver high power at a relatively low voltage with an energy conversion efficiency of over 65%. The capillary pressure generated grows linearly with shrinking capillary diameter, as does the frequency of actuation. We present the pressure, frequency, and power scaling properties of these actuators and demonstrate that power density scales up as the inverse capillary diameter squared, leading to high-efficiency actuators with a strength density exceeding biological muscle. Two potential applications for microhydraulics are also demonstrated: soft-microrobotics and energy harvesting. [2015-0214]


Mapping Publication Trends and Identifying Hot Spots of Research on Internet Health Information Seeking Behavior: A Quantitative and Co-Word Biclustering Analysis


title: Acoustic Correlates of Stress in Thai

abstract: Acoustic correlates of stress [duration, fundamental frequency (F₀), and intensity] were investigated in a language (Thai) in which both duration and F₀ are employed to signal lexical cont


title: Lacunarity Analysis on Image Patterns for Texture Classification

abstract: Based on the concept of lacunarity in fractal geometry, we developed a statistical approach to texture description, which yields highly discriminative feature with strong robustness to a wide range of transformations, including pho- tometric changes and geometric changes. The texture feature is constructed by concatenating the lacunarity-related parameters estimated from the multi-scale local binary patterns of image. Benefiting from the ability of lacunarity analysis to distinguish spatial patterns, our method is able to characterize the spatial distribution of local image structures from multiple scales. The proposed feature was applied to texture classification and has demonstrated excellent performance in comparison with several state-of-the- art approaches on four benchmark datasets.


title: Rule Optimization of Boosted C5.0 Classification Using Genetic Algorithm for Liver disease Prediction

abstract: One of the interesting and important subjects among researchers in the field of medical and computer science is diagnosing illness by considering the features that have the most impact on recognitions. The subject discusses a new concept which is called Medical Data Mining (MDM). Indeed, data mining methods use different ways such as classification and clustering to classify diseases and their symptoms which are helpful for diagnosing. This paper introduces a new method of liver disease diagnosis to help doctors and their patients in finding the disease symptoms and reduce a long time of diagnosing and prevent deaths. The proposed method will optimize the rules released from Boosted C5.0 classification method with the Genetic Algorithm (GA), to increase the diagnosis time and accuracy. So instead of using an evolutionary algorithm for producing rules, the genetic algorithm is used for improving and reducing rules of another algorithm. We show that our proposed approach has better performance and throughput in comparison with other work in the field. The accuracy is improved from 81% in [1] to 93% in our work.


title: Filtering of Hall-Sensor Signals for Improved Operation of Brushless DC Motors

abstract: Brushless dc motors controlled by Hall-effect sensors are used in variety of applications, wherein the Hall sensors should be placed 120 electrical degrees apart. This is difficult to achieve in practice especially in low-precision motors, which leads to unsymmetrical operation of the inverter/motor phases. To mitigate this phenomenon, an approach of filtering the Hall-sensor signals has been recently proposed. This letter extends the previous work and presents a very efficient digital implementation of such filters that can be easily included into various brushless dc motor-drive systems for restoring their operation in steady state and transients.


Governance in League of Legends: A hybrid system


title: Extending customer relationship management: from empowering firms to empowering customers

abstract: Purpose – The focus of customer relationship management (CRM) literature has been predominantly on the firm perspective and on IT, not on customer or service orientation and value co‐creation. This paper seeks to explore and analyse contemporary CRM frameworks and suggests future research directions. To achieve this, a thorough literature review on CRM is conducted focusing on recent advances within CRM. This provides a good basis for critically analysing the current status of both CRM theory and practice.Design/methodology/approach – The paper reviews CRM literature published 2003‐2011. Based on the literature review, it introduces a conceptual framework of the changing role of customer data in the CRM framework.Findings – Literature has not adequately addressed the role of the emerging service orientation, value co‐creation and the opportunities provided by new technology and communication channels. Drawing on a thorough CRM literature review, we argue that a fundamental change in CRM thinking is needed...


title: Image Classification Based on Hierarchical Temporal Memory and Color Features

abstract: The research in the domain of content-based image retrieval (CBIR) is concentrated on several problems, among which two problems are essential: - development of efficient image classification algorithms, and selection of suitable image features. Recently, a biologically inspired Hierarchical Temporal Memory (HTM) network demonstrated promising results in image classification tasks. The focus of this paper is to explore possibilities of this network to be applied to CBIR. In particular, we study the performance of the HTM network when, instead of conventional grey-level images and features, color features are used. The results of our experiments show that using color texture features, defined for a reduced range of color quantization values, performs comparably well as the grey-scale image features.


title: Examining the Peak-End Effects of Subjective Experience

abstract: Psychological research has shown that 'peak-end' effects influence people's retrospective evaluation of hedonic and affective experience. Rather than objectively reviewing the total amount of pleasure or pain during an experience, people's evaluation is shaped by the most intense moment (the peak) and the final moment (end). We describe an experiment demonstrating that peak-end effects can influence a user's preference for interaction sequences that are objectively identical in their overall requirements. Participants were asked to choose which of two interactive sequences of five pages they preferred. Both sequences required setting a total of 25 sliders to target values, and differed only in the distribution of the sliders across the five pages -- with one sequence intended to induce positive peak-end effects, the other negative. The study found that manipulating only the peak or the end of the series did not significantly change preference, but that a combined manipulation of both peak and end did lead to significant differences in preference, even though all series had the same overall effort.


title: Accelerating Dynamic Time Warping Clustering with a Novel Admissible Pruning Strategy

abstract: Clustering time series is a useful operation in its own right, and an important subroutine in many higher-level data mining analyses, including data editing for classifiers, summarization, and outlier detection. While it has been noted that the general superiority of Dynamic Time Warping (DTW) over Euclidean Distance for similarity search diminishes as we consider ever larger datasets, as we shall show, the same is not true for clustering. Thus, clustering time series under DTW remains a computationally challenging task. In this work, we address this lethargy in two ways. We propose a novel pruning strategy that exploits both upper and lower bounds to prune off a large fraction of the expensive distance calculations. This pruning strategy is admissible; giving us provably identical results to the brute force algorithm, but is at least an order of magnitude faster. For datasets where even this level of speedup is inadequate, we show that we can use a simple heuristic to order the unavoidable calculations in a most-useful-first ordering, thus casting the clustering as an anytime algorithm. We demonstrate the utility of our ideas with both single and multidimensional case studies in the domains of astronomy, speech physiology, medicine and entomology.


title: String Similarity Search: A Hash-Based Approach

abstract: String similarity search is a fundamental query that has been widely used for DNA sequencing, error-tolerant query auto-completion, and data cleaning needed in database, data warehouse, and data mining. In this paper, we study string similarity search based on edit distance that is supported by many database management systems such as Oracle and PostgreSQL . Given the edit distance, ${\mathsf {ed}} (s,t)$ , between two strings, $s$ and $t$ , the string similarity search is to find every string $t$ in a string database $D$ which is similar to a query string $s$ such that ${\mathsf {ed}} (s, t) \leq \tau$ for a given threshold $\tau$ . In the literature, most existing work takes a filter-and-verify approach, where the filter step is introduced to reduce the high verification cost of two strings by utilizing an index built offline for $D$ . The two up-to-date approaches are prefix filtering and local filtering. In this paper, we study string similarity search where strings can be either short or long. Our approach can support long strings, which are not well supported by the existing approaches due to the size of the index built and the time to build such index. We propose two new hash-based labeling techniques, named $\mathsf {OX}$ label and $\mathsf {XX}$ label, for string similarity search. We assign a hash-label, ${\mathsf {H}} _s$ , to a string $s$ , and prune the dissimilar strings by comparing two hash-labels, ${\mathsf {H}} _s$ and ${\mathsf {H}} _t$ , for two strings $s$ and $t$ in the filter step. The key idea is to take the dissimilar bit-patterns between two hash-labels. We discuss our hash-based approaches, address their pruning power, and give the algorithms. Our hash-based approaches achieve high efficiency, and keep its index size and index construction time one order of magnitude smaller than the existing approaches in our experiment at the same time.


title: A New Solution for N-Queens Problem using Blind Approaches: DFS and BFS Algorithms

abstract: The N×N queen’s puzzle is the problem of placing N chess queen on an N×N chess board so that no two queens attack each other. This approach is a classical problem in the artificial intelligence area. A solution requires that no two queens share the same row, column or diagonal. These problems for computer scientists present practical solution to many useful applications and have become an important issue. In this paper we proposed new resolution for solving nQueens used combination of depth firs search (DFS) and breathe first search (BFS) techniques. The proposed algorithm act based on placing queens on chess board directly. This is possible by regular pattern on the basis of the work law of minister. The results show that performance and run time in this approach better then back tracking methods and hill climbing modes.


title: Designing kinetic interactions for organic user interfaces

abstract: Composite building structures, such as foam core panels or the like. The panels comprise one or more foam core sections, preferably cores which include fire retardant additives. The exterior sheathing for the foam cores is a multiple laminate containing an inorganic filler material, such as glass fibers or ground carbonaceous or siliceous material; the filler is bound in place by a fire retardant, furan-based resin. The method includes forming a plurality of layers by depositing the resin and the filler alternately in a mold, thereafter placing boards or like core structures thereover and covering the exposed surfaces by multi-ply laminates of the fire retardant furan-based resin containing the glass fiber, carbonaceous or siliceous fillers. The method is also applicable to cover existing foam core installations used in building structures.


Trade negotiations and the new international monetary system


title: Developing Model-Based Reasoning in Mathematics and Science

abstract: Abstract It is essential to base instruction on a foundation of understanding of children's thinking, but it is equally important to adopt the longer-term view that is needed to stretch these early competencies into forms of thinking that are complex, multifaceted, and subject to development over years, rather than weeks or months. We pursue this topic through our studies of model-based reasoning. We have identified four forms of models and related modeling practices that show promise for developing model-based reasoning. Models have the fortuitous feature of making forms of student reasoning public and inspectable—not only among the community of modelers, but also to teachers. Modeling provides feedback about student thinking that can guide teaching decisions, an important dividend for improving professional practice.


title: A robust fault modeling and prediction technique for heterogeneous network

abstract: This paper proposes a modelling technique for fault identification in a heterogeneous network. Heterogeneous network is the integration of wired network, cellular network, wireless local area network (WLAN), and Adhoc network, etc. There may be several numbers of causes and sub-causes of fault in each type of network. These causes of fault must be mitigated in order to have a secure and reliable system. The paper introduces a model based approach that starts with the enumeration of possible causes of each fault over the heterogeneous network. A framework is then created by such causes which diagrammatically describe the causes and sub-causes responsible for the occurrence of a fault. This paper suggests a conceptual fault cause tree model based on the probabilistic framework for ranking the faults and their possible causes. An effective mitigation strategy is required to mitigate the causes and sub-causes. Once mitigation of the cause creating a fault is achieved, the system is analyzed and tested for accuracy and reliability. The Proposed technique assures that all sub-causes even at the lowest level of abstraction is taken into consideration in making the system more robust against the particular fault. The proposed model is also used to analyze the faulty probability of a heterogeneous network.


title: Effect of complementary triangular split ring resonator on microstrip patch antenna

abstract: In this paper, a new complementary triangular split ring resonator (CTSRR) is proposed and the impact of CTSRR on conventional patch antenna is investigated at 2.44 GHz for WLAN applications. The electromagnetic behavior of CTSRR, as well as their equivalent circuit is developed and explained. The demonstrated theoretical approaches result in a meaningful improvement than in comparison to conventional patch antenna. The parametric studies are completed in this work with the different orientation pattern of CTSRR structure. This is exposed that with the increment of CTSRR on patch, antenna performances improve, but resonance frequency shifts toward lower frequency region.


What’s trending now@ #organizational research methods


title: A Deep Neural Network Approach for Sentence Boundary Detection in Broadcast News

abstract: This paper presents a deep neural network (DNN) approach to sentence boundary detection in broadcast news. We extract prosodic and lexical features at each inter-word position in the transcripts and learn a sequential classifier to label these positions as either boundary or non-boundary. This work is realized by a hybrid DNN-CRF (conditional random field) architecture. The DNN accepts prosodic feature inputs and non-linearly maps them into boundary/non-boundary posterior probability outputs. Subsequently, the posterior probabilities are combined with lexical features and the integrated features are modeled by a linear-chain CRF. The CRF finally labels the inter-word positions as boundary or non-boundary by Viterbi decoding. Experiments show that, as compared with the state-of-the-art DTCRF approach [1], the proposed DNN-CRF approach achieves 16.7% and 4.1% reduction in NIST boundary detection error in reference and speech recognition transcripts, respectively.


title: An overview of MIMO communications - a key to gigabit wireless

abstract: High data rate wireless communications, nearing 1 Gb/s transmission rates, is of interest in emerging wireless local area networks and home audio/visual networks. Designing very high speed wireless links that offer good quality-of-service and range capability in non-line-of-sight (NLOS) environments constitutes a significant research and engineering challenge. Ignoring fading in NLOS environments, we can, in principle, meet the 1 Gb/s data rate requirement with a single-transmit single-receive antenna wireless system if the product of bandwidth (measured in hertz) and spectral efficiency (measured in bits per second per hertz) is equal to 10/sup 9/. A variety of cost, technology and regulatory constraints make such a brute force solution unattractive, if not impossible. The use of multiple antennas at transmitter and receiver, popularly known as multiple-input multiple-output (MIMO) wireless, is an emerging cost-effective technology that offers substantial leverages in making 1 Gb/s wireless links a reality. The paper provides an overview of MIMO wireless technology covering channel models, performance limits, coding, and transceiver design.


title: Perceptual learning style and learning proficiency: A test of the hypothesis

abstract: Given the potential importance of using modality preference with instruction, the authors tested whether learning style preference correlated with memory performance in each of 3 sensory modalities: visual, auditory, and kinesthetic. In Study 1, participants completed objective measures of pictorial, auditory, and tactile learning and learning style self-assessments. The results indicate that objective test performance did not correlate with learning style preference. In Study 2, the authors examined in more detail the information participants used to answer the learning style self-assessment. The findings indicate that participants answered the inventory using general memories and beliefs rather than specific examples of learning in different modalities. These results challenge the hypothesis that individuals learn best with material presented in a particular sensory modality.


title: Smiley face: why we use emoticon stickers in mobile messaging

abstract: As mobile instant messaging has become a major means of communication with the widespread use of smartphones, emoticons, symbols that are meant to indicate particular emotions in instant messages, have also developed into various forms. The primary purpose of this study is to classify the usage patterns of emoticons focusing on a particular variant known as "stickers" to observe individual and social characteristics of emoticon use and reinterpret the meaning of emoticons in instant messages. A qualitative approach with an in-depth semi-structured interview was used to uncover the motive in using emoticon stickers. The study suggests that besides using emoticon stickers for expressing emotions, users may have other motives: strategic and functional purposes.


title: A Proposal for Semantic Map Representation and Evaluation

abstract: Semantic mapping is the incremental process of “mapping” relevant information of the world (i.e., spatial information, temporal events, agents and actions) to a formal description supported by a reasoning engine. Current research focuses on learning the semantic of environments based on their spatial location, geometry and appearance. Many methods to tackle this problem have been proposed, but the lack of a uniform representation, as well as standard benchmarking suites, prevents their direct comparison. In this paper, we propose a standardization in the representation of semantic maps, by defining an easily extensible formalism to be used on top of metric maps of the environments. Based on this, we describe the procedure to build a dataset (based on real sensor data) for benchmarking semantic mapping techniques, also hypothesizing some possible evaluation metrics. Nevertheless, by providing a tool for the construction of a semantic map ground truth, we aim at the contribution of the scientific community in acquiring data for populating the dataset.


title: Shape from focus system

abstract: A shape-from-focus method that uses different focus levels to obtain a sequence of object images is described. A sum-modified-Laplacian operator is developed to provide local measures of the quality of image focus. The operator is applied to the sequence of images of the object to determine a set of focus measures as each image point. A model is developed to describe the variation of focus measure values due to defocusing. This model is used by a depth estimation algorithm to interpolate focus measure values and obtain accurate depth estimates. A fully automated system that has been implemented using an optical microscope and tested on a variety of industrial samples is described. >


title: MVAPICH2 over OpenStack with SR-IOV: An Efficient Approach to Build HPC Clouds

abstract: Cloud Computing with Virtualization offers attractive flexibility and elasticity to deliver resources by providing a platform for consolidating complex IT resources in a scalable manner. However, efficiently running HPC applications on Cloud Computing systems is still full of challenges. One of the biggest hurdles in building efficient HPC clouds is the unsatisfactory performance offered by underlying virtualized environments, more specifically, virtualized I/O devices. Recently, Single Root I/O Virtualization (SR-IOV) technology has been steadily gaining momentum for high-performance interconnects such as InfiniBand and 10GigE. Due to its near native performance for inter-node communication, many cloud systems such as Amazon EC2 have been using SR-IOV in their production environments. Nevertheless, recent studies have shown that the SR-IOV scheme lacks locality aware communication support, which leads to performance overheads for inter-VM communication within the same physical node. In this paper, we propose an efficient approach to build HPC clouds based on MVAPICH2 over Open Stack with SR-IOV. We first propose an extension for Open Stack Nova system to enable the IV Shmem channel in deployed virtual machines. We further present and discuss our high-performance design of virtual machine aware MVAPICH2 library over Open Stack-based HPC Clouds. Our design can fully take advantage of high-performance SR-IOV communication for inter-node communication as well as Inter-VM Shmem (IVShmem) for intra-node communication. A comprehensive performance evaluation with micro-benchmarks and HPC applications has been conducted on an experimental Open Stack-based HPC cloud and Amazon EC2. The evaluation results on the experimental HPC cloud show that our design and extension can deliver near bare-metal performance for implementing SR-IOV-based HPC clouds with virtualization. Further, compared with the performance on EC2, our experimental HPC cloud can exhibit up to 160X, 65X, 12X improvement potential in terms of point-to-point, collective and application for future HPC clouds.


title: Differential Privacy with Bounded Priors: Reconciling Utility and Privacy in Genome-Wide Association Studies

abstract: Differential privacy (DP) has become widely accepted as a rigorous definition of data privacy, with stronger privacy guarantees than traditional statistical methods. However, recent studies have shown that for reasonable privacy budgets, differential privacy significantly affects the expected utility. Many alternative privacy notions which aim at relaxing DP have since been proposed, with the hope of providing a better tradeoff between privacy and utility. At CCS'13, Li et al. introduced the membership privacy framework, wherein they aim at protecting against set membership disclosure by adversaries whose prior knowledge is captured by a family of probability distributions. In the context of this framework, we investigate a relaxation of DP, by considering prior distributions that capture more reasonable amounts of background knowledge. We show that for different privacy budgets, DP can be used to achieve membership privacy for various adversarial settings, thus leading to an interesting tradeoff between privacy guarantees and utility. We re-evaluate methods for releasing differentially private chi2-statistics in genome-wide association studies and show that we can achieve a higher utility than in previous works, while still guaranteeing membership privacy in a relevant adversarial setting.


title: Question Classification in Question Answering Systems

abstract: Question answering systems can be seen as the next step in information retrieval, allowing users to pose questions in natural language and receive succinct answers. In order for a question answerin ...


title: Almost 2-SAT Is Fixed-Parameter Tractable (Extended Abstract)

abstract: We consider the following problem. Given a 2-cnf formula, is it possible to remove at most kclauses so that the resulting 2-cnf formula is satisfiable? This problem is known to different research communities in theoretical computer science under the names Almost 2-SAT, All-but-k2-SAT, 2-cnf deletion, and 2-SAT deletion. The status of the fixed-parameter tractability of this problem is a long-standing open question in the area of parameterized complexity. We resolve this open question by proposing an algorithm that solves this problem in O(15k*k*m3) time showing that this problem is fixed-parameter tractable.


title: Towards a multi-DOF passive balancing mechanism for upper limbs

abstract: Gravity balancing has great significance in many rehabilitation applications and one example is in minimizing the resistance of lifting a patient's upper limb due to its own weight. As compared to commonly used active method in exoskeleton, passive weight compensation offers many other advantages such as elimination of motors which reduces the overall load acting on the patient. However, the torque about each joint due to the weight of the upper and fore arm is dependent on the orientation and location of each other, which makes it a challenge to compensate. Hence, this paper proposes and develop a passive method to compensate the torques on each joint. The proposed design includes a decoupling mechanism for isolating the torsional effect between various linkages and a realizable torsional compliant beam which can provide a specific torsional stiffness function to compensate the torque due to the arm's weight. In place of the usual expensive zero-length spring as the solution, an algorithm that generates the appropriate compliant beam design is also presented in this paper. The end result of the manipulator arm is a reduced required joint torque for gravity compensation using only passive means. A two-DOF prototype, with easy possible extension of additional DOF, has been built, and experiments are performed to illustrate, verify and quantify the effectiveness of the proposed method.


Trusted Machine Learning: Model Repair and Data Repair for Probabilistic Models.


title: A framework for MAC protocol misbehavior detection in wireless networks

abstract: The pervasiveness of wireless devices and the architectural organization of wireless networks in distributed communities, where no notion of trust can be assumed, are the main reasons for the growing interest in the issue of compliance to protocol rules. Reliable and timely detection of deviation from legitimate protocol operation is recognized as a prerequisite for ensuring efficient and fair use of network resources and minimizing performance losses. Nevertheless, the random nature of protocol operation together with the inherent difficulty of monitoring in the open and highly volatile wireless medium poses significant challenges. In this paper, we consider the fundamental problem of detection of node misbehavior at the MAC layer. Starting from a model where the behavior of a node is observable, we cast the problem within a minimax robust detection framework, with the objective to provide a detection rule of optimum performance for the worst-case attack. The performance is measured in terms of required number of observations in order to derive a decision. This framework is meaningful for studying misbehavior because it captures the presence of uncertainty of attacks and concentrates on the attacks that are most significant in terms of incurred performance losses. It also refers to the case of an intelligent attacker that can adapt its policy to avoid being detected. Although the basic model does not include interference, we show that our ideas can be extended to the case where observations are hindered by interference due to concurrent transmissions. We also present some hints for the problem of notifying the rest of the network about a misbehavior event. Our work provides interesting insights and performance bounds and serves as a prelude to a future study that would capture more composite instances of the problem.


title: Evaluating contour segment descriptors

abstract: Contour segment (CS) is the fundamental element of partial boundaries or edges in shapes and images. So far, CS has been widely used in many applications, including object detection/matching and open curve matching. To increase the matching accuracy and efficiency, a variety of CS descriptors have been proposed. A CS descriptor is formed by a chain of boundary or edge points and is able to encode the geometric configuration of a CS. Because many different CS descriptors exist, a structured overview and quantitative evaluation are required in the context of CS matching. This paper assesses 27 CS descriptors in a structured way. Firstly, the analytical invariance properties of CS descriptors are explored with respect to scaling, rotation and transformation. Secondly, their distinctiveness is evaluated experimentally on three datasets. Lastly, their computation complexity is studied. Based on results, we find that both CS lengths and matching algorithms affect the CS matching performance while matching algorithms have higher affection. The results also reveal that, with different combinations of CS descriptors and matching algorithms, several requirements in terms of matching speed and accuracy can be fulfilled. Furthermore, a proper combination of CS descriptors can improve the matching accuracy over the individuals.


title: A review of unsupervised feature learning and deep learning for time-series modeling

abstract: This paper gives a review of the recent developments in deep learning and unsupervised feature learning for time-series problems. While these techniques have shown promise for modeling static data, such as computer vision, applying them to time-series data is gaining increasing attention. This paper overviews the particular challenges present in time-series data and provides a review of the works that have either applied time-series data to unsupervised feature learning algorithms or alternatively have contributed to modifications of feature learning algorithms to take into account the challenges present in time-series data.


title: The thoracolumbar fascia: anatomy, function and clinical considerations

abstract: In this overview, new and existent material on the organization and composition of the thoracolumbar fascia (TLF) will be evaluated in respect to its anatomy, innervation biomechanics and clinical relevance. The integration of the passive connective tissues of the TLF and active muscular structures surrounding this structure are discussed, and the relevance of their mutual interactions in relation to low back and pelvic pain reviewed. The TLF is a girdling structure consisting of several aponeurotic and fascial layers that separates the paraspinal muscles from the muscles of the posterior abdominal wall. The superficial lamina of the posterior layer of the TLF (PLF) is dominated by the aponeuroses of the latissimus dorsi and the serratus posterior inferior. The deeper lamina of the PLF forms an encapsulating retinacular sheath around the paraspinal muscles. The middle layer of the TLF (MLF) appears to derive from an intermuscular septum that developmentally separates the epaxial from the hypaxial musculature. This septum forms during the fifth and sixth weeks of gestation. The paraspinal retinacular sheath (PRS) is in a key position to act as a ‘hydraulic amplifier’, assisting the paraspinal muscles in supporting the lumbosacral spine. This sheath forms a lumbar interfascial triangle (LIFT) with the MLF and PLF. Along the lateral border of the PRS, a raphe forms where the sheath meets the aponeurosis of the transversus abdominis. This lateral raphe is a thickened complex of dense connective tissue marked by the presence of the LIFT, and represents the junction of the hypaxial myofascial compartment (the abdominal muscles) with the paraspinal sheath of the epaxial muscles. The lateral raphe is in a position to distribute tension from the surrounding hypaxial and extremity muscles into the layers of the TLF. At the base of the lumbar spine all of the layers of the TLF fuse together into a thick composite that attaches firmly to the posterior superior iliac spine and the sacrotuberous ligament. This thoracolumbar composite (TLC) is in a position to assist in maintaining the integrity of the lower lumbar spine and the sacroiliac joint. The three-dimensional structure of the TLF and its caudally positioned composite will be analyzed in light of recent studies concerning the cellular organization of fascia, as well as its innervation. Finally, the concept of a TLC will be used to reassess biomechanical models of lumbopelvic stability, static posture and movement.


title: Sex differences in intrinsic aptitude for mathematics and science? A critical review

abstract: This article considers 3 claims that cognitive sex differences account for the differential representation of men and women in high-level careers in mathematics and science: (a) males are more focused on objects from the beginning of life and therefore are predisposed to better learning about mechanical systems; (b) males have a profile of spatial and numerical abilities producing greater aptitude for mathematics; and (c) males are more variable in their cognitive abilities and therefore predominate at the upper reaches of mathematical talent. Research on cognitive development in human infants, preschool children, and students at all levels fails to support these claims. Instead, it provides evidence that mathematical and scientific reasoning develop from a set of biologically based cognitive capacities that males and females share. These capacities lead men and women to develop equal talent for mathematics and science.


5 Categorization Theory and Research in Consumer Psychology Category Representation and Category-Based Inference


title: Robust Convolutional Neural Networks for Image Recognition

abstract: Recently image recognition becomes vital task using several methods. One of the most interesting used methods is using Convolutional Neural Network (CNN). It is widely used for this purpose. However, since there are some tasks that have small features that are considered an essential part of a task, then classification using CNN is not efficient because most of those features diminish before reaching the final stage of classification. In this work, analyzing and exploring essential parameters that can influence model performance. Furthermore different elegant prior contemporary models are recruited to introduce new leveraging model. Finally, a new CNN architecture is proposed which achieves state-of-the-art classification results on the different challenge benchmarks. The experimented are conducted on MNIST, CIFAR-10, and CIFAR-100 datasets. Experimental results showed that the results outperform and achieve superior results comparing to the most contemporary approaches.


title: Design of Planar Artificial Magnetic Conductor Ground Plane Using Frequency-Selective Surfaces for Frequencies Below 1 GHz

abstract: A novel design of planar artificial magnetic conductor (AMC) based on a frequency-selective surface (FSS) for frequencies below 1 GHz is presented. A discussion about design parameters and the influence of dielectric substrate permittivity in the operating band and the operating bandwidth is presented. The discussion is supported by a finite element method (FEM) simulation.


title: Enabling reliable and secure IoT-based smart city applications

abstract: Smart Cities are considered recently as a promising solution for providing efficient services to citizens with the use of Information and Communication Technologies. With the latest advances on the Internet of Things, a new era has emerged in the Smart City domain, opening new opportunities for the development of efficient and low-cost applications that aim to improve the Quality of Life in cities. Although there is much research in this area, which has resulted in the development of many commercial products, significant parameters like reliability, security and privacy have not been considered as very important up until now. The newly launched FP7-SmartCities-2013 project RERUM aims to build upon the advances in the area of Internet of Things in Smart Cities and develop a framework to enhance reliability and security of smart city applications, with the citizen at the center of attention. This work presents four applications that will be developed within RERUM, gives a general description of the open reliability and security issues that have to be taken into account and gives an overall view of the solutions that RERUM will develop to address these issues.


title: A stable target-tracking control for unicycle mobile robots

abstract: This paper deals with target tracking control of unicycle type mobile robots. Target tracking function is essential for autonomous robots such as guide robots, security guard robots, etc. In the field of mobile robot control, many control schemes for posture stabilization and trajectory tracking problem have been proposed. Target tracking control, however, cannot be achieved using these kinds of control laws. Therefore, a new global asymptotic stable controller for this problem is designed using the backstepping method. The stability of the system is proved using the Lyapunov function. Various simulation results validate the performance and theoretical analysis.


title: WiDraw: Enabling Hands-free Drawing in the Air on Commodity WiFi Devices

abstract: This paper demonstrates that it is possible to leverage WiFi signals from commodity mobile devices to enable hands-free drawing in the air. While prior solutions require the user to hold a wireless transmitter, or require custom wireless hardware, or can only determine a pre-defined set of hand gestures, this paper introduces WiDraw, the first hand motion tracking system using commodity WiFi cards, and without any user wearables. WiDraw harnesses the Angle-of-Arrival values of incoming wireless signals at the mobile device to track the user's hand trajectory. We utilize the intuition that whenever the user's hand occludes a signal coming from a certain direction, the signal strength of the angle representing the same direction will experience a drop. Our software prototype using commodity wireless cards can track the user's hand with a median error lower than 5 cm. We use WiDraw to implement an in-air handwriting application that allows the user to draw letters, words, and sentences, and achieves a mean word recognition accuracy of 91%.


title: Analysis and Design of an Ultrabroadband Stacked Power Amplifier in CMOS Technology

abstract: This brief presents the analysis and design of a two-stage stacked power amplifier (PA) with very broadband gain frequency response and power performance in a small chip size. The broadband load impedance match is realized using modified stacked field-effect transistors (FETs) with a resistive feedback by analyzing the matching condition of the source input impedance of the stacked FET. In order to further improve the broadband gain frequency response, the effectiveness of a gain expansion from a stacked driver amplifier is demonstrated to compensate the gain compression of the last-stage amplifier. To verify the design concept, a two-stage three-stacked PA has been implemented in a 0.18- $\mu\mbox{m}$ CMOS technology. The PA achieves a saturated output power of 22–24.3 dBm and a power added efficiency of 13%–20% within a 194% fractional bandwidth from 0.1 to 6.5 GHz. It also demonstrates better than 11-dB input return loss (RL) and better than 5.1-dB output RL. This PA occupies a chip size of 0.64 mm2 including pads.


title: SmartCast: An Incentive Compatible Consensus Protocol Using Smart Contracts

abstract: Motivated by the desire for high-throughput public databases (i.e., “blockchains”), we design incentive compatible protocols that run “off-chain”, but rely on an existing cryptocurrency to implement a reward and/or punishment mechanism. Our protocols are incentive compatible in the sense that behaving honestly is a weak Nash equilibrium, even in spite of potentially malicious behavior from a small fraction of the participants (i.e., the BAR model from Clement et al. [7]). To show the feasibility of our approach, we build a prototype implementation, called SmartCast, comprising an Ethereum smart contract, and an off-chain consensus protocol based on Dolev-Strong [10]. SmartCast also includes a “marketplace” smart contract that randomly assigns workers to protocol instances. We evaluate the communication costs of our system, as well as the “gas” transaction costs that are involved in running the Ethereum smart contract.


title: On the Efficiency of Image Metrics for Evaluating the Visual Quality of 3D Models

abstract: 3D meshes are deployed in a wide range of application processes (e.g., transmission, compression, simplification, watermarking and so on) which inevitably introduce geometric distortions that may alter the visual quality of the rendered data. Hence, efficient model-based perceptual metrics, operating on the geometry of the meshes being compared, have been recently introduced to control and predict these visual artifacts. However, since the 3D models are ultimately visualized on 2D screens, it seems legitimate to use images of the models (i.e., snapshots from different viewpoints) to evaluate their visual fidelity. In this work we investigate the use of image metrics to assess the visual quality of 3D models. For this goal, we conduct a wide-ranging study involving several 2D metrics, rendering algorithms, lighting conditions and pooling algorithms, as well as several mean opinion score databases. The collected data allow (1) to determine the best set of parameters to use for this image-based quality assessment approach and (2) to compare this approach to the best performing model-based metrics and determine for which use-case they are respectively adapted. We conclude by exploring several applications that illustrate the benefits of image-based quality assessment.


title: Phased array systems for air, land and naval defence applications in Selex ES

abstract: After a brief recall of 3D Long Range phased array concepts, the focus is on the G-band multifunction radar systems and on the technological capabilities that the Land&Naval Division of Selex ES is developing by taking advantage of its Research and Development facilities. At the end, future trends about new challenging applications and technological issues are presented and discussed.


Light Linear Logic


title: Attribute bagging : improving accuracy of classi # er ensembles by using random feature subsets

abstract: We present attribute bagging (AB), a technique for improving the accuracy and stability of classifier ensembles induced using random subsets of features. AB is a wrapper method that can be used with any learning algorithm. It establishes an appropriate attribute subset size and then randomly selects subsets of features, creating projections of the training set on which the ensemble classifiers are built. The induced classifiers are then used for voting. This article compares the performance of our AB method with bagging and other algorithms on a hand-pose recognition dataset. It is shown that AB gives consistently better results than bagging, both in accuracy and stability. The performance of ensemble voting in bagging and the AB method as a function of the attribute subset size and the number of voters for both weighted and unweighted voting is tested and discussed. We also demonstrate that ranking the attribute subsets by their classification accuracy and voting using only the best subsets further improves the resulting performance of the ensemble.


title: Agents that Learn to Explain Themselves

abstract: Intelligent artificial agents need to be able to explain and justify their actions. They must therefore understand the rationales for their own actions. This paper describes a technique for acquiring this understanding, implemented in a multimedia explanation system. The system determines the motivation for a decision by recalling the situation in which the decision was made, and replaying the decision under variants of the original situation. Through experimentation the agent is able to discover what factors led to the decisions, and what alternatives might have been chosen had the situation been slightly different. The agent learns to recognize similar situations where the same decision would be made for the same reasons. This approach is implemented in an artificial fighter pilot that can explain the motivations for its actions, situation assessments, and beliefs.


title: Seeing without knowing: Limitations of the transparency ideal and its application to algorithmic accountability:

abstract: Models for understanding and holding systems accountable have long rested upon ideals and logics of transparency. Being able to see a system is sometimes equated with being able to know how it work...


title: Model of the Ankyrin and SOCS Box Protein, ASB9, E3 Ligase Reveals a Mechanism for Dynamic Ubiquitin Transfer

abstract: Cullin-RING E3 ligases (CRLs) are elongated and bowed protein complexes that transfer ubiquitin over 60 A to proteins targeted for proteasome degradation. One such CRL contains the ankyrin repeat and SOCS box protein 9 (ASB9), which binds to and partially inhibits creatine kinase (CK). While current models for the ASB9-CK complex contain some known interface residues, the overall structure and precise interface of the ASB9-CK complex remains unknown. Through an integrative modeling approach, we report a third-generation model that reveals precisely the interface interactions and also fits the shape of the ASB9-CK complex as determined by small-angle X-ray scattering. We constructed an atomic model for the entire CK-targeting CRL to uncover dominant modes of motion that could permit ubiquitin transfer. Remarkably, only the correctly docked CK-containing E3 ligase and not incorrectly docked structures permitted close approach of ubiquitin to the CK substrate.


title: L1-Regularized Multiway Canonical Correlation Analysis for SSVEP-Based BCI

abstract: Canonical correlation analysis (CCA) between recorded electroencephalogram (EEG) and designed reference signals of sine-cosine waves usually works well for steady-state visual evoked potential (SSVEP) recognition in brain-computer interface (BCI) application. However, using the reference signals of sine- cosine waves without subject-specific and inter-trial information can hardly give the optimal recognition accuracy, due to possible overfitting, especially within a short time window length. This paper introduces an L1-regularized multiway canonical correlation analysis (L1-MCCA) for reference signal optimization to improve the SSVEP recognition performance further. A multiway extension of the CCA, called MCCA, is first presented, in which collaborative CCAs are exploited to optimize the reference signals in correlation analysis for SSVEP recognition alternatingly from the channel-way and trial-way arrays of constructed EEG tensor. L1-regularization is subsequently imposed on the trial-way array optimization in the MCCA, and hence results in the more powerful L1-MCCA with function of effective trial selection. Both the proposed MCCA and L1-MCCA methods are validated for SSVEP recognition with EEG data from 10 healthy subjects, and compared to the ordinary CCA without reference signal optimization. Experimental results show that the MCCA significantly outperforms the CCA for SSVEP recognition. The L1-MCCA further improves the recognition accuracy which is significantly higher than that of the MCCA.


title: Neurogaming Technology Meets Neuroscience Education: A Cost-Effective, Scalable, and Highly Portable Undergraduate Teaching Laboratory for Neuroscience.

abstract: Active research-driven approaches that successfully incorporate new technology are known to catalyze student learning. Yet achieving these objectives in neuroscience education is especially challenging due to the prohibitive costs and technical demands of research-grade equipment. Here we describe a method that circumvents these factors by leveraging consumer EEG-based neurogaming technology to create an affordable, scalable, and highly portable teaching laboratory for undergraduate courses in neuroscience. This laboratory is designed to give students hands-on research experience, consolidate their understanding of key neuroscience concepts, and provide a unique real-time window into the working brain. Survey results demonstrate that students found the lab sessions engaging. Students also reported the labs enhanced their knowledge about EEG, their course material, and neuroscience research in general.


title: A 98.55% Efficiency Switched-Tank Converter for Data Center Application

abstract: Achieving high efficiency and high power density features of intermediate bus converter (IBC) in data center application is very critical. This paper proposes a nonisolated switched-tank dc–dc converter (STC) to meet the high requirement of IBC. The proposed STC has fixed voltage conversion ratio. Theoretical analysis provides the guideline for designing resonant tank and nonresonant tank as well as selecting proper deadtime for the proposed circuit. With resonant operation feature of the proposed circuit, zero current switching is realized on all switching devices to help the high-efficiency operation of the proposed converter. Through the proposed design methodology in this paper, power loss on switching devices and planar inductors can be minimized. In addition, detailed power loss analysis and breakdown provide hints for further efficiency improvement. Simulation has been performed to validate the operating principle of the proposed STC. A designed 6x STC (450 W nominal/600 W maximum) based on eGaNFETs is demonstrated. With 54 V typical input voltage, the output of the 6x STC is 9 V. The GaN-based prototype achieves a peak efficiency of 98.55%. Also, it achieves 97.18% efficiency and 750 W/in3 power density at 450 W.


title: A Survey of Textual Emotion Detection

abstract: Emotion detection in text is a research field that gained an extensive interest recently. The advancement in communication networks and the spread usage of social web is the reason of massive amount of emotions that can be extracted frequently. The extraction and analysis of such emotions provides a powerful tool in detecting and recognizing the feelings, and this provides many advantages in diverse areas. In this survey, we focused on the textual emotion detection as a task of sentiment analysis. This survey explored the latest state of art approaches for emotion detection in text, and discussed their classification according to the used techniques, the used emotional model and the different used datasets. The outcome of this paper tends to highlight the limitation and gaps of these recent works and direct the possible future research to fulfill these gaps in this increasingly developed field.


title: Analysis of two Pit-1 gene polymorphisms: Single nucleotide polymorphisms (SNPs) distribution patterns in Podolica cattle breed

abstract: Pit-1 is a pituitary-specific transcription factor responsible for pituitary development and hormone expression in mammals. Pit-1 is a member of the POU domain containing proteins, a group of transcriptional regulators with a critical role in cell differentiation and proliferation. It was shown that this group of proteins control the transcription of the growth hormone (GH), the prolactin (PRL), the thyroid-stimulating hormone �-subunit (TSH-�), the GHRH receptor genes and the Pit-1 gene itself. In this study, the Pit-1/HinfI and Pit-1/TaqI loci were investigated using PCR-RFLP approach in a sample of 104 Podolica cattle. All the possible genotypes for both single nucleotide polymorphisms (SNPs) were identified. The allelic frequencies at Pit-1/TaqI locus were 0.76 (G) and 0.24 (A), while those at Pit-1/HinfI locus were 0.70 (B) and 0.30 (A). Combined genotypic frequencies and possible haplotypes frequencies were also reported. Moreover, some population genetic indexes, namely: gene heterozygosity (He), gene homozygosity (Ho), effective allele numbers (Ne), fixation index (FIS) and polymorphism information content (PIC) were calculated.


title: Deep3: Leveraging Three Levels of Parallelism for Efficient Deep Learning

abstract: This paper proposes Deep3, an automated platform-aware Deep Learning (DL) framework that brings orders of magnitude performance improvement to DL training and execution. Deep3 is the first to simultaneously leverage three levels of parallelism for performing DL: data, network, and hardware. It uses platform profiling to abstract physical characterizations of the target platform. The core of Deep3 is a new extensible methodology that enables incorporation of platform characteristics into the higher-level data and neural network transformation. We provide accompanying libraries to ensure automated customization and adaptation to different datasets and platforms. Proof-of-concept evaluations demonstrate 10-100 fold physical performance improvement compared to the state-of-the-art DL frameworks, e.g., TensorFlow.


title: Fasciola gigantica: anthelmintic effect of the aqueous extract of Artocarpus lakoocha.

abstract: Abstract The effect of the crude extract of Artocarpus lakoocha (70% composition is 2,4,3′,5′- tetrahydroxystilbene -THS) on adult Fasciola gigantica was evaluated after incubating the parasites in M-199 medium containing 250, 500, 750 and 1000 μg/ml of the crude extract, or triclabendazole (TCZ) at the concentrations of 80 and 175 μg/ml as the positive control, for 3, 6, 12 and 24 h, using relative motility (RM) assay and observation by scanning electron microscope (SEM). Decreased contraction and motility were first observed after 3 h incubation with TCZ at the concentration 80 and 175 μg/ml. TCZ markedly reduced the parasite’s motility at the concentration of 175 μg/ml at 6 h, and killed the worms after 12 h exposure. The crude extract of A. lakoocha at all concentrations reduced the parasite’s motility similar to TCZ at 3 h incubation. In 250 and 500 μg/ml of the crude extract, the values were decreased from 3 to 12 h, then they were stable between 12 and 24 h and reduced to the level approximately 30–40% of the control. At 750 and 1000 μg/ml concentrations the crude extract rapidly reduced the RM values from the start to 12 h and killed the parasites between 12 and 24 h incubation. The crude extract also inhibited the larval migration by 75% and 100% at the concentrations of 250–500 and 750–1000 μg/ml, respectively. TCZ and the crude extract caused sequentially changes in the tegument including swelling, followed by blebbings that later ruptured, leading to the erosion and desquamation of the tegument syncytium. As the result, lesion was formed which exposed the basal lamina. The damage appeared more severe on the dorsal than the ventral surface, and earlier on the anterior part and lateral margins when compared to the posterior part. The severity and rapidity of the damages were enhanced with increasing concentration of the crude extract. Hence, the crude extract of A. lakoocha , may exert its fasciolicidal effect against adult F. gigantica by initially causing the tegumental damage.


title: Attribute-Based Access Control Scheme in Federated IoT Platforms

abstract: The Internet of Things (IoT) introduced the possibility to connect electronic things from everyday life to the Internet, while making them ubiquitously available. With advanced IoT services, based on a trusted federation among heterogeneous IoT platforms, new security problems (including authentication and authorization) emerge. This contribution aims at describing the main facets of the preliminary security architecture envisaged in the context of the symbIoTe project, recently launched by European Commission under the Horizon 2020 EU program. Our approach features distributed and decoupled mechanisms for authentication and authorization services in complex scenarios embracing heterogeneous and federated IoT platforms, by leveraging Attribute Based Access Control and token-based authorization techniques.


title: Reproductive biology of Venus clam Gafrarium tumidum (Roding, 1798) from Southeast coast of India

abstract: The reproductive biology of tumid venus clamGafrarium ::: tumidum (Roding) was studied in detail from the ::: Southeast coast of India. Sample size ranged from ::: 10.3 to 41.3mm. Sexes are separate but not diierentiated ::: externally. Annual sex ratio male:female (M:F) ::: deviated signi¢cantly from the 1:1 ratio with females ::: dominating. Based on ova diameter progression of ::: gonad smear and histology, four and two maturity ::: stages of gonads were diierentiated in female and ::: male clams, respectively. Peak spawning was observed ::: during November and a minor one in April.


title: A keyed-hashing based self-synchronization mechanism for port address hopping communication

abstract: Port address hopping (PAH) communication is a powerful network moving target defense (MTD) mechanism. It was inspired by frequency hopping in wireless communications. One of the critical and difficult issues with PAH is synchronization. Existing schemes usually provide hops for each session lasting only a few seconds/minutes, making them easily influenced by network events such as transmission delays, traffic jams, packet dropouts, reordering, and retransmission. To address these problems, in this paper we propose a novel self-synchronization scheme, called ‘keyed-hashing based self-synchronization (KHSS)’. The proposed method generates the message authentication code (MAC) based on the hash based MAC (HMAC), which is then further used as the synchronization information for port address encoding and decoding. Providing the PAH communication system with one-packet-one-hopping and invisible message authentication abilities enables both clients and servers to constantly change their identities as well as perform message authentication over unreliable communication mediums without synchronization and authentication information transmissions. Theoretical analysis and simulation and experiment results show that the proposed method is effective in defending against man-in-the-middle (MITM) attacks and network scanning. It significantly outperforms existing schemes in terms of both security and hopping efficiency.


title: Design and analysis of switched-capacitor-based step-up resonant converters

abstract: A switched-capacitor-based step-up resonant converter is proposed. The voltage conversion of the converters is in step-up mode. By adding a different number of switched-capacitor cells, different output voltage conversion ratios can be obtained. The voltage conversion ratio from 2 to any whole number can therefore be generated by these switching-capacitor techniques. A resonant tank is used to assist in zero-current switching hence the current spike, which usually exists for classical switched-capacitor can be eliminated. Both high-frequency operations and high efficiency are possible. Generalized analysis and design method of the converters are also presented. Experimental results verified the theoretical analysis.


title: Requirements engineering as a success factor in software projects

abstract: Requirements engineering might be the single most important part of any software project. Based on their field study of 15 RE teams in nine software organizations, the authors identify the RE practices that clearly contribute to project success, particularly in terms of team knowledge, resource allocation, and process. They then summarize the best practices exhibited by the most successful teams.


title: A Bound on the Error of Cross Validation Using the Approximation and Estimation Rates, with Consequences for the Training-Test Split

abstract: We give a theoretical and experimental analysis of the generalization error of cross validation using two natural measures of the problem under consideration. The approximation rate measures the accuracy to which the target function can be ideally approximated as a function of the number of parameters, and thus captures the complexity of the target function with respect to the hypothesis model. The estimation rate measures the deviation between the training and generalization errors as a function of the number of parameters, and thus captures the extent to which the hypothesis model suffers from overfitting. Using these two measures, we give a rigorous and general bound on the error of the simplest form of cross validation. The bound clearly shows the dangers of making γ —the fraction of data saved for testing—too large or too small. By optimizing the bound with respect to γ, we then argue that the following qualitative properties of cross-validation behavior should be quite robust to significant changes ...


title: A learning algorithm for adaptive canonical correlation analysis of several data sets

abstract: Canonical correlation analysis (CCA) is a classical tool in statistical analysis to find the projections that maximize the correlation between two data sets. In this work we propose a generalization of CCA to several data sets, which is shown to be equivalent to the classical maximum variance (MAXVAR) generalization proposed by Kettenring. The reformulation of this generalization as a set of coupled least squares regression problems is exploited to develop a neural structure for CCA. In particular, the proposed CCA model is a two layer feedforward neural network with lateral connections in the output layer to achieve the simultaneous extraction of all the CCA eigenvectors through deflation. The CCA neural model is trained using a recursive least squares (RLS) algorithm. Finally, the convergence of the proposed learning rule is proved by means of stochastic approximation techniques and their performance is analyzed through simulations.


title: Improving a Credit Scoring Model by Incorporating Bank Statement Derived Features

abstract: In this paper, we investigate the extent to which features derived from bank statements provided by loan applicants, and which are not declared on an application form, can enhance a credit scoring model for a New Zealand lending company. Exploring the potential of such information to improve credit scoring models in this manner has not been studied previously. We construct a baseline model based solely on the existing scoring features obtained from the loan application form, and a second baseline model based solely on the new bank statement-derived features. A combined feature model is then created by augmenting the application form features with the new bank statement derived features. Our experimental results using ROC analysis show that a combined feature model performs better than both of the two baseline models, and show that a number of the bank statement-derived features have value in improving the credit scoring model. The target data set used for modelling was highly imbalanced, and Naive Bayes was found to be the best performing model, and outperformed a number of other classifiers commonly used in credit scoring, suggesting its potential for future use on highly imbalanced data sets.


title: K-means based segmentation for real-time zenithal people counting

abstract: The paper presents an efficient and reliable approach to automatic people segmentation, tracking and counting, designed for a system with an overhead mounted (zenithal) camera. Upon the initial block-wise background subtraction, k-means clustering is used to enable the segmentation of single persons in the scene. The number of people in the scene is estimated as the maximal number of clusters with acceptable inter-cluster separation. Tracking of segmented people is addressed as a problem of dynamic cluster assignment between two consecutive frames and it is solved in a greedy fashion. Systems for people counting are applied to people surveillance and management and lately within the ambient intelligence solutions. Experimental results suggest that the proposed method is able to achieve very good results in terms of counting accuracy and execution speed.


title: Optimal coil size ratios for wireless power transfer applications

abstract: Optimization of coil sizes for wireless power transfer applications has a significant impact on the range and efficiency of the wireless power system. Often it is difficult to accurately analyze how a set of coils will perform before they are constructed due to the complexity of approximating the parasitic components and coupling coefficients associated with the coils. Also, for certain wireless power applications such as consumer electronic devices, implanted medical devices and industrial equipment that have physical constraints on the size and shape of the coils, it can be a difficult and time-consuming process to design and evaluate several different coil configurations for optimal range and efficiency. This paper provides simplified design equations to accurately calculate the self inductance, capacitance, resistance, quality factor and coupling coefficient in terms of coil geometries for flat, spiral coils. Experimental results validate the analysis and a design example is provided to optimize the size of a transmit coil for maximum range and wireless power efficiency to a 5.8cm receive coil used in a four-element wireless power transfer system.


title: Terpene synthases of oregano (Origanum vulgare L.) and their roles in the pathway and regulation of terpene biosynthesis

abstract: The aroma, flavor and pharmaceutical value of cultivated oregano (Origanum vulgare L.) is a consequence of its essential oil which consists mostly of monoterpenes and sesquiterpenes. To investigate the biosynthetic pathway to oregano terpenes and its regulation, we identified and characterized seven terpene synthases, key enzymes of terpene biosynthesis, from two cultivars of O. vulgare. Heterologous expression of these enzymes showed that each forms multiple mono- or sesquiterpene products and together they are responsible for the direct production of almost all terpenes found in O. vulgare essential oil. The correlation of essential oil composition with relative and absolute terpene synthase transcript concentrations in different lines of O. vulgare demonstrated that monoterpene synthase activity is predominantly regulated on the level of transcription and that the phenolic monoterpene alcohol thymol is derived from gamma-terpinene, a product of a single monoterpene synthase. The combination of heterologously-expressed terpene synthases for in vitro assays resulted in blends of mono- and sesquiterpene products that strongly resemble those found in vivo, indicating that terpene synthase expression levels directly control the composition of the essential oil. These results will facilitate metabolic engineering and directed breeding of O. vulgare cultivars with higher quantity of essential oil and improved oil composition.


title: Low-Latency FPGA Based Financial Data Feed Handler

abstract: Financial exchanges provide real time data feeds containing trade, order and status information to brokers, traders and other market makers. ITCH is one such market data feed that is disseminated by the NASDAQ exchange. The work presented in this paper describes an FPGA based ITCH feed handler and processing system. The handler, built on the Stone Ridge RDX-11 hardware platform with a combination of HDL and Impulse C, accepts and processes ITCH packet sat line speed with extremely low latency. Our implementation parses sixteen different stock symbols in the feed and generates an outbound packet on the NASDAQ one second heartbeat. The unit was tested with an artificial feed that could be adjusted in speed to simulate market surges. The system demonstrated a turnaround latency of 2.7µs with very little variation for all tested feed rates. The CPU equivalent demonstrated 38±22µs(1x rate) with a long tail illustrating the variability inherent to processing by the host O/S. The FPGA solution demonstrated ultra-low, deterministic latency and was able to continue processing data at the line rate limit.


Phonetic Universals in Vowel Systems