title: On Relevance, Probabilistic Indexing and Information Retrieval

abstract: This paper reports on a novel technique for literature indexing and searching in a mechanized library system. The notion of relevance is taken as the key concept in the theory of information retrieval and a comparative concept of relevance is explicated in terms of the theory of probability. The resulting technique called “Probabilistic Indexing,” allows a computing machine, given a request for information, to make a statistical inference and derive a number (called the “relevance number”) for each document, which is a measure of the probability that the document will satisfy the given request. The result of a search is an ordered list of those documents which satisfy the request ranked according to their probable relevance. The paper goes on to show that whereas in a conventional library system the cross-referencing (“see” and “see also”) is based solely on the “semantical closeness” between index terms, statistical measures of closeness between index terms can be defined and computed. Thus, given an arbitrary request consisting of one (or many) index term(s), a machine can elaborate on it to increase the probability of selecting relevant documents that would not otherwise have been selected. Finally, the paper suggests an interpretation of the whole library problem as one where the request is considered as a clue on the basis of which the library system makes a concatenated statistical inference in order to provide as an output an ordered list of those documents which most probably satisfy the information needs of the user.


title: A CFCC-LSTM Model for Sea Surface Temperature Prediction

abstract: Sea surface temperature (SST) prediction is not only theoretically important but also has a number of practical applications across a variety of ocean-related fields. Although a large amount of SST data obtained via remote sensor are available, previous work rarely attempted to predict future SST values from history data in spatiotemporal perspective. This letter regards SST prediction as a sequence prediction problem and builds an end-to-end trainable long short term memory (LSTM) neural network model. LSTM naturally has the ability to learn the temporal relationship of time series data. Besides temporal information, spatial information is also included in our LSTM model. The local correlation and global coherence of each pixel can be expressed and retained by patches with fixed dimensions. The proposed model essentially combines the temporal and spatial information to predict future SST values. Its structure includes one fully connected LSTM layer and one convolution layer. Experimental results on two data sets, i.e., one Advanced Very High Resolution Radiometer SST data set covering China Coastal waters and one National Oceanic and Atmospheric Administration High-Resolution SST data set covering the Bohai Sea, confirmed the effectiveness of the proposed model.


title: A Printed Transition for Matching Improvement of SIW Horn Antennas

abstract: The substrate integrated waveguide (SIW) technology allows to construct several types of commonly used antennas in a planar way. However, frequency limitations associated to commercial substrates appear in the implementation of certain types of antennas, e.g., SIW horn antennas are not well matched when the substrate thickness is much smaller than the wavelength. A printed transition is proposed to overcome this problem. Differently from current solutions, no bulky elements are required allowing to maintain the most important features of this technology namely its compactness and ease of manufacturing. In order to quickly analyze and design the transition, both a coupled resonator and a transmission line models are developed, together with design guidelines. The proposed transition is designed to match a H-plane SIW horn antenna built in a thin substrate $({\rm thickness} at different frequency bands at the Ku-band. Experimental results for 3 different transitions show that the matching characteristics are efficiently improved compared with the conventional SIW horn antenna and validates the proposed models.


title: Finding prototypes of answers for improving answer sentence selection

abstract: Answer sentence selection has been widely adopted recently for benchmarking techniques in Question Answering. Previous proposals for the task are essentially general solutions taking the form of neural networks that measure semantic similarity. In contrast, the present paper describes a simple technique to take advantage of such general-purpose tools for dealing with questions and answer sentences without changing the base system. The technique involves replacing wh-words in input questions with a word denoting the prototype of all answers. These transformed questions are passed as input to an existing neural network built for measuring semantic similarity. This technique is evaluated on two different neural network architectures over two datasets: TrecQA and WikiQA. Results of our experiments show improvement in overall accuracy across most question types we are interested in: 'who', 'when' and 'where'-type questions.


title: Understanding the Adoption of Voice Activated Personal Assistants

abstract: This study aims to investigate the factors that affect the usage of voice-activated personal assistants VAPA which are mobile device applications such as Siri, Google Now, S Voice, Cortana, Alexa, etc. A theoretical framework is proposed based on the relative technology acceptance model constructs in the light of literature. Data are collected from a total of 183 people with a survey questionnaire. Structural equation modelling SEM was applied as the major statistical technique for data analysis. The proposed model has the potential to help technology companies to understand some of the factors influencing user' behaviors and attitudes toward VAPA and improve the technology quality. There are few researches about voice-activated technology adoption in the literature and this is first paper that proposes a research model for acceptance of VAPA.


title: Mapping distributed brain function and networks with diffuse optical tomography

abstract: High-resolution diffuse optical tomography employing a large array of light sources and detectors arranged around the head can perform functional brain imaging. It provides an alternative to magnetic resonance imaging for monitoring activity in different areas of the brain.


title: A simple more general boxplot method for identifying outliers

abstract: Abstract The boxplot method (Exploratory Data Analysis, Addison-Wesley, Reading, MA, 1977) is a graphically-based method of identifying outliers which is appealing not only in its simplicity but also because it does not use the extreme potential outliers in computing a measure of dispersion. The inner and outer fences are defined in terms of the hinges (or fourths), and therefore are not distorted by a few extreme values. Such distortion could lead to failing to detect some outliers, a problem known as “masking”. A method for determining the probability associated with any fence or observation is proposed based on the cumulative distribution function of the order statistics. This allows the statistician to easily assess, in a probability sense, the degree to which an observation is dissimilar to the majority of the observations. In addition, an adaptation for approximately normal but somewhat asymmetric distributions is suggested.


title: Efficient Authentication and Key Management Mechanisms for Smart Grid Communications

abstract: A smart grid (SG) consists of many subsystems and networks, all working together as a system of systems, many of which are vulnerable and can be attacked remotely. Therefore, security has been identified as one of the most challenging topics in SG development, and designing a mutual authentication scheme and a key management protocol is the first important step. This paper proposes an efficient scheme that mutually authenticates a smart meter of a home area network and an authentication server in SG by utilizing an initial password, by decreasing the number of steps in the secure remote password protocol from five to three and the number of exchanged packets from four to three. Furthermore, we propose an efficient key management protocol based on our enhanced identity-based cryptography for secure SG communications using the public key infrastructure. Our proposed mechanisms are capable of preventing various attacks while reducing the management overhead. The improved efficiency for key management is realized by periodically refreshing all public/private key pairs as well as any multicast keys in all the nodes using only one newly generated function broadcasted by the key generator entity. Security and performance analyses are presented to demonstrate these desirable attributes.


title: Measuring inconsistency in meta-analyses.

abstract: Cochrane Reviews have recently started including the quantity I 2 to help readers assess the consistency of the results of studies in meta-analyses. What does this new quantity mean, and why is assessment of heterogeneity so important to clinical practice? ::: ::: Systematic reviews and meta-analyses can provide convincing and reliable evidence relevant to many aspects of medicine and health care.1 Their value is especially clear when the results of the studies they include show clinically important effects of similar magnitude. However, the conclusions are less clear when the included studies have differing results. In an attempt to establish whether studies are consistent, reports of meta-analyses commonly present a statistical test of heterogeneity. The test seeks to determine whether there are genuine differences underlying the results of the studies (heterogeneity), or whether the variation in findings is compatible with chance alone (homogeneity). However, the test is susceptible to the number of trials included in the meta-analysis. We have developed a new quantity, I 2, which we believe gives a better measure of the consistency between trials in a meta-analysis. ::: ::: Assessment of the consistency of effects across studies is an essential part of meta-analysis. Unless we know how consistent the results of studies are, we cannot determine the generalisability of the findings of the meta-analysis. Indeed, several hierarchical systems for grading evidence state that the results of studies must be consistent or homogeneous to obtain the highest grading.2–4 ::: ::: Tests for heterogeneity are commonly used to decide on methods for combining studies and for concluding consistency or inconsistency of findings.5 6 But what does the test achieve in practice, and how should the resulting P values be interpreted? ::: ::: A test for heterogeneity examines the null hypothesis that all studies are evaluating the same effect. The usual test statistic …


title: Youth texting: Help or hindrance to literacy?

abstract: An extensive amount of research has been performed in recent years into the widespread practice of text messaging in youth. As part of this broad area of research, the associations between youth texting and literacy have been investigated in a variety of contexts. A comprehensive, semi-systematic review of the literature into texting and literacy was conducted, with a particular focus on quantitative empirical studies. Media reports, teacher surveys, and qualitative studies were also taken into consideration as part of this wide-ranging examination of previous research into this phenomenon. There were no clear positive or negative links discovered between adolescent texting practices and literacy, with the research findings in this study area best summarized as mixed and inconclusive. More studies in this research area are required, especially of an experimental or longitudinal kind. In addressing the debate on the impact of texting on literacy, research analyses as well as media reports would benefit from a more balanced presentation of existing findings.


title: A Low-Noise, Non-Contact EEG/ECG Sensor

abstract: Typical electroencephalogram (EEG) and electrocardiogram (ECG) sensors require conductive gel to ensure low-impedance electrical contact between the sensor and skin, making set-up time-consuming and long-term recording problematic. We present a gel-free, non-contact EEG/ECG sensor with on-board electrode that capacitively couples to the skin. Active shielding of the high-impedance input significantly reduces noise pickup, and reduces variations in gain as a function of gap distance. The integrated sensor combines amplification, bandpass filtering, and analog-to-digital conversion within a 1 inch diameter enclosure. The measured input-referred noise, over 1-100 Hz frequency range, is 2 muVrms at 0.2 mm sensor distance, and 17 muVrms at 3.2 mm distance. Experiments coupling the sensor to human scalp through hair and to chest through clothing produce clear EEG and ECG recorded signals.


title: Selective HTTPS traffic manipulation at middleboxes for BYOD devices

abstract: HTTPS has become a vital component of the WWW ecosystem. However, today's application-layer middleboxes in the cloud are largely “blind” to HTTPS traffic. We propose a novel system infrastructural solution, called CloudEye, that allows middleboxes to selectively manipulate HTTPS traffic. A key design philosophy of CloudEye is to hide all the complexity from client and server applications (thus being transparent to them) and to have middlebox-related functions managed by a dedicated OS service. CloudEye provides control of what information the middlebox can access through new techniques such as HTTPS tags and shadow connections, without changing the TLS/SSL or HTTP protocol. CloudEye is secure and easy to use. We implemented its prototype on Linux/Android, and demonstrated its low overhead and rich use cases on off-the-shelf mobile devices and cloud servers.


title: PLAY: Semantics-Based Event Marketplace

abstract: In this paper we present PLAY Platform, a Web-oriented distributed semantic middleware that serves as an Event Marketplace: the place where heterogeneous events can be integrated and combined. The purpose of the platform is to derive useful information from diverse real-time sources such as collaborative processes. The platform provides technology where instant results are needed or where heterogeneous data must be integrated on the fly or where the data arrive fast enough to require the stream processing nature of our approach. The main advantages of the platforms are its scalability (cloud-based nature) and the expressivity of the event combinations that can be defined (using both real-time and historical data). The platform has been applied in a use case about Personal data management. In this paper we present some results from the validation, focusing on smartphone and social media integration.


title: Theory of Planned Behavior and Ethics Theory in Digital Piracy: An Integrated Model

abstract: Since digital piracy has posed a significant threat to the development of the software industry and the growth of the digital media industry, it has, for the last decade, held considerable interest for researchers and practitioners. This article will propose an integrated model that combines the theory of planned behavior (TPB) and ethics theory, the two theories that are most often used in digital piracy studies. Data were obtained from university students in China, and the model was examined using the structural equation model (SEM). The results show that moral obligation and justice, derived from ethics theories and TPB variables, such as attitude, subjective norms, and perceived behavioral control, influence the behavioral intentions of individuals to commit digital piracy. The attitude of individuals toward digital piracy is also found to be influenced by perceived benefits, perceived risk, and habit.


title: HiExpan: Task-Guided Taxonomy Construction by Hierarchical Tree Expansion

abstract: Taxonomies are of great value to many knowledge-rich applications. As the manual taxonomy curation costs enormous human effects, automatic taxonomy construction is in great demand. However, most existing automatic taxonomy construction methods can only build hypernymy taxonomies wherein each edge is limited to expressing the "is-a" relation. Such a restriction limits their applicability to more diverse real-world tasks where the parent-child may carry different relations. In this paper, we aim to construct a task-guided taxonomy from a domain-specific corpus and allow users to input a "seed" taxonomy, serving as the task guidance. We propose an expansion-based taxonomy construction framework, namely HiExpan, which automatically generates key term list from the corpus and iteratively grows the seed taxonomy. Specifically, HiExpan views all children under each taxonomy node forming a coherent set and builds the taxonomy by recursively expanding all these sets. Furthermore, HiExpan incorporates a weakly-supervised relation extraction module to extract the initial children of a newly-expanded node and adjusts the taxonomy tree by optimizing its global structure. Our experiments on three real datasets from different domains demonstrate the effectiveness of HiExpan for building task-guided taxonomies.


title: Protecting Adolescents From Harm: Findings From the National Longitudinal Study on Adolescent Health

abstract: Context. —The main threats to adolescents' health are the risk behaviors they choose. How their social context shapes their behaviors is poorly understood. Objective. —To identify risk and protective factors at the family, school, and individual levels as they relate to 4 domains of adolescent health and morbidity: emotional health, violence, substance use, and sexuality. Design. —Cross-sectional analysis of interview data from the National Longitudinal Study of Adolescent Health. Participants. —A total of 12118 adolescents in grades 7 through 12 drawn from an initial national school survey of 90118 adolescents from 80 high schools plus their feeder middle schools. Setting. —The interview was completed in the subject's home. Main Outcome Measures. —Eight areas were assessed: emotional distress; suicidal thoughts and behaviors; violence; use of 3 substances (cigarettes, alcohol, marijuana); and 2 types of sexual behaviors (age of sexual debut and pregnancy history). Independent variables included measures of family context, school context, and individual characteristics. Results. —Parent-family connectedness and perceived school connectedness were protective against every health risk behavior measure except history of pregnancy. Conversely, ease of access to guns at home was associated with suicidality (grades 9-12: P P P P P P P P P P P P P P P Conclusions. —Family and school contexts as well as individual characteristics are associated with health and risky behaviors in adolescents. The results should assist health and social service providers, educators, and others in taking the first steps to diminish risk factors and enhance protective factors for our young people.


title: A controller to avoid both occlusions and obstacles during a vision-based navigation task in a cluttered environment

abstract: This paper presents a sensor-based controller allowing to visually drive a mobile robot towards a target while avoiding visual features occlusions and obstacle collisions. We consider the model of a cart-like robot equipped with proximetric sensors and a camera mounted on a pan-platform. The proposed method relies on the continuous switch between three controllers realizing respectively the nominal vision-based task, the obstacle by passing and the occlusion avoidance. Simulation results are given at the end of the paper.


Fractional Order ControlA Tutorial


title: A lower limb exoskeleton research platform to investigate human-robot interaction

abstract: Designing the underlying mechanical structure of lower limb exoskeletons for assistance and rehabilitation is a demanding task that requires a good understanding of the interaction that takes place between the exoskeleton and the human user wearing it. Often the effects of a given mechanical design on the user are not straightforward or intuitive. One obstacle for research is that existing rehabilitation systems do not offer the flexibility that is necessary to investigate different designs and ideas. In this paper we present a passive experimental lower limb exoskeleton that is specifically built to evaluate exoskeleton design-elements and different characteristics. These are namely a joint misalignment compensation mechanism, a three DOF hip joint design, the lack of mechanical transparency as well as the placement of the interfacing cuffs. The motivation and mechanical design of the system is presented along with the results of pilot trials to validate the system as a suitable experimental platform for our investigations


title: Extractive Text Summarization using Neural Networks

abstract: Text Summarization has been an extensively studied problem. Traditional approaches to text summarization rely heavily on feature engineering. In contrast to this, we propose a fully data-driven approach using feedforward neural networks for single document summarization. We train and evaluate the model on standard DUC 2002 dataset which shows results comparable to the state of the art models. The proposed model is scalable and is able to produce the summary of arbitrarily sized documents by breaking the original document into fixed sized parts and then feeding it recursively to the network.


title: Factors influencing cloud computing adoption for e-government implementation in developing countries: Instrument development

abstract: This paper aims to develop a model and measurement to investigate the factors influencing cloud computing adoption as a part of developing countries’ alternatives to implement e-government services.,This study proposes a theoretical model based on the literature of technology adoption models. It constructs scale measurements for the proposed model constructs by extracting and adapting the items from the literature. The authors verify the scales’ content validity and reliability by applying face validity, pre-testing and pilot study. For the pilot study, the authors collect the data from 26 information technology staff in five public organizations in Yemen. The authors test the reliability of the scales using Cronbach’s alpha criterion, and then conduct exploratory factor analysis to evaluate the validity of the scales.,The results show that the scale measurements meet the conventional criteria of reliability and validity.,Theoretically, this paper provides an integrated model for examining cloud computing adoption for e-government implementation in developing countries. In addition, it develops an instrument to empirically investigate the influencing factors of cloud computing adoption in the context of developing countries’ e-government initiatives.


Fauna and flora of Nepal Himalaya


title: Compact 3D MIMO radar — Antenna array design and experimental results

abstract: We present a compact multiple-input multiple-output (MIMO) radar sensor for near-to medium-range threedimensional (3D) radar imaging and target object detection. The MIMO radar sensor contains 16 transmit (Tx) and 16 receive (Rx) antenna elements and employs a frequency modulated continuous wave (FMCW) modulation scheme. Orthogonality between the transmitted signals is ensured by means of a timedivision multiplexing (TDM) scheme. First, a brief description of the overall system architecture is given. Then, two distinctive antenna array designs are discussed, which address different design objectives. Finally, selected experimental results from our measurement campaigns are presented, which highlight the angular resolution and 3D imaging capability achieved by our MIMO radar sensor.


title: Angle of arrival estimation based on warped delay-and-sum (WDAS) beamforming technique

abstract: In antisubmarine warfare (ASW), fast and accurate angle of arrival (AOA) estimation is required in order to localize fast motion targets such as torpedoes. For this purpose we introduce in this paper a new approach called warped delay-and-sum (WDAS) beamforming to estimate the AOA of a narrowband source with uniform linear array (ULA). The basic idea of warped delay-and-sum (WDAS) beamforming is based on calculating the spatial spectrum of the conventional delay-and-sum (DAS) beamforming at nonuniform spaced angles obtained by a spatial frequency transformation using a first order allpass function with a complex valued parameter. This research proves the perfection of the proposed method through simulation work. Moreover, a comparative study will be conducted to compare the performance with the conventional DAS beamforming.


title: The interplay between system identification and machine learning

abstract: Learning from examples is one of the key problems in science and engineering. It deals with function reconstruction from a finite set of direct and noisy samples. Regularization in reproducing kernel Hilbert spaces (RKHSs) is widely used to solve this task and includes powerful estimators such as regularization networks. Recent achievements include the proof of the statistical consistency of these kernel- based approaches. Parallel to this, many different system identification techniques have been developed but the interaction with machine learning does not appear so strong yet. One reason is that the RKHSs usually employed in machine learning do not embed the information available on dynamic systems, e.g. BIBO stability. In addition, in system identification the independent data assumptions routinely adopted in machine learning are never satisfied in practice. This paper provides new results which strengthen the connection between system identification and machine learning. Our starting point is the introduction of RKHSs of dynamic systems. They contain functionals over spaces defined by system inputs and allow to interpret system identification as learning from examples. In both linear and nonlinear settings, it is shown that this perspective permits to derive in a relatively simple way conditions on RKHS stability (i.e. the property of containing only BIBO stable systems or predictors), also facilitating the design of new kernels for system identification. Furthermore, we prove the convergence of the regularized estimator to the optimal predictor under conditions typical of dynamic systems.


title: Difficulty Rating of Sudoku Puzzles: An Overview and Evaluation

abstract: How can we predict the difficulty of a Sudoku puzzle? We give an overview of difficulty rating metrics and evaluate them on extensive dataset on human problem solving (more then 1700 Sudoku puzzles, hundreds of solvers). The best results are obtained using a computational model of human solving activity. Using the model we show that there are two sources of the problem difficulty: complexity of individual steps (logic operations) and structure of dependency among steps. We also describe metrics based on analysis of solutions under relaxed constraints -- a novel approach inspired by phase transition phenomenon in the graph coloring problem. In our discussion we focus not just on the performance of individual metrics on the Sudoku puzzle, but also on their generalizability and applicability to other problems.


title: Compact and Low-Profile Omnidirectional Circularly Polarized Antenna With Four Coupling Arcs for UAV Applications

abstract: A compact and low-profile omnidirectional circularly polarized (CP) antenna with four coupling arcs is presented. In this structure, four outer arcs are utilized to generate horizontal polarization, and an inner disk-loaded feeding pin can produce vertical polarization. Thus, omnidirectional CP radiation is achieved by combining the radiation from the arcs and the feeding pin. Four shorting pins are employed at the end of the outer arcs, which play a great role on the miniaturization of the antenna. Furthermore, the antenna's dimensions can be reduced more by loading four inner coupling arcs inside the outer arcs. An L-band prototype of the antenna is calculated, fabricated, and measured. Its final dimensions are $0.16{\lambda _0} \times 0.16{\lambda _0} \times 0.03{\lambda _0}$ ( ${\lambda _0}$ is the free-space wavelength at operating frequency). The results show the impedance bandwidth (VSWR ≤2) of 13 MHz (1429–1442 MHz). In the operating frequency band, the axial ratios are less than 2.4 dB, and the peak gain reaches 1.01 dBic. This type of antenna can meet the requirements of the weight, dimensions, and frequency bands for special unmanned aerial vehicle communications in China.


Quaternions : From Classical Mechanics to Computer Graphics , and Beyond


title: Reranking And Self-Training For Parser Adaptation

abstract: Statistical parsers trained and tested on the Penn Wall Street Journal (WSJ) treebank have shown vast improvements over the last 10 years. Much of this improvement, however, is based upon an ever-increasing number of features to be trained on (typically) the WSJ treebank data. This has led to concern that such parsers may be too finely tuned to this corpus at the expense of portability to other genres. Such worries have merit. The standard "Charniak parser" checks in at a labeled precision-recall f-measure of 89.7% on the Penn WSJ test set, but only 82.9% on the test set from the Brown treebank corpus.This paper should allay these fears. In particular, we show that the reranking parser described in Charniak and Johnson (2005) improves performance of the parser on Brown to 85.2%. Furthermore, use of the self-training techniques described in (McClosky et al., 2006) raise this to 87.8% (an error reduction of 28%) again without any use of labeled Brown data. This is remarkable since training the parser and reranker on labeled Brown data achieves only 88.4%.


title: A fuzzy filter for images corrupted by impulse noise

abstract: A new operator is presented which adopts a fuzzy logic approach for the enhancement of images corrupted by impulse noise. The proposed operator is based on two-step fuzzy reasoning, and it is able to perform a very strong noise cancellation while preserving image details very well. The new fuzzy filter is favorably compared with other nonlinear operators in the literature.


title: Simultaneous Blood–Tissue Exchange of Oxygen, Carbon Dioxide, Bicarbonate, and Hydrogen Ion

abstract: A detailed nonlinear four-region (red blood cell, plasma, interstitial fluid, and parenchymal cell) axially distributed convection-diffusion-permeation-reaction-binding computational model is developed to study the simultaneous transport and exchange of oxygen (O\(_{2}\)) and carbon dioxide (CO\(_{2}\)) in the blood–tissue exchange system of the heart. Since the pH variation in blood and tissue influences the transport and exchange of O\(_{2}\) and CO\(_{2}\) (Bohr and Haldane effects), and since most CO\(_{2}\) is transported as HCO\(_{3}^{-}\) (bicarbonate) via the CO\(_{2}\) hydration (buffering) reaction, the transport and exchange of HCO\(_{3}^{-}\) and H\(^{+}\) are also simulated along with that of O\(_{2}\) and CO\(_{2}\). Furthermore, the model accounts for the competitive nonlinear binding of O\(_{2}\) and CO\(_{2}\) with the hemoglobin inside the red blood cells (nonlinear \({\rm O_{2}\hbox{--}CO_{2}}\) interactions, Bohr and Haldane effects), and myoglobin-facilitated transport of O\(_{2}\) inside the parenchymal cells. The consumption of O\(_{2}\) through cytochrome-c oxidase reaction inside the parenchymal cells is based on Michaelis–Menten kinetics. The corresponding production of CO\(_{2}\) is determined by respiratory quotient (RQ), depending on the relative consumption of carbohydrate, protein, and fat. The model gives a physiologically realistic description of O\(_{2}\) transport and metabolism in the microcirculation of the heart. Furthermore, because model solutions for tracer transients and steady states can be computed highly efficiently, this model may be the preferred vehicle for routine data analysis where repetitive solutions and parameter optimization are required, as is the case in PET imaging for estimating myocardial O\(_{2}\) consumption.


title: Unsupervised Feature Learning with Discriminative Encoder

abstract: In recent years, deep discriminative models have achieved extraordinary performance on supervised learning tasks, significantly outperforming their generative counterparts. However, their success relies on the presence of a large amount of labeled data. How can one use the same discriminative models for learning useful features in the absence of labels? We address this question in this paper, by jointly modeling the distribution of data and latent features in a manner that explicitly assigns zero probability to unobserved data. Rather than maximizing the marginal probability of observed data, we maximize the joint probability of the data and the latent features using a two step EM-like procedure. To prevent the model from overfitting to our initial selection of latent features, we use adversarial regularization. Depending on the task, we allow the latent features to be one-hot or real-valued vectors and define a suitable prior on the features. For instance, one-hot features correspond to class labels and are directly used for the unsupervised and semi-supervised classification task, whereas real-valued feature vectors are fed as input to simple classifiers for auxiliary supervised discrimination tasks. The proposed model, which we dub discriminative encoder (or DisCoder), is flexible in the type of latent features that it can capture. The proposed model achieves state-of-the-art performance on several challenging tasks.


title: UVAR: An intersection UAV-assisted VANET routing protocol

abstract: It is a challenging task to develop an efficient routing solution for a reliable data delivery in urban vehicular environments. Indeed, it is difficult to find a shortest end-to-end connected path especially in urban city given the mobility pattern of the vehicles and the various obstructions to a clear transmission such as buildings. To overcome these difficulties, we investigate how unmanned aerial vehicles (UAVs) can assist vehicles on the ground in relaying in urban areas. In this paper, we propose UVAR (UAV-Assisted VANET Routing Protocol), a new routing technique for Vehicular Ad hoc Networks (VANets). This protocol is based on the use of the traffic density and the knowledge of vehicular connectivity in the streets. With this approach UAVs collect information about the traffic density on the ground and the state of vehicles connectivity, and exchange them with vehicles through Hello messages. These information allow UAV to place themselves so as to allow relaying data when connectivity between sole vehicles on the ground is not possible. Through vehicle-to-UAV (V2U) communication, the overall connectivity between vehicles is improved and therefore the routing process is efficiently improved. The performance of the proposed protocol is evaluated and the results to different scenarios are discussed.


Image Based Visual Servoing with Linear-Quadratic Regulator Control for Mobile Robots


Performance of Botnet Detection by Neural Networks in Software-Defined Networks


title: Choosing Your IoT Programming Framework: Architectural Aspects

abstract: The Internet of Things (IoT) is turning into practice. To drive innovations, it is crucial that programmers have means to develop IoT applications in the form of IoT programming frameworks. These are toolkits to develop applications according to a certain style or method and that let developers focus on the essence of their applications. New IoT programming frameworks are emerging frequently and this can be overwhelming. To gain useful insights on these frameworks, we define a taxonomy that classifies their architecture. At the same time, the process of developing this taxonomy makes the essential architectural aspects of these frameworks explicit. For each of these aspects, we provide discriminating categories with respect to the effects each category has on certain extra-functional (quality) properties in IoT. We apply the taxonomy on three IoT programming frameworks, namely Works with Nest [1], ARM mbed IoT Device Platform [2] and Alljoyn [3]. Our analysis results in useful insights not only on the architecture of the frameworks but also on the basic differences between the frameworks. Comparing multiple candidate frameworks based on the taxonomy helps to find the most suitable framework for a desired IoT application.


title: Haswell: The Fourth-Generation Intel Core Processor

abstract: Haswell, Intel's fourth-generation core processor architecture, delivers a range of client parts, a converged core for the client and server, and technologies used across many products. It uses an optimized version of Intel 22-nm process technology. Haswell provides enhancements in power-performance efficiency, power management, form factor and cost, core and uncore microarchitecture, and the core's instruction set.


title: Ganoderma lucidum (Curt.: Fr.) Karst. – health-promoting properties. A review

abstract: Summary This paper presents the characteristics of the species Ganoderma lucidum in terms of health-promoting properties. This species is rare in Poland, and is subject to strict protection. Reishi is classified as a medicinal mushroom which fruiting bodies are characterized by a content of active substances with diverse positive effects on human health. G. lucidum is particularly rich source of bioactive compounds, which are obtained from fruiting bodies, mycelium and spores of this species. The therapeutic effect of G. lucidum extracts has been demonstrated in many scientific studies. The most important pharmacological and physiological effects include: immunomodulatory, anti-cancer, anti-inflammatory, antiviral, anti-atherosclerosis, antidiabetic and anti-aging. Reishi has also a beneficial effect on liver cells and the cardiovascular system and protects in case of stomach ulcers. Due to its properties G. lucidum can be used in the prevention and treatment of life-threatening diseases, such as cancer, stroke and heart diseases.


Automated Discovery of Lead Users and Latent Product Features by Mining Large Scale Social Media Networks


title: A case study on the relationship between code ownership and refactoring activities in a Java software system

abstract: Refactoring, the activity of changing source code design without affecting its external behavior, is a widely used practice among developers, since it is considered to positively affect the quality of software systems. However, there are some "human factors" to be considered while performing refactoring, including developers knowledge of systems architecture. Recent studies showed how much "people" metrics, such as code ownership, might affect software quality as well. In this preliminary study we investigated the relationship between code ownership and refactoring activity performed by developers. This study can provide useful insights on who performs refactoring and help team leaders to properly manage human resources during software development.


title: Multimodal Emotion Recognition Using Multimodal Deep Learning

abstract: To enhance the performance of affective models and reduce the cost of acquiring physiological signals for real-world applications, we adopt multimodal deep learning approach to construct affective models from multiple physiological signals. For unimodal enhancement task, we indicate that the best recognition accuracy of 82.11% on SEED dataset is achieved with shared representations generated by Deep AutoEncoder (DAE) model. For multimodal facilitation tasks, we demonstrate that the Bimodal Deep AutoEncoder (BDAE) achieves the mean accuracies of 91.01% and 83.25% on SEED and DEAP datasets, respectively, which are much superior to the state-of-the-art approaches. For cross-modal learning task, our experimental results demonstrate that the mean accuracy of 66.34% is achieved on SEED dataset through shared representations generated by EEG-based DAE as training samples and shared representations generated by eye-based DAE as testing sample, and vice versa.


title: Posterior urethral valve.

abstract: BACKGROUND ::: Posterior urethral valve (PUV) is a significant cause of morbidity, mortality and ongoing renal damage in children. It accounts for end-stage renal disease in a proportion of children. This article aims at highlighting the current trend in the management of boys with posterior urethral valve. ::: ::: ::: DATA SOURCES ::: PubMed/Medline and bibliographic search for posterior urethral valve was done. Relevant literatures on presentation, pathology, evaluation, management and outcomes of PUV were reviewed. ::: ::: ::: RESULTS ::: PUV which is increasingly diagnosed prenatally presents a spectrum of severity. The varied severity and degree of obstruction caused by this abnormality depend on the configuration of the obstructive membrane within the urethra. The decision to intervene prenatally is dependent on gestational age, amniotic volume, and renal function of fetal urine aspiration. Identification of the patients who may benefit from early intervention remains inconclusive. Endoscopic ablation of the valve is the gold standard of treatment but use of Mohan's valvotome and other modalities are invaluable in developing countries where endoscopic facilities are limited. Proximal urinary diversion may result in poor bladder compliance and should be reserved for patients with persisting or increasing upper urinary tract dilatation, increasing serum creatinine or inappropriate instruments. The behavior of the bladder and its subsequent management after valve ablation may influence the long-term renal outcome in PUV patients. ::: ::: ::: CONCLUSIONS ::: The care of children with PUV continues to improve as a result of earlier diagnosis by ultrasound, developments in surgical technique and meticulous attention to neonatal care. The ultimate goal of management should be to maximize renal function, maintain normal bladder function, minimize morbidity and prevent iatrogenic problems.


Identification of Karaka relations in an English sentence.


title: A New Six-Switch Five-Level Active Neutral Point Clamped Inverter for PV Applications

abstract: Multilevel inverters are one of the preferred solutions for medium-voltage and high-power applications and have found successful industrial applications. Five-level active neutral point clamped inverter (5L-ANPC) is one of the most popular topologies among five-level inverters. A six-switch 5L-ANPC (6S-5L-ANPC) topology is proposed. Compared to the conventional 5L-ANPC inverters, the 6S-5L-ANPC reduces two active switches and has lower conduction loss. The proposed modulation enables the 6S-5L-ANPC inverter to operate under both active and reactive power conditions. The flying-capacitor capacitance is designed under both active and reactive power conditions. The analysis shows the proposed topology is suitable for photovoltaic grid-connected applications. A 1 KVA single-phase experimental prototype is built to verify the validity and flexibility of the proposed topology and modulation method.


title: Deep Learning in Medical Image Analysis.

abstract: This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.


title: Sentence classification experiments for legal text summarisation

abstract: We describe experiments in building a classifier which determines the rhetorical status of sentences. The research is part of a text summarisation project for the legal domain and we use a newly compiled and annotated corpus of judgments of the UK House of Lords. Rhetorical role classification is an initial step which pro- vides input to the sentence selection component of the system. We report results from experiments with four classifiers from the Weka package (C4.5, naive Bayes, Winnow and SVMs). We also report results using maximum entropy models both in a standard classification framework and in a sequence labelling framework. The SVM classifier and the maximum entropy sequence tagger yield the most promising results.


Survey on Satellite Image Enhancement Techniques


title: Generating test data with enhanced context-free grammars

abstract: The use of context-free grammars to improve functional testing of very-large-scale integrated circuits is described. It is shown that enhanced context-free grammars are effective tools for generating test data. The discussion covers preliminary considerations, the first tests, generating systematic tests, and testing subroutines. The author's experience using context-free grammars to generate tests for VLSI circuit simulators indicates that they are remarkably effective tools that virtually anyone can use to debug virtually any program. >


title: #CHImoney: financial interactions, digital cash, capital exchange and mobile money

abstract: Interactions around money and financial services are a critical part of our lives on and off-line. New technologies and new ways of interacting with these technologies are of huge interest; they enable new business models and ways of making sense of this most important aspect of our everyday lives. At the same time, money is an essential element in HCI research and design. This workshop is intended to bring together researchers and practitioners involved in the design and use of systems that combine digital and new media with monetary and financial interactions to build on an understanding of these technologies and their impacts on users' behaviors. The workshop will focus on social, technical, and economic aspects around everyday user interactions with money and emerging financial technologies and systems.


title: Using wireless technologies in healthcare

abstract: With an increasingly mobile society and the worldwide deployment of mobile and wireless networks, wireless infrastructure can support many current and emerging healthcare applications. However, before wireless infrastructure can be used in a wide scale, there are several challenges that must be overcome. These include how to best utilise the capabilities of diverse wireless technologies and how to effectively manage the complexity of wireless and mobile networks in healthcare applications. In this paper, we discuss how wireless technologies can be applied in the healthcare environment. Additionally, some open issues and challenges are also discussed.


title: A Self-Supervised Decision Fusion Framework for Building Detection

abstract: In this study, a new building detection framework for monocular satellite images, called self-supervised decision fusion (SSDF) is proposed. The model is based on the idea of self-supervision, which aims to generate training data automatically from each individual test image, without human interaction. This approach allows us to use the advantages of the supervised classifiers in a fully automated framework. We combine our previous supervised and unsupervised building detection frameworks to suggest a self-supervised learning architecture. Hence, we borrow the major strength of the unsupervised approach to obtain one of the most important clues, the relation of a building, and its cast shadow. This important information is, then, used in order to satisfy the requirement of training sample selection. Finally, an ensemble learning algorithm, called fuzzy stacked generalization (FSG), fuses a set of supervised classifiers trained on the automatically generated dataset with various shape, color, and texture features. We assessed the building detection performance of the proposed approach over 19 test sites and compare our results with the state of the art algorithms. Our experiments show that the supervised building detection method requires more than 30% of the ground truth (GT) training data to reach the performance of the proposed SSDF method. Furthermore, the SSDF method increases the F-score by 2 percentage points (p.p.) on the average compared to performance of the unsupervised method.


title: Wearable electromyography sensor based outdoor-indoor seamless pedestrian navigation using motion recognition method

abstract: Navigation and position applications are now becoming standard built-in features in a smart phone. However, locating a mobile user in GNSS unfriendly and denied environments such as urban canyons and indoor environments ubiquitously is still a challenging task. Several self-contained sensors, such as accelerometer, digital compass, gyroscope and barometer, have been adapted as assistance augmentation technologies to a GPS receiver to make a seamless outdoor-indoor pedestrian navigation system. Since the indoor environment is more complex than an open-sky environment, such GNSS signal-degraded areas are typically also contaminated with disturbance sources that affect sensor measurements, a digital compass can be disturbed significantly by e.g. an elevator that bears magnetic perturbance. And a ventilation facility may cause inconsistencies in the barometer's measurements; not to mention that the indoor surrounding attenuates or blocks the GNSS signal. In this paper, a novel outdoor-indoor seamless solution for pedestrian navigation is introduced, which is based on Electromyography (EMG) sensors. The EMG sensor measures the electrical potentials generated by muscle contractions of human body. Therefore it is immune against the environment disturbance; moreover, it has potential capability to exploit the health situation of the pedestrian, since the EMG sensor has been applied on the biomedical field for decades. In the paper, five different motions are classified to estimate the stride length, including: walking horizontally, walking up along a slope, stepping upstairs/downstairs and standing still. The stride length estimation is based on a simple empirical module where fix stride length is donated to each classified motion. In order to evaluate the EMG-based pedestrian dead reckoning (PDR) solution developed in this study, an outdoor-indoor field test had been carried out in the Finnish Geodetic Institute. The test results demonstrated that the EMG-based PDR solutions are comparable to the commercial GPS stand-alone solutions for a period of 9 minutes outdoors, which is equivalent to a walking distance of 667 meters and also demonstrates its robustness for indoor navigation for a period of 3 minutes.


title: A Standard Methodology for the Construction of Symptoms Ontology for Diabetes Diagnosis

abstract: because of life style, food habits, work environment, tension which are major causes of the diabetes at early stage. Prediabetes is the state that occurs when a person's blood glucose levels are higher than normal but not high enough for a diagnosis of diabetes. This stage may lead to diabetes stage if patient has not followed the proper diet and exercise. In the symptoms ontology for diabetes consists related terms for various types like type1, type 2, prediabetes, gestational diabetes etc. It is a complex disorder that has many possible indicators. These include unexplained weight loss, excessive hunger (polyphagia), thirst (polydipsia), frequent urination (polyuria), dehydration, leg pain when walking (claudication), fatigue, dizziness and itching (pruritus). Awareness of these signs and symptoms can keep people alert to potential onset of diabetes. The above information can be processed intelligently using ontology. The symptoms ontology has the explicit specification of concepts of symptoms and their attributes as causes, which helps to provide the entire scenario of the diabetes diagnosis. Mainly, three basic elements in ontology are concepts, relations, and axioms to formalize the definitions. Concepts represent the underlying domain knowledge and the relation represents the relationship between the concepts and relations should be concentrated while constructing it. The reasons for construction of ontology are [5]:


title: Nonsurgical Medical Penile Girth Augmentation: Experience-Based Recommendations

abstract: Penile augmentation is increasingly sought by men who are dissatisfied with the size and/or appearance of their penis. However, augmentation procedures are still considered to be highly controversial with no standardized recommendations reported in the medical literature and limited outcome data. Nevertheless, these procedures continue to be performed in increasing numbers in private settings. Therefore, there is a need for safe, effective, and minimally invasive procedures to be developed, evaluated, and reported in the research literature. In this article, we focus particularly on girth enhancement procedures rather than lengthening procedures as penile girth appears to be particularly important for sexual satisfaction. We discuss the advantages and disadvantages of the common techniques to date, with a focus on the minimally invasive injectable girth augmentation techniques. Based on considerable operative experience, we offer our own suggestions for patient screening, technique selection, and perioperative care.


title: Optimizing the Minimum Vertex Guard Set on Simple Polygons via a Genetic Algorithm

abstract: The problem of minimizing the number of vertex-guards necessary to cover a given simple polygon (MINIMUM VERTEX GUARD (MVG) problem) is NP-hard. This computational complexity opens two lines of investigation: the development of algorithms that establish approximate solutions and the determination of optimal solutions for special classes of simple polygons. In this paper we follow the first line of investigation and propose an approximation algorithm based on general metaheuristic genetic algorithms to solve the MVG problem. Based on our algorithm, we conclude that on average the minimum number of vertex-guards needed to cover an arbitrary and an orthogonal polygon with n vertices is 38 n , respectively. We also conclude that this result is very satisfactory in the sense that it is always close to optimal (with an approximation ratio of 2, for arbitrary polygons; and with an approximation ratio of 1.9, for orthogonal polygons).


title: Map-aided localization with lateral perception

abstract: Accurate localization of a vehicle is a challenging task as GPS available on the market are not designed for lane-level accuracy application. Although dead reckoning helps, cumulative errors from inertial sensors result in a integration drift. This paper presents a new method of localization based on sensors data fusion. An accurate digital map of the lane marking is used as a powerful additional sensor. Road markings are detected by processing two lateral cameras to estimate their distance to the vehicle. Coupled with the map data in a EKF filter it improves the ego-localization obtained with inertial and GPS measurements. The result is a vehicle localization at an ego-lane level of accuracy, with a lateral error of less than 10 centimeters.


title: An Active Contour Method for Bone Cement Reconstruction From C-Arm X-Ray Images

abstract: A novel algorithm is presented to segment and reconstruct injected bone cement from a sparse set of X-ray images acquired at arbitrary poses. The sparse X-ray multi-view active contour (SxMAC-pronounced "smack") can 1) reconstruct objects for which the background partially occludes the object in X-ray images, 2) use X-ray images acquired on a noncircular trajectory, and 3) incorporate prior computed tomography (CT) information. The algorithm's inputs are preprocessed X-ray images, their associated pose information, and prior CT, if available. The algorithm initiates automated reconstruction using visual hull computation from a sparse number of X-ray images. It then improves the accuracy of the reconstruction by optimizing a geodesic active contour. Experiments with mathematical phantoms demonstrate improvements over a conventional silhouette based approach, and a cadaver experiment demonstrates SxMAC's ability to reconstruct high contrast bone cement that has been injected into a femur and achieve sub-millimeter accuracy with four images.


title: Discovering Coherent Topics with Entity Topic Models

abstract: Probabilistic topic models are powerful techniques which are widely used for discovering topics or semantic content from a large collection of documents. However, because topic models are entirely unsupervised, they may lead to topics that are not understandable in applications. Recently, several knowledge-based topic models have been proposed which primarily use word-level domain knowledge in the model to enhance the topic coherence and ignore the rich information carried by entities (e.g persons, location, organizations, etc.) associated with the documents. Additionally, there exists a vast amount of prior knowledge (background knowledge) represented as ontologies and Linked Open Data (LOD), which can be incorporated into the topic models to produce coherent topics. In this paper, we introduce a novel entity-based topic model, called EntLDA, to effectively integrate an ontology with an entity topic model to improve the topic modeling process. Furthermore, to increase the coherence of the identified topics, we introduce a novel ontology-based regularization framework, which is then integrated with the EntLDA model. Our experimental results demonstrate the effectiveness of the proposed model in improving the coherence of the topics.


Non-Native Text-to-Speech Preserving Speaker Individuality Based on Partial Correction of Prosodic and Phonetic Characteristics


Power and Sample Size Determination for Linear Models


title: CSV: visualizing and mining cohesive subgraphs

abstract: Extracting dense sub-components from graphs efficiently is an important objective in a wide range of application domains ranging from social network analysis to biological network analysis, from the World Wide Web to stock market analysis. Motivated by this need recently we have seen several new algorithms to tackle this problem based on the (frequent) pattern mining paradigm. A limitation of most of these methods is that they are highly sensitive to parameter settings, rely on exhaustive enumeration with exponential time complexity, and often fail to help the users understand the underlying distribution of components embedded within the host graph. In this article we propose an approximate algorithm, to mine and visualize cohesive subgraphs (dense sub components) within a large graph. The approach, refereed to as Cohesive Subgraph Visualization (CSV) relies on a novel mapping strategy that maps edges and nodes to a multi-dimensional space wherein dense areas in the mapped space correspond to cohesive subgraphs. The algorithm then walks through the dense regions in the mapped space to output a visual plot that effectively captures the overall dense sub-component distribution of the graph. Unlike extant algorithms with exponential complexity, CSV has a complexity of O(V2logV) when fixing the parameter mapping dimension, where V corresponds to the number of vertices in the graph, although for many real datasets the performance is typically sub-quadratic. We demonstrate the utility of CSV as a stand-alone tool for visual graph exploration and as a pre-filtering step to significantly scale up exact subgraph mining algorithms such as CLAN.


title: Botulinum Toxin Type A Treatment for Contouring of the Lower Face

abstract: BACKGROUND ::: Since type A botulinum toxin was first reported for the treatment of masseter muscle hypertrophy in 1994, there have been few reports about cosmetic indications for contouring procedures of the lower face with injection of botulinum toxin type A, and this procedure remains unpredictable. ::: ::: ::: OBJECTIVES ::: This study attempted a quantitative prospective analysis of reduction of masseter muscle hypertrophy after Botox injection, using ultrasound and computerized tomography (CT) scans to analyze the possible use of botulinum toxin type A as a contouring procedure for the lower face. ::: ::: ::: METHODS ::: Forty-five patients consented to the study and received a contouring procedure of the lower face from November 2001 to April 2002. Twenty five to 30 U of Botox per side was injected at five to six points into the prominent portions of the mandibular angle. Serial measurements were made of the thickness of the masseter muscle by ultrasound and CT before the injections and at 1 and 3 months thereafter. A quantitative analysis for the masseter thickness changes was performed on just one patient who underwent all ultrasound and CT scans. Statistical analysis of the masseter thickness change was by two-way and multiple comparison analysis. To evaluate clinical long-term effects, the patient's satisfaction with the procedure and any side effects after injections were monitored during 4 to 10 months of follow-up. ::: ::: ::: RESULTS ::: Among the total of 45 patients, 15 underwent the three ultrasound measurements, and 14 had the three CT measurements. With regard to quantitative analysis of the thickness change to the masseter on both sides of the face according to time points, this was gradually reduced by both the ultrasound and CT measurements during the first 3 months. By ultrasound, the maximum reduction in masseter thickness was seen 1 month after the injections, with a slight increase being observed at 3 months after injection. A continued reduction of masseteric muscle thickness was seen on the CT up to 3 months after injection. In terms of patient satisfaction for up to 10 months of follow-up, the results were as follows: very satisfied, 1; satisfied, 36; slight improvement, 3; no change or dissatisfied, 5. Main local side effects included masticatory difficulties, muscle aching at injection sites, and speech disturbance. However, these side effects were transitory, usually lasting from 1 to 4 weeks after the injections. ::: ::: ::: CONCLUSIONS ::: Preliminary results from this study suggest that an injection of Botox resulted in relatively satisfactory clinical effects, although there was only a short-term follow-up. It is suggested that the use of botulinum toxin type A for contouring of the lower face can be established as a simple, predictable, alternative facial contouring procedure without a prolonged recovery time.


title: Discrete Spectral Hashing for Efficient Similarity Retrieval

abstract: To meet the required huge data analysis, organization, and storage demand, the hashing technique has got a lot of attention as it aims to learn an efficient binary representation from the original high-dimensional data. In this paper, we focus on the unsupervised spectral hashing due to its effective manifold embedding. Existing spectral hashing methods mainly suffer from two problems, i.e., the inefficient spectral candidate and intractable binary constraint for spectral analysis. To overcome these two problems, we propose to employ spectral rotation to seek a better spectral solution and adopt the alternating projection algorithm to settle the complex code constraints, which are therefore named as Spectral Hashing with Spectral Rotation and Alternating Discrete Spectral Hashing , respectively. To enjoy the merits of both methods, the spectral rotation technique is finally combined with the original spectral objective, which aims to simultaneously learn better spectral solution and more efficient discrete codes and is called as Discrete Spectral Hashing . Furthermore, the efficient optimization algorithms are also provided, which just take comparable time complexity to existing hashing methods. To evaluate the proposed three methods, extensive comparison experiments and studies are conducted on four large-scale data sets for the image retrieval task, and the noticeable performance beats several state-of-the-art spectral hashing methods on different evaluation metrics.


title: WordRecorder: Accurate Acoustic-based Handwriting Recognition Using Deep Learning

abstract: This paper presents WordRecorder, an efficient and accurate handwriting recognition system that identifies words using acoustic signals generated by pens and paper, thus enabling ubiquitous handwriting recognition. To achieve this, we carefully craft a new deep-learning based acoustic sensing framework with three major components, i.e., segmentation, classification, and word suggestion. First, we design a dual-window approach to segment the raw acoustic signal into a series of words and letters by exploiting subtle acoustic signal features of handwriting. Then we integrate a set of simple yet effective signal processing techniques to further refine raw acoustic signals into normalized spectrograms which are suitable for deep-learning classification. After that, we customize a deep neural network that is suitable for smart devices. Finally, we incorporate a word suggestion module to enhance the recognition performance. Our framework achieves both computation efficiency and desirable classification accuracy simultaneously. We prototype our design using off-the-shelf smartwatches and conduct extensive evaluations. Our results demonstrate that WordRecorder robustly archives 81% accuracy rate for trained users, and 75% for users without training, across a range of different environment, users, and writing habits.


title: A conceptual framework for cyber security awareness and education in SA

abstract: The Internet is becoming increasingly interwoven in the daily lives of many individuals, organisations and nations. It has, to a large extent, had a positive effect on the way people communicate. It has also introduced new avenues for business; and it has offered nations an opportunity to govern online. Nevertheless, although cyberspace offers an endless list of services and opportunities, it is also accompanied by many risks, of which many Internet users are not aware. As such, various countries have developed and implemented cyber-security awareness and education measures to counter the perceived ignorance of the Internet users. However, there is currently a definite lack in South Africa (SA) in this regard; as there are currently, little government-led and sponsored cyber-security awareness and education initiatives. The primary research objective of this paper, therefore, is to propose a cyber-security awareness and education framework for SA that would assist in creating a cyber-secure culture in SA among all of the users of the Internet. This framework will be developed on the basis of key factors extrapolated from a comparative analysis of relevant developed countries.


title: Reliability Analysis of Computer Systems Using Dataflow Graph Models

abstract: We use dataflow graphs to represent the computational structure, analogous to Petri nets and Turing machines, and have developed a method for analyzing the reliability of computer systems modeled as dataflow graphs. Because of the hierarchical nature of dataflow graphs, systems can be analyzed at several levels of abstraction. Reliabilities of subgraphs can be calculated using either traditional techniques or dataflow approach presented here (recursively). The reliabilities of subgraphs can then be combined leading to decomposition-aggregation approach. The time needed for an actor to complete its operation is not included in our analysis of dataflow graphs. Incorporation of the time element compounds the problem and we have not studied it yet.


title: Dark current reduction in InAs/InAsSb superlattice mid-wave infrared detectors through restoration etch

abstract: An investigation on the suppression of surface-related dark current in InAs/InAsSb superlattice mid-wave infrared detectors with pin architecture through the optimisation of device fabrication scheme is presented. In particular, the ‘restoration’ chemical etch for the removal of redeposited dry etch by-products that resulted in more than order of magnitude dark current level reduction is utilised. Further, electrochemical sulphur passivation was applied with no substantial effect on device performance. Thus, different passivation schemes shall be developed for the InAs/InAsSb-based detectors.


title: Security metrics and security investment models

abstract: Planning information security investment is somewhere between art and science. This paper reviews and compares existing scientific approaches and discusses the relation between security investment models and security metrics. To structure the exposition, the high-level security production function is decomposed into two steps: cost of security is mapped to a security level, which is then mapped to benefits. This allows to structure data sources and metrics, to rethink the notion of security productivity, and to distinguish sources of indeterminacy as measurement error and attacker behavior. It is further argued that recently proposed investment models, which try to capture more features specific to information security, should be used for all strategic security investment decisions beneath defining the overall security budget.


title: 芸術科学会論文 誌 Vol.3 No.4 pp.216-223 CG Representation of Wood Aging with Distortion, Cracking and Erosion

abstract: Materials exposed to the elements change in appearance because of aging. Because wood is an organic substance, cracks and the surface erosion occur easily. To produce realistic computer graphic images, we need simulate the aging phenomenon also. Here, we propose a visual simulation of the distortion, cracking, and erosion of wood. In this method, wood is represented by a tetrahedral mesh. By setting semi-physical variables at each vertex in this mesh, a visual simulation of wood aging can be accomplished. The surface of the wood is defined by values assigned to the superficial tetrahedral mesh vertices. Changes in the surface are achieved by value changes. The effectiveness of this method is demonstrated by applications on a plank and shapes such as a bunny and an armadillo statue.


title: Sensorless pump control strategy for hydraulic systems of heating applications driven by robust encoderless PMSM drive

abstract: The paper presents developed algorithms for energy efficient sensorless control of pump for hydraulic systems of heating applications. It is presented the complex solution including both robust encoderless PMSM drive control and intelligent master control of hydraulic performance of the pump ensuring transformation of the pump natural hydraulic curve which leads to significant energy savings in the heating applications. Proposed solution has been proved on the laboratory model of the heating system. Achieved results and comparison to fixed speed heating pump are presented including limitations of the developed algorithm and perspective ways of further research.


title: Defect prediction on a legacy industrial software: a case study on software with few defects

abstract: Context: Building defect prediction models for software projects is helpful for reducing the effort in locating defects. In this paper, we share our experiences in building a defect prediction model for a large industrial software project. We extract product and process metrics to build models and show that we can build an accurate defect prediction model even when 4% of the software is defective. Objective: Our goal in this project is to integrate a defect predictor into the continuous integration (CI) cycle of a large software project and decrease the effort in testing. Method: We present our approach in the form of an experience report. Specifically, we collected data from seven older versions of the software project and used additional features to predict defects of current versions. We compared several classification techniques including Naive Bayes, Decision Trees, and Random Forest and resampled our training data to present the company with the most accurate defect predictor. Results: Our results indicate that we can focus testing efforts by guiding the test team to only 8% of the software where 53% of actual defects can be found. Our model has 90% accuracy. Conclusion: We produce a defect prediction model with high accuracy for a software with defect rate of 4%. Our model uses Random Forest, that which we show has more predictive power than Naive Bayes, Logistic Regression and Decision Trees in our case.


title: Calculation of computational complexity for radix-2 p fast fourier transform algorithms for medical signals

abstract: Due to its simplicity radix-2 is a popular algorithm to implement Fast Fourier Transform. Radix - 2 p algorithms have the same order of computational complexity as higher radices algorithms, but still retain the simplicity of radix-2 . By defining a new concept, Twiddle Factor Template , in this paper we propose a method for exact calculation of multiplicative complexity for radix-2 p algorithms. The methodology is described for radix-2 , radix-2 2 and radix-2 3 algorithms. Results show that radix-2 2 and radix-2 3 have significantly less computational complexity compared to radix-2 . Another interesting result is that while the number of complex multiplications in radix-2 3 algorithm is slightly more than radix-2 2 , the number of real multiplications for radix-2 3 is less than radix-2 2 .


title: Learning photographic global tonal adjustment with a database of input/output image pairs

abstract: Adjusting photographs to obtain compelling renditions requires skill and time. Even contrast and brightness adjustments are challenging because they require taking into account the image content. Photographers are also known for having different retouching preferences. As the result of this complexity, rule-based, one-size-fits-all automatic techniques often fail. This problem can greatly benefit from supervised machine learning but the lack of training data has impeded work in this area. Our first contribution is the creation of a high-quality reference dataset. We collected 5,000 photos, manually annotated them, and hired 5 trained photographers to retouch each picture. The result is a collection of 5 sets of 5,000 example input-output pairs that enable supervised learning. We first use this dataset to predict a user's adjustment from a large training set. We then show that our dataset and features enable the accurate adjustment personalization using a carefully chosen set of training photos. Finally, we introduce difference learning: this method models and predicts difference between users. It frees the user from using predetermined photos for training. We show that difference learning enables accurate prediction using only a handful of examples.


imputeTS: Time Series Missing Value Imputation in R


title: Antibiotics in Dutch general practice: nationwide electronic GP database and national reimbursement rates

abstract: PURPOSE ::: In order to assess whether different databases generate information which can be reliable compared with each other, this study aimed to assess to which degree prescribing rates for systemic antibiotics from a nationwide electronic general practitioner (GP) database correspond with national reimbursement rates, and to investigate for which indications antibiotics are prescribed. ::: ::: ::: METHODS ::: Nationwide GP prescribing data were collected from the Second Dutch National Survey of General Practice (DNSGP-2) based on 90 general practices serving 358 008 patients in 2001. Dutch national reimbursement rates for GPs were derived from claims data of the Dutch Drug Information System/Health Care Insurance Board (GIP/CVZ) from 2001. We calculated antibiotic prescribing rates per 1000 patients/inhabitants for each database, and these rates were compared for the total rates and according to antibiotic subgroups. Indications for which GPs prescribed antibiotics were described. ::: ::: ::: RESULTS ::: In national reimbursement data, 339 antibiotic prescriptions per 1000 inhabitants were prescribed by GPs, while the nationwide GP database showed 255 prescriptions per 1000 patients (75% coverage with reimbursement rates). The nationwide GP database showed high volumes of sulphonamides & trimethoprim, and small volumes of macrolides and quinolones. Half of the prescriptions (48%) were prescribed for respiratory diseases, a quarter (26%) for urinary diseases and 7% for ear diseases. ::: ::: ::: CONCLUSIONS ::: GPs voluntarily participating in a research network prescribe less antibiotics than Dutch GPs in general, and are cautious in prescribing newer and more broad-spectrum antibiotics. This point has to be taken into account when databases will be compared with each other.


title: Detecting Malicious Behaviors in JavaScript Applications

abstract: JavaScript applications are widely used in a range of scenarios, including Web applications, mobile applications, and server-side applications. On one hand, due to its excellent cross-platform support, Javascript has become the core technology of social network platforms. On the other hand, the flexibility of the JavaScript language makes such applications prone to attacks that inject malicious behaviors. In this paper, we propose a detection technique to identify malicious behaviors in JavaScript applications. Our method models an application’s normal behavior on function activation, which is used as a basis to detect attacks. We prototyped our solution on the popular JavaScript engine V8 and used it to detect attacks on the android system. Our evaluation shows the effectiveness of our approach in detecting injection attacks to JavaScript applications.


title: N‐acetylcysteine attenuates the development of cardiac fibrosis and remodeling in a mouse model of heart failure

abstract: Abstract Oxidative stress plays a central role in the pathogenesis of heart failure. We aimed to determine whether the antioxidant N‐ acetylcysteine can attenuate cardiac fibrosis and remodeling in a mouse model of heart failure. Minipumps were implanted subcutaneously in wild‐type mice ( n = 20) and mice with cardiomyopathy secondary to cardiac specific overexpression of mammalian sterile 20‐like kinase 1 (MST‐1; n = 18) to administer N‐ acetylcysteine (40 mg/kg per day) or saline for a period of 8 weeks. At the end of this period, cardiac remodeling and function was assessed via echocardiography. Fibrosis, oxidative stress, and expression of collagen types I and III were quantified in heart tissues. Cardiac perivascular and interstitial fibrosis were greater by 114% and 209%, respectively, in MST‐1 compared to wild type ( P ≤ 0.001). In MST‐1 mice administered N‐ acetylcysteine, perivascular and interstitial fibrosis were 40% and 57% less, respectively, compared to those treated with saline ( P ≤ 0. 03). Cardiac oxidative stress was 119% greater in MST‐1 than in wild type ( P N‐ acetylcysteine attenuated oxidative stress in MST‐1 by 42% ( P = 0.005). These data indicate that N‐ acetylcysteine can blunt cardiac fibrosis and related remodeling in the setting of heart failure potentially by reducing oxidative stress. This study provides the basis to investigate the role of N‐ acetylcysteine in chronic heart failure.


title: Failing to Get the Gist: Reduced False Recognition of Semantic Associates in Semantic Dementia

abstract: In 2 experiments involving patients with semantic dementia, the authors investigated the impact of semantic memory loss on both true and false recognition. Experiment 1 involved recognition memory for categories of everyday objects that shared a predominantly semantic relationship. The patients showed preserved item-specific recollection for the pictorial stimuli but, compared with control participants, exhibited significantly reduced utilization of gist information regarding the categories of objects. The latter result is consistent with the patients’ degraded semantic knowledge. Experiment 2 involved categories of abstract objects that were related to one another perceptually rather than semantically. Patients with semantic dementia obtained item-specific recollection and gist memory scores that were indistinguishable from those of control participants. These results suggest that the reduction in gist memory in semantic dementia is largely specific to semantic representations and cannot be attributed to general difficulty with abstracting and/or utilizing gistlike commonalities between stimuli.


title: Apologizing versus asking permission: optimistic concurrency control for abstract data types

abstract: An optimistic concurrency control technique is one that allows transactions to execute without synchronization, relying on commit-time validation to ensure serializability. Several new optimistic concurrency control techniques for objects in decentralized distributed systems are described here, their correctness and optimality properties are proved, and the circumstances under which each is likely to be useful are characterized. Unlike many methods that classify operations only as Reads or Writes, these techniques systematically exploit type-specific properties of objects to validate more interleavings. Necessary and sufficient validation conditions can be derived directly from an object's data type specification. These techniques are also modular: they can be applied selectively on a per-object (or even per-operation) basis in conjunction with standard pessimistic techniques such as two-phase locking, permitting optimistic methods to be introduced exactly where they will be most effective. These techniques can be used to reduce the algorithmic complexity of achieving high levels of concurrency, since certain scheduling decisions that are NP-complete for pessimistic schedulers can be validated after the fact in time, independent of the level of concurrency. These techniques can also enhance the availability of replicated data, circumventing certain tradeoffs between concurrency and availability imposed by comparable pessimistic techniques.


title: Contrast-Guided Image Interpolation

abstract: In this paper a contrast-guided image interpolation method is proposed that incorporates contrast information into the image interpolation process. Given the image under interpolation, four binary contrast-guided decision maps (CDMs) are generated and used to guide the interpolation filtering through two sequential stages: 1) the 45(°) and 135(°) CDMs for interpolating the diagonal pixels and 2) the 0(°) and 90(°) CDMs for interpolating the row and column pixels. After applying edge detection to the input image, the generation of a CDM lies in evaluating those nearby non-edge pixels of each detected edge for re-classifying them possibly as edge pixels. This decision is realized by solving two generalized diffusion equations over the computed directional variation (DV) fields using a derived numerical approach to diffuse or spread the contrast boundaries or edges, respectively. The amount of diffusion or spreading is proportional to the amount of local contrast measured at each detected edge. The diffused DV fields are then thresholded for yielding the binary CDMs, respectively. Therefore, the decision bands with variable widths will be created on each CDM. The two CDMs generated in each stage will be exploited as the guidance maps to conduct the interpolation process: for each declared edge pixel on the CDM, a 1-D directional filtering will be applied to estimate its associated to-be-interpolated pixel along the direction as indicated by the respective CDM; otherwise, a 2-D directionless or isotropic filtering will be used instead to estimate the associated missing pixels for each declared non-edge pixel. Extensive simulation results have clearly shown that the proposed contrast-guided image interpolation is superior to other state-of-the-art edge-guided image interpolation methods. In addition, the computational complexity is relatively low when compared with existing methods; hence, it is fairly attractive for real-time image applications.


title: Unsupervised Disaggregation of Low Frequency Power Measurements

abstract: Fear of increasing prices and concern about climate change are motivating residential power conservation efforts. We investigate the effectiveness of several unsupervised disaggregation methods on low frequency power measurements collected in real homes. Specifically, we consider variants of the factorial hidden Markov model. Our results indicate that a conditional factorial hidden semi-Markov model, which integrates additional features related to when and how appliances are used in the home and more accurately represents the power use of individual appliances, outperforms the other unsupervised disaggregation methods. Our results show that unsupervised techniques can provide perappliance power usage information in a non-invasive manner, which is ideal for enabling power conservation efforts.


title: Automated Massecuite Sucro Crystal Size characterization system for crystallization pan in sugar industry

abstract: Automated Massecuite Sucro Crystal Sizing System has been developed to analyze the sucrose crystals, size and shape in the crystallization pan. The sucrose crystals present in the massecuite are analyzed by a microscopic camera. This paper proposes an automated method for identifying the size of sucro crystal by performing a series of contrast enhancement techniques including statistical momentary analysis through standard deviation. This helps to equalize the intensity distribution in the massecuite images at the preprocessing stage. This work Identifies the crystal cluster from the massecuite which helps to characterize crystal size distribution in the crystallization pan. Crystal Size analysis guides the plant operator to improve the quality of the final product in sugar industry.


title: Fast Perceptual Image Enhancement

abstract: The vast majority of photos taken today are by mobile phones. While their quality is rapidly growing, due to physical limitations and cost constraints, mobile phone cameras struggle to compare in quality with DSLR cameras. This motivates us to computationally enhance these images. We extend upon the results of Ignatov et al., where they are able to translate images from compact mobile cameras into images with comparable quality to high-resolution photos taken by DSLR cameras. However, the neural models employed require large amounts of computational resources and are not lightweight enough to run on mobile devices. We build upon the prior work and explore different network architectures targeting an increase in image quality and speed. With an efficient network architecture which does most of its processing in a lower spatial resolution, we achieve a significantly higher mean opinion score (MOS) than the baseline while speeding up the computation by 6.3 times on a consumer-grade CPU. This suggests a promising direction for neural-network-based photo enhancement using the phone hardware of the future.


title: New dynamical models of lead-acid batteries

abstract: This paper documents the main results of studies that have been carried out, during a period of more than a decade, at University of Pisa in co-operation with other technical Italian institutions, about models of electrochemical batteries suitable for the use of the electrical engineer, in particular for the analysis of electrical systems with batteries. The problem of simulating electrochemical batteries by means of equivalent electric circuits is defined in a general way; then particular attention is then devoted to the problem of modeling of lead-acid batteries. For this kind of battery, a general model structure is defined from which specific models can be inferred, having different degrees of complexity and simulation quality. In particular, the implementation of the third-order model, that shows a good compromise between complexity and precision, is developed in detail. The behavior of the proposed models is compared with results obtained with extensive lab tests on different types of lead-acid batteries.


title: Web-Based Measure of Semantic Relatedness

abstract: Semantic relatedness measures quantify the degree in which some words or concepts are related, considering not only similarity but any possible semantic relationship among them. Relatedness computation is of great interest in different areas, such as Natural Language Processing, Information Retrieval, or the Semantic Web. Different methods have been proposed in the past; however, current relatedness measures lack some desirable properties for a new generation of Semantic Web applications: maximum coverage, domain independence, and universality. ::: ::: In this paper, we explore the use of a semantic relatedness measure between words, that uses the Web as knowledge source. This measure exploits the information about frequencies of use provided by existing search engines. Furthermore, taking this measure as basis, we define a new semantic relatedness measure among ontology terms. The proposed measure fulfils the above mentioned desirable properties to be used on the Semantic Web. We have tested extensively this semantic measure to show that it correlates well with human judgment, and helps solving some particular tasks, as word sense disambiguation or ontology matching.


title: The Evolution of the Supermarket Industry: From A&P to Wal-Mart

abstract: This paper examines the evolution of the supermarket industry, from the birth of the chain store concept in the early 1900s to the recent rise of the Wal-Mart supercenter. The central message is that the major themes relevant today (the importance of scale and standardization, technological innovation, the introduction of new formats, and the rapt attention of anti-trust authorities) appear throughout its hundred year history. The goal of this paper is to provide a coherent context for current merger policy and broaden our understanding of the processes shaping the economic geography of retail trade.


Using deep neural networks to improve proficiency assessment for children English language learners.


title: Abstract Grammatical Processing of Nouns and Verbs in Broca's Area: Evidence from FMRI

abstract: The role of Broca's area in grammatical computation is unclear, because syntactic processing is often confounded with working memory, articulation, or semantic selection. Morphological processing potentially circumvents these problems. Using event-related functional magnetic resonance imaging (fMRI), we had 18 subjects silently inflect words or read them verbatim. Subtracting the activity pattern for reading from that for inflection, which indexes processes involved in inflection (holding constant lexical processing and articulatory planning) highlighted left Brodmann area (BA) 44/45 (Broca's area), BA 47, anterior insula, and medial supplementary motor area. Subtracting activity during zero inflection (the hawk; they walk) from that during overt inflection (the hawks; they walked), which highlights manipulation of phonological content, implicated subsets of the regions engaged by inflection as a whole. Subtracting activity during verbatim reading from activity during zero inflection (which highlights the manipulation of inflectional features) implicated distinct regions of BA 44, 47, and a premotor region (thereby tying these regions to grammatical features), but failed to implicate the insula or BA 45 (thereby tying these to articulation). These patterns were largely similar in nouns and verbs and in regular and irregular forms, suggesting these regions implement inflectional features cutting across word classes. Greater activity was observed for irregular than regular verbs in the anterior cingulate and supplementary motor area (SMA), possibly reflecting the blocking of regular or competing irregular candidates. The results confirm a role for Broca's area in abstract grammatical processing, and are interpreted in terms of a network of regions in left prefrontal cortex (PFC) that are recruited for processing abstract morphosyntactic features and overt morphophonological content.


title: Mitigation of Policy Manipulation Attacks on Deep Q-Networks with Parameter-Space Noise

abstract: Recent developments have established the vulnerability of deep reinforcement learning to policy manipulation attacks via intentionally perturbed inputs, known as adversarial examples. In this work, we propose a technique for mitigation of such attacks based on addition of noise to the parameter space of deep reinforcement learners during training. We experimentally verify the effect of parameter-space noise in reducing the transferability of adversarial examples, and demonstrate the promising performance of this technique in mitigating the impact of whitebox and blackbox attacks at both test and training times.


title: CSLA: A LANGUAGE FOR IMPROVING CLOUD SLA MANAGEMENT

abstract: Cloud computing is a paradigm for enabling remote, on-demand access to a set of configurable computing resources as a service. The pay-per-use model enables service providers to offer their services to customers in different Quality-of-Service (QoS) levels. Service Level Agreement (SLA) is a negotiated agreement between a service provider and a customer where QoS parameters specify the quality level of service that the service provider have to guarantee. However, due to the dynamic nature of the Cloud and its instability, some SLA violations can occurred and the service providers can be charged for penalties. In this paper, we aim at addressing the Cloud instability to better control SLA management (in particular SLA violations) and indirectly the Cloud elasticity. We propose CSLA, a new SLA language directly integrating some features dealing with QoS uncertainty and Cloud fluctuation. In our evaluation, we present a novel profit model for service provider and new algorithms (for admission control and scheduling) to meet SLA requirements (e.g. prevent SLA violations) while tackling scalability and dynamic issues.


title: T1-MRI Fluorescent Iron Oxide Nanoparticles by Microwave Assisted Synthesis

abstract: Iron oxide nanoparticles have long been studied as a T2 contrast agent in MRI due to their superparamagnetic behavior. T1-based positive contrast, being much more favorable for clinical application due to brighter and more accurate signaling is, however, still limited to gadolinium- or manganese-based imaging tools. Though being the only available commercial positive-contrast agents, they lack an efficient argument when it comes to biological toxicity and their circulatory half-life in blood. The need arises to design a biocompatible contrast agent with a scope for easy surface functionalization for long circulation in blood and/or targeted imaging. We hereby propose an extremely fast microwave synthesis for fluorescein-labeled extremely-small iron oxide nanoparticles (fdIONP), in a single step, as a viable tool for cell labeling and T1-MRI. We demonstrate the capabilities of such an approach through high-quality magnetic resonance angiographic images of mice.


title: Prevalence of Problematic Mobile Phone Use in British Adolescents

abstract: Abstract The problematic use of mobile phones among adolescents has not been widely studied. There are very few instruments for assessing potential technological addiction to mobile phones, or for categorizing different types of users or uses. The most widely used scale is the Mobile Phone Problem Use Scale (MPPUS), which is used to study adult populations, and has been applied in various forms in international contexts. The aims of this study were to adapt the Spanish version of this scale (MPPUSA) to British adolescents, and then to estimate the prevalence of possible problematic users. A questionnaire was administered to a sample of 1,529 secondary school pupils aged between 11 and 18 years, with 1,026 completed questionnaires being collected. The analysis showed that the factor and construct validity and reliability were comparable to those obtained in previous studies. The prevalence of problematic users among the students was 10%, and the typical problematic user tended to be an adolescent between 1...


title: A Framework for the Cooperation of Learning Algorithms

abstract: We introduce a framework for training architectures composed of several modules. This framework, which uses a statistical formulation of learning systems, provides a unique formalism for describing many classical connectionist algorithms as well as complex systems where several algorithms interact. It allows to design hybrid systems which combine the advantages of connectionist algorithms as well as other learning algorithms.


title: What is an anxiety disorder

abstract: Initiated as part of the ongoing deliberation about the nosological structure of DSM, this review aims to evaluate whether the anxiety disorders share features of responding that define them and make them distinct from depressive disorders, and/or that differentiate fear disorders from anxious-misery disorders. The review covers symptom self-report as well as on-line indices of behavioral, physiological, cognitive, and neural responding in the presence of aversive stimuli. The data indicate that the anxiety disorders share self-reported symptoms of anxiety and fear; heightened anxiety and fear responding to cues that signal threat, cues that signal no threat, cues that formerly signaled threat, and contexts associated with threat; elevated stress reactivity to aversive stimuli; attentional biases to threat-relevant stimuli and threat-based appraisals of ambiguous stimuli; and elevated amygdala responses to threat-relevant stimuli. Some differences exist among anxiety disorders, and between anxiety disorders and depressive disorders. However, the differences are not fully consistent with proposed subdivisions of fear disorders vs. anxious misery disorders, and comparative data in large part are lacking. Given the high rates of co-morbidity, advances in our understanding of the features of responding that are shared across vs. unique to anxiety and depressive disorders will require dimensional approaches. In summary, the extant data help to define the features of responding that are shared across anxiety disorders, but are insufficient to justify revisions to the DSM nosology at this time. Depression and Anxiety, 2009. © 2009 Wiley-Liss, Inc.