query: Stochastic Variational Deep Kernel Learning

positive: Manifold Gaussian Processes for regression Off-the-shelf Gaussian Process (GP) covariance functions encode smoothness assumptions on the structure of the function to be modeled. To model complex and non-differentiable functions, these smoothness assumptions are often too restrictive. One way to alleviate this limitation is to find a different representation of the data by introducing a feature space. This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task. In this paper, we propose Manifold Gaussian Processes, a novel supervised method that jointly learns a transformation of the data into a feature space and a GP regression from the feature space to observed space. The Manifold GP is a full GP and allows to learn data representations, which are useful for the overall regression task. As a proof-of-concept, we evaluate our approach on complex non-smooth functions where standard GPs perform poorly, such as step functions and robotics tasks with contacts.

positive: Deep Gaussian Processes In this paper we introduce deep Gaussian process (GP) models. Deep GPs are a deep belief network based on Gaussian process mappings. The data is modeled as the output of a multivariate GP. The inputs to that Gaussian process are then governed by another GP. A single layer model is equivalent to a standard GP or the GP latent variable model (GP-LVM). We perform inference in the model by approximate variational marginalization. This results in a strict lower bound on the marginal likelihood of the model which we use for model selection (number of layers and nodes per layer). Deep belief networks are typically applied to relatively large data sets using stochastic gradient descent for optimization. Our fully Bayesian treatment allows for the application of deep models even when data is scarce. Model selection by our variational bound shows that a five layer hierarchy is justified even when modelling a digit data set containing only 150 examples.

positive: Variational Auto-encoded Deep Gaussian Processes We develop a scalable deep non-parametric generative model by augmenting deep Gaussian processes with a recognition model. Inference is performed in a novel scalable variational framework where the variational posterior distributions are reparametrized through a multilayer perceptron. The key aspect of this reformulation is that it prevents the proliferation of variational parameters which otherwise grow linearly in proportion to the sample size. We derive a new formulation of the variational lower bound that allows us to distribute most of the computation in a way that enables to handle datasets of the size of mainstream deep learning tasks. We show the efficacy of the method on a variety of challenges including deep unsupervised learning and deep Bayesian optimization.

positive: A Unifying View of Sparse Approximate Gaussian Process Regression We provide a new unifying view, including all existing prope r probabilistic sparse approximations for Gaussian process regression. Our approach relies on exp ressing theeffective priorwhich the methods are using. This allows new insights to be gained, and highlights the relationship between existing methods. It also allows for a clear theoretically j ustified ranking of the closeness of the known approximations to the corresponding full GPs. Finall y we point directly to designs of new better sparse approximations, combining the best of the exi sting strategies, within attractive computational constraints.

positive:
Caffe: Convolutional Architecture for Fast Feature Embedding Caffe provides multimedia scientists and practitioners with a clean and modifiable framework for state-of-the-art deep learning algorithms and a collection of reference models. The framework is a BSD-licensed C++ library with Python and MATLAB bindings for training and deploying general-purpose convolutional neural networks and other deep models efficiently on commodity architectures. Caffe fits industry and internet-scale media needs by CUDA GPU computation, processing over 40 million images a day on a single K40 or Titan GPU (approx 2 ms per image). By separating model representation from actual implementation, Caffe allows experimentation and seamless switching among platforms for ease of development and deployment from prototyping machines to cloud environments.
 Caffe is maintained and developed by the Berkeley Vision and Learning Center (BVLC) with the help of an active community of contributors on GitHub. It powers ongoing research projects, large-scale industrial applications, and startup prototypes in vision, speech, and multimedia.

negative: Multilayer networks: an architecture framework We present an architecture framework for the control and management of multilayer networks and associated advanced network services. This material is identified as an "architecture framework" to emphasize its role in providing guidance and structure to our subsequent detailed architecture, design, and implementation activities. Our work is motivated by requirements from the Department of Energy science application community for real-time on-demand science-domain-specific network services and resource provisioning. We also summarize the current state of deployments and use of network services based on this multilayer network architecture framework.

negative: IoT for Healthcare The Internet of Things (IoT) is the network of physical objects or things embedded with electronics, software, sensors, and network connectivity, which enables these objects to collect and exchange data. The Internet of Things allows objects to be sensed and controlled remotely across existing network infrastructure. The IoT is enabled by the latest developments in RFID, smart sensors, communication technologies, and Internet protocols. The basic premise is to have smart sensors collaborate directly without human involvement to deliver a new class of applications. The current revolution in Internet, mobile, and machine-to-machine (M2M) technologies can be seen as the first phase of the IoT. In the coming years, the IoT is expected to bridge diverse technologies to enable new applications by connecting physical objects together in support of intelligent decision making. Smart healthcare plays a significant role in healthcare applications through embedding sensors and actuators in patients and their medicine for monitoring and tracking purposes. The IoT is used by clinical care to monitor physiological statuses of patients through sensors by collecting and analyzing their information and then sending analyzed patient’s data remotely to processing centers to make suitable actions. Not only for patients, it also useful for normal people to check the health status by using wearable devices with sensors.

negative: User Experience in Service Design: A Case Study from Algeria The design of crisis management services is crucial for emerging countries such as Algeria. It must take into account the experiences of diverse stakeholders. The authors investigate user experience (UX) practices from a service design perspective and describe a case study from Algeria exploring UX-driven service design for crisis management.

negative: Towards Accurate Predictions of Customer Purchasing Patterns A range of algorithms was used to classify online retail customers of a UK company using historical transaction data. The predictive capabilities of the classifiers were assessed using linear regression, Lasso and regression trees. Unlike most related studies, classifications were based upon specific and marketing focused customer behaviours. Prediction accuracy on untrained customers was generally better than 80%. The models implemented (and compared) for classification were: Logistic Regression, Quadratic Discriminant Analysis, Linear SVM, RBF SVM, Gaussian Process, Decision Tree, Random Forest and Multi-layer Perceptron (Neural Network). Postcode data was then used to classify solely on demographics derived from the UK Land Registry and similar public data sources. Prediction accuracy remained better than 60%.

negative: Discriminative learning of visual words for 3D human pose estimation This paper addresses the problem of recovering 3D human pose from a single monocular image, using a discriminative bag-of-words approach. In previous work, the visual words are learned by unsupervised clustering algorithms. They capture the most common patterns and are good features for coarse-grain recognition tasks like object classification. But for those tasks which deal with subtle differences such as pose estimation, such representation may lack the needed discriminative power. In this paper, we propose to jointly learn the visual words and the pose regressors in a supervised manner. More specifically, we learn an individual distance metric for each visual word to optimize the pose estimation performance. The learned metrics rescale the visual words to suppress unimportant dimensions such as those corresponding to background. Another contribution is that we design an appearance and position context (APC) local descriptor that achieves both selectivity and invariance while requiring no background subtraction. We test our approach on both a quasi-synthetic dataset and a real dataset (HumanEva) to verify its effectiveness. Our approach also achieves fast computational speed thanks to the integral histograms used in APC descriptor extraction and fast inference of pose regressors.

negative: A graph-based recommender system for digital library Research shows that recommendations comprise a valuable service for users of a digital library [11]. While most existing recommender systems rely either on a content-based approach or a collaborative approach to make recommendations, there is potential to improve recommendation quality by using a combination of both approaches (a hybrid approach). In this paper, we report how we tested the idea of using a graph-based recommender system that naturally combines the content-based and collaborative approaches. Due to the similarity between our problem and a concept retrieval task, a Hopfield net algorithm was used to exploit high-degree book-book, user-user and book-user associations. Sample hold-out testing and preliminary subject testing were conducted to evaluate the system, by which it was found that the system gained improvement with respect to both precision and recall by combining content-based and collaborative approaches. However, no significant improvement was observed by exploiting high-degree associations.

negative: Real-time people counting system using video camera In this MTech thesis experiments will be tried out on a people counting system in an effort to enhance the accuracy when separating counting groups of people, and nonhuman objects. This system features automatic color equalization, adaptive background subtraction, shadow detection algorithm and Kalman tracking. The aim is to develop a reliable and accurate computer vision alternative to sensor or contact based mechanisms. The problem for many computer vision based systems are making good separation between the background and foreground, and teaching the computers what parts make up a scene. We also want to find features to classify the foreground moving objects, an easy task for a human, but a complex task for a computer. Video has been captured with a birds eye view close to one of the entrances at the school about ten meters above the floor. From this video troublesome parts have been selected to test the changes done to the algorithms and program code.

negative: Bicycle dynamics and control: adapted bicycles for education and research In this paper, the dynamics of bicycles is analyzed from the perspective of control. Models of different complexity are presented, starting with simple ones and ending with more realistic models generated from multibody software. Models that capture essential behavior such as self-stabilization as well as models that demonstrate difficulties with rear wheel steering are considered. Experiences using bicycles in control education along with suggestions for fun and thought-provoking experiments with proven student attraction are presented. Finally, bicycles and clinical programs designed for children with disabilities are described.

negative: A neostriatal habit learning system in humans. Amnesic patients and nondemented patients with Parkinson's disease were given a probabilistic classification task in which they learned which of two outcomes would occur on each trial, given the particular combination of cues that appeared. Amnesic patients exhibited normal learning of the task but had severely impaired declarative memory for the training episode. In contrast, patients with Parkinson's disease failed to learn the probabilistic classification task, despite having intact memory for the training episode. This double dissociation shows that the limbic-diencephalic regions damaged in amnesia and the neostriatum damaged in Parkinson's disease support separate and parallel learning systems. In humans, the neostriatum (caudate nucleus and putamen) is essential for the gradual, incremental learning of associations that is characteristic of habit learning. The neostriatum is important not just for motor behavior and motor learning but also for acquiring nonmotor dispositions and tendencies that depend on new associations.

negative: On Removing Routing Protocol from Future Wireless Networks: A Real-time Deep Learning Approach for Intelligent Traffic Control Recently, deep learning has appeared as a breakthrough machine learning technique for various areas in computer science as well as other disciplines. However, the application of deep learning for network traffic control in wireless/heterogeneous networks is a relatively new area. With the evolution of wireless networks, efficient network traffic control such as routing methodology in the wireless backbone network appears as a key challenge. This is because the conventional routing protocols do not learn from their previous experiences regarding network abnormalities such as congestion and so forth. Therefore, an intelligent network traffic control method is essential to avoid this problem. In this article, we address this issue and propose a new, real-time deep learning based intelligent network traffic control method, exploiting deep Convolutional Neural Networks (deep CNNs) with uniquely characterized inputs and outputs to represent the considered Wireless Mesh Network (WMN) backbone. Simulation results demonstrate that our proposal achieves significantly lower average delay and packet loss rate compared to those observed with the existing routing methods. We particularly focus on our proposed method's independence from existing routing protocols, which makes it a potential candidate to remove routing protocol(s) from future wired/ wireless networks.

negative: Development of a 6-DOF manipulator actuated with a straight-fiber-type artificial muscle Robots have become an integral part of human life, and the relationship between humans and robots has grown closer. Thus, it is desired that robots have characteristics similar to humans. In this context, we paid attention to an artificial muscle actuator. We used straight-fiber-type artificial muscles, derived from the McKibben type, which have excellent characteristics with respect to the contraction rate and force. We developed a 6-DOF manipulator actuated by a straight fiber artificial muscle. Furthermore, we tried to control the manipulator position by considering its characteristics.

negative: From smart to smarter cities: Bridging the dimensions of technology and urban planning This paper discusses the importance of urban form in smart cities. Development of urban form has been a major concern for urban planners, designers and policy makers. Sprawl is one way of urban development which is not considered good for a better living standard. Compact form arose as an opposed idea to urban sprawl. Form based codes (FBCs) is a tool for urban planning and design that attempts to mitigate the problem of urban sprawl, whereas conventional zoning strengthens this. This paper highlights the importance of physical place in smart city and how FBCs attempt to create better physical place than conventional zoning. Our study shows that FBCs can lead to smart growth which can be a solution to bridge technology and urban planning together in a single platform.

negative: How to learn a graph from smooth signals We propose a framework that learns the graph structure underlying a set of smooth signals. Given X ∈ Rm×n whose rows reside on the vertices of an unknown graph, we learn the edge weights w ∈ R + under the smoothness assumption that tr ( X>LX ) is small. We show that the problem is a weighted `-1 minimization that leads to naturally sparse solutions. We point out how known graph learning or construction techniques fall within our framework and propose a new model that performs better than the state of the art in many settings. We present efficient, scalable primal-dual based algorithms for both our model and the previous state of the art, and evaluate their performance on artificial and real data.

negative: Thin-Slicing for Pose: Learning to Understand Pose without Explicit Pose Estimation We address the problem of learning a pose-aware, compact embedding that projects images with similar human poses to be placed close-by in the embedding space. The embedding function is built on a deep convolutional network, and trained with triplet-based rank constraints on real image data. This architecture allows us to learn a robust representation that captures differences in human poses by effectively factoring out variations in clothing, background, and imaging conditions in the wild. For a variety of pose-related tasks, the proposed pose embedding provides a cost-efficient and natural alternative to explicit pose estimation, circumventing challenges of localizing body joints. We demonstrate the efficacy of the embedding on pose-based image retrieval and action recognition problems.

negative: Regions, Periods, Activities: Uncovering Urban Dynamics via Cross-Modal Representation Learning With the ever-increasing urbanization process, systematically modeling people’s activities in the urban space is being recognized as a crucial socioeconomic task. This task was nearly impossible years ago due to the lack of reliable data sources, yet the emergence of geo-tagged social media (GTSM) data sheds new light on it. Recently, there have been fruitful studies on discovering geographical topics from GTSM data. However, their high computational costs and strong distributional assumptions about the latent topics hinder them from fully unleashing the power of GTSM. To bridge the gap, we present CrossMap, a novel crossmodal representation learning method that uncovers urban dynamics with massive GTSM data. CrossMap first employs an accelerated mode seeking procedure to detect spatiotemporal hotspots underlying people’s activities. Those detected hotspots not only address spatiotemporal variations, but also largely alleviate the sparsity of the GTSM data. With the detected hotspots, CrossMap then jointly embeds all spatial, temporal, and textual units into the same space using two different strategies: one is reconstructionbased and the other is graph-based. Both strategies capture the correlations among the units by encoding their cooccurrence and neighborhood relationships, and learn lowdimensional representations to preserve such correlations. Our experiments demonstrate that CrossMap not only significantly outperforms state-of-the-art methods for activity recovery and classification, but also achieves much better efficiency.

negative: A 3.4 W Digital-In Class-D Audio Amplifier in 0.14 $\mu $m CMOS In this paper a class-D audio amplifier for mobile applications is presented realized in a 0.14 μm CMOS technology tailored for mobile applications. The amplifier has a simple PDM-based digital interface for audio and control that requires only two pins and enables assembly in 9-bump WL-CSP. The complete audio path is discussed that consists of a Parser, Digital PWM controller, 1-bit DA-converters, analog feedback loop and the Class-D power stage. A reconfigurable gate driver is used that reduces quiescent current consumption and radiated emission.

negative: A new paradigm of human gait analysis with Kinect Analysis of human gait helps to find an intrinsic gait signature through which ubiquitous human identification and medical disorder problems can be investigated in a broad spectrum. The gait biometric provides an unobtrusive feature by which video gait data can be captured at a larger distance without prior awareness of the subject. In this paper, a new technique has been addressed to study the human gait analysis with Kinect Xbox device. It ensures us to minimize the segmentation errors with automated background subtraction technique. The closely similar human skeleton model can be generated from background subtracted gait images, altered by covariate conditions, such as change in walking speed and variations in clothing type. The gait signatures are captured from joint angle trajectories of left hip, left knee, right hip and right knee of subject's skeleton model. The experimental verification on Kinect gait data has been compared with our in-house development of sensor based biometric suit, Intelligent Gait Oscillation Detector (IGOD). An endeavor has been taken to investigate whether this sensor based biometric suit can be altered with a Kinect device for the proliferation of robust gait identification system. The Fisher discriminant analysis has been applied on training gait signature to look into the discriminatory power of feature vector. The Naïve Bayesian classifier demonstrates an encouraging classification result with estimation of errors on limited dataset captured by Kinect sensor.

negative: Employee Engagement From a Self-Determination Theory Perspective Macey and Schneider (2008) draw on numerous theories to explain what engagement is and how it is similar to and different from related constructs in the organizational behavior literature. As a result, we now have a better understanding of some of the key ‘‘components’’ of engagement. What appears to be missing, however, is a strong unifying theory to guide research and practice. We believe that such a theory exists in the form of self-determination theory (SDT; Deci & Ryan, 1985; Ryan & Deci, 2000) and its various corollaries, self-concordance theory (SCT; Sheldon & Elliot, 1999), hierarchical theory (Vallerand, 1997), and passion theory (Vallerand et al., 2003). Although Macey and Schneider acknowledged the relevance of SDT and SCT, we believe that much greater use of these theories could be made to justify and extend their conceptual model.

negative: Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or "things" to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

negative: Handshaking, gender, personality, and first impressions. Although people's handshakes are thought to reflect their personality and influence our first impressions of them, these relations have seldom been formally investigated. One hundred twelve participants had their hand shaken twice by 4 trained coders (2 men and 2 women) and completed 4 personality measures. The participants' handshakes were stable and consistent across time and coders. There were also gender differences on most of the handshaking characteristics. A firm handshake was related positively to extraversion and emotional expressiveness and negatively to shyness and neuroticism; it was also positively related to openness to experience, but only for women. Finally, handshake characteristics were related to the impressions of the participants formed by the coders. These results demonstrate that personality traits, assessed through self-report, can predict specific behaviors assessed by trained observers. The pattern of relations among openness, gender, handshaking, and first impressions suggests that a firm handshake may be an effective form of self-promotion for women.

negative:
Large-Scale Network Dysfunction in Major Depressive Disorder: A Meta-analysis of Resting-State Functional Connectivity. IMPORTANCE
Major depressive disorder (MDD) has been linked to imbalanced communication among large-scale brain networks, as reflected by abnormal resting-state functional connectivity (rsFC). However, given variable methods and results across studies, identifying consistent patterns of network dysfunction in MDD has been elusive.

OBJECTIVE
To investigate network dysfunction in MDD through a meta-analysis of rsFC studies.

DATA SOURCES
Seed-based voxelwise rsFC studies comparing individuals with MDD with healthy controls (published before June 30, 2014) were retrieved from electronic databases (PubMed, Web of Science, and EMBASE) and authors contacted for additional data.

STUDY SELECTION
Twenty-seven seed-based voxel-wise rsFC data sets from 25 publications (556 individuals with MDD and 518 healthy controls) were included in the meta-analysis.

DATA EXTRACTION AND SYNTHESIS
Coordinates of seed regions of interest and between-group effects were extracted. Seeds were categorized into seed-networks by their location within a priori functional networks. Multilevel kernel density analysis of between-group effects identified brain systems in which MDD was associated with hyperconnectivity (increased positive or reduced negative connectivity) or hypoconnectivity (increased negative or reduced positive connectivity) with each seed-network.

RESULTS
Major depressive disorder was characterized by hypoconnectivity within the frontoparietal network, a set of regions involved in cognitive control of attention and emotion regulation, and hypoconnectivity between frontoparietal systems and parietal regions of the dorsal attention network involved in attending to the external environment. Major depressive disorder was also associated with hyperconnectivity within the default network, a network believed to support internally oriented and self-referential thought, and hyperconnectivity between frontoparietal control systems and regions of the default network. Finally, the MDD groups exhibited hypoconnectivity between neural systems involved in processing emotion or salience and midline cortical regions that may mediate top-down regulation of such functions.

CONCLUSIONS AND RELEVANCE
Reduced connectivity within frontoparietal control systems and imbalanced connectivity between control systems and networks involved in internal or external attention may reflect depressive biases toward internal thoughts at the cost of engaging with the external world. Meanwhile, altered connectivity between neural systems involved in cognitive control and those that support salience or emotion processing may relate to deficits regulating mood. These findings provide an empirical foundation for a neurocognitive model in which network dysfunction underlies core cognitive and affective abnormalities in depression.

negative: Greedy Function Approximation : A Gradient Boosting Machine Function approximation is viewed from the perspective of numerical optimization in function space, rather than parameter space. A connection is made between stagewise additive expansions and steepest{descent minimization. A general gradient{descent \boosting" paradigm is developed for additive expansions based on any tting criterion. Speci c algorithms are presented for least{squares, least{absolute{deviation, and Huber{M loss functions for regression, and multi{class logistic likelihood for classi cation. Special enhancements are derived for the particular case where the individual additive components are decision trees, and tools for interpreting such \TreeBoost" models are presented. Gradient boosting of decision trees produces competitive, highly robust, interpretable procedures for regression and classi cation, especially appropriate for mining less than clean data. Connections between this approach and the boosting methods of Freund and Shapire 1996, and Friedman, Hastie, and Tibshirani 1998 are discussed. 1 Function estimation In the function estimation problem one has a system consisting of a random \output" or \response" variable y and a set of random \input" or \explanatory" variables x = fx1; ; xng. Given a \training" sample fyi;xig N 1 of known (y;x){values, the goal is to nd a function F (x) that maps x to y, such that over the joint distribution of all (y;x){values, the expected value of some speci ed loss function (y; F (x)) is minimized F (x) = argmin F (x) Ey;x (y; F (x)) = argmin F (x) Ex [Ey( (y; F (x)) jx] : (1) Frequently employed loss functions (y; F ) include squared{error (y F ) and absolute error jy F j for y 2 R (regression), and negative binomial log{likelihood, log(1 + e 2yF ), when y 2 f 1; 1g (classi cation). A common procedure is to take F (x) to be a member of a parameterized class of functions F (x;P), where P = fP1; P2; g is a set of parameters. In this paper we focus on \additive" expansions of the form

negative: FPGA implementations of fast fourier transforms for real-time signal and image processing Applications based on Fast Fourier Transform (FFT) such as signal and image processing require high computational power, plus the ability to experiment with algorithms. Reconfigurable hardware devices in the form of Field Programmable Gate Arrays (FPGAs) have been proposed as a way of obtaining high performance at an economical price. At present, however, users must program FPGAs at a very low level and have a detailed knowledge of the architecture of the device being used. To try to reconcile the dual requirements of high performance and ease of development, this paper reports on the design and realisation of a High Level framework for the implementation of 1-D and 2-D FFTs for real-time applications. Results show that the parallel implementation of 2-D FFT achieves virtually linear speed-up and real-time performance for large matrix sizes. Finally, an FPGA-based parametrisable environment based on the developed parallel 2-D FFT architecture is presented as a solution for frequencydomain image filtering application.

negative: Priors for people tracking from small training sets We advocate the use of scaled Gaussian process latent variable models (SGPLVM) to learn prior models of 3D human pose for 3D people tracking. The SGPLVM simultaneously optimizes a low-dimensional embedding of the high-dimensional pose data and a density function that both gives higher probability to points close to training data and provides a nonlinear probabilistic mapping from the low-dimensional latent space to the full-dimensional pose space. The SGPLVM is a natural choice when only small amounts of training data are available. We demonstrate our approach with two distinct motions, golfing and walking. We show that the SGPLVM sufficiently constrains the problem such that tracking can be accomplished with straightforward deterministic optimization.

negative: Three dual polarized 2.4GHz microstrip patch antennas for active antenna and in-band full duplex applications This paper presents the design, implementation and interport isolation performance evaluation of three dual port, dual polarized 2.4GHz microstrip patch antennas. Input matching and interport isolation (both DC and RF) performance of implemented antennas has been compared by measuring the input return losses (Sii, S22) and interport isolation (S12) respectively at 2.4GHz. Two implemented single layer antennas provide around 40dB RF interport isolation while the multilayer antenna has 60dB isolation between transmit and receive ports at centre frequency with DC isolated ports. The multilayer antenna provides more than 55dB interport isolation for antenna's 10 dB input impedance bandwidth of 50MHz.

negative:
Learning a Variational Network for Reconstruction of Accelerated MRI Data PURPOSE
To allow fast and high-quality reconstruction of clinical accelerated multi-coil MR data by learning a variational network that combines the mathematical structure of variational models with deep learning.

THEORY AND METHODS
Generalized compressed sensing reconstruction formulated as a variational model is embedded in an unrolled gradient descent scheme. All parameters of this formulation, including the prior model defined by filter kernels and activation functions as well as the data term weights, are learned during an offline training procedure. The learned model can then be applied online to previously unseen data.

RESULTS
The variational network approach is evaluated on a clinical knee imaging protocol for different acceleration factors and sampling patterns using retrospectively and prospectively undersampled data. The variational network reconstructions outperform standard reconstruction algorithms, verified by quantitative error measures and a clinical reader study for regular sampling and acceleration factor 4.

CONCLUSION
Variational network reconstructions preserve the natural appearance of MR images as well as pathologies that were not included in the training data set. Due to its high computational performance, that is, reconstruction time of 193 ms on a single graphics card, and the omission of parameter tuning once the network is trained, this new approach to image reconstruction can easily be integrated into clinical workflow. Magn Reson Med 79:3055-3071, 2018. © 2017 International Society for Magnetic Resonance in Medicine.

negative: Semi-Supervised Learning with the Deep Rendering Mixture Model Semi-supervised learning algorithms reduce the high cost of acquiring labeled training data by using both labeled and unlabeled data during learning. Deep Convolutional Networks (DCNs) have achieved great success in supervised tasks and as such have been widely employed in the semi-supervised learning. In this paper we leverage the recently developed Deep Rendering Mixture Model (DRMM), a probabilistic generative model that models latent nuisance variation, and whose inference algorithm yields DCNs. We develop an EM algorithm for the DRMM to learn from both labeled and unlabeled data. Guided by the theory of the DRMM, we introduce a novel nonnegativity constraint and a variational inference term. We report state-of-the-art performance on MNIST and SVHN and competitive results on CIFAR10. We also probe deeper into how a DRMM trained in a semi-supervised setting represents latent nuisance variation using synthetically rendered images. Taken together, our work provides a unified framework for supervised, unsupervised, and semisupervised learning.

negative: Deep discriminative manifold learning This paper presents a new non-linear dimensionality reduction with stochastic neighbor embedding. A deep neural network is developed for discriminative manifold learning where the class information in transformed low-dimensional space is preserved. Importantly, the objective function for deep manifold learning is formed as the Kullback-Leibler divergence between the probability measures of the labeled samples in high-dimensional and low-dimensional spaces. Different from conventional methods, the derived objective does not require the empirically-tuned parameter. This objective is optimized to attractive those samples from the same class to be close together and simultaneously impose those samples from different classes to be far apart. In the experiments on image and audio tasks, we illustrate the effectiveness of the proposed discriminative manifold learning in terms of visualization and classification performance.

negative: On the importance of initialization and momentum in deep learning Deep and recurrent neural networks (DNNs and RNNs respectively) are powerful models that were considered to be almost impossible to train using stochastic gradient descent with momentum. In this paper, we show that when stochastic gradient descent with momentum uses a well-designed random initialization and a particular type of slowly increasing schedule for the momentum parameter, it can train both DNNs and RNNs (on datasets with long-term dependencies) to levels of performance that were previously achievable only with Hessian-Free optimization. We find that both the initialization and the momentum are crucial since poorly initialized networks cannot be trained with momentum and well-initialized networks perform markedly worse when the momentum is absent or poorly tuned. Our success training these models suggests that previous attempts to train deep and recurrent neural networks from random initializations have likely failed due to poor initialization schemes. Furthermore, carefully tuned momentum methods su ce for dealing with the curvature issues in deep and recurrent network training objectives without the need for sophisticated second-order methods.

negative: On optimization methods for deep learning The predominant methodology in training deep learning advocates the use of stochastic gradient descent methods (SGDs). Despite its ease of implementation, SGDs are difficult to tune and parallelize. These problems make it challenging to develop, debug and scale up deep learning algorithms with SGDs. In this paper, we show that more sophisticated off-the-shelf optimization methods such as Limited memory BFGS (L-BFGS) and Conjugate gradient (CG) with line search can significantly simplify and speed up the process of pretraining deep algorithms. In our experiments, the difference between LBFGS/CG and SGDs are more pronounced if we consider algorithmic extensions (e.g., sparsity regularization) and hardware extensions (e.g., GPUs or computer clusters). Our experiments with distributed optimization support the use of L-BFGS with locally connected networks and convolutional neural networks. Using L-BFGS, our convolutional network model achieves 0.69% on the standard MNIST dataset. This is a state-of-theart result on MNIST among algorithms that do not use distortions or pretraining.

negative: Graphite: Iterative Generative Modeling of Graphs Graphs are a fundamental abstraction for modeling relational data. However, graphs are discrete and combinatorial in nature, and learning representations suitable for machine learning tasks poses statistical and computational challenges. In this work, we propose Graphite an algorithmic framework for unsupervised learning of representations over nodes in a graph using deep latent variable generative models. Our model is based on variational autoencoders (VAE), and uses graph neural networks for parameterizing both the generative model (i.e., decoder) and inference model (i.e., encoder). The use of graph neural networks directly incorporates inductive biases due to the spatial, local structure of graphs directly in the generative model. We draw novel connections of our framework with approximate inference via kernel embeddings. Empirically, Graphite outperforms competing approaches for the tasks of density estimation, link prediction, and node classification on synthetic and benchmark datasets.

negative: Large Scale Distributed Deep Networks Recent work in unsupervised feature learning and deep learning has shown that being able to train large models can dramatically improve performance. In this paper, we consider the problem of training a deep network with billions of parameters using tens of thousands of CPU cores. We have developed a software framework called DistBelief that can utilize computing clusters with thousands of machines to train large models. Within this framework, we have developed two algorithms for large-scale distributed training: (i) Downpour SGD, an asynchronous stochastic gradient descent procedure supporting a large number of model replicas, and (ii) Sandblaster, a framework that supports a variety of distributed batch optimization procedures, including a distributed implementation of L-BFGS. Downpour SGD and Sandblaster L-BFGS both increase the scale and speed of deep network training. We have successfully used our system to train a deep network 30x larger than previously reported in the literature, and achieves state-of-the-art performance on ImageNet, a visual object recognition task with 16 million images and 21k categories. We show that these same techniques dramatically accelerate the training of a more modestlysized deep network for a commercial speech recognition service. Although we focus on and report performance of these methods as applied to training large neural networks, the underlying algorithms are applicable to any gradient-based machine learning algorithm.


query: Literature Fingerprinting: A New Method for Visual Literary Analysis

positive: Data Mining : Concepts and Techniques Association rule mining was first proposed by Agrawal, Imielinski, and Swami [AIS93]. The Apriori algorithm discussed in Section 5.2.1 for frequent itemset mining was presented in Agrawal and Srikant [AS94b]. A variation of the algorithm using a similar pruning heuristic was developed independently by Mannila, Tiovonen, and Verkamo [MTV94]. A joint publication combining these works later appeared in Agrawal, Mannila, Srikant, Toivonen, and Verkamo [AMS96]. A method for generating association rules from frequent itemsets is described in Agrawal and Srikant [AS94a].

positive: Combining Text and Linguistic Document Representations for Authorship Attribution In this paper, we provide several alternatives to the classical Bag-Of-Words model for automatic authorship attribution. To this end, we consider linguistic and writing style information such as grammatical structures to construct different document representations. Furthermore we describe two techniques to combine the obtained representations: combination vectors and ensemble based meta classification. Our experiments show the viability of our approach.

positive: Finding Interesting Rules from Large Sets of Discovered Association Rules Association rules, introduced by Agrawal, Imielinski, and Swami, are rules of the form “for 90% of the rows of the relation, if the row has value 1 in the columns in set W, then it has 1 also in column B”. Efficient methods exist for discovering association rules from large collections of data. The number of discovered rules can, however, be so large that browsing the rule set and finding interesting rules from it can be quite difficult for the user. We show how a simple formalism of rule templates makes it possible to easily describe the structure of interesting rules. We also give examples of visualization of rules, and show how a visualization tool interfaces with rule templates.

positive: Probabilistic outputs for support vector machines and comparisons to regularized likelihood methods

positive: Automatically Categorizing Written Texts by Author Gender The problem of automatically determining the gender of a document's author would appear to be a more subtle problem than those of categorization by topic or authorship attribution. Nevertheless, it is shown that automated text categorization techniques can exploit combinations of simple lexical and syntactic features to infer the gender of the author of an unseen formal written document with approximately 80% accuracy. The same techniques can be used to determine if a document is fiction or non-fiction with approximately 98% accuracy.

negative: To Create What You Tell: Generating Videos from Captions We are creating multimedia contents everyday and everywhere. While automatic content generation has played a fundamental challenge to multimedia community for decades, recent advances of deep learning have made this problem feasible. For example, the Generative Adversarial Networks (GANs) is a rewarding approach to synthesize images. Nevertheless, it is not trivial when capitalizing on GANs to generate videos. The difficulty originates from the intrinsic structure where a video is a sequence of visually coherent and semantically dependent frames. This motivates us to explore semantic and temporal coherence in designing GANs to generate videos. In this paper, we present a novel Temporal GANs conditioning on Captions, namely TGANs-C, in which the input to the generator network is a concatenation of a latent noise vector and caption embedding, and then is transformed into a frame sequence with 3D spatio-temporal convolutions. Unlike the naive discriminator which only judges pairs as fake or real, our discriminator additionally notes whether the video matches the correct caption. In particular, the discriminator network consists of three discriminators: video discriminator classifying realistic videos from generated ones and optimizes video-caption matching, frame discriminator discriminating between real and fake frames and aligning frames with the conditioning caption, and motion discriminator emphasizing the philosophy that the adjacent frames in the generated videos should be smoothly connected as in real ones. We qualitatively demonstrate the capability of our TGANs-C to generate plausible videos conditioning on the given captions on two synthetic datasets (SBMG and TBMG) and one real-world dataset (MSVD). Moreover, quantitative experiments on MSVD are performed to validate our proposal via Generative Adversarial Metric and human study.

negative: Number : AUTCON-D16-00058 R 1 Title : BIM for Infrastructure : An Overall Review and Constructor Perspective 6 The subject of Building Information Modelling (BIM) has become a central topic to the improvement of the AECOO 7 (Architecture, Engineering, Construction, Owner and Operator) industry around the world, to the point where the 8 concept is being expanded into domains it was not originally conceived to address. Transitioning BIM into the 9 domain of infrastructure projects has provided challenges and emphasized the constructor perspective of BIM. 10 Therefore, this study aims to collect the relevant literature regarding BIM within the Infrastructure domain and its 11 use from the constructor perspective to review and analyse the current industry positioning and research state of 12 the art, with regards to the set criteria. The review highlighted a developing base of BIM for infrastructure. From 13 the analysis, the related research gaps were identified regarding information integration, alignment of BIM 14 processes to constructor business processes & the effective governance and value of information. From this a 15 unique research strategy utilising a framework for information governance coupled with a graph based distributed 16 data environment is outlined to further progress the integration and efficiency of AECOO Infrastructure projects. 17

negative: Enterprise Database Applications and the Cloud: A Difficult Road Ahead There is considerable interest in moving DBMS applications from inside enterprise data centers to the cloud, both to reduce cost and to increase flexibility and elasticity. Some of these applications are "green field" projects (i.e., new applications), others are existing legacy systems that must be migrated to the cloud. In another dimension, some are decision support applications while others are update-oriented. In this paper, we discuss the technical and political challenges that these various enterprise applications face when considering cloud deployment. In addition, a requirement for quality-of-service (QoS) guarantees will generate additional disruptive issues. In some circumstances, achieving good DBMS performance on current cloud architectures and future hardware technologies will be non-trivial. In summary, there is a difficult road ahead for enterprise database applications.

negative: Laughbot: Detecting Humor in Spoken Language with Language and Audio Cues We propose detecting and responding to humor in spoken dialogue by extracting language and audio cues and subsequently feeding these features into a combined recurrent neural network (RNN) and logistic regression model. In this paper, we parse Switchboard phone conversations to build a corpus of punchlines and unfunny lines where punchlines precede laughter tokens in Switchboard transcripts. We create a combined RNN and logistic regression model that uses both acoustic and language cues to predict whether a conversational agent should respond to an utterance with laughter. Our model achieves an F1-score of 63.2 and accuracy of 73.9. This model outperforms our logistic language model (F1-score 56.6) and RNN acoustic model (59.4) as well as the final RNN model of D. Bertero, 2016 (52.9). Using our final model, we create a “laughbot” that audibly responds to a user with laughter when their utterance is classified as a punchline. A conversational agent outfitted with a humorrecognition system such as the one we present in this paper would be valuable as these agents gain utility in everyday life. Keywords—Chatbots; spoken natural language processing; deep learning; machine learning

negative:
Reconstruction of dorsal and/or caudal nasal septum deformities with septal battens or by septal replacement: an overview and comparison of techniques. OBJECTIVES
The objectives of this study were to describe and compare two techniques used to correct nasal septum deviations located in the dorsal and/or caudal septum.

STUDY DESIGN
The authors conducted a retrospective clinical chart review.

METHODS
The authors conducted a comparison of functional and technical results between surgery in the L-strut of the septum in 114 patients with septal battens or by septal replacement by subjective self-evaluation and by examination of the position of the septum during follow up.

RESULTS
There was subjective improvement in nasal breathing in 86% of the septal batten group and in 94% of the septal replacement group. This difference was not statistically significant. The technical result was judged by examining the position of the septum during follow up as midline, slightly deviated, or severely deviated. The septum was significantly more often located in the midline during follow up in the septal replacement group than in the septal batten group.

CONCLUSION
Treatment of deformities located in the structurally important L-strut of the septum may be technically challenging and many functional, structural, and esthetic considerations must be taken into account. On the basis of this series, both septal battens and septal replacement techniques may be considered for correction of deviations in this area. The functional improvement rates were not significantly different between the techniques, although during follow up, the septum appeared to be significantly more often located in the midline in the septal replacement group. The techniques are described and their respective advantages and potential drawbacks are discussed.

negative: DroidTrace: A ptrace based Android dynamic analysis system with forward execution capability Android, being an open source smartphone operating system, enjoys a large community of developers who create new mobile services and applications. However, it also attracts malware writers to exploit Android devices in order to distribute malicious apps in the wild. In fact, Android malware are becoming more sophisticated and they use advanced “dynamic loading” techniques like Java reflection or native code execution to bypass security detection. To detect dynamic loading, one has to use dynamic analysis. Currently, there are only a handful of Android dynamic analysis tools available, and they all have shortcomings in detecting dynamic loading. The aim of this paper is to design and implement a dynamic analysis system which allows analysts to perform systematic analysis of dynamic payloads with malicious behaviors. We propose “DroidTrace”, a ptrace based dynamic analysis system with forward execution capability. Our system uses ptrace to monitor selected system calls of the target process which is running the dynamic payloads, and classifies the payloads behaviors through the system call sequence, e.g., behaviors such as file access, network connection, inter-process communication and even privilege escalation. Also, DroidTrace performs “physical modification” to trigger different dynamic loading behaviors within an app. Using DroidTrace, we carry out a large scale analysis on 36,170 dynamic payloads in 50,000 apps and 294 malware in 10 families (four of them are zero-day) with various dynamic loading behaviors.

negative: Understanding the Dynamic Interplay of Social Buzz and Contribution Behavior within and between Online Platforms - Evidence from Crowdfunding Motivated by the growing interconnection between online platforms, we examine the dynamic interplay between social buzz and contribution behavior in the crowdfunding context. Since the utility of crowdfunding projects is usually difficult to ascertain, prospective backers draw on quality signals, such as social buzz and prior-contribution behavior, to make their funding decisions. We employ the panel vector autoregression (PVAR) methodology to investigate both intraand cross-platform effects based on data collected from three platforms: Indiegogo, one of the largest crowdfunding platforms on the web, Twitter and Facebook. Our results show a positive influence of social buzz on project backing, but a negative relationship in the reverse direction. Furthermore, we observe strong positive feedback cycles within each platform. Our results are supplemented by split-sample analyses for project orientation (Social, Cause and Entrepreneurial) and project success (Winners vs. Losers), in which Facebook shares were identified as a critical success factor.

negative: Comparing exploration strategies for Q-learning in random stochastic mazes Balancing the ratio between exploration and exploitation is an important problem in reinforcement learning. This paper evaluates four different exploration strategies combined with Q-learning using random stochastic mazes to investigate their performances. We will compare: UCB-1, softmax, ∈-greedy, and pursuit. For this purpose we adapted the UCB-1 and pursuit strategies to be used in the Q-learning algorithm. The mazes consist of a single optimal goal state and two suboptimal goal states that lie closer to the starting position of the agent, which makes efficient exploration an important part of the learning agent. Furthermore, we evaluate two different kinds of reward functions, a normalized one with rewards between 0 and 1, and an unnormalized reward function that penalizes the agent for each step with a negative reward. We have performed an extensive grid-search to find the best parameters for each method and used the best parameters on novel randomly generated maze problems of different sizes. The results show that softmax exploration outperforms the other strategies, although it is harder to tune its temperature parameter. The worst performing exploration strategy is ∈-greedy.

negative: Character-based movie summarization A decent movie summary is helpful for movie producer to promote the movie as well as audience to capture the theme of the movie before watching the whole movie. Most exiting automatic movie summarization approaches heavily rely on video content only, which may not deliver ideal result due to the semantic gap between computer calculated low-level features and human used high-level understanding. In this paper, we incorporate script into movie analysis and propose a novel character-based movie summarization approach, which is validated by modern film theory that what actually catches audiences' attention is the character. We first segment scenes in the movie by analysis and alignment of script and movie. Then we conduct substory discovery and content attention analysis based on the scent analysis and character interaction features. Given obtained movie structure and content attention value, we calculate movie attraction scores at both shot and scene levels and adopt this as criterion to generate movie summary. The promising experimental results demonstrate that character analysis is effective for movie summarization and movie content understanding.

negative: Distributed Denial-of-Service (DDoS) Threat in Collaborative Environment - A Survey on DDoS Attack Tools and Traceback Mechanisms Collaborative applications are feasible nowadays and are becoming more popular due to the advancement in internetworking technology. The typical collaborative applications, in India include the Space research, Military applications, Higher learning in Universities and Satellite campuses, State and Central government sponsored projects, e-governance, e-healthcare systems, etc. In such applications, computing resources for a particular institution/organization spread across districts and states and communication is achieved through internetworking. Therefore the computing and communication resources must be protected against security attacks as any compromise on these resources would jeopardize the entire application/mission. Collaborative environment is prone for various threats, of which Distributed Denial of Service (DDoS) attacks are of major concern. DDoS attack prevents legitimate access to critical resources. A survey by Arbor networks reveals that approximately 1,200 DDoS attacks occur per day. As the DDoS attack is coordinated, the defense for the same has to be a collaborative one. To counter DDoS attacks in a collaborative environment, all the routers need to work collaboratively by exchanging their caveat messages with their neighbors. This paper analyses the security measures in a collaborative environment, identifles the popular DDoS attack tools, and surveys the existing traceback mechanisms to trace the real attacker.

negative: Geolocation of RF Emitters by Many UAVs This paper presents an approach to using a large team of UAVs to find radio frequency (RF) emitting targets in a large area. Small, inexpensive UAVs that can collectively and rapidly determine the approximate location of intermittently broadcasting and mobile RF emitters have a range of applications in both military, e.g., for finding SAM batteries, and civilian, e.g., for finding lost hikers, domains. Received Signal Strength Indicator (RSSI) sensors on board the UAVs measure the strength of RF signals across a range of frequencies. The signals, although noisy and ambiguous due to structural noise, e.g., multipath effects, overlapping signals and sensor noise, allow estimates to be made of emitter locations. Generating a probability distribution over emitter locations requires integrating multiple signals from different UAVs into a Bayesian filter, hence requiring cooperation between the UAVs. Once likely target locations are identified, EO-camera equipped UAVs must be tasked to provide a video stream of the area to allow a user to identify the emitter.

negative: 75–85 GHz flip-chip phased array RFIC with simultaneous 8-transmit and 8-receive paths for automotive radar applications This paper presents the first simultaneous 8-transmit and 8-receive paths 75-85 GHz phased array RFIC for FMCW automotive radars. The receive path has two separate I/Q mixers each connected to 4-element phased arrays for RF and digital beamforming. The chip also contains a build-in-self-test system (BIST) for the transmit and receive paths. Measurements on a flip-chip prototype show a gain >24 dB at 77 GHz, -25 dB coupling between adjacent channels in the transmit and receive paths (<;-45 dB between non-adjacent channels), and <;-50 dB coupling between the transmit and receive portions of the chip.

negative: Globally and locally consistent image completion We present a novel approach for image completion that results in images that are both locally and globally consistent. With a fully-convolutional neural network, we can complete images of arbitrary resolutions by filling-in missing regions of any shape. To train this image completion network to be consistent, we use global and local context discriminators that are trained to distinguish real images from completed ones. The global discriminator looks at the entire image to assess if it is coherent as a whole, while the local discriminator looks only at a small area centered at the completed region to ensure the local consistency of the generated patches. The image completion network is then trained to fool the both context discriminator networks, which requires it to generate images that are indistinguishable from real ones with regard to overall consistency as well as in details. We show that our approach can be used to complete a wide variety of scenes. Furthermore, in contrast with the patch-based approaches such as PatchMatch, our approach can generate fragments that do not appear elsewhere in the image, which allows us to naturally complete the images of objects with familiar and highly specific structures, such as faces.

negative: DEFO-NET: Learning Body Deformation Using Generative Adversarial Networks Modelling the physical properties of everyday objects is a fundamental prerequisite for autonomous robots. We present a novel generative adversarial network (DEFO-NET), able to predict body deformations under external forces from a single RGB-D image. The network is based on an invertible conditional Generative Adversarial Network (IcGAN) and is trained on a collection of different objects of interest generated by a physical finite element model simulator. Defo-netinherits the generalisation properties of GANs. This means that the network is able to reconstruct the whole 3-D appearance of the object given a single depth view of the object and to generalise to unseen object configurations. Contrary to traditional finite element methods, our approach is fast enough to be used in real-time applications. We apply the network to the problem of safe and fast navigation of mobile robots carrying payloads over different obstacles and floor materials. Experimental results in real scenarios show how a robot equipped with an RGB-D camera can use the network to predict terrain deformations under different payload configurations and use this to avoid unsafe areas.

negative: Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples We identify obfuscated gradients, a kind of gradient masking, as a phenomenon that leads to a false sense of security in defenses against adversarial examples. While defenses that cause obfuscated gradients appear to defeat iterative optimizationbased attacks, we find defenses relying on this effect can be circumvented. We describe characteristic behaviors of defenses exhibiting the effect, and for each of the three types of obfuscated gradients we discover, we develop attack techniques to overcome it. In a case study, examining noncertified white-box-secure defenses at ICLR 2018, we find obfuscated gradients are a common occurrence, with 7 of 9 defenses relying on obfuscated gradients. Our new attacks successfully circumvent 6 completely, and 1 partially, in the original threat model each paper considers.

negative: Graphical SLAM - a self-correcting map We describe an approach to simultaneous localization and mapping, SLAM. This approach has the highly desirable property of robustness to data association errors. Another important advantage of our algorithm is that non-linearities are computed exactly, so that global constraints can be imposed even if they result in large shifts to the map. We represent the map as a graph and use the graph to find an efficient map update algorithm. We also show how topological consistency can be imposed on the map, such as, closing a loop. The algorithm has been implemented on an outdoor robot and we have experimental validation of our ideas. We also explain how the graph can be simplified leading to linear approximations of sections of the map. This reduction gives us a natural way to connect local map patches into a much larger global map.

negative: A novel approach to American sign language recognition using MAdaline neural network Sign language interpretation is gaining a lot of research attention because of its social contributions which is proved to be extremely beneficial for the people suffering from hearing or speaking disabilities. This paper proposes a novel image processing sign language detection framework that employs MAdaline network for classification purpose. This paper mainly highlights two novel aspects, firstly it introduces an advanced feature set comprising of seven distinct features that has not been used widely for sign language interpretation purpose, more over utilization of such features negates the cumbersome step of cropping of irrelevant background image, thus reducing system complexity. Secondly it suggests a possible solution of the concerned problem can be obtained using an extension of the traditional Adaline network, formally termed as MAdaline Network. Although the concept of MAdaline network has originated much earlier, the provision of application of this framework in this domain definitely help in designing an improved sign language interpreting interface. The newly formulated framework has been implemented to recognize standardized American sign language containing 26 English alphabets from ‘A’ to ‘Z’. The performance of the proposed algorithm has also been compared with the standardized algorithms, and in each case the former one outperformed its contender algorithms by a large margin establishing the efficiency of the same.

negative: Design and Optimization a Circular Shape Network Antenna Micro Strip for Some Application To meet the demands of high speed required by mobile communication of past generations ,one solution is to increase the number of antennas to the show and the reception of the wireless link this is called MIMO (Multiple input ,Multiple output )technology .however ,the integration of multiple antennas on the same PCB is delicate because of the small volume that require some applications and electromagnetic antenna between the coupling ,phenomena that we cannot neglect them .indeed a strong isolation between them has been reached to reduce fading of the signal caused by the electromagnetic antenna reached to reduce fading of the signal caused by the electromagnetic coupling and maximize the overall gain .in this article we are interested then integration on the same printed circuit of eight antennas MIMO are not operation in the same frequency band .the first antenna of this last work at 2.4GHz .other antennas have resonance frequency folling each with 20MHz offset this device is characterized by its original form that keeps is highly isolated antennas from the point of view electromagnetic coupling . INDEX TERMS MIMO, Technology Micro-strip, Microwave, Network Antenna

negative: Network Traffic Anomaly Detection This paper presents a tutorial for network anomaly detection, focusing on non-signature-based approaches. Network traffic anomalies are unusual and significant changes in the traffic of a network. Networks play an important role in today’s social and economic infrastructures. The security of the network becomes crucial, and network traffic anomaly detection constitutes an important part of network security. In this paper, we present three major approaches to non-signature-based network detection: PCA-based, sketch-based, and signal-analysis-based. In addition, we introduce a framework that subsumes the three approaches and a scheme for network anomaly extraction. We believe network anomaly detection will become more important in the future because of the increasing importance of network security.

negative: Forward-Private Dynamic Searchable Symmetric Encryption with Efficient Search Dynamic Searchable Symmetric Encryption (DSSE) allows to delegate keyword search and file update over an encrypted database via encrypted indexes, and therefore provides opportunities to mitigate the data privacy and utilization dilemma in cloud storage platforms. Despite its merits, recent works have shown that efficient DSSE schemes are vulnerable to statistical attacks due to the lack of forward-privacy, whereas forward-private DSSE schemes suffers from practicality concerns as a result of their extreme computation overhead. Due to significant practical impacts of statistical attacks, there is a critical need for new DSSE schemes that can achieve the forward-privacy in a more practical and efficient manner. We propose a new DSSE scheme that we refer to as Forward-private Sublinear DSSE (FS-DSSE). FS-DSSE harnesses special secure update strategies and a novel caching strategy to reduce the computation cost of repeated queries. Therefore, it achieves forward-privacy, sublinear search complexity, low end-to-end delay, and parallelization capability simultaneously. We fully implemented our proposed method and evaluated its performance on a real cloud platform. Our experimental evaluation results showed that the proposed scheme is highly secure and highly efficient compared with state-of-the-art DSSE techniques. Specifically, FS-DSSE is up to three magnitude of times faster than forward-secure DSSE counterparts, depending on the frequency of the searched keyword in the database.

negative: Performance Analysis of Classification Algorithms on Medical Diagnoses-a Survey Corresponding Author: Vanaja, S., Research Scholar and Research guide, Research and Development, Bharathiar University, Coimbatore, Tamil Nadu, India Email: vanajasha@yahoo.com Abstract: The aim of this research paper is to study and discuss the various classification algorithms applied on different kinds of medical datasets and compares its performance. The classification algorithms with maximum accuracies on various kinds of medical datasets are taken for performance analysis. The result of the performance analysis shows the most frequently used algorithms on particular medical dataset and best classification algorithm to analyse the specific disease. This study gives the details of different classification algorithms and feature selection methodologies. The study also discusses about the data constraints such as volume and dimensionality problems. This research paper also discusses the new features of C5.0 classification algorithm over C4.5 and performance of classification algorithm on high dimensional datasets. This research paper summarizes various reviews and technical articles which focus on the current research on Medical diagnosis.

negative: Semantic MEDLINE: An advanced information management application for biomedicine Semantic MEDLINE integrates information retrieval, advanced natural language processing, automatic summarization, and visualization into a single Web portal. The application is intended to help manage the results of PubMed searches by condensing core semantic content in the citations retrieved. Output is presented as a connected graph of semantic relations, with links to the original MEDLINE citations. The ability to connect salient information across documents helps users keep up with the research literature and discover connections which might otherwise go unnoticed. Semantic MEDLINE can make an impact on biomedicine by supporting scientific discovery and the timely translation of insights from basic research into advances in clinical practice and patient care. Marcelo Fiszman has an M.D. from the State University of Rio de Janeiro and a Ph.D. in biomedical informatics from the University of Utah. He was awarded a postdoctoral fellowship in biomedical informatics at the National Library of Medicine (NLM) and is currently a research scientist there. His work focuses on natural language processing algorithms that exploit symbolic, rule-based techniques for semantic interpretation of biomedical text. He is also interested in using extracted semantic information for automatic abstraction summarization and literaturebased discovery. These efforts underpin Semantic MEDLINE, which is currently under development at NLM. This innovative biomedical information management application combines document retrieval, semantic interpretation, automatic summarization, and knowledge visualization into a single application.

negative: Effects of Professional Development on Teachers ' Instruction : Results from a Three-Year Longitudinal Study JSTOR is a not-for-profit service that helps scholars, researchers, and students discover, use, and build upon a wide range of content in a trusted digital archive. We use information technology and tools to increase productivity and facilitate new forms of scholarship. For more information about JSTOR, please contact support@jstor.org.. American Educational Research Association is collaborating with JSTOR to digitize, preserve and extend access to Educational Evaluation and Policy Analysis. This article examines the effects of professional development on teachers' instruction. Using a purposefully selected sample of about 207 teachers in 30 schools, in 10 districts infive states, we examine features of teachers' professional development and its effects on changing teaching practice in mathematics and science from 1996-1999. We found that professional developmentfocused on specific instructional practices increases teachers' use of those practices in the classroom. Furthermore, we found that specificfeatures, such as active learning opportunities, increase the effect of the professional development on teacher's instruction. What are the characteristics of professional development that affect teaching practice? This study adds to the knowledge base on effective professional development. The success of standards-based reform depends on teachers' ability to foster both basic knowledge and advanced thinking and problem solving among their students (Loucks-Desimone et al. Education 1999a), and surveys of teachers about their preservice preparation and in-service professional development experiences (e.g., Carey & Frechtling, 1997). In addition, there is a considerable amount of literature describing "best practices" in professional development, drawing on expert experiences (e.g., Loucks-Horsley et al., 1998). A professional consensus is emerging about particular characteristics of "high quality" professional development. These characteristics include a focus on content and how students learn content; in-depth, active learning opportunities; links to high standards, opportunities for teachers to engage in leadership roles; extended duration ; and the collective participation of groups of teachers from the same school, grade, or department. Although lists of characteristics such as these commonly appear in the literature on effective professional development, there is little direct evidence on the extent to which these characteristics are related to better teaching and increased student achievement. Some studies conducted over the past decade suggest that professional development experiences that share all or most of these characteristics can have a substantial, positive influence on teachers' classroom practice and student achieve-A few recent studies have begun to examine the relative importance of specific characteristics of professional development. Several studies have found that the intensity and …

negative: "It's alive, it's magic, it's in love with you": opportunities, challenges and open questions for actuated interfaces Actuated Interfaces are receiving a great deal of interest from the research community. The field can now present a range of point designs, illustrating the potential design space of Actuated Interfaces. However, despite the increasing interest in Actuated Interfaces, the research carried out is nevertheless primarily preoccupied with the technical challenges and potential application areas, rather than how users actually approach, experience, interpret and understand Actuated Interfaces. Based on three case studies, investigating how people experience Actuated Interfaces, we point to; magic, movement and ambiguity as fruitful perspectives for understanding users' experiences with Actuated Interfaces. The three perspectives are employed to reflect upon opportunities and challenges, as well as point to open questions and relevant areas for future research for Actuated Interfaces.

negative: Automatic Airspace Sectorisation: A Survey Airspace sectorisation provides a partition of a given airspace into sectors, subject to geometric constraints and workload constraints, so that some cost metric is minimised. We survey the algorithmic aspects of methods for automatic airspace sectorisation, for an intended readership of experts on air traffic management.

negative: The Function of Fiction is the Abstraction and Simulation of Social Experience. Fiction literature has largely been ignored by psychology researchers because its only function seems to be entertainment, with no connection to empirical validity. We argue that literary narratives have a more important purpose. They offer models or simulations of the social world via abstraction, simplification, and compression. Narrative fiction also creates a deep and immersive simulative experience of social interactions for readers. This simulation facilitates the communication and understanding of social information and makes it more compelling, achieving a form of learning through experience. Engaging in the simulative experiences of fiction literature can facilitate the understanding of others who are different from ourselves and can augment our capacity for empathy and social inference.

negative: High frequency ultrasound imaging of surface and subsurface structures of fingerprints High frequency ultrasound is suitable for non-invasive evaluation of skin because it can obtain both morphological and biomechanical information. A specially developed acoustic microscope system with the central frequency of 100 MHz was developed. The system was capable of (1) conventional C-mode acoustic microscope imaging of thinly sliced tissue, (2) ultrasound impedance imaging of the surface of in vivo thick tissue and (3) 3D ultrasound imaging of inside of the in vivo tissue. In the present study, ultrasound impedance imaging and 3D ultrasound imaging of in vivo fingerprints were obtained. The impedance image showed pores of the sweat glands in the surface of fingerprint and 3D ultrasound imaging showed glands of the rear surface of fingerprint. Both findings were not visualized by normal optical imaging, thus the system can be applied to pathological diagnosis of skin lesions and assessment of aging of the skin in cosmetic point of view.

negative: A review on fingerprint orientation estimation Fingerprint orientation plays important roles in fingerprint enhancement, fingerprint classification, and fingerprint recognition. This paper critically reviews the primary advances on fingerprint orientation estimation. Advantages and limitations of existing methods have been addressed. Issues on future development have been discussed. Copyright © 2010 John Wiley & Sons, Ltd.

negative: A Kinect-Based Wearable Face Recognition System to Aid Visually Impaired Users In this paper, we introduce a real-time face recognition (and announcement) system targeted at aiding the blind and low-vision people. The system uses a Microsoft Kinect sensor as a wearable device, performs face detection, and uses temporal coherence along with a simple biometric procedure to generate a sound associated with the identified person, virtualized at his/her estimated 3-D location. Our approach uses a variation of the K-nearest neighbors algorithm over histogram of oriented gradient descriptors dimensionally reduced by principal component analysis. The results show that our approach, on average, outperforms traditional face recognition methods while requiring much less computational resources (memory, processing power, and battery life) when compared with existing techniques in the literature, deeming it suitable for the wearable hardware constraints. We also show the performance of the system in the dark, using depth-only information acquired with Kinect's infrared camera. The validation uses a new dataset available for download, with 600 videos of 30 people, containing variation of illumination, background, and movement patterns. Experiments with existing datasets in the literature are also considered. Finally, we conducted user experience evaluations on both blindfolded and visually impaired users, showing encouraging results.

negative: Analysis of H.264 Bitstream Prioritization for Dual TCP/UDP Streaming of HD video over WLANs Flexible Dual-TCP/UDP Streaming Protocol (FDSP) is a new method for streaming H.264-encoded HD video over wireless networks. The method takes advantage of the hierarchical structure of H.264/AVC syntax and uses TCP to transmit important syntax elements of H.264/AVC video and UDP to transmit less important elements. FDSP was shown to outperform pure-UDP streaming in visual quality and pure-TCP streaming in delay. In this work, FDSP is expanded to include a new parameter called Bitstream Prioritization (BP). The newly modified algorithm, FDSP-BP, is analyzed to measure the impact of BP on the quality of streaming for partially and fully congested networks. Our analysis shows that FDSP-BP is superior to pure-TCP streaming methods with respect to rebuffering instances, while still maintaining high visual quality.

negative: Indoor Localization Algorithm based on Fingerprint Using a Single Fifth Generation Wi-Fi Access Point This paper proposes an indoor positioning system (IPS) based on WLAN using a single fifth-generation (5G) Wi-Fi access point. The proposed method uses fingerprint and classification models based on KNN (K-nearest neighbor) and Bayes rule. The fingerprint is formed by beam RSS (Received Signal Strength) samples, collected in some 2D locations of the indoor environment. Numerical simulations shown that using the best beam samples, it is possible to locate the stationary user's mobile device with average error less than 2.5 m.

negative: Development of a credible and integrated electronic voting machine based on contactless IC cards, biometrie fingerprint credentials and POS printer In recent times there has been a decline in the confidence of common people over electronic voting machines (EVMs). More anticipated than what the reality actually is, today's automated vote casting methods have faced immense controversy related to being vulnerable to hacking and questions have been raised about their transparency and security. This paper proposes the design and development of a novel tamper resistant electronic voting system that aims to mitigate the recurring issues and flaws of existing voting machines of today. To elucidate the system in brief, multiple layered verification process would be carried out on a potential voter by the means of fingerprint recognition and a Near Field Communication (NFC) smart card entry in order to authenticate his or her identity. Subsequently, the person would cast the vote by pressing a button corresponding to a particular candidate which would be recorded in the system providing the vote caster a visual confirmation. The final vote would then be printed out spontaneously onto a ballot box using a POS (Point of Sales) printer for an added level of validation. This system not only prevents multiple vote casts, but also eliminates the discrepancies that commonly arise with a person claiming not to have voted, whereas his or her name is present in the list of vote casters. The project is an incremental extension of a previous research.