Procedural facade variations from a single layout

We introduce a framework to generate many variations of a facade design that look similar to a given facade layout. Starting from an input image, the facade is hierarchically segmented and labeled with a collection of manual and automatic tools. The user can then model constraints that should be maintained in any variation of the input facade design. Subsequently, facade variations are generated for different facade sizes, where multiple variations can be produced for a certain size. Computing such new facade variations has many unique challenges, and we propose a new algorithm based on interleaving heuristic search and quadratic programming. In contrast to most previous work, we focus on the generation of new design variations and not on the automatic analysis of the input's structure. Adding a modeling step with the user in the loop ensures that our results routinely are of high quality.


Survey of Insurance Fraud Detection Using Data Mining Techniques

62 Abstract— With an increase in financial accounting fraud in the current economic scenario experienced, financial accounting fraud detection has become an emerging topics of great importance for academics, research and industries. Financial fraud is a deliberate act that is contrary to law, rule or policy with intent to obtain unauthorized financial benefit and intentional misstatements or omission of amounts by deceiving users of financial statements, especially investors and creditors. Data mining techniques are providing great aid in financial accounting fraud detection, since dealing with the large data volumes and complexities of financial data are big challenges for forensic accounting. Financial fraud can be classified into four: bank fraud, insurance fraud, securities and commodities fraud. Fraud is nothing but wrongful or criminal trick planned to result in financial or personal gains. This paper describes the more details on insurance sector related frauds and related solutions. In finance, insurance sector is doing important role and also it is unavoidable sector of every human being.


A Region Ensemble for 3-D Face Recognition

In this paper, we introduce a new system for 3D face recognition based on the fusion of results from a committee of regions that have been independently matched. Experimental results demonstrate that using 28 small regions on the face allow for the highest level of 3D face recognition. Score-based fusion is performed on the individual region match scores and experimental results show that the Borda count and consensus voting methods yield higher performance than the standard sum, product, and min fusion rules. In addition, results are reported that demonstrate the robustness of our algorithm by simulating large holes and artifacts in images. To our knowledge, no other work has been published that uses a large number of 3D face regions for high-performance face matching. Rank one recognition rates of 97.2% and verification rates of 93.2% at a 0.1% false accept rate are reported and compared to other methods published on the face recognition grand challenge v2 data set.


Deformation Modeling for Robust 3D Face Matching

Face recognition based on 3D surface matching is promising for overcoming some of the limitations of current 2D image-based face recognition systems. The 3D shape is generally invariant to the pose and lighting changes, but not invariant to the nonrigid facial movement such as expressions. Collecting and storing multiple templates to account for various expressions for each subject in a large database is not practical. We propose a facial surface modeling and matching scheme to match 2.5D facial scans in the presence of both nonrigid deformations and pose changes (multiview) to a stored 3D face model with neutral expression. A hierarchical geodesic-based resampling approach is applied to extract landmarks for modeling facial surface deformations. We are able to synthesize the deformation learned from a small group of subjects (control group) onto a 3D neutral model (not in the control group), resulting in a deformed template. A user-specific (3D) deformable model is built for each subject in the gallery with respect to the control group by combining the templates with synthesized deformations. By fitting this generative deformable model to a test scan, the proposed approach is able to handle expressions and pose changes simultaneously. A fully automatic and prototypic deformable model based 3D face matching system has been developed. Experimental results demonstrate that the proposed deformation modeling scheme increases the 3D face matching accuracy in comparison to matching with 3D neutral models by 7 and 10 percentage points, respectively, on a subset of the FRGC v2.0 3D benchmark and the MSU multiview 3D face database with expression variations.


Monitoring critical temperatures in permanent magnet synchronous motors using low-order thermal models

Monitoring critical temperatures in electric motors is crucial for preventing shortened motor life spans due to excessive thermal stress. With regard to interior permanent magnet synchronous motors (IPMSM), critical temperatures typically occur in the magnets and in the stator end winding. As directly measuring temperatures, especially on the rotating part, is costly, sensitive and thus not applicable with respect to automotive applications, model-based approaches are preferred. In this paper, two low-order thermal models for accurate temperature estimations in the permanent magnets, the winding and the end winding are introduced and compared. The model parameters are estimated solely based on experimental data via a multistep identification approach for linear parameter-varying systems. The model performances are validated by extensive experimental results based on a high-speed PMSM typically used as traction motor in subcompact electric cars.


RC-RCD clamp circuit for ringing losses reduction in a flyback converter

An RCD clamp circuit is usually used in flyback converters, in order to limit the voltage spikes caused by leakage transformer inductance. Oscillation ringing appears due to the clamp diode, which deteriorates the converter's power rate. This brief describes this ringing phenomenon and the use of an RC-RCD clamp circuit for damping the clamp diode's oscillation. This clamp circuit is capable for improving a flyback converter's power ratio.


A secure and unclonable embedded system using instruction-level PUF authentication

In this paper we present a secure and unclonable embedded system design that can target either an FPGA or an ASIC technology. The premise of the security is that the executed machine code and the executing environment (the embedded processor) will authenticate each other at a per-instruction basis using Physical Unclonable Functions (PUFs) that are built into the processor. The PUFs ensure that the execution of the binary code may only proceed if the binary is compiled with the correct intrinsic knowledge of the PUFs, and that such intrinsic knowledge is virtually unique to each processor and therefore unclonable. We will explain how to implement and integrate the PUFs into the processor's execution environment such that each instruction is authenticated and de-obfuscated on-demand and how to transform an ordinary binary executable into PUF-aware, obfuscated binaries. We will also present a prototype system on a Xilinx Spartan6-based FPGA board.


Augmenting feature model through customer preference mining by hybrid sentiment analysis

A feature model is an essential tool to identify variability and commonality within a product line of an enterprise, assisting stakeholders to configure product lines and to discover opportunities for reuse. However, the number of product variants needed to satisfy individual customer needs is still an open question, as feature models do not incorporate any direct customer preference information. In this paper, we propose to incorporate customer preference information into feature models using sentiment analysis of user-generated online product reviews. The proposed sentiment analysis method is a hybrid combination of affective lexicons and a rough-set technique. It is able to predict sentence sentiments for individual product features with acceptable accuracy, and thus augment a feature model by integrating positive and negative opinions of the customers. Such opinionated customer preference information is regarded as one attribute of the features, which helps to decide the number of variants needed within a product line. Finally, we demonstrate the feasibility and potential of the proposed method via an application case of Kindle Fire HD tablets.


Inferring tumor progression from genomic heterogeneity.

Cancer progression in humans is difficult to infer because we do not routinely sample patients at multiple stages of their disease. However, heterogeneous breast tumors provide a unique opportunity to study human tumor progression because they still contain evidence of early and intermediate subpopulations in the form of the phylogenetic relationships. We have developed a method we call Sector-Ploidy-Profiling (SPP) to study the clonal composition of breast tumors. SPP involves macro-dissecting tumors, flow-sorting genomic subpopulations by DNA content, and profiling genomes using comparative genomic hybridization (CGH). Breast carcinomas display two classes of genomic structural variation: (1) monogenomic and (2) polygenomic. Monogenomic tumors appear to contain a single major clonal subpopulation with a highly stable chromosome structure. Polygenomic tumors contain multiple clonal tumor subpopulations, which may occupy the same sectors, or separate anatomic locations. In polygenomic tumors, we show that heterogeneity can be ascribed to a few clonal subpopulations, rather than a series of gradual intermediates. By comparing multiple subpopulations from different anatomic locations, we have inferred pathways of cancer progression and the organization of tumor growth.


Will NoSQL Databases Live Up to Their Promise?

Many organizations collect vast amounts of customer, scientific, sales, and other data for future analysis. Traditionally, most of these organizations have stored structured data in relational databases for subsequent access and analysis. However, a growing number of developers and users have begun turning to various types of nonrelational, now frequently called NoSQL-databases. Nonrelational databases, including hierarchical, graph, and object-oriented databases-have been around since the late 1960s. However, new types of NoSQL databases are being developed. And only now are they beginning to gain market traction. Different NoSQL databases take different approaches. What they have in common is that they're not relational. Their primary advantage is that, unlike relational databases, they handle unstructured data such as word-processing files, e-mail, multimedia, and social media efficiently. This paper discuss issues such as limitations, advantages, concerns and doubts regarding NoSQL databases.


Wide-band microstrip antenna with an H-shaped coupling aperture

Theoretical and experimental results of a wide-band planar antenna are presented. This antenna can achieve a wide bandwidth, low cross-polarization levels, and low backward radiation levels. For wide bandwidth and easy integration with active circuits, it uses aperture-coupled stacked square patches. The coupling aperture is an H-shaped aperture. Based on the finite-difference time-domain method, a parametric study of the input impedance of the antenna is presented, and effects of each parameter on the antenna impedance are illustrated. One antenna is also designed, fabricated, and measured. The measured return loss exhibits an impedance bandwidth of 21.7%. The cross-polarization levels in both and planes are better than 23 dB. The front-to-back ratio of the antenna radiation pattern is better than 22 dB. Both theoretical and experimental results of parameters and radiation patterns are presented and discussed.


Putting Egocentric and Allocentric into Perspective

In the last decade many studies examined egocentric and allocentric spatial relations. For various tasks, navigators profit from both kinds of relations. However, their interrelation seems to be underspecified. We present four elementary representations of allocentric and egocentric relations (sensorimotor contingencies, egocentric coordinate systems, allocentric coordinate systems, and perspective-free representations) and discuss them with respect to their encoding and retrieval. Elementary representations are problematic for capturing large spaces and situations which encompass both allocentric and egocentric relations at the same time. Complex spatial representations provide a solution to this problem. They combine elementary coordinate representations either by pair-wise connections or by hierarchical embedding. We discuss complex spatial representations with respect to computational requirements and their plausibility regarding behavioral and neural findings. This work is meant to clarify concepts of egocentric and allocentric, to show their limitations, benefits and empirical plausibility and to point out new directions for future research.


Novel Video Prediction for Large-scale Scene using Optical Flow

Making predictions of future frames is a critical challenge in autonomous driving research. Most of the existing methods for video prediction attempt to generate future frames in simple and fixed scenes. In this paper, we propose a novel and effective optical flow conditioned method for the task of video prediction with an application to complex urban scenes. In contrast with previous work, the prediction model only requires video sequences and optical flow sequences for training and testing. Our method uses the rich spatial-temporal features in video sequences. The method takes advantage of the motion information extracting from optical flow maps between neighbor images as well as previous images. Empirical evaluations on the KITTI dataset and the Cityscapes dataset demonstrate the effectiveness of our method.


Impact of timing and frequency offsets on multicarrier waveform candidates for 5G

This paper presents a study of the candidate waveforms for 5G when they are subject to timing and carrier frequency offset. These waveforms are: orthogonal frequency division multiplexing (OFDM), generalized frequency division multiplexing (GFDM), universal filtered multicarrier (UFMC), circular filter bank multicarrier (C-FBMC), and linear filter bank multicarrier (FBMC). We are particularly interested in multiple access interference (MAI) when a number of users transmit their signals to a base station in an asynchronous or a quasi-synchronous manner. We identify the source of MAI in these waveforms and present some numerical analysis that confirm our findings. The goal of this study is to answer the following question, “Which one of the 5G candidate waveforms has more relaxed synchronization requirements?”.


Contextmapping : experiences from practice

In recent years, various methods and techniques have emerged for mapping the contexts of people's interaction with products. Designers and researchers use these techniques to gain deeper insight into the needs and dreams of prospective users of new products. As most of these techniques are still under development, there is a lack of practical knowledge about how such studies can be conducted. In this paper we share our insights, based on several projects from research and many years of industrial practice, of conducting user studies with generative techniques. The appendix contains a single case illustrating the application of these techniques in detail.


Enhancing Test Compression With Dependency Analysis for Multiple Expansion Ratios

Scan test data compression is widely used in industry to reduce test data volume (TDV) and test application time (TAT). This paper shows how multiple scan chain expansion ratios can help to obtain high test data compression in system-on-chips. Scan chains are partitioned with a higher expansion ratio than normal in scan compression mode and then are gradually concatenated based on a cost function to detect any faults that could not be detected at the higher expansion ratios. It improves the overall test compression ratio since it potentially allows faults to be detected at the highest expansion ratio. This paper introduces a new cost function to choose scan chain concatenation candidates for concatenation for multiple expansion ratios. To avoid TDV and TAT increase by scan concatenation, the proposed method takes a logic structure and scan chain length into consideration. Experiment results show the proposed method reduces TAT and TDV by 53%–64% compared with a traditional scan compression method.


Implementation Challenges for Information Security Awareness Initiatives in E-Government

With the widespread adoption of electronic government services, there has been a need to ensure a seamless flow of information across public sector organizations, while at the same time, maintaining confidentiality, integrity and availability. Governments have put in place various initiatives and programs including information security awareness to provide the needed understanding on how public sector employees can maintain security and privacy. Nonetheless, the implementation of such initiatives often faces a number of challenges that impede further take-up of e-government services. This paper aims to provide a better understanding of the challenges contributing towards the success of information security awareness initiatives implementation in the context of e-government. Political, organizational, social as well as technological challenges have been utilized in a conceptual framework to signify such challenges in e-government projects. An empirical case study conducted in a public sector organization in Greece was exploited in this research to reflect on these challenges. While, the results from this empirical study confirm the role of the identified challenges for the implementation of security awareness programs in e-government, it has been noticed that awareness programmers often pursue different targets of preserving security and privacy, which sometimes results in adding more complexity to the organization.


Positive emotions in early life and longevity: findings from the nun study.

Handwritten autobiographies from 180 Catholic nuns, composed when participants were a mean age of 22 years, were scored for emotional content and related to survival during ages 75 to 95. A strong inverse association was found between positive emotional content in these writings and risk of mortality in late life (p < .001). As the quartile ranking of positive emotion in early life increased, there was a stepwise decrease in risk of mortality resulting in a 2.5-fold difference between the lowest and highest quartiles. Positive emotional content in early-life autobiographies was strongly associated with longevity 6 decades later. Underlying mechanisms of balanced emotional states are discussed.


Targetless Calibration of a Lidar - Perspective Camera Pair

A novel method is proposed for the calibration of a camera - 3D lidar pair without the use of any special calibration pattern or point correspondences. The proposed method has no specific assumption about the data source: plain depth information is expected from the lidar scan and a simple perspective camera is used for the 2D images. The calibration is solved as a 2D-3D registration problem using a minimum of one (for extrinsic) or two (for intrinsic-extrinsic) planar regions visible in both cameras. The registration is then traced back to the solution of a non-linear system of equations which directly provides the calibration parameters between the bases of the two sensors. The method has been tested on a large set of synthetic lidar-camera image pairs as well as on real data acquired in outdoor environment.


ISCEV standard for clinical visual evoked potentials: (2016 update)

Visual evoked potentials (VEPs) can provide important diagnostic information regarding the functional integrity of the visual system. This document updates the ISCEV standard for clinical VEP testing and supersedes the 2009 standard. The main changes in this revision are the acknowledgment that pattern stimuli can be produced using a variety of technologies with an emphasis on the need for manufacturers to ensure that there is no luminance change during pattern reversal or pattern onset/offset. The document is also edited to bring the VEP standard into closer harmony with other ISCEV standards. The ISCEV standard VEP is based on a subset of stimulus and recording conditions that provide core clinical information and can be performed by most clinical electrophysiology laboratories throughout the world. These are: (1) Pattern-reversal VEPs elicited by checkerboard stimuli with large 1 degree (°) and small 0.25° checks. (2) Pattern onset/offset VEPs elicited by checkerboard stimuli with large 1° and small 0.25° checks. (3) Flash VEPs elicited by a flash (brief luminance increment) which subtends a visual field of at least 20°. The ISCEV standard VEP protocols are defined for a single recording channel with a midline occipital active electrode. These protocols are intended for assessment of the eye and/or optic nerves anterior to the optic chiasm. Extended, multi-channel protocols are required to evaluate postchiasmal lesions.


Satellite Imagery Multiscale Rapid Detection with Windowed Networks

Detecting small objects over large areas remains a significant challenge in satellite imagery analytics. Among the challenges is the sheer number of pixels and geographical extent per image: a single DigitalGlobe satellite image encompasses over 64 km and over 250 million pixels. Another challenge is that objects of interest are often minuscule (∼ 10 pixels in extent even for the highest resolution imagery), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (SIMRDWN) that evaluates satellite images of arbitrarily large size at native resolution at a rate of ≥ 0.2 km/s. Building upon the tensorflow object detection API paper [9], this pipeline offers a unified approach to multiple object detection frameworks that can run inference on images of arbitrary size. The SIMRDWN pipeline includes a modified version of YOLO (known as YOLT [25]), along with the models in [9]: SSD [14], Faster R-CNN [22], and R-FCN [3]. The proposed approach allows comparison of the performance of these four frameworks, and can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. For objects of very different scales (e.g. airplanes versus airports) we find that using two different detectors at different scales is very effective with negligible runtime cost. We evaluate large test images at native resolution and find mAP scores of 0.2 to 0.8 for vehicle localization, with the YOLT architecture achieving both the highest mAP and fastest inference speed.


N-gram Counts and Language Models from the Common Crawl

We contribute 5-gram counts and language models trained on the Common Crawl corpus, a collection over 9 billion web pages. This release improves upon the Google n-gram counts in two key ways: the inclusion of low-count entries and deduplication to reduce boilerplate. By preserving singletons, we were able to use Kneser-Ney smoothing to build large language models. This paper describes how the corpus was processed with emphasis on the problems that arise in working with data at this scale. Our unpruned Kneser-Ney English 5-gram language model, built on 975 billion deduplicated tokens, contains over 500 billion unique n-grams. We show gains of 0.5–1.4 BLEU by using large language models to translate into various languages.


Empirical Study on Deep Learning Models for QA

In this paper we explore deep learning models with memory component or attention mechanism for question answering task. We combine and compare three models, Neural Machine Translation [1], Neural Turing Machine [5], and Memory Networks [15] for a simulated QA data set [14]. This paper is the first one that uses Neural Machine Translation and Neural Turing Machines for solving QA tasks. Our results suggest that the combination of attention and memory have potential to solve certain QA problem.


Depth-Gated LSTM

In this short note, we present an extension of long short-term memory (LSTM) neural networks to using a depth gate to connect memory cells of adjacent layers. Doing so introduces a linear dependence between lower and upper layer recurrent units. Importantly, the linear dependence is gated through a gating function, which we call depth gate. This gate is a function of the lower layer memory cell, the input to and the past memory cell of this layer. We conducted experiments and verified that this new architecture of LSTMs was able to improve machine translation and language modeling performances.


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.


Clustering Spatial Data in the Presence of Obstacles: a Density-Based Approach

Clustering spatial data is a well-known problem that has been extensively studied. Grouping similar data in large 2-dimensional spaces to find hidden patterns or meaningful sub-groups has many applications such as satellite imagery, geographic information systems, medical image analysis, marketing, computer visions, etc. Although many methods have been proposed in the literature, very few have considered physical obstacles that may have significant consequences on the effectiveness of the clustering. Taking into account these constraints during the clustering process is costly and the modeling of the constraints is paramount for good performance. In this paper, we investigate the problem of clustering in the presence of constraints such as physical obstacles and introduce a new approach to model these constraints using polygons. We also propose a strategy to prune the search space and reduce the number of polygons to test during clustering. We devise a density-based clustering algorithm, DBCluC, which takes advantage of our constraint modeling to efficiently cluster data objects while considering all physical constraints. The algorithm can detect clusters of arbitrary shape and is insensitive to noise, the input order, and the difficulty of constraints. Its average running complexity is O(NlogN) where N is the number of


The selection of fusion levels in thoracic idiopathic scoliosis.

From the material and data reviewed in our study of 405 patients, it appears that postoperative correction of the thoracic spine approximately equals the correction noted on preoperative side-bending roentgenograms. Selective thoracic fusion can be safely performed on a Type-II curve of less than 80 degrees, but care must be taken to use the vertebra that is neutral and stable so that the lower level of the fusion is centered over the sacrum. The lumbar curve spontaneously corrects to balance the thoracic curve when selective thoracic fusion is performed and the lower level of fusion is properly selected. In Type-III, IV, and V thoracic curves the lower level of fusion should be centered over the sacrum to achieve a balanced, stable spine.


Image quality measures and their performance

A number of quality measures are evaluated for gray scale image compression. They are all bivariate, exploiting the differences between corresponding pixels in the original and degraded images. It is shown that although some numerical measures correlate well with the observers’ response for a given compression technique, they are not reliable for an evaluation across different techniques. A graphical measure called Hosaka plots, however, can be used to appropriately specify not only the amount, but also the type of degradation in reconstructed images.


CacheAudit: A Tool for the Static Analysis of Cache Side Channels

We present CacheAudit, a versatile framework for the automatic, static analysis of cache side channels. CacheAudit takes as input a program binary and a cache configuration and derives formal, quantitative security guarantees for a comprehensive set of side-channel adversaries, namely, those based on observing cache states, traces of hits and misses, and execution times. Our technical contributions include novel abstractions to efficiently compute precise overapproximations of the possible side-channel observations for each of these adversaries. These approximations then yield upper bounds on the amount of information that is revealed.
 In case studies, we apply CacheAudit to binary executables of algorithms for sorting and encryption, including the AES implementation from the PolarSSL library, and the reference implementations of the finalists of the eSTREAM stream cipher competition. The results we obtain exhibit the influence of cache size, line size, associativity, replacement policy, and coding style on the security of the executables and include the first formal proofs of security for implementations with countermeasures such as preloading and data-independent memory access patterns.


A correlation game for unsupervised learning yields computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synapse elimination

Much has been learned about plasticity of biological synapses from empirical studies. Hebbian plasticity is driven by correlated activity of presynaptic and postsynaptic neurons. Synapses that converge onto the same neuron often behave as if they compete for a fixed resource; some survive the competition while others are eliminated. To provide computational interpretations of these aspects of synaptic plasticity, we formulate unsupervised learning as a zero-sum game between Hebbian excitation and anti-Hebbian inhibition in a neural network model. The game formalizes the intuition that Hebbian excitation tries to maximize correlations of neurons with their inputs, while anti-Hebbian inhibition tries to decorrelate neurons from each other. We further include a model of synaptic competition, which enables a neuron to eliminate all connections except those from its most strongly correlated inputs. Through empirical studies, we show that this facilitates the learning of sensory features that resemble parts of objects. Much has been learned about plasticity of biological synapses from empirical studies. One line of research has explored Hebbian plasticity, which is triggered by correlated presynaptic and postsynaptic activity. Many kinds of excitatory synapses are strengthened by correlated activity, and are said to be Hebbian [Bi and Poo, 2001]. Strengthening of inhibitory synapses by correlated activity has also been observed [Gaiarsa et al., 2002]. The phenomenon is sometimes said to be anti-Hebbian, because strengthening an inhibitory synapse is like making a negative number more negative. Another line of research has shown that synapses converging onto the same neuron often behave as if they compete for shares of a fixed “pie”; some survive the competition while others are eliminated [Lichtman and Colman, 2000]. According to this viewpoint, Hebbian plasticity does not increase or decrease the pie of synaptic resources; it only allocates resources across convergent synapses [Miller, 1996] Theoretical neuroscientists have proposed a number of computational functions for Hebbian excitation, anti-Hebbian inhibition, and synaptic competition/elimination. Hebbian excitation has long been invoked as a mechanism for learning features from sensory input [Von der Malsburg, 1973]. Lateral inhibition has been used to sparsen neural activity, thereby facilitating Hebbian feature learning [Von der Malsburg, 1973, Fukushima, 1980, Rumelhart and Zipser, 1985, Kohonen, 1990]. Endowing the lateral inhibition with anti-Hebbian plasticity can give more robust control over sparseness of activity. Földiák [1990] demonstrated this with numerical experiments, but did not provide an interpretation in terms of an optimization principle. Less relevant here are antiHebbian models without reference to sparse activity in linear [Foldiak, 1989, Rubner and Tavan, 1989, Rubner and Schulten, 1990] and nonlinear [Carlson, 1990, Girolami and Fyfe, 1997] networks. Also less relevant is the application of anti-Hebbian plasticity to feedforward rather than lateral connections [Hyvärinen and Oja, 1998]. 1 ar X iv :1 70 4. 00 64 6v 1 [ cs .N E ] 3 A pr 2 01 7 Leen [1991] performed a stability analysis for a linear network with Hebbian feedforward connections and anti-Hebbian lateral connections. Plumbley [1993] derived a linear network with antiHebbian lateral inhibition (but no plasticity of feedforward connections) from the principle of information maximization with a power constraint. Pehlevan et al. [2015] showed that a linear network with Hebbian feedforward connections and anti-Hebbian lateral inhibition can be interpreted as online gradient optimization of a “similarity matching” cost function. Pehlevan and Chklovskii [2014] and Hu et al. [2014] went on to extend the similarity matching principle to derive nonlinear neural networks for unsupervised learning. Synaptic competition and elimination have been studied in models of cortical development, and have been shown to play an important role in the emergence of feature selectivity [Miller, 1996]. The subject of the present work is a mathematical formalism that provides computational interpretations of Hebbian excitation, anti-Hebbian inhibition, and synaptic competition/elimination in nonlinear neural networks. We start by formulating unsupervised learning as the maximization of output-input correlations subject to upper bound constraints on output-output correlations. We motivate our formulation by describing its relation to previous theoretical frameworks for unsupervised learning, such as maximization [Linsker, 1988, Atick and Redlich, 1990, Plumbley, 1993, Bell and Sejnowski, 1995] or minimization of mutual information [Hyvärinen and Oja, 2000], (2) projection onto a subspace that maximizes a moment-based statistic such as variance [Oja, 1982, Linsker, 1988] or kurtosis [Huber, 1985], and the (3) similarity matching principle [Pehlevan et al., 2015]. To solve our constrained maximization problem, we introduce Lagrange multipliers. This Lagrangian dual formulation of unsupervised learning can in turn be solved by a nonlinear neural network with Hebbian excitation and anti-Hebbian inhibition. The network is very similar to the original model of Földiák [1990], differing mainly by its use of rectification rather than sigmoidal nonlinearity. (The latter can also be handled by our formalism, as shown in Appendix B.) Lagrange multipliers were also used to study anti-Hebbian plasticity by Plumbley [1993], but only for linear networks. Effectively, excitation and inhibition behave like players in a game, and the inhibitory connections can be interpreted as Lagrange multipliers. The game is zero-sum, in that excitation tries to maximize a payoff function and inhibition tries to minimize exactly the same payoff function. Roughly speaking, however, one could say that excitation aims to maximize the correlation of each output neuron with its inputs, while inhibition aims to decorrelate the output neurons from each other. Our term “correlation game” is derived from this intuitive picture. Within our mathematical formalism, we also consider a dynamics of synaptic competition and elimination that is drawn from models of cortical development [Miller and MacKay, 1994]. Competition between the excitatory synapses convergent on a single neuron is capable of driving the strengths of some synapses to zero. In numerical experiments with the MNIST dataset, we show that synapse elimination has the computational function of facilitating the learning of features that resemble “parts” of objects. Theoretical analysis shows that the surviving synapses converging onto an output neuron come from its most strongly correlated inputs; synapses from weakly correlated inputs are eliminated. Our correlation game is closely related to the similarity matching principle of Pehlevan and Chklovskii [2014] and Pehlevan et al. [2015]. The major novelty is the introduction of decorrelation as a constraint for the optimization. Paralleling our work, Pehlevan et al. [2017] have shown that the similarity matching principle leads to a game theoretic formulation through Hubbard-Stratonovich duality. Again our novelty is the use of decorrelation as a constraint, which leads to our correlation game through Lagrangian duality. Our model of synaptic competition and elimination was borrowed with slight modification from the literature on modeling neural development [Miller and MacKay, 1994]. It can be viewed as a more biologically plausible alternative to previous unsupervised learning algorithms that sparsen features. For example, Hoyer [2004] is similar to ours because it can be interpreted as independently sparsening each set of convergent synapses, rather than applying a global L1 regularizer to all synapses.


Uncertainty-based competition between prefrontal and dorsolateral striatal systems for behavioral control

A broad range of neural and behavioral data suggests that the brain contains multiple systems for behavioral choice, including one associated with prefrontal cortex and another with dorsolateral striatum. However, such a surfeit of control raises an additional choice problem: how to arbitrate between the systems when they disagree. Here, we consider dual-action choice systems from a normative perspective, using the computational theory of reinforcement learning. We identify a key trade-off pitting computational simplicity against the flexible and statistically efficient use of experience. The trade-off is realized in a competition between the dorsolateral striatal and prefrontal systems. We suggest a Bayesian principle of arbitration between them according to uncertainty, so each controller is deployed when it should be most accurate. This provides a unifying account of a wealth of experimental evidence about the factors favoring dominance by either system.


Stakeholders in Explainable AI

There is general consensus that it is important for artificial intelligence (AI) and machine learning systems to be explainable and/or interpretable. However, there is no general consensus over what is meant by ‘explainable’ and ‘interpretable’. In this paper, we argue that this lack of consensus is due to there being several distinct stakeholder communities. We note that, while the concerns of the individual communities are broadly compatible, they are not identical, which gives rise to different intents and requirements for explainability/interpretability. We use the software engineering distinction between validation and verification, and the epistemological distinctions between knowns/unknowns, to tease apart the concerns of the stakeholder communities and highlight the areas where their foci overlap or diverge. It is not the purpose of the authors of this paper to ‘take sides’ — we count ourselves as members, to varying degrees, of multiple communities — but rather to help disambiguate what stakeholders mean when they ask ‘Why?’ of an AI.


Loughborough University Institutional Repository Product attachment and replacement : implications for sustainable design

This research investigated a number of main areas of attachment in order to determine how consumer-product relationships are formed and to identify whether these feelings influence replacement decisions. Primary research comprised of interviews with consumers to discuss the topic area in relation to three possessions selected for their attachment qualities. The research highlighted how attachment is determined by multiple themes, many of which are circumstantial to consumers’ experiences and therefore difficult for designers to control. Findings showed that memories were the most prominent theme of participants’ attachment, closely followed by pleasure and usability. Enjoyment and pleasure were found to be the primary reason for attachment to newly purchased items, whereas nostalgia was highest for older possessions. Appearance and reliability were found to have considerable influence on participants’ attitudes towards replacement.


PID: the Pathway Interaction Database

The Pathway Interaction Database (PID, http://pid.nci.nih.gov) is a freely available collection of curated and peer-reviewed pathways composed of human molecular signaling and regulatory events and key cellular processes. Created in a collaboration between the US National Cancer Institute and Nature Publishing Group, the database serves as a research tool for the cancer research community and others interested in cellular pathways, such as neuroscientists, developmental biologists and immunologists. PID offers a range of search features to facilitate pathway exploration. Users can browse the predefined set of pathways or create interaction network maps centered on a single molecule or cellular process of interest. In addition, the batch query tool allows users to upload long list(s) of molecules, such as those derived from microarray experiments, and either overlay these molecules onto predefined pathways or visualize the complete molecular connectivity map. Users can also download molecule lists, citation lists and complete database content in extensible markup language (XML) and Biological Pathways Exchange (BioPAX) Level 2 format. The database is updated with new pathway content every month and supplemented by specially commissioned articles on the practical uses of other relevant online tools.


Global Inference for Sentence Compression: An Integer Linear Programming Approach

Sentence compression holds promise for many applications ranging from summarization to subtitle generation. Our work views sentence compression as an optimization problem and uses integer linear programming (ILP) to infer globally optimal compressions in the presence of linguistically motivated constraints. We show how previous formulations of sentence compression can be recast as ILPs and extend these models with novel global constraints. Experimental results on written and spoken texts demonstrate improvements over state-of-the-art models.


Toward tense as a clinical marker of specific language impairment in English-speaking children.

A critical clinical issue is the identification of a clinical marker, a linguistic form or principle that can be shown to be characteristic of children with Specific Language Impairment (SLI). In this paper we evaluate, as candidate clinical markers, a set of morphemes that mark Tense. In English, this includes -s third person singular, -ed regular past, BE, and DO. According to the Extended Optional Infinitive Account (EOI) of Rice, Wexler, and Cleave (1995), this set of morphemes is likely to appear optionally in the grammars of children with SLI at a rate lower than the optionality evident in younger controls. Three groups of preschool children participated: 37 children with SLI, and two control groups, one of 40 MLU-equivalent children and another of 45 age-equivalent children. Three kinds of evidence support the conclusion that a set of morphemes that marks Tense can be considered a clinical marker: (a) low levels of accuracy for the target morphemes for the SLI group relative to either of the two control groups; (b) affectedness for the set of morphemes defined by the linguistic function of Tense, but not for morphemes unrelated to Tense; and (c) a bimodal distribution for Tense-marking morphemes relative to age peers, in which the typical children are at essentially adult levels of the grammar, whereas children in the SLI group were at low (i.e., non-adultlike) levels of performance. The clinical symptoms are evident in omissions of surface forms. Errors of subject-verb agreement and syntactic misuses are rare, showing that, as predicted, children in an EOI stage who are likely to mark Tense optionally at the same time know a great deal about the grammatical properties of finiteness and agreement in the adult grammar. The findings are discussed in terms of alternative accounts of the grammatical limitations of children with SLI and implications for clinical identification.


A Privacy-Aware Authentication Scheme for Distributed Mobile Cloud Computing Services

In modern societies, the number of mobile users has dramatically risen in recent years. In this paper, an efficient authentication scheme for distributed mobile cloud computing services is proposed. The proposed scheme provides security and convenience for mobile users to access multiple mobile cloud computing services from multiple service providers using only a single private key. The security strength of the proposed scheme is based on bilinear pairing cryptosystem and dynamic nonce generation. In addition, the scheme supports mutual authentication, key exchange, user anonymity, and user untraceability. From system implementation point of view, verification tables are not required for the trusted smart card generator (SCG) service and cloud computing service providers when adopting the proposed scheme. In consequence, this scheme reduces the usage of memory spaces on these corresponding service providers. In one mobile user authentication session, only the targeted cloud service provider needs to interact with the service requestor (user). The trusted SCG serves as the secure key distributor for distributed cloud service providers and mobile clients. In the proposed scheme, the trusted SCG service is not involved in individual user authentication process. With this design, our scheme reduces authentication processing time required by communication and computation between cloud service providers and traditional trusted third party service. Formal security proof and performance analyses are conducted to show that the scheme is both secure and efficient.


A Real Options Approach for the Valuation of Highway Concessions

T theory of real options offers an approach for the valuation of investments in real assets, based on the methodology developed for financial options. This approach is especially appropriate in the context of strategic decision making under conditions of uncertainty. This is the case for the valuation of highway concessions, where real options arise from certain clauses of the contracts, for example, a minimum traffic guarantee. The possible exercise of these kinds of rights means an added value for the project that cannot be easily captured using traditional procedures for investment valuation. In this paper, we develop a methodology to value these clauses of highway concessions and, for that purpose, we consider the traffic on the highway as the underlying asset in an option contract, taking into account that, when a nonfinancial asset is used, some adjustments have to be made to the options approach. This methodology is applied to the case of an already operating highway concession, using real data, and the authors obtain an estimate of the value of a minimum traffic guarantee, which depends on several parameters that are analyzed. The authors conclude that this methodology is an appropriate tool for the valuation of highway concessions that have operational flexibility.


Pedestrian detection in low resolution videos can be challenging. In outdoor surveillance scenarios, the size of pedestrians in the images is often very small (around 20 pixels tall). The most common and successful approaches for single frame pedestrian detection use gradient-based features and a support vector machine classifier. We propose an extension of these ideas, and develop a new algorithm that extracts gradient features from a spatiotemporal volume, consisting of a short sequence of images (about one second in duration). The additional information provided by the motion of the person compensates for the loss of resolution. On standard datasets (PETS2001, VIRAT) we show a significant improvement in performance over single-frame detection.


Computer-generated pen-and-ink illustration

This paper describes the principles of traditional pen-and-ink illustration, and shows how a great number of them can be implemented as part of an automated rendering system. It introduces “stroke textures,” which can be used for achieving both texture and tone with line drawing. Stroke textures also allow resolution-dependent rendering, in which the choice of strokes used in an illustration is appropriately tied to the resolution of the target medium. We demonstrate these techniques using complex architectural models, including Frank Lloyd Wright's “Robie House.”


No need to war-drive: unsupervised indoor localization

We propose UnLoc, an unsupervised indoor localization scheme that bypasses the need for war-driving. Our key observation is that certain locations in an indoor environment present identifiable signatures on one or more sensing dimensions. An elevator, for instance, imposes a distinct pattern on a smartphone's accelerometer; a corridor-corner may overhear a unique set of WiFi access points; a specific spot may experience an unusual magnetic fluctuation. We hypothesize that these kind of signatures naturally exist in the environment, and can be envisioned as internal landmarks of a building. Mobile devices that "sense" these landmarks can recalibrate their locations, while dead-reckoning schemes can track them between landmarks. Results from 3 different indoor settings, including a shopping mall, demonstrate median location errors of 1:69m. War-driving is not necessary, neither are floorplans the system simultaneously computes the locations of users and landmarks, in a manner that they converge reasonably quickly. We believe this is an unconventional approach to indoor localization, holding promise for real-world deployment.


jModelTest 2: more models, new heuristics and parallel computing Diego Darriba, Guillermo L. Taboada, Ramón Doallo and David Posada Supplementary Table 1. New features in jModelTest 2 Supplementary Table 2. Model selection accuracy Supplementary Table 3. Mean square errors for model averaged estimates Supplementary Note 1. Hill-climbing hierarchical clustering algorithm Supplementary Note 2. Heuristic filtering Supplementary Note 3. Simulations from prior distributions Supplementary Note 4. Speed-up benchmark on real and simulated datasets


Vegan diets: practical advice for athletes and exercisers

With the growth of social media as a platform to share information, veganism is becoming more visible, and could be becoming more accepted in sports and in the health and fitness industry. However, to date, there appears to be a lack of literature that discusses how to manage vegan diets for athletic purposes. This article attempted to review literature in order to provide recommendations for how to construct a vegan diet for athletes and exercisers. While little data could be found in the sports nutrition literature specifically, it was revealed elsewhere that veganism creates challenges that need to be accounted for when designing a nutritious diet. This included the sufficiency of energy and protein; the adequacy of vitamin B12, iron, zinc, calcium, iodine and vitamin D; and the lack of the long-chain n-3 fatty acids EPA and DHA in most plant-based sources. However, via the strategic management of food and appropriate supplementation, it is the contention of this article that a nutritive vegan diet can be designed to achieve the dietary needs of most athletes satisfactorily. Further, it was suggested here that creatine and β-alanine supplementation might be of particular use to vegan athletes, owing to vegetarian diets promoting lower muscle creatine and lower muscle carnosine levels in consumers. Empirical research is needed to examine the effects of vegan diets in athletic populations however, especially if this movement grows in popularity, to ensure that the health and performance of athletic vegans is optimised in accordance with developments in sports nutrition knowledge.


An approach to automotive ECG measurement validation using a car-integrated test framework

Development and integration of physiological sensors into automotive applications is gaining importance. Assistance systems which possess knowledge about the driver's cognitive state could increase road safety. In this paper we present a flexible framework that enables the development, evaluation and verification of sensors and algorithms for automotive applications using physiological signals under realistic driving conditions. We have integrated a custom capacitive ECG measurement system into a test car and validated its performance in real world driving tests. During first test runs, the capacitive system achieved a sensitivity of up to 95.5% and a precision rate of up to 92.6%. Our system also records synchronized vehicle dynamics. We discuss the road test measurements which suggest that the driving situation highly impacts the quality of ECG signal. Therefore, information on driving dynamics could be used to improve the precision rate of future capacitive ECG measurement.


Spatial delineation of soil erosion vulnerability in the Lake Tana Basin , Ethiopia

The main objective of this study was to identify the most vulnerable areas to soil erosion in the Lake Tana Basin, Blue Nile, Ethiopia using the Soil and Water Assessment Tool (SWAT), a physically based distributed hydrological model, and a Geographic Information System based decision support system that uses multi-criteria evaluation (MCE). The SWAT model was used to estimate the sediment yield within each sub-basin and identify the most sediment contributing areas in the basin. Using the MCE analysis, an attempt was made to combine a set of factors (land use, soil, slope and river layers) to make a decision according to the stated objective. On the basis of simulated SWAT, sediment yields greater than 30 tons/ha for each of the sub-basin area, 18Ð4% of the watershed was determined to be high erosion potential area. The MCE results indicated that 12–30Ð5% of the watershed is high erosion potential area. Both approaches show comparable watershed area with high soil erosion susceptibility. The output of this research can aid policy and decision makers in determining the soil erosion ‘hot spots’ and the relevant soil and water conservation measures. Copyright  2009 John Wiley & Sons, Ltd.


The development of the SGI-16: a shortened sensory gating deficit and distractibility questionnaire for adults with ADHD.

The Sensory Gating Inventory (SGI) is a questionnaire composed of 36 items designed to investigate abnormal perception related to the inability to control sensitivity to sensory stimuli frequently reported in adult with ADHD. This questionnaire can be considered too lengthy to be taken by people with ADHD, and a shortened version is needed. One hundred and sixty-three adults with ADHD responded to the SGI-36. An item reduction process took into account both the results of statistical analyses and the expertise of a steering committee. Construct validity, reliability, and external validity were tested for a short version (16 items). The structure of the SGI-16 was confirmed by principal components factor analysis. Cronbach's alpha coefficients ranged from 0.78 to 0.89. The SGI-16 dimension scores were highly correlated with their respective SGI-36 dimension scores. The SGI-16 seems to be both appropriate and useful for use in clinical practice to investigate perceptual abnormalities in adults with ADHD.


Vehicular Ad Hoc Networks (VANETs): Challenges and Perspectives

Vehicular ad hoc network (VANET), a subclass of mobile ad hoc networks (MANETs), is a promising approach for future intelligent transportation system (ITS). These networks have no fixed infrastructure and instead rely on the vehicles themselves to provide network functionality. However, due to mobility constraints, driver behavior, and high mobility, VANETs exhibit characteristics that are dramatically different from many generic MANETs. This article provides a comprehensive study of challenges in these networks, which we concentrate on the problems and proposed solutions. Then we outline current state of the research and future perspectives. With this article, readers can have a more thorough understanding of vehicle ad hoc networking and the research trends in this area


Design of 5.9 ghz dsrc-based vehicular safety communication

The automotive industry is moving aggressively in the direction of advanced active safety. Dedicated short-range communication (DSRC) is a key enabling technology for the next generation of communication-based safety applications. One aspect of vehicular safety communication is the routine broadcast of messages among all equipped vehicles. Therefore, channel congestion control and broadcast performance improvement are of particular concern and need to be addressed in the overall protocol design. Furthermore, the explicit multichannel nature of DSRC necessitates a concurrent multichannel operational scheme for safety and non-safety applications. This article provides an overview of DSRC based vehicular safety communications and proposes a coherent set of protocols to address these requirements


Group signatures with verifier-local revocation

Group signatures have recently become important for enabling privacy-preserving attestation in projects such as Microsoft's ngscb effort (formerly Palladium). Revocation is critical to the security of such systems. We construct a short group signature scheme that supports Verifier-Local Revocation (VLR). In this model, revocation messages are only sent to signature verifiers (as opposed to both signers and verifiers). Consequently there is no need to contact individual signers when some user is revoked. This model is appealing for systems providing attestation capabilities. Our signatures are as short as standard RSA signatures with comparable security. Security of our group signature (in the random oracle model) is based on the Strong Diffie-Hellman assumption and the Decision Linear assumption in bilinear groups. We give a precise model for VLR group signatures and discuss its implications.


A Survey of Inter-Vehicle Communication ∗

As a component of the intelligent transportation system (ITS) and one of the concrete applications of mobile ad hoc networks, inter-vehicle communication (IVC) has attracted research attention from both the academia and industry of, notably, US, EU, and Japan. The most important feature of IVC is its ability to extend the horizon of drivers and on-board devices (e.g., radar or sensors) and, thus, to improve road traffic safety and efficiency. This paper surveys IVC with respect to key enabling technologies ranging from physical radio frequency to group communication primitives and security issues. The mobility models used to evaluate the feasibility of these technologies are also briefly described. We focus on the discussion of various MAC protocols that seem to be indispensable components in the network protocol stack of IVC. By analyzing the application requirements and the protocols built upon the MAC layer to meet these requirements, we also advocate our perspective that ad hoc routing protocols and group communication primitives migrated from wired networks might not be an efficient way to support the envisioned applications, and that new coordination algorithms directly based on MAC could be designed for this purpose.


Detecting Satire in the News with Machine Learning

We built models with Logistic Regression and linear Support Vector Machines on a large dataset consisting of regular news articles and news from satirical websites, and showed that such linear classifiers on a corpus with about 60,000 articles can perform with a precision of 98.7% and a recall of 95.2% on a random test set of the news. On the other hand, when testing the classifier on “publication sources” which are completely unknown during training, only an accuracy of 88.2% and an F1-score of 76.3% are achieved. As another result, we showed that the same algorithm can distinguish between news written by the news agency itself and paid articles from customers. Here the results had an accuracy of 99%.


Dual Grid Array Antennas in a Thin-Profile Package for Flip-Chip Interconnection to Highly Integrated 60-GHz Radios

We examine the current development of highly integrated 60-GHz radios with an interest in antenna-circuit interfaces. We design and analyze grid array antennas with special attention to the differential feeding and the patterned ground plane. More importantly, we integrate two grid array antennas in a package; propose the way of assembling it to the system printed circuit board; and demonstrate a total solution of low cost and thin profile to highly integrated 60-GHz radios. We show that the package in low temperature cofired ceramic (LTCC) technology measures only 13×13×0.575 mm3 ; can carry a 60-GHz radio die of current and future sizes with flip-chip bonding; and achieves good antenna performance in the 60-GHz band with maximum gain of 13.5 and 14.5 dBi for the single-ended and differential antennas, respectively.


NLOS Identification in Range-Based Source Localization: Statistical Approach

Least squares estimation is a widely-used technique for range-based source localization, which obtains the most probable position of mobile station. These methods cannot provide desirable accuracy in the case with a non line of sight (NLOS) path between mobile station and base stations. To circumvent this drawback, many algorithms have been proposed to identify and mitigate this error; however, they have a large run-time overhead. On the other hand, new positioning systems utilize a large set of base stations, and a practical algorithm should be fast enough to deal with them. In this paper, we propose a novel algorithm based on subspace method to identify and eliminate the NLOS error. Simulation studies show that our algorithm is faster and more accurate compared with other conventional methods, especially in the large-scale cases.


Visual Place Recognition: A Survey

Visual place recognition is a challenging problem due to the vast range of ways in which the appearance of real-world places can vary. In recent years, improvements in visual sensing capabilities, an ever-increasing focus on long-term mobile robot autonomy, and the ability to draw on state-of-the-art research in other disciplines-particularly recognition in computer vision and animal navigation in neuroscience-have all contributed to significant advances in visual place recognition systems. This paper presents a survey of the visual place recognition research landscape. We start by introducing the concepts behind place recognition-the role of place recognition in the animal kingdom, how a “place” is defined in a robotics context, and the major components of a place recognition system. Long-term robot operations have revealed that changing appearance can be a significant factor in visual place recognition failure; therefore, we discuss how place recognition solutions can implicitly or explicitly account for appearance change within the environment. Finally, we close with a discussion on the future of visual place recognition, in particular with respect to the rapid advances being made in the related fields of deep learning, semantic scene understanding, and video description.


Concepts Not Alone: Exploring Pairwise Relationships for Zero-Shot Video Activity Recognition

Vast quantities of videos are now being captured at astonishing rates, but the majority of these are not labelled. To cope with such data, we consider the task of content-based activity recognition in videos without any manually labelled examples, also known as zero-shot video recognition. To achieve this, videos are represented in terms of detected visual concepts, which are then scored as relevant or irrelevant according to their similarity with a given textual query. In this paper, we propose a more robust approach for scoring concepts in order to alleviate many of the brittleness and low precision problems of previous work. Not only do we jointly consider semantic relatedness, visual reliability, and discriminative power. To handle noise and non-linearities in the ranking scores of the selected concepts, we propose a novel pairwise order matrix approach for score aggregation. Extensive experiments on the large-scale TRECVID Multimedia Event Detection data show the superiority of our approach.


Strategic human resource management ( SHRM ) practices and its effect on financial performance : evidence from some selected scheduled private commercial banks in Bangladesh

Formulation and execution of Strategic Human Resource Management (SHRM) practices and its effect on perceived financial performance of any organization is a burning issue in the globalized competitive business era. This study aims to find out the relationship between SHRM and financial performance to ensure the sustainability and competitive advantage of the selected scheduled private commercial banks in Bangladesh. The research has been conducted on managers as sample of some private commercial banks during the period of January to November 2012 to collect the primary data. To evaluate the financial performance researchers have used annual reports of 2011 and 2012, journals, web sites etc. as secondary source. Survey research findings indicate that strategic integration and development of HRM were practiced to a full extent in the sampled firms. From the cross sectional analysis, the financial performance indicators show that the capital adequacy was mostly at satisfactory level compared with eh industry average. The quality of assets loan varies from bank to bank but most of them are performing at the desired level. Management efficiency was out-performing the standard in most of the cases. The profitability indicators ratio was also better than the average of private commercial banks. The result presented in this study suggests practicing intensive SHRM so that improved financial performance can be asserted to sustain in the competitive environment.


Stock Market Classification Model Using Sentiment Analysis on Twitter Based on Hybrid Naive Bayes Classifiers

Sentiment analysis has become one of the most popular process to predict stock market behaviour based on consumer reactions. Concurrently, the availability of data from Twitter has also attracted researchers towards this research area. Most of the models related to sentiment analysis are still suffering from inaccuracies. The low accuracy in classification has a direct effect on the reliability of stock market indicators. The study primarily focuses on the analysis of the Twitter dataset. Moreover, an improved model is proposed in this study; it is designed to enhance the classification accuracy. The first phase of this model is data collection, and the second involves the filtration and transformation, which are conducted to get only relevant data. The most crucial phase is labelling, in which polarity of data is determined and negative, positive or neutral values are assigned to people opinion. The fourth phase is the classification phase in which suitable patterns of the stock market are identified by hybridizing Naïve Bayes Classifiers (NBCs), and the final phase is the performance and evaluation. This study proposes Hybrid Naïve Bayes Classifiers (HNBCs) as a machine learning method for stock market classification. The outcome is instrumental for investors, companies, and researchers whereby it will enable them to formulate their plans according to the sentiments of people. The proposed method has produced a significant result; it has achieved accuracy equals 90.38%.


When and why do retrieval attempts enhance subsequent encoding?

Unsuccessful retrieval attempts can enhance subsequent encoding and learning. In three experiments, subjects either attempted to retrieve word pairs prior to studying them (e.g., attempting to recall tide-? before studying tide-beach) or did not attempt retrieval and retention of the studied targets was assessed on a subsequent cued recall test. Experiment 1 showed that attempting retrieval enhanced subsequent encoding and recall relative to not attempting retrieval when the word pairs were semantically related, but not when the pairs were unrelated. In Experiment 2, studying a different word pair prior to the correct pair (e.g., studying tide-wave prior to tide-beach) did not produce the same effect as attempting retrieval prior to studying. Constraining retrieval to a particular candidate word prior to study (e.g., recalling tide-wa__ before studying tide-beach) produced a negative effect on subsequent recall. Experiment 3 showed that attempting retrieval did not enhance encoding when a brief delay occurred between the retrieval attempt and the subsequent study trial. The results support the idea that a search set of candidates related to the retrieval cue is activated during retrieval and that this retrieval-specific activation can enhance subsequent encoding of those candidates.


Clustered Data Management in Virtual Docker Networks Spanning Geo-Redundant Data Centers : A Performance Evaluation Study of Docker Networking

...................................................................................................................................... iii Acknowledgement ....................................................................................................................... iv Table of


Nonparametric variational inference

Variational methods are widely used for approximate posterior inference. However, their use is typically limited to families of distributions that enjoy particular conjugacy properties. To circumvent this limitation, we propose a family of variational approximations inspired by nonparametric kernel density estimation. The locations of these kernels and their bandwidth are treated as variational parameters and optimized to improve an approximate lower bound on the marginal likelihood of the data. Unlike most other variational approximations, using multiple kernels allows the approximation to capture multiple modes of the posterior. We demonstrate the efficacy of the nonparametric approximation with a hierarchical logistic regression model and a nonlinear matrix factorization model. We obtain predictive performance as good as or better than more specialized variational methods and MCMC approximations. The method is easy to apply to graphical models for which standard variational methods are difficult to derive.


Movie trailer quality evaluation using real-time human electroencephalogram

The total US box office revenue exceeds ten billion dollars a year. Inevitably, new product forecasting and diagnosis have high financially stakes. The motion picture industry has a huge incentive to perform early prediction of movie success or failure. In this paper, we present a study that evaluate viewer responses to short movie trailers using a cap instrumented with Electroencephalogram (EEG) sensors. The method can be used to evaluate prerelease movies regarding how engaging they are to viewers. The primary advantage of our approach is that responses of a viewer can be recorded without any manual inputs from the viewer. The data collected, if analyzed properly, can reveal more accurate information regarding the viewer's emotion states while watching the movie than post-viewing surveys. This approach also enables the delivery of personalized entertainment through the brain interface.


A fast procedure for computing the distance between complex objects in three-dimensional space

An efficient and reliable algorithm for computing the Euclidean distance between a pair of convex sets in Rm is described. Extensive numerical experience with a broad family of polytopes in R 3 shows that the computational cost is approximately linear in the total number of vertices specifying the two polytopes. The algorithm has special features which makes its application in a variety of robotics problems attractive. These are discussed and an example of collision


Depression and Psychosocial Risk Factors among Community-Dwelling Older Adults in Singapore.

Depression is the most common mental and emotional disorder that emerges in the late stages of life. It is closely associated with poor health, disability, mortality, and suicide. The study examines the risk factors of depression in late life, especially the psychosocial factors, among a sample comprising 162 community-dwelling Singaporean adults aged 65 years and above. An interview-based structured survey was conducted in multiple senior activity centers located in different parts of Singapore. Results from the hierarchical regression analysis show that 32.9% of the variance in geriatric depression can be explained by the three psychosocial factors, among which loneliness, perceived social support, and the emotional regulation component of resilience are significantly associated with depression in older adults. Large-scale studies should be conducted to confirm the findings of the present study, and to further examine the predictive effects of these psychosocial factors on depression among older adults.


The least mean fourth (LMF) adaptive algorithm and its family

New steepest descent algorithms for adaptive filtering and have been devised which allow error minimization in the mean fourth and mean sixth, etc., sense. During adaptation, the weights undergo exponential relaxation toward their optimal solutions. T ime constants have been derived, and surprisingly they turn out to be proportional to the time constants that would have been obtained if the steepest descent least mean square (LMS) algorithm of Widrow and Hoff had been used. The new gradient algorithms are insignificantly more complicated to program and to compute than the LMS algorithm. Their general form is W J+l = w, t 2plqK-lx,, where W, is the present weight vector, W, + 1 is the next weight vector, r, is the present error, X, is the present input vector, u is a constant controlling stability and rate of convergence, and 2 K is the exponent of the error being minimized. Conditions have been derived for weight-vector convergence of the mean and of the variance for the new gradient algorithms. The behavior of the least mean fourth (LMF) algorithm is of special interest. In comparing this algorithm to the LMS algorithm, when both are set to have exactly the same time constants for the weight relaxation process, the LMF algorithm, under some circumstances, will have a substantially lower weight noise than the LMS algorithm. It is possible, therefore, that a min imum mean fourth error algorithm can do a better job of least squares estimation than a mean square error algorithm. This intriguing concept has implications for all forms of adaptive algorithms, whether they are based on steepest descent or otherwise.


Towards Linked Research Data: an Institutional Approach

For Open Science to be widely adopted, a strong institutional support for scientists will be essential. Bielefeld University and the associated Center of Excellence Cognitive Interaction Technology (CITEC) have developed a platform that enables researchers to manage their publications and the underlying research data in an easy and efficient way. Following a Linked Data approach we integrate this data into a unified linked data store and interlink it with additional data sources from inside the university and outside sources like DBpedia. Based on the existing platform, a concrete case study from the domain of biology is implemented that releases optical motion tracking data of stick insect locomotion. We investigate the cost and usefulness of such a detailed, domain-specific semantic enrichment in order to evaluate whether this approach might be considered for large-scale deployment.


Efficient Exploratory Learning of Inverse Kinematics on a Bionic Elephant Trunk

We present an approach to learn the inverse kinematics of the “bionic handling assistant”-an elephant trunk robot. This task comprises substantial challenges including high dimensionality, restrictive and unknown actuation ranges, and nonstationary system behavior. We use a recent exploration scheme, online goal babbling, which deals with these challenges by bootstrapping and adapting the inverse kinematics on the fly. We show the success of the method in extensive real-world experiments on the nonstationary robot, including a novel combination of learning and traditional feedback control. Simulations further investigate the impact of nonstationary actuation ranges, drifting sensors, and morphological changes. The experiments provide the first substantial quantitative real-world evidence for the success of goal-directed bootstrapping schemes, moreover with the challenge of nonstationary system behavior. We thereby provide the first functioning control concept for this challenging robot platform.


Winning the DARPA Grand Challenge with an AI Robot

This paper describes the software architecture of Stanley, an autonomous land vehicle developed for high-speed desert driving without human intervention. The vehicle recently won the DARPA Grand Challenge, a major robotics competition. The article describes the software architecture of the robot, which relied pervasively on state-of-the-art AI technologies, such as machine learning and probabilistic reasoning.


A 3-$\mu\hbox{W}$  CMOS Glucose Sensor for Wireless Contact-Lens Tear Glucose Monitoring

This paper presents a noninvasive wireless sensor platform for continuous health monitoring. The sensor system integrates a loop antenna, wireless sensor interface chip, and glucose sensor on a polymer substrate. The IC consists of power management, readout circuitry, wireless communication interface, LED driver, and energy storage capacitors in a 0.36-mm2 CMOS chip with no external components. The sensitivity of our glucose sensor is 0.18 μA·mm-2·mM-1. The system is wirelessly powered and achieves a measured glucose range of 0.05-1 mM with a sensitivity of 400 Hz/mM while consuming 3 μW from a regulated 1.2-V supply.


A Review and Characterization of Progressive Visual Analytics

Progressive Visual Analytics (PVA) has gained increasing attention over the past years. It brings the user into the loop during otherwise long-running and non-transparent computations by producing intermediate partial results. These partial results can be shown to the user for early and continuous interaction with the emerging end result even while it is still being computed. Yet as clear-cut as this fundamental idea seems, the existing body of literature puts forth various interpretations and instantiations that have created a research domain of competing terms, various definitions, as well as long lists of practical requirements and design guidelines spread across different scientific communities. This makes it more and more difficult to get a succinct understanding of PVA’s principal concepts, let alone an overview of this increasingly diverging field. The review and discussion of PVA presented in this paper address these issues and provide (1) a literature collection on this topic, (2) a conceptual characterization of PVA, as well as (3) a consolidated set of practical recommendations for implementing and using PVA-based visual analytics solutions.


A closer look at recognition-based graphical passwords on mobile devices

Graphical password systems based on the recognition of photographs are candidates to alleviate current over-reliance on alphanumeric passwords and PINs. However, despite being based on a simple concept -- and user evaluations consistently reporting impressive memory retention -- only one commercial example exists and overall take-up is low. Barriers to uptake include a perceived vulnerability to observation attacks; issues regarding deployability; and the impact of innocuous design decisions on security not being formalized. Our contribution is to dissect each of these issues in the context of mobile devices -- a particularly suitable application domain due to their increasing significance, and high potential to attract unauthorized access. This produces: 1) A novel yet simple solution to the intersection attack that permits greater variability in login challenges; 2) Detailed analysis of the shoulder surfing threat that considers both simulated and human testing; 3) A first look at image processing techniques to contribute towards automated photograph filtering. We operationalize our observations and gather data in a field context where decentralized mechanisms of varying entropy were installed on the personal devices of participants. Across two working weeks success rates collected from users of a high entropy version were similar to those of a low entropy version at 77%, and login durations decreased significantly across the study.


Robust Inference via Generative Classifiers for Handling Noisy Labels

Large-scale datasets may contain significant proportions of noisy (incorrect) class labels, and it is well-known that modern deep neural networks (DNNs) poorly generalize from such noisy training datasets. To mitigate the issue, we propose a novel inference method, termed Robust Generative classifier (RoG), applicable to any discriminative (e.g., softmax) neural classifier pre-trained on noisy datasets. In particular, we induce a generative classifier on top of hidden feature spaces of the pre-trained DNNs, for obtaining a more robust decision boundary. By estimating the parameters of generative classifier using the minimum covariance determinant estimator, we significantly improve the classification accuracy with neither re-training of the deep model nor changing its architectures. With the assumption of Gaussian distribution for features, we prove that RoG generalizes better than baselines under noisy labels. Finally, we propose the ensemble version of RoG to improve its performance by investigating the layer-wise characteristics of DNNs. Our extensive experimental results demonstrate the superiority of RoG given different learning models optimized by several training techniques to handle diverse scenarios of noisy labels.


Domain Specific Sentence Level Mood Extraction from Malayalam Text

Natural Language Processing (NLP) is a field which studies the interactions between computers and natural languages. NLP is used to enable computers to attain the capability of manipulating natural languages with a level of expertise equivalent to humans. There exists a wide range of applications for NLP, of which sentiment analysis(SA) plays a major role. In sentimental analysis, the emotional polarity of a given text is analysed, and classified as positive, negative or neutral. A more difficult task is to refine the classification into different moods such as happy, sad, angry etc. Analysing a natural language for mood extraction is not at all an easy task for a computer. Even after achieving capabilities of massive amount of computation within a matter of seconds, understanding the sentiments embodied in phrases and sentences of textual information remains one of the toughest tasks for a computer till date. This paper focuses on tagging the appropriate mood in Malayalam text. Tagging is used to specify whether a sentence indicates a sad, happy or angry mood of the person involved or if the sentence contains just facts, devoid of emotions. This research work is heavily dependent on the language since the structures vary from language to language. Mood extraction and tagging has been successfully implemented for English and other European languages. For the south Indian language Malayalam, no significant work has yet been done on mood extraction. We will be focusing on domain-specific sentence-level mood extraction from Malayalam text. The extraction process involves parts-of-speech tagging of the input sentence, extracting the patterns from the input sentence which will contribute to the mood of the sentence, such as the adjective, adverb etc., and finally scoring the sentence on an emotive scale by calculating the semantic orientation of the sentence using the extracted patterns. Sentiment classification is becoming a promising topic with the rise of social media such as blogs, social networking sites, where people express their views on various topics. Mood extraction enables computers to automate the activities performed by human for making decisions based on the moods of opinions expressed in Malayalam text.


A Reinforcement Learning Approach to Interactive-Predictive Neural Machine Translation

We present an approach to interactivepredictive neural machine translation that attempts to reduce human effort from three directions: Firstly, instead of requiring humans to select, correct, or delete segments, we employ the idea of learning from human reinforcements in form of judgments on the quality of partial translations. Secondly, human effort is further reduced by using the entropy of word predictions as uncertainty criterion to trigger feedback requests. Lastly, online updates of the model parameters after every interaction allow the model to adapt quickly. We show in simulation experiments that reward signals on partial translations significantly improve character F-score and BLEU compared to feedback on full translations only, while human effort can be reduced to an average number of 5 feedback requests for every input.


A Critical Evaluation of User Participation Research: Gaps and Future Directions

In this theoretical synthesis, we juxtapose three traditions of prior research on user participation and involvement: the survey and experimental literature on the relationship between user participation and IS success, the normative literature on alternative development approaches, and qualitative studies that examine user participation from a variety of theoretical perspectives. We also assess progress made in the three bodies of literature, and identify gaps and directions of future research for improving user participation.


Epidermoid cyst of the clitoris: a case report.

We report a rare case of spontaneous clitoral epidermal cyst without any declared previous female genital mutilation. This patient was successfully and surgically resected with good local and cosmetic results.


Multi-threshold CMOS design for low power digital circuits

Multi-threshold CMOS (MTCMOS) power gating is a design technique in which a power gating transistor is connected between the logic transistors and either power or ground, thus creating a virtual supply rail or virtual ground rail, respectively. Power gating transistor sizing, transition (sleep mode to active mode) current, short circuit current and transition time are design issues for power gating design. The use of power gating design results in the delay overhead in the active mode. If both nMOS and pMOS sleep transistor are used in power gating, delay overhead will increase. This paper proposes the design methodology for reducing the delay of the logic circuits during active mode. This methodology limits the maximum value of transition current to a specified value and eliminates short circuit current. Experiment results show 16.83% reduction in the delay.


A 0.4 V 63 $\mu$W 76.1 dB SNDR 20 kHz Bandwidth Delta-Sigma Modulator Using a Hybrid Switching Integrator

This paper presents a delta-sigma modulator operating at a supply voltage of 0.4 V. The designed delta-sigma modulator uses a proposed hybrid switching integrator and operates at a low supply voltage without clock boosting or bootstrapped switches. The proposed integrator consists of both switched-resistor and switched-capacitor operations and significantly reduces distortion at a low supply voltage. Variation in the turn-on resistance, which is the main source of distortion, is avoided by placing the switches at the virtual ground node of the amplifier. The proposed low-voltage design scheme can replace commonly-used clock boosting techniques, which rely on internal high-voltage generation circuits. A fabricated modulator achieves a 76.1 dB signal-to-noise-plus-distortion ratio (SNDR) and an 82 dB dynamic range at a 20 kHz bandwidth. The measured total power consumption is 63 μW from a 0.4 V supply voltage. The measured results show robust SNDR performance, even at ±10% supply voltage variations. The measured results also show stable performance over a wide temperature range.


PyDial: A Multi-domain Statistical Dialogue System Toolkit

Statistical Spoken Dialogue Systems have been around for many years. However, access to these systems has always been difficult as there is still no publicly available end-to-end system implementation. To alleviate this, we present PyDial, an opensource end-to-end statistical spoken dialogue system toolkit which provides implementations of statistical approaches for all dialogue system modules. Moreover, it has been extended to provide multidomain conversational functionality. It offers easy configuration, easy extensibility, and domain-independent implementations of the respective dialogue system modules. The toolkit is available for download under the Apache 2.0 license.


A study of injection locking and pulling in oscillators

Injection locking characteristics of oscillators are derived and a graphical analysis is presented that describes injection pulling in time and frequency domains. An identity obtained from phase and envelope equations is used to express the requisite oscillator nonlinearity and interpret phase noise reduction. The behavior of phase-locked oscillators under injection pulling is also formulated.


Quantum generalisation of feedforward neural networks

We propose a quantum generalisation of a classical neural network. The classical neurons are firstly rendered reversible by adding ancillary bits. Then they are generalised to being quantum reversible, i.e. unitary. (The classical networks we generalise are called feedforward, and have step-function activation functions.) The quantum network can be trained efficiently using gradient descent on a cost function to perform quantum generalisations of classical tasks. We demonstrate numerically that it can: (i) compress quantum states onto a minimal number of qubits, creating a quantum autoencoder, and (ii) discover quantum communication protocols such as teleportation. Our general recipe is theoretical and implementation-independent. The quantum neuron module can naturally be implemented photonically.


Depth Selective Camera: A Direct, On-Chip, Programmable Technique for Depth Selectivity in Photography

Time of flight (ToF) cameras use a temporally modulated light source and measure correlation between the reflected light and a sensor modulation pattern, in order to infer scene depth. In this paper, we show that such correlational sensors can also be used to selectively accept or reject light rays from certain scene depths. The basic idea is to carefully select illumination and sensor modulation patterns such that the correlation is non-zero only in the selected depth range - thus light reflected from objects outside this depth range do not affect the correlational measurements. We demonstrate a prototype depth-selective camera and highlight two potential applications: imaging through scattering media and virtual blue screening. This depth-selectivity can be used to reject back-scattering and reflection from media in front of the subjects of interest, thereby significantly enhancing the ability to image through scattering media-critical for applications such as car navigation in fog and rain. Similarly, such depth selectivity can also be utilized as a virtual blue-screen in cinematography by rejecting light reflecting from background, while selectively retaining light contributions from the foreground subject.


Power factor improvement of a transverse flux machine with high torque density

Design of a permanent magnet transverse flux machine optimized for high torque density and high power factor is presented. The optimization process focuses on performance enhancement through improvement of power factor without sacrificing torque density. Simple magnetostatic simulation based method is proposed for the optimization process. Magnetic couplings among axially separated phases are also discussed in the article. Experimental results demonstrating the correlation of measured inductances with that of simulation results are provided.


A probabilistic model for item-based recommender systems

Recommender systems estimate the conditional probability P(χj|χi) of item χj being bought, given that a customer has already purchased item χi. While there are different ways of approximating this conditional probability, the expression is generally taken to refer to the frequency of co-occurrence of items in the same basket, or other user-specific item lists, rather than being seen as the co-occurrence of χj with χi as a proportion of all other items bought alongside χi. This paper proposes a probabilistic calculus for the calculation of conditionals based on item rather than basket counts. The proposed method has the consequence that items bough together as part of small baskets are more predictive of each other than if they co-occur in large baskets. Empirical results suggests that this may result in better take-up of personalised recommendations.


Efficacy of music therapy treatment based on cycles of sessions: a randomised controlled trial.

We undertook a randomised controlled trial to assess whether a music therapy (MT) scheme of administration, including three working cycles of one month spaced out by one month of no treatment, is effective to reduce behavioural disturbances in severely demented patients. Sixty persons with severe dementia (30 in the experimental and 30 in the control group) were enrolled. Baseline multidimensional assessment included demographics, Mini Mental State Examination (MMSE), Barthel Index and Neuropsychiatry Inventory (NPI) for all patients. All the patients of the experimental and control groups received standard care (educational and entertainment activities). In addition, the experimental group received three cycles of 12 active MT sessions each, three times a week. Each 30-min session included a group of three patients. Every cycle of treatment was followed by one month of wash-out. At the end of this study, MT treatment resulted to be more effective than standard care to reduce behavioural disorders. We observed a significant reduction over time in the NPI global scores in both groups (F(7,357) = 9.06, p < 0.001) and a significant difference between groups (F(1,51) = 4.84, p < 0.05) due to a higher reduction of behavioural disturbances in the experimental group at the end of the treatment (Cohen's d = 0.63). The analysis of single NPI items shows that delusions, agitation and apathy significantly improved in the experimental, but not in the control group. This study suggests the effectiveness of MT approach with working cycles in reducing behavioural disorders of severely demented patients.


Uninterruptible power supply systems provide protection

Nowadays, uninterruptible power supply (UPS) systems are in use throughout the world, helping to supply a wide variety of critical loads, in situations of power outage or anomalies of the mains. This article describes the most common line problems and the relationship between these and the different existing kinds of UPS, showing their operation modes as well as the existent energy storage systems. It also addresses an overview of the control schemes applied to different distributed UPS configurations. Finally, it points out the applicability of such systems in distributed generation, microgrids, and renewable energy systems.


The examination timetabling problem at Universiti Malaysia Pahang: Comparison of a constructive heuristic with an existing software solution

This paper presents a real-world, capacitated examination timetabling problem from Universiti Malaysia Pahang (UMP), Malaysia. The problem has constraints which have not been modelled before, these being the distance between examination rooms and splitting exams across several rooms. These constraints provide additional challenges in defining a suitable model and in developing a constructive heuristic. One of the contributions of this paper is to formally define this real-world problem. A further contribution is the constructive heuristic that is able to produce good quality solutions for the problem, which are superior to the solutions that are produced using the university’s current software. Moreover, our method adheres to all hard constraints which the current systems fails to do. 2010 Elsevier B.V. All rights reserved.


No-Reference Image Quality Assessment in the Spatial Domain

We propose a natural scene statistic-based distortion-generic blind/no-reference (NR) image quality assessment (IQA) model that operates in the spatial domain. The new model, dubbed blind/referenceless image spatial quality evaluator (BRISQUE) does not compute distortion-specific features, such as ringing, blur, or blocking, but instead uses scene statistics of locally normalized luminance coefficients to quantify possible losses of “naturalness” in the image due to the presence of distortions, thereby leading to a holistic measure of quality. The underlying features used derive from the empirical distribution of locally normalized luminances and products of locally normalized luminances under a spatial natural scene statistic model. No transformation to another coordinate frame (DCT, wavelet, etc.) is required, distinguishing it from prior NR IQA approaches. Despite its simplicity, we are able to show that BRISQUE is statistically better than the full-reference peak signal-to-noise ratio and the structural similarity index, and is highly competitive with respect to all present-day distortion-generic NR IQA algorithms. BRISQUE has very low computational complexity, making it well suited for real time applications. BRISQUE features may be used for distortion-identification as well. To illustrate a new practical application of BRISQUE, we describe how a nonblind image denoising algorithm can be augmented with BRISQUE in order to perform blind image denoising. Results show that BRISQUE augmentation leads to performance improvements over state-of-the-art methods. A software release of BRISQUE is available online: http://live.ece.utexas.edu/research/quality/BRISQUE_release.zip for public use and evaluation.