title: Learning the Speech Front-end with Raw Waveform CLDNNs

abstract: Learning an acoustic model directly from the raw waveform has been an active area of research. However, waveformbased models have not yet matched the performance of logmel trained neural networks. We will show that raw waveform features match the performance of log-mel filterbank energies when used with a state-of-the-art CLDNN acoustic model trained on over 2,000 hours of speech. Specifically, we will show the benefit of the CLDNN, namely the time convolution layer in reducing temporal variations, the frequency convolution layer for preserving locality and reducing frequency variations, as well as the LSTM layers for temporal modeling. In addition, by stacking raw waveform features with log-mel features, we achieve a 3% relative reduction in word error rate.


title: A brief history and review of modern casting techniques in early onset scoliosis

abstract: Purpose ::: Body casts have a long history in the treatment of spinal deformity. Currently the use of body casts is limited to early onset scoliosis. Here, we aim to provide a brief narrative of the evolution of cast application for the management of spinal deformity.


title: Knowledge base completion using embeddings and rules

abstract: Knowledge bases (KBs) are often greatly incomplete, necessitating a demand for KB completion. A promising approach is to embed KBs into latent spaces and make inferences by learning and operating on latent representations. Such embedding models, however, do not make use of any rules during inference and hence have limited accuracy. This paper proposes a novel approach which incorporates rules seamlessly into embedding models for KB completion. It formulates inference as an integer linear programming (ILP) problem, with the objective function generated from embedding models and the constraints translated from rules. Solving the ILP problem results in a number of facts which 1) are the most preferred by the embedding models, and 2) comply with all the rules. By incorporating rules, our approach can greatly reduce the solution space and significantly improve the inference accuracy of embedding models. We further provide a slacking technique to handle noise in KBs, by explicitly modeling the noise with slack variables. Experimental results on two publicly available data sets show that our approach significantly and consistently outperforms state-of-the-art embedding models in KB completion. Moreover, the slacking technique is effective in identifying erroneous facts and ambiguous entities, with a precision higher than 90%.


title: Brain growth across the life span in autism: Age-specific changes in anatomical pathology

abstract: Autism is marked by overgrowth of the brain at the earliest ages but not at older ages when decreases in structural volumes and neuron numbers are observed instead. This has led to the theory of age-specific anatomic abnormalities in autism. Here we report age-related changes in brain size in autistic and typical subjects from 12 months to 50 years of age based on analyses of 586 longitudinal and cross-sectional MRI scans. This dataset is several times larger than the largest autism study to date. Results demonstrate early brain overgrowth during infancy and the toddler years in autistic boys and girls, followed by an accelerated rate of decline in size and perhaps degeneration from adolescence to late middle age in this disorder. We theorize that underlying these age-specific changes in anatomic abnormalities in autism, there may also be age-specific changes in gene expression, molecular, synaptic, cellular, and circuit abnormalities. A peak age for detecting and studying the earliest fundamental biological underpinnings of autism is prenatal life and the first three postnatal years. Studies of the older autistic brain may not address original causes but are essential to discovering how best to help the older aging autistic person. Lastly, the theory of age-specific anatomic abnormalities in autism has broad implications for a wide range of work on the disorder including the design, validation, and interpretation of animal model, lymphocyte gene expression, brain gene expression, and genotype/CNV-anatomic phenotype studies.


title: Personalization in cultural heritage: the road travelled and the one ahead

abstract: Over the last 20 years, cultural heritage has been a favored domain for personalization research. For years, researchers have experimented with the cutting edge technology of the day; now, with the convergence of internet and wireless technology, and the increasing adoption of the Web as a platform for the publication of information, the visitor is able to exploit cultural heritage material before, during and after the visit, having different goals and requirements in each phase. However, cultural heritage sites have a huge amount of information to present, which must be filtered and personalized in order to enable the individual user to easily access it. Personalization of cultural heritage information requires a system that is able to model the user (e.g., interest, knowledge and other personal characteristics), as well as contextual aspects, select the most appropriate content, and deliver it in the most suitable way. It should be noted that achieving this result is extremely challenging in the case of first-time users, such as tourists who visit a cultural heritage site for the first time (and maybe the only time in their life). In addition, as tourism is a social activity, adapting to the individual is not enough because groups and communities have to be modeled and supported as well, taking into account their mutual interests, previous mutual experience, and requirements. How to model and represent the user(s) and the context of the visit and how to reason with regard to the information that is available are the challenges faced by researchers in personalization of cultural heritage. Notwithstanding the effort invested so far, a definite solution is far from being reached, mainly because new technology and new aspects of personalization are constantly being introduced. This article surveys the research in this area. Starting from the earlier systems, which presented cultural heritage information in kiosks, it summarizes the evolution of personalization techniques in museum web sites, virtual collections and mobile guides, until recent extension of cultural heritage toward the semantic and social web. The paper concludes with current challenges and points out areas where future research is needed.


title: Approximation Algorithms for the Max-Min Allocation problem

abstract: The Max-Min allocation problem is to distribute indivisible goods to people so as to maximize the minimum utility of the people. We show a (2ki¾? 1)-approximation algorithm for Max-Min when there are kpeople with subadditive utility functions. We also give a k/i¾?-approximation algorithm (for i¾?≤ k/2) if the utility functions are additive and the utility of an item for a person is restricted to 0, 1 or Ufor some U> 1. The running time of this algorithm depends exponentially on the parameter i¾?. Both the algorithms are combinatorial, simple and easy to analyze.


title: Learning Morphological Disambiguation Rules For Turkish

abstract: In this paper, we present a rule based model for morphological disambiguation of Turkish. The rules are generated by a novel decision list learning algorithm using supervised training. Morphological ambiguity (e.g. lives = live+s or life+s) is a challenging problem for agglutinative languages like Turkish where close to half of the words in running text are morphologically ambiguous. Furthermore, it is possible for a word to take an unlimited number of suffixes, therefore the number of possible morphological tags is unlimited. We attempted to cope with these problems by training a separate model for each of the 126 morphological features recognized by the morphological analyzer. The resulting decision lists independently vote on each of the potential parses of a word and the final parse is selected based on our confidence on these votes. The accuracy of our model (96%) is slightly above the best previously reported results which use statistical models. For comparison, when we train a single decision list on full tags instead of using separate models on each feature we get 91% accuracy.


No . 067 April 4 , 2017 Theory of Deep Learning III : Generalization Properties of SGD by


title: Waste Gasification by Thermal Plasma : A Review

abstract: This paper proposes an overview of waste-to-energy conversion by gasification processes based on thermal plasma. In the first part, basic aspects of the gasification process have been discussed: chemical reaction in gasification, main reactor configuration, chemical conversion performances, tar content in syngas and performances in function of the design and the operation conditions (temperature, pressure, oxidizing agent…). In the second part of the paper are compared the performances, available in the scientific literature, of various waste gasification processes based on thermal plasma (DC or AC plasma torches) at lab scale versus typical performances of waste autothermal gasification: LHV of the syngas, cold gas efficiency and net electrical efficiency. In the last part, a review has been done on the various torch technologies used for waste gasification by plasma at industrial scale, the major companies on this market and the perspectives of the industrial development of the waste gasification by thermal plasma. The main conclusions are that plasma technology is considered as a highly attractive route for the processing of waste-to-energy and can be easily adapted to the treatment of various wastes (municipal solid wastes, heavy oil, used car tires, medical wastes…). The high enthalpy, the residence time and high temperature in plasma can advantageously improve the conditions for gasification, which are inaccessible in other thermal processes and can allow reaching, due to low tar content in the syngas, better net electrical efficiency than autothermal processes.


title: A novel method for binarization of scene text images and its application in text identification

abstract: The aim of this article is twofold. First, we propose an effective methodology for binarization of scene images. For our present study, we use the publicly available ICDAR 2011 Born Digital Data set. We introduce a new concept of variance map of a gray-level image for detection of text boundary in an image. Based on this boundary information, the image is binarized by means of adaptive thresholding. This binarization procedure produces a number of connected components. Next, these connected components are examined in order to identify possible text components. In this context, a number of shape-based features that distinguish between text and non-text components are proposed. We consider text component identification as an one-class classification problem, i.e., the ground truth information for only the text class is available for the ICDAR 2011 Born Digital Data set. Then, the ground truth text components are used to obtain a certain statistical distribution of the shape-based features. Here, we observe that all the features may not follow a single family of distributions. Therefore, we construct a joint distribution by using multivariate Gaussian copula which allows a coupling of different marginal distributions. As our experiments suggest, the copula-based method is superior to multivariate Gaussian distribution in describing the feature distribution. Finally, a text connected component of an unknown class is subjected to the trained statistical model, and by performing a hypothesis test we successfully identify a possible text component. For a comparative study, we consider a number of state-of-the-art methods. Our proposed approach significantly outperforms most of these methods in terms of recall, precision and F-measure in both the binarization and text identification tasks.


title: Mathematical optimization for earliness/tardiness minimization in a multiple automated guided vehicle manufacturing system via integrated heuristic algorithms

abstract: In this paper, a scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system is proposed and formulated. Considering the due date of AGVs requiring for material handling among shops in a jobshop layout, their earliness and tardiness are significant in satisfying the expected cycle time and from an economic view point. Earliness results in AGVs waiting and tardiness causes temporary part storages in the shop floor. Therefore, we propose a mathematical program to minimize the penalized earliness and tardiness. Since the mathematical program is difficult to solve with a conventional method, an optimization method in two stages, namely searching the solution space and finding optimal solutions are proposed. The performance of the proposed mathematical model is tested in a numerical example and compared with several methods in the current literature. Scheduling problem for multiple automated guided vehicles (AGVs) in a manufacturing system is proposed and formulated.Propose a mathematical program to minimize the penalized earliness and tardiness.Optimization method in two stages, namely searching the solution space and finding optimal solutions are proposed.The performance of the proposed mathematical model is tested in a numerical example and compared with several methods in the current literature.


title: Enhancing Mobile Cloud Computing Security Using Steganography

abstract: Cloud computing is an emerging and popular method of accessing shared and dynamically configurable resources via the computer network on demand. Cloud computing is excessively used by mobile applications to offload data over the network to the cloud. There are some security and privacy concerns using both mobile devices to offload data to the facilities provided by the cloud providers. One of the critical threats facing cloud users is the unauthorized access by the insiders (cloud administrators) or the justification of location where the cloud providers operating. Although, there exist variety of security mechanisms to prevent unauthorized access by unauthorized user by the cloud administration, but there is no security provision to prevent unauthorized access by the cloud administrators to the client data on the cloud computing. In this paper, we demonstrate how steganography, which is a secrecy method to hide information, can be used to enhance the security and privacy of data (images) maintained on the cloud by mobile applications. Our proposed model works with a key, which is embedded in the image along with the data, to provide an additional layer of security, namely, confidentiality of data. The practicality of the proposed method is represented via a simple case study.


title: Employing Color Theory to Visualize Volume-rendered Multivariate Ensembles of Asteroid Impact Simulations

abstract: We describe explorations and innovations developed to help scientists understand an ensemble of large scale sim- ulations of asteroid impacts in the ocean. The simulations were run to help scientists determine the characteristics of asteroids that NASA should track, so that communities at risk from impact can be given advanced notice. Of rel- evance to the CHI community are 1) hands-on workflow issues specific to exploring ensembles of large scientific data, 2) innovations in exploring such data ensembles with color, and 3) examples of multidisciplinary collaboration.


title: Combination of Antimicrobials and Essential Oils as an Alternative for the Control of Salmonella enterica Multiresistant Strains Related to Foodborne Disease

abstract: Due to the increase in bacterial resistance to antimicrobials (AMBs) commonly used in veterinary and human medicine, the new strategies for controlling zoonoses focus on the study of natural products with demonstrated AMB activity, such as essential oils (EOs). The aim of this study was to evaluate the in vitro effect of the combination of enrofloxacin (ENR), ceftiofur (CEF), and trimethoprim-sulfamethoxazole (SXT) with cinnamon, clove, oregano, and red thyme EOs against multiple drug-resistant strains of Salmonella enterica. The minimum inhibitory concentration (MIC) of each product was determined by microdilution and "Checkerboard" methods and their combined effect was evaluated against 15 strains of S. enterica. The results were interpreted by the calculation of fractional inhibitory concentration (FIC) and their respective indexes (FICI). Significant susceptibility of all strains to the four EOs was observed. The results showed a synergistic effect between EOs and AMBs tested, highlighting the upper percentage of total synergies of the SXT with the four EOs (FICI ≤0.5 in 60% of assays), and the most effective combination being the one of ENR and cinnamon. The MIC of cinnamon was reduced from 1250 to 312.5 μg/mL and the MIC of ENR from 2 to 0.031 μg/mL. There was no antagonism in the tested combinations (AMBs-EOs). Our results support the combined use of EOs and AMBs for the control of multiresistant strains of S. enterica with a reduction of the minimum effective dose of AMBs and their adverse effects.


title: A comparative analysis of digital innovation ecosystems

abstract: In this paper, we examine how Apple and Google have used third-party led innovation to create viable digital innovation ecosystems, through the App Store and the Android platform respectively. In many ways, this is similar to the strategy adopted in an earlier decade by NTT DoCoMo in the mobile communications space, through the iMode platform. We compare and contrast alternative approaches to accelerating innovation through such an ecosystem oriented approach, and outline some key lessons for content providers, developers, and other stakeholders. We analyze these developments through the theories of network externalities, social network communication theory, and related frameworks in strategic management


Fenofibrate Induces Ketone Body Production in Melanoma and Glioblastoma Cells


title: Probiotics in aquaculture: importance and future perspectives

abstract: Aquaculture is one of the fastest developing growth sectors in the world and Asia presently contributes about 90% to the global production. However, disease outbreaks are constraint to aquaculture production thereby affects both economic development of the country and socio-economic status of the local people in many countries of Asia-Pacifi c region. Disease control in aquaculture industry has been achieved by following different methods using traditional ways, synthetic chemicals and antibiotics. However, the use of such expensive chemotherapeutants for controlling diseases has been widely criticized for their negative impacts like accumulation of residues, development of drug resistance, immunosuppressants and reduced consumer preference for aqua products treated with antibiotics and traditional methods are ineffective against controlling new diseases in large aquaculture systems. Therefore, alternative methods need to be developed to maintain a healthy microbial environment in the aquaculture systems there by to maintain the health of the cultured organisms. Use of probiotics is one of such method that is gaining importance in controlling potential pathogens. This review provides a summary of the criteria for the selection of the potential probiotics, their importance and future perspectives in aquaculture industry.


title: Multimodal Co-Training for Selecting Good Examples from Webly Labeled Video

abstract: We tackle the problem of learning concept classifiers from videos on the web without using manually labeled data. Although metadata attached to videos (e.g., video titles, descriptions) can be of help collecting training data for the target concept, the collected data is often very noisy. The main challenge is therefore how to select good examples from noisy training data. Previous approaches firstly learn easy examples that are unlikely to be noise and then gradually learn more complex examples. However, hard examples that are much different from easy ones are never learned. In this paper, we propose an approach called multimodal co-training (MMCo) for selecting good examples from noisy training data. MMCo jointly learns classifiers for multiple modalities that complement each other to select good examples. Since MMCo selects examples by consensus of multimodal classifiers, a hard example for one modality can still be used as a training example by exploiting the power of the other modalities. The algorithm is very simple and easily implemented but yields consistent and significant boosts in example selection and classification performance on the FCVID and YouTube8M benchmarks.


title: Comparative Analysis of Block Cipher-Based Encryption Algorithms: A Survey

abstract: Nowadays, network security is increasing rapidly and becoming an important and challenging issue. Information and internet security threats and attacks are becoming difficult to be detected. Therefore, encryption has come up as a solution, and plays an important role in information security system. Many techniques are needed to protect the shared data. In this paper we implemented four encrypt techniques AES, DES, 3DES and E-DES algorithms and compared their performance. A comparative analysis on the above symmetric encryption algorithms has been made. These algorithms consume a significant amount of computing resources such as CPU time, memory and battery power. Experiments results are given to analyses the effectiveness of each algorithm. The comparison is made on the basis of these parameters: speed, block size, and key size etc. Educational-DES has better performance than other DES, 3DES, and AES algorithms.


title: Motivation through the design of work: test of a theory

abstract: Abstract A model is proposed that specifies the conditions under which individuals will become internally motivated to perform effectively on their jobs. The model focuses on the interaction among three classes of variables: (a) the psychological states of employees that must be present for internally motivated work behavior to develop; (b) the characteristics of jobs that can create these psychological states; and (c) the attributes of individuals that determine how positively a person will respond to a complex and challenging job. The model was tested for 658 employees who work on 62 different jobs in seven organizations, and results support its validity. A number of special features of the model are discussed (including its use as a basis for the diagnosis of jobs and the evaluation of job redesign projects), and the model is compared to other theories of job design.


title: Global, Dense Multiscale Reconstruction for a Billion Points

abstract: We present a variational approach for surface reconstruction from a set of oriented points with scale information. We focus particularly on scenarios with nonuniform point densities due to images taken from different distances. In contrast to previous methods, we integrate the scale information in the objective and globally optimize the signed distance function of the surface on a balanced octree grid. We use a finite element discretization on the dual structure of the octree minimizing the number of variables. The tetrahedral mesh is generated efficiently with a lookup table which allows to map octree cells to the nodes of the finite elements. We optimize memory efficiency by data aggregation, such that robust data terms can be used even on very large scenes. The surface normals are explicitly optimized and used for surface extraction to improve the reconstruction at edges and corners.


title: Optimal control for the PWM switching-mode power supply

abstract: An optimum control approach for the control loop design of a category of the PWM switching mode power supply (SMPS) is presented. Based on the nonlinear programming technique, the modeling technique of the optimum control problem for a full-bridge ZVS-PWM SMPS and a flyback PWM SMPS with current mode control is introduced. A discrete nonlinear optimization algorithm is employed to find the optimum parameters of the compensation network with given power stage parameters in the meaning of minimizing the ISE index. The design results are verified by computer simulation and experiments on a two-loop flyback PWM SMPS. By using the optimum control approach, some transient performance specifications (such as stability, fast response, line and load rejection capabilities) for both full-bridge ZVS-PWM and two-loop flyback PWM SMPSs can be achieved simultaneously. >


Automatic On-line Monitoring of animal Health and welfare by Precision livestock farming


title: Weed detection in soybean crops using ConvNets

abstract: The use of Deep Learning for the detection of weeds in soybean crops is proposed.The approach uses ConvNets in images segmented by the SLIC Superpixels algorithm.An image database was created using photographs captured by UAVs.The performance of ConvNets were compared with others classifiers as SVM.Using ConvNets, this work achieved above 97% accuracy in weed detection. Weeds are undesirable plants that grow in agricultural crops, such as soybean crops, competing for elements such as sunlight and water, causing losses to crop yields. The objective of this work was to use Convolutional Neural Networks (ConvNets or CNNs) to perform weed detection in soybean crop images and classify these weeds among grass and broadleaf, aiming to apply the specific herbicide to weed detected. For this purpose, a soybean plantation was carried out in Campo Grande, Mato Grosso do Sul, Brazil, and the Phantom DJI 3 Professional drone was used to capture a large number of crop images. With these photographs, an image database was created containing over fifteen thousand images of the soil, soybean, broadleaf and grass weeds. The Convolutional Neural Networks used in this work represent a Deep Learning architecture that has achieved remarkable success in image recognition. For the training of Neural Network the CaffeNet architecture was used. Available in Caffe software, it consists of a replication of the well known AlexNet, network which won the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012). A software was also developed, Pynoviso, which through the use of the superpixel segmentation algorithm SLIC, was used to build a robust image dataset and classify images using the model trained by Caffe software. In order to compare the results of ConvNets, Support Vector Machines, AdaBoost and Random Forests were used in conjunction with a collection of shape, color and texture feature extraction techniques. As a result, this work achieved above 98% accuracy using ConvNets in the detection of broadleaf and grass weeds in relation to soil and soybean, with an accuracy average between all images above 99%.


title: Millimeter-Wave Shaped-Beam Substrate Integrated Conformal Array Antenna

abstract: A millimeter-wave shaped-beam substrate integrated conformal array antenna is demonstrated in this paper. After discussing the influence of conformal shape on the characteristics of a substrate integrated waveguide (SIW) and a radiating slot, an array mounted on a cylindrical surface with a radius of 20 mm, i.e., 2.3 λ, is synthesized at the center frequency of 35 GHz. All components, including a 1-to-8 divider, a phase compensated network and an 8 × 8 slot array are fabricated in a single dielectric substrate together. In measurement, it has a - 27.4 dB sidelobe level (SLL) beam in H-plane and a flat-topped fan beam with -38° ~ 37° 3 dB beamwidth in E-plane at the center frequency of 35 GHz. The cross polarization is lower than -41.7 dB at the beam direction. Experimental results agree well with simulations, thus validating our design. This SIW scheme is able to solve the difficulty of integration between conformal array elements and a feed network in millimeter-wave frequency band, while avoid radiation leakage and element-to-element parasitic cross-coupling from the feed network.


title: Automatic Simplification of Obfuscated JavaScript Code

abstract: Javascript is a scripting language that is commonly used to create sophisticated interactive client-side web applications. It can also be used to carry out browser-based attacks on users. Malicious JavaScript code is usually highly obfuscated, making detection a challenge. This paper describes a simple approach to deobfuscation of JavaScript code based on dynamic analysis and slicing. Experiments using a prototype implementation indicate that our approach is able to penetrate multiple layers of complex obfuscations and extract the core logic of the computation.


title: Inferring Internet denial-of-service activity

abstract: In this article, we seek to address a simple question: “How prevalent are denial-of-service attacks in the Internet?” Our motivation is to quantitatively understand the nature of the current threat as well as to enable longer-term analyses of trends and recurring patterns of attacks. We present a new technique, called “backscatter analysis,” that provides a conservative estimate of worldwide denial-of-service activity. We use this approach on 22 traces (each covering a week or more) gathered over three years from 2001 through 2004. Across this corpus we quantitatively assess the number, duration, and focus of attacks, and qualitatively characterize their behavior. In total, we observed over 68,000 attacks directed at over 34,000 distinct victim IP addresses---ranging from well-known e-commerce companies such as Amazon and Hotmail to small foreign ISPs and dial-up connections. We believe our technique is the first to provide quantitative estimates of Internet-wide denial-of-service activity and that this article describes the most comprehensive public measurements of such activity to date.


title: CB: a humanoid research platform for exploring neuroscience

abstract: This paper presents a 50-d.o.f. humanoid robot, Computational Brain (CB). CB is a humanoid robot created for exploring the underlying processing of the human brain while dealing with the real world. We place our investigations within real—world contexts, as humans do. In so doing, we focus on utilizing a system that is closer to humans—in sensing, kinematics configuration and performance. We present the real-time network-based architecture for the control of all 50 d.o.f. The controller provides full position/velocity/force sensing and control at 1 kHz, allowing us the flexibility in deriving various forms of control. A dynamic simulator is also presented; the simulator acts as a realistic testbed for our controllers and acts as a common interface to our humanoid robots. A contact model developed to allow better validation of our controllers prior to final testing on the physical robot is also presented. Three aspects of the system are highlighted in this paper: (i) physical power for walking, (ii) full-b...


title: Better with bubbles: enhancing the visual realism of simulated fluid

abstract: We present a method for including the visual effect of bubbles in a computer graphics fluid simulation, thus enhancing the illusion of realism for a splashing fluid. Previous fluid simulation methods have not included bubbles. Bubble creation is integrated into the particle level-set fluid simulation algorithm. Individual bubbles are approximated by spheres, which form more complex shapes where they intersect. The rendering of bubbles and fluid are integrated to create the appearance of one continuous surface. At the fluid-air boundary, we integrate bubbles whenever level-set marker particles pass from from the outside to the inside of the fluid. Thus, these particles represent air that has become trapped within the fluid surface. In addition, we detect empty pockets within the fluid, that are often formed due to turbulence, and create bubbles within this space. This is an inexpensive way of giving the impression that the air trapped in air pockets has become bubbles. Photo-realistic images of simulation results are rendered with a raytracer that has been enhanced to include caustics, and to handle bubble-bubble interfaces. Comparison of these images with images rendered without bubbles supports our position that the simple addition of bubbles to a fluid simulation greatly enhances visual realism.


title: Deep Discrete Supervised Hashing

abstract: Hashing has been widely used for large-scale search due to its low storage cost and fast query speed. By using supervised information, supervised hashing can significantly outperform unsupervised hashing. Recently, discrete supervised hashing and feature learning based deep hashing are two representative progresses in supervised hashing. On one hand, hashing is essentially a discrete optimization problem. Hence, utilizing supervised information to directly guide discrete (binary) coding procedure can avoid sub-optimal solution and improve the accuracy. On the other hand, feature learning based deep hashing, which integrates deep feature learning and hash-code learning into an end-to-end architecture, can enhance the feedback between feature learning and hash-code learning. The key in discrete supervised hashing is to adopt supervised information to directly guide the discrete coding procedure in hashing. The key in deep hashing is to adopt the supervised information to directly guide the deep feature learning procedure. However, most deep supervised hashing methods cannot use the supervised information to directly guide both discrete (binary) coding procedure and deep feature learning procedure in the same framework. In this paper, we propose a novel deep hashing method, called deep discrete supervised hashing (DDSH). DDSH is the first deep hashing method which can utilize pairwise supervised information to directly guide both discrete coding procedure and deep feature learning procedure and thus enhance the feedback between these two important procedures. Experiments on four real datasets show that DDSH can outperform other state-of-the-art baselines, including both discrete hashing and deep hashing baselines, for image retrieval.


title: On classification of distorted images with deep convolutional neural networks

abstract: Image blur and image noise are common distortions during image acquisition. In this paper, we systematically study the effect of image distortions on the deep neural network (DNN) image classifiers. First, we examine the DNN classifier performance under four types of distortions. Second, we propose two approaches to alleviate the effect of image distortion: re-training and fine-tuning with noisy images. Our results suggest that, under certain conditions, fine-tuning with noisy images can alleviate much effect due to distorted inputs, and is more practical than re-training.


title: Neuromorphic Silicon Neuron Circuits

abstract: Hardware implementations of spiking neurons can be extremely useful for a large variety of applications, ranging from high-speed modeling of large-scale neural systems to real-time behaving systems, to bidirectional brain–machine interfaces. The specific circuit solutions used to implement silicon neurons depend on the application requirements. In this paper we describe the most common building blocks and techniques used to implement these circuits, and present an overview of a wide range of neuromorphic silicon neurons, which implement different computational models, ranging from biophysically realistic and conductance-based Hodgkin–Huxley models to bi-dimensional generalized adaptive integrate and fire models. We compare the different design methodologies used for each silicon neuron design described, and demonstrate their features with experimental results, measured from a wide range of fabricated VLSI chips.


title: Compact Thermal Model for Vertical Nanowire Phase-Change Memory Cells

abstract: We introduce a compact model for the temperature distribution in cylindrical nanowire (NW) phase-change memory (PCM) cells for both transient (nanoseconds) and steady-state time scales. The model takes advantage of the symmetry of the cell to efficiently calculate temperature distribution dependence on geometry and material/interface properties. The results are compared with data from the literature and with finite-element simulations, showing improved computation speed by two orders of magnitude. Programming current sensitivity to cell dimensions and material properties is investigated, indicating that NW diameter (D) and thermal boundary resistance (TBR) play the strongest role in enhancing PCM energy efficiency.


title: A comparison of four algorithms for estimating 3-d rigid transformations

abstract: A common need in machine vision is to compute the 3-D rigid transformation that exists between two sets of points for which corresponding pairs have been determined. In this paper a comparative analysis of four popular and efficient algorithms is given. Each computes the translational and rotational components of the transform in closed-form as the solution to a least squares formulation of the problem. They differ in terms of the representation of the transform and the method of solution, using respectively: singular value decomposition of a matrix, orthonormal matrices, unit quaternions and dual quaternions. This comparison presents results of several experiments designed to determine the (1) accuracy in the presence of noise, (2) stability with respect to degenerate data sets, and (3) relative computation time of each approach.


title: Marine defaunation: Animal loss in the global ocean

abstract: Marine defaunation, or human-caused animal loss in the oceans, emerged forcefully only hundreds of years ago, whereas terrestrial defaunation has been occurring far longer. Though humans have caused few global marine extinctions, we have profoundly affected marine wildlife, altering the functioning and provisioning of services in every ocean. Current ocean trends, coupled with terrestrial defaunation lessons, suggest that marine defaunation rates will rapidly intensify as human use of the oceans industrializes. Though protected areas are a powerful tool to harness ocean productivity, especially when designed with future climate in mind, additional management strategies will be required. Overall, habitat degradation is likely to intensify as a major driver of marine wildlife loss. Proactive intervention can avert a marine defaunation disaster of the magnitude observed on land.


title: Designing to Mitigate Effects of Flicker in LED Lighting: Reducing risks to health and safety

abstract: How often has this scenario happened? You are driving at night behind a car that has bright light-emitting diode (LED) taillights. When looking directly at the taillights, the light is not blurry, but when glancing at other objects, a trail of lights appears, known as a phantom array. The reason for this trail of lights might not be what you expected: it is not due to glare, degradation of eyesight, or astigmatism. The culprit may be the flickering of the LED lights caused by pulse-width modulating (PWM) drive circuitry. Actually, many LED taillights flicker on and off at frequencies between 200 and 500 Hz, which is too fast to notice when the eye is not in rapid motion. However, during a rapid eye movement (saccade), the images of the LED lights appear in different positions on the retina, causing a trail of images to be perceived (Figure 1). This disturbance of vision may not occur with all LED taillights because some taillights keep a constant current through the LEDs. However, when there is a PWM current through the LEDs, the biological effect of the light flicker may become noticeable during the eye saccade.


title: What Do We Talk About When We Talk About Credibility? Refugee Appeals in Italy

abstract: This chapter focuses on the first level of appeal in the Italian refugee status determination procedure. I use the “credibility issue” as a lens through which to observe how this notion is employed at the tribunals of Bologna and Turin, simultaneously as a core issue in the determination procedure, a sensitive category to handle with care, and an almost-empty shell used for purposes stretching out far beyond the tribunals themselves. Such purposes can only be grasped when shifting the gaze from the mechanics of civil law as enacted inside specific local sites, towards national and supra-national migratory policies, rooted in an entangling culture of denial that strongly impacts on local-level decisional procedures of recognition or rejection.


title: BeAMS: A Beacon-Based Angle Measurement Sensor for Mobile Robot Positioning

abstract: Positioning is a fundamental issue in mobile robot applications, and it can be achieved in multiple ways. Among these methods, triangulation based on angle measurements is widely used, robust, accurate, and flexible. This paper presents BeAMS, which is a new active beacon-based angle measurement system used for mobile robot positioning. BeAMS introduces several major innovations. One innovation is the use of a unique unsynchronized channel with on–off keying modulated infrared signals to measure angles and to identify the beacons. We also introduce a new mechanism to measure angles: Our system detects a beacon when it enters and leaves an angular window. We show that the estimator resulting from the center of this angular window provides an unbiased estimate of the beacon angle. A theoretical framework for a thorough performance analysis of BeAMS is provided. We establish the upper bound of the variance and validate this bound through experiments and simulations; the overall error measure of BeAMS is lower than $\hbox{0.24}^\circ$ for an acquisition rate of $10\,\hbox{Hz}$ . In conclusion, BeAMS is a low-power, flexible, and robust solution for angle measurement and a reliable component for robot positioning.


title: Students' experiences of blended learning across a range of postgraduate programmes

abstract: summary article i nfo Article history: Accepted 17 May 2011 The article describes the students' experiences of taking a blended learning postgraduate programme in a school of nursing and midwifery. The indications to date are that blended learning as a pedagogical tool has the potential to contribute and improve nursing and midwifery practice and enhance student learning. Little is reported about the students' experiences to date. Focus groups were conducted with students in the first year of introducing blended learning. The two main themes that were identified from the data were (1) the benefits of blended learning and (2) the challenges to blended learning. The blended learning experience was received positively by the students. A significant finding that was not reported in previous research was that the online component meant little time away from study for the students suggesting that it was more invasive on their everyday life. It is envisaged that the outcomes of the study will assist educators who are considering delivering programmes through blended learning. It should provide guidance for further developments and improvements in using Virtual Learning Environment (VLE) and blended learning in nurse education.


title: Emotion Recognition System by a Neural Network Based Facial Expression Analysis

abstract: Human-computer interfaces are getting more complex every day with the purpose of easing the use of computers and enhancing the overall user experience. Since research has shown that a majority of human interaction comes from non-verbal communication, user emotion detection is one of the directions that can be taken to enhance the overall user experience. This paper proposes a system for human emotion recognition by analyzing key facial regions using principal component analysis and neural networks. The proposed system has been trained and tested on the FEEDTUM database where it achieved a relatively high average score of correct recognition and therefore showed promise for future development.


title: Improve the Prediction Accuracy of Naïve Bayes Classifier with Association Rule Mining

abstract: Nowadays, big data contains infinite business opportunities. Companies begin to analyze their data to predict their potential customers and business decisions using Naive Bayes Classifier, Association Rule Mining, Decision Tree and other famous algorithms. An accurate classification result may help companies leading in its industry. Companies seek to find feasible business intelligences to obtain reliable prediction results. In this paper we propose an association rule mining to improve Naive Bayes Classifier. Naive Bayes Classifier is one of the famous algorithm in big data classification but based on an independent assumptions between features. Association rule mining is popular and useful for discovering relations between inputs in big data analysis. We use bank marketing data set to illustrate in this work. In general, this work is helpful to all the business data set.


title: Potential challenges and issues in implementation of MIM diodes for rectenna application

abstract: Metal-Insulator-Metal (MIM) diodes are one of the widely explored diodes for high frequency applications. The tunneling based transport mechanism in such diodes provides an ultra-fast switching speed which makes it a promising candidate for high frequency rectification in rectennas (Antenna coupled diode). However, despite of a lot of headway in the innovation of MIM diodes, the desired performance has not been achieved yet. This paper brings forward the various challenges and issues confronted in the successful implementation of MIM diodes for rectenna energy harvesting system.


title: Estimation, Control, and Planning for Aggressive Flight With a Small Quadrotor With a Single Camera and IMU

abstract: We address the state estimation, control, and planning for aggressive flight with a 150 cm diameter, 250 g quadrotor equipped only with a single camera and an inertial measurement unit (IMU). The use of smartphone grade hardware and the small scale provides an inexpensive and practical solution for autonomous flight in indoor environments. The key contributions of this paper are: 1) robust state estimation and control using only a monocular camera and an IMU at speeds of 4.5 m/s, accelerations of over 1.5 g, roll and pitch angles of up to 90 $^\circ$ , and angular rate of up to 800 $^\circ$ /s without requiring any structure in the environment; 2) planning of dynamically feasible three-dimensional trajectories for slalom paths and flights through narrow windows; and 3) extensive experimental results showing aggressive flights through and around obstacles with large rotation angular excursions and accelerations.


title: Self-propagation of pathogenic protein aggregates in neurodegenerative diseases

abstract: For several decades scientists have speculated that the key to understanding age-related neurodegenerative disorders may be found in the unusual biology of the prion diseases. Recently, owing largely to the advent of new disease models, this hypothesis has gained experimental momentum. In a remarkable variety of diseases, specific proteins have been found to misfold and aggregate into seeds that structurally corrupt like proteins, causing them to aggregate and form pathogenic assemblies ranging from small oligomers to large masses of amyloid. Proteinaceous seeds can therefore serve as self-propagating agents for the instigation and progression of disease. Alzheimer's disease and other cerebral proteopathies seem to arise from the de novo misfolding and sustained corruption of endogenous proteins, whereas prion diseases can also be infectious in origin. However, the outcome in all cases is the functional compromise of the nervous system, because the aggregated proteins gain a toxic function and/or lose their normal function. As a unifying pathogenic principle, the prion paradigm suggests broadly relevant therapeutic directions for a large class of currently intractable diseases.


title: Impedance joint torque control of an active-passive composited driving self-adaptive end effector for space manipulator

abstract: An active-passive composited driving self-adaptive end effector is proposed for the space manipulator, that is a two-finger gripper with an active motor and a passive spring. The problems of dynamics and control are challenging because of the strong coupling and their heavy computational burdens for the implementation of model-based control algorithms. To avoid solving differential-algebraic equations, the Virtual Spring Approach (VSA) is presented for formulating the appropriate dynamics model. Next, the impedance joint torque controller is proposed to control the system dynamics of capturing because the finger appears as active mechanical impedance with variable impedance parameters when it makes contact with an unknown object. Finally, the simulation results validate the self-adaptability of the active-passive composited driving end effector, and the proposed control scheme is adequate.


title: Spatiotemporal atlas parameterization for evolving meshes

abstract: We convert a sequence of unstructured textured meshes into a mesh with incrementally changing connectivity and atlas parameterization. Like prior work on surface tracking, we seek temporally coherent mesh connectivity to enable efficient representation of surface geometry and texture. Like recent work on evolving meshes, we pursue local remeshing to permit tracking over long sequences containing significant deformations or topological changes. Our main contribution is to show that both goals are realizable within a common framework that simultaneously evolves both the set of mesh triangles and the parametric map. Sparsifying the remeshing operations allows the formation of large spatiotemporal texture charts. These charts are packed as prisms into a 3D atlas for a texture video. Reducing tracking drift using mesh-based optical flow helps improve compression of the resulting video stream.


Beyond News Contents : The Role of Social Context for Fake News Detection


title: “ Snap-nEat ” : Food Recognition and Nutrition Estimation on a Smartphone

abstract: We present snap-n-eat, a mobile food recognition system. The system can recognize food and estimate the calorific and nutrition content of foods automatically without any user intervention. To identify food items, the user simply snaps a photo of the food plate. The system detects the salient region, crops its image, and subtracts the background accordingly. Hierarchical segmentation is performed to segment the image into regions. We then extract features at different locations and scales and classify these regions into different kinds of foods using a linear support vector machine classifier. In addition, the system determines the portion size which is then used to estimate the calorific and nutrition content of the food present on the plate. Previous approaches have mostly worked with either images captured in a lab setting, or they require additional user input (eg, user crop bounding boxes). Our system achieves automatic food detection and recognition in real-life settings containing cluttered backgrounds. When multiple food items appear in an image, our system can identify them and estimate their portion size simultaneously. We implemented this system as both an Android smartphone application and as a web service. In our experiments, we have achieved above 85% accuracy when detecting 15 different kinds of foods.


title: Provenance browser: Displaying and querying scientific workflow provenance graphs

abstract: This demonstration presents an interactive provenance browser for visualizing and querying data dependency (lineage) graphs produced by scientific workflow runs. The browser allows users to explore different views of provenance as well as to express complex and recursive graph queries through a high-level query language (QLP). Answers to QLP queries are lineage preserving in that queries return sets of lineage dependencies (denoting provenance graphs), which can be further queried and visually displayed (as graphs) in the browser. By combining provenance visualization, navigation, and query, the provenance browser can enable scientists to more easily access and explore scientific workflow provenance information.


title: Automotive Radars: A review of signal processing techniques

abstract: Automotive radars, along with other sensors such as lidar, (which stands for "light detection and ranging"), ultrasound, and cameras, form the backbone of self-driving cars and advanced driver assistant systems (ADASs). These technological advancements are enabled by extremely complex systems with a long signal processing path from radars/sensors to the controller. Automotive radar systems are responsible for the detection of objects and obstacles, their position, and speed relative to the vehicle. The development of signal processing techniques along with progress in the millimeter-wave (mm-wave) semiconductor technology plays a key role in automotive radar systems. Various signal processing techniques have been developed to provide better resolution and estimation performance in all measurement dimensions: range, azimuth-elevation angles, and velocity of the targets surrounding the vehicles. This article summarizes various aspects of automotive radar signal processing techniques, including waveform design, possible radar architectures, estimation algorithms, implementation complexity-resolution trade off, and adaptive processing for complex environments, as well as unique problems associated with automotive radars such as pedestrian detection. We believe that this review article will combine the several contributions scattered in the literature to serve as a primary starting point to new researchers and to give a bird's-eye view to the existing research community.


title: Y chromosomal haplogroup J as a signature of the post-neolithic colonization of Europe

abstract: In order to attain a finer reconstruction of the peopling of southern and central-eastern Europe from the Levant, we determined the frequencies of eight lineages internal to the Y chromosomal haplogroup J, defined by biallelic markers, in 22 population samples obtained with a fine-grained sampling scheme. Our results partially resolve a major multifurcation of lineages within the haplogroup. Analyses of molecular variance show that the area covered by haplogroup J dispersal is characterized by a significant degree of molecular radiation for unique event polymorphisms within the haplogroup, with a higher incidence of the most derived sub-haplogroups on the northern Mediterranean coast, from Turkey westward; here, J diversity is not simply a subset of that present in the area in which this haplogroup first originated. Dating estimates, based on simple tandem repeat loci (STR) diversity within each lineage, confirmed the presence of a major population structuring at the time of spread of haplogroup J in Europe and a punctuation in the peopling of this continent in the post-Neolithic, compatible with the expansion of the Greek world. We also present here, for the first time, a novel method for comparative dating of lineages, free of assumptions of STR mutation rates.


title: Modal identification of simple structures with high-speed video using motion magnification

abstract: Abstract Video cameras offer the unique capability of collecting high density spatial data from a distant scene of interest. They can be employed as remote monitoring or inspection sensors for structures because of their commonplace availability, simplicity, and potentially low cost. An issue is that video data is difficult to interpret into a format familiar to engineers such as displacement. A methodology called motion magnification has been developed for visualizing exaggerated versions of small displacements with an extension of the methodology to obtain the optical flow to measure displacements. In this paper, these methods are extended to modal identification in structures and the measurement of structural vibrations. Camera-based measurements of displacement are compared against laser vibrometer and accelerometer measurements for verification. The methodology is demonstrated on simple structures, a cantilever beam and a pipe, to identify and visualize the operational deflection shapes. Suggestions for applications of this methodology and challenges in real-world implementation are given.


title: On the Sample Complexity of Random Fourier Features for Online Learning: How Many Random Fourier Features Do We Need?

abstract: We study the sample complexity of random Fourier features for online kernel learning—that is, the number of random Fourier features required to achieve good generalization performance. We show that when the loss function is strongly convex and smooth, online kernel learning with random Fourier features can achieve an O(log T /T) bound for the excess risk with only O(1/λ2) random Fourier features, where T is the number of training examples and λ is the modulus of strong convexity. This is a significant improvement compared to the existing result for batch kernel learning that requires O(T) random Fourier features to achieve a generalization bound O(1/√T). Our empirical study verifies that online kernel learning with a limited number of random Fourier features can achieve similar generalization performance as online learning using full kernel matrix. We also present an enhanced online learning algorithm with random Fourier features that improves the classification performance by multiple passes of training examples and a partial average.


User behavior analysis based on Identity management systems ’ log data


title: Outcome of Glansectomy and Skin Grafting in the Management of Penile Cancer

abstract: Purpose. To report outcome data for patients with penile cancer treated surgically with glansectomy and skin grafting. Materials and Methods. We retrospectively reviewed data on all patients undergoing surgical management of penile cancer by a single surgeon between 1998 and 2008. Outcomes in patients who underwent glansectomy and skin grafting were analysed. Results. Between 1998 and 2008 a total of 25 patients with a mean age 60 (39–83) underwent glansectomy and skin grafting. Six patients had carcinoma in situ (CIS); the stage in the remaining patients ranged from T1G1 to T3G3. Mean followup for patients was 28 months (range 6–66). Disease specific survival was 92% with 2 patients who had positive nodes at lymph node dissection developing groin recurrence. One patient developed a local recurrence requiring a partial penectomy. Conclusions. Penile preserving surgery with glansectomy and skin grafting is a successful technique with minimal complications for local control of penile carcinoma arising on the glans. Careful followup to exclude local recurrence is required.


title: FADEWICH: Fast Deauthentication over the Wireless Channel

abstract: Both authentication and deauthentication are instrumental for preventing unauthorized access to computers and other resources. While there are obvious motivating factors for using strong authentication mechanisms, convincing users to deauthenticate is not straight-forward, since deauthentication is not considered mandatory. A user who leaves a logged-in workstation unattended (especially for a short time) is typically not inconvenienced in any way; in fact, the other way around - no annoying reauthentication is needed upon return. However, an unattended workstation is trivially susceptible to the well-known "lunchtime attack" by any nearby adversary who simply takes over the departed user's log-in session. At the same time, since deauthentication does not intrinsically require user secrets, it can, in principle, be made unobtrusive. To this end, this paper designs the first automatic user deauthentication system - FADEWICH - that does not rely on biometric-or behavior-based techniques (e.g., keystroke dynamics) and does not require users to carry any devices. It uses physical properties of wireless signals and the effect of human bodies on their propagation. To assess FADEWICH's feasibility and performance, extensive experiments were conducted with its prototype. Results show that it suffices to have nine inexpensive wireless sensors deployed in a shared office setting to correctly deauthenticate all users within six seconds (90% within four seconds) after they leave their workstation's vicinity. We considered two realistic scenarios where the adversary attempts to subvert FADEWICH and showed that lunchtime attacks fail.


View morphing


title: Monte Carlo Strength Evaluation: Fast and Reliable Password Checking

abstract: Modern password guessing attacks adopt sophisticated probabilistic techniques that allow for orders of magnitude less guesses to succeed compared to brute force. Unfortunately, best practices and password strength evaluators failed to keep up: they are generally based on heuristic rules designed to defend against obsolete brute force attacks. Many passwords can only be guessed with significant effort, and motivated attackers may be willing to invest resources to obtain valuable passwords. However, it is eminently impractical for the defender to simulate expensive attacks against each user to accurately characterize their password strength. This paper proposes a novel method to estimate the number of guesses needed to find a password using modern attacks. The proposed method requires little resources, applies to a wide set of probabilistic models, and is characterised by highly desirable convergence properties. The experiments demonstrate the scalability and generality of the proposal. In particular, the experimental analysis reports evaluations on a wide range of password strengths, and of state-of-the-art attacks on very large datasets, including attacks that would have been prohibitively expensive to handle with existing simulation-based approaches.


title: Unified Architecture for Multichannel End-to-End Speech Recognition With Neural Beamforming

abstract: This paper proposes a unified architecture for end-to-end automatic speech recognition (ASR) to encompass microphone-array signal processing such as a state-of-the-art neural beamformer within the end-to-end framework. Recently, the end-to-end ASR paradigm has attracted great research interest as an alternative to conventional hybrid paradigms with deep neural networks and hidden Markov models. Using this novel paradigm, we simplify ASR architecture by integrating such ASR components as acoustic, phonetic, and language models with a single neural network and optimize the overall components for the end-to-end ASR objective: generating a correct label sequence. Although most existing end-to-end frameworks have mainly focused on ASR in clean environments, our aim is to build more realistic end-to-end systems in noisy environments. To handle such challenging noisy ASR tasks, we study multichannel end-to-end ASR architecture, which directly converts multichannel speech signal to text through speech enhancement. This architecture allows speech enhancement and ASR components to be jointly optimized to improve the end-to-end ASR objective and leads to an end-to-end framework that works well in the presence of strong background noise. We elaborate the effectiveness of our proposed method on the multichannel ASR benchmarks in noisy environments (CHiME-4 and AMI). The experimental results show that our proposed multichannel end-to-end system obtained performance gains over the conventional end-to-end baseline with enhanced inputs from a delay-and-sum beamformer (i.e., BeamformIT) in terms of character error rate. In addition, further analysis shows that our neural beamformer, which is optimized only with the end-to-end ASR objective, successfully learned a noise suppression function.


title: Biogeography of bacterial communities exposed to progressive long-term environmental change

abstract: The response of microbial communities to long-term environmental change is poorly understood. Here, we study bacterioplankton communities in a unique system of coastal Antarctic lakes that were exposed to progressive long-term environmental change, using 454 pyrosequencing of the 16S rDNA gene (V3–V4 regions). At the time of formation, most of the studied lakes harbored marinecoastal microbial communities, as they were connected to the sea. During the past 20000 years, most lakes isolated from the sea, and subsequently they experienced a gradual, but strong, salinity change that eventually developed into a gradient ranging from freshwater (salinity 0) to hypersaline (salinity 100). Our results indicated that present bacterioplankton community composition was strongly correlated with salinity and weakly correlated with geographical distance between lakes. A few abundant taxa were shared between some lakes and coastal marine communities. Nevertheless, lakes contained a large number of taxa that were not detected in the adjacent sea. Abundant and rare taxa within saline communities presented similar biogeography, suggesting that these groups have comparable environmental sensitivity. Habitat specialists and generalists were detected among abundant and rare taxa, with specialists being relatively more abundant at the extremes of the salinity gradient. Altogether, progressive long-term salinity change appears to have promoted the diversification of bacterioplankton communities by modifying the composition of ancestral communities and by allowing the establishment of new taxa.


title: Learning from Demonstration for Autonomous Navigation in Complex Unstructured Terrain

abstract: Rough terrain autonomous navigation continues to pose a challenge to the robotics community. Robust navigation by a mobile robot depends not only on the individual performance of perception and planning systems, but on how well these systems are coupled. When traversing complex unstructured terrain, this coupling (in the form of a cost function) has a large impact on robot behavior and performance, necessitating a robust design. This paper explores the application of Learning from Demonstration to this task for the Crusher autonomous navigation platform. Using expert examples of desired navigation behavior, mappings from both online and offline perceptual data to planning costs are learned. Challenges in adapting existing techniques to complex online planning systems and imperfect demonstration are addressed, along with additional practical considerations. The benefits to autonomous performance of this approach are examined, as well as the decrease in necessary designer effort. Experimental results are presented from autonomous traverses through complex natural environments.


title: An Interactive 3D Holographic Pyramid for Museum Exhibition

abstract: In this paper, an interactive holographic system, realized with the aim of creating, exchanging, discussing and disseminating cultural heritage information, is presented. By using low-cost and off-the-shelf devices, the system provides the visitors with a 'floating' computer generated representation of a virtual cultural artefact that, unlike the real one, can be examined in detail through a touchless natural interface. The proposed system is realized in such a way that it can be easily placed in a cultural exhibition without requiring any structural intervention. As such, it could represent a useful instrument complementary to a museum visit thanks to its capacity both to convey different types of digital cultural information and especially to allow the visitor to become an active actor, able to enjoy different perspectives and all the details of the artefact sharing her/his experience with other visitors. The paper describes the system modules and the hardware design to physically realize the pyramid, and details the user interface composed of two main actions designed to obtain a simple exploration of a virtual cultural heritage artefact.


title: Enabling team learning when members are prone to contentious communication: The role of team leader coaching

abstract: Members of teams are often prone to interpersonal communication patterns that can undermine the team’s capacity to engage in self-learning processes that are critical to team adaptation and performance improvement. We argue that team leader coaching behaviors are critical to ensuring that team discussions that may foster learning new teamwork skills and strategies are unfettered by the tendency of two or more members to exhibit contentious interpersonal communications. We accordingly test a model in which team contentious communication moderates the mediated relationship of team leader coaching behaviors on team innovation effectiveness and team task performance. In a study of 82 work teams, team leader coaching behaviors exhibited indirect, positive relationships with both team innovation effectiveness and team task performance through team learning, but only among teams with an average or higher level of contentious interpersonal communication. We discuss theoretical and practical implications for the leadership of teams.


title: Normalized cuts and image segmentation

abstract: We propose a novel approach for solving the perceptual grouping problem in vision. Rather than focusing on local features and their consistencies in the image data, our approach aims at extracting the global impression of an image. We treat image segmentation as a graph partitioning problem and propose a novel global criterion, the normalized cut, for segmenting the graph. The normalized cut criterion measures both the total dissimilarity between the different groups as well as the total similarity within the groups. We show that an efficient computational technique based on a generalized eigenvalue problem can be used to optimize this criterion. We have applied this approach to segmenting static images and found results very encouraging.


title: Compact ring monopole antenna with double meander lines for

abstract: A novel compact ring monopole antenna with double meander lines is proposed for wireless local area networks (WLAN) applications in IEEE 802.11b/g/a systems. The designed antenna, fed by a 50 Ω microstrip transmission line, is only 32 mm in height and 16 mm in width. By introducing a horizontal and a vertical branched strips to a closed rectangular strip ring, the proposed antenna can generate two separate impedance bandwidths. Prototypes of the proposed antenna have been constructed and tested. The obtained impedance bandwidths reach about 12% for the 2.4 GHz band and 45.3% for the 5 GHz band, which meet the required bandwidth specification of 2.4/5 GHz WLAN standard. Also, good radiation performance and antenna gain over the two frequency ranges have been obtained.


A SUPPORT VECTOR MACHINE APPROACH FOR OBJECT BASED IMAGE ANALYSIS


title: A Clustering Approach For Nearly Unsupervised Recognition Of Nonliteral Language

abstract: In this paper we present TroFi (Trope Finder), a system for automatically classifying literal and nonliteral usages of verbs through nearly unsupervised word-sense disambiguation and clustering techniques. TroFi uses sentential context instead of selectional constraint violations or paths in semantic hierarchies. It also uses literal and nonliteral seed sets acquired and cleaned without human supervision in order to bootstrap learning. We adapt a word-sense disambiguation algorithm to our task and augment it with multiple seed set learners, a voting schema, and additional features like SuperTags and extrasentential context. Detailed experiments on hand-annotated data show that our enhanced algorithm outperforms the baseline by 24.4%. Using the TroFi algorithm, we also build the TroFi Example Base, an extensible resource of annotated literal/nonliteral examples which is freely available to the NLP research community.


title: Genetics of pigmentary disorders

abstract: The genetic and molecular bases of various types of congenital pigmentary disorders have been classified in the past 10 years, as follows: (1) disorders of melanoblast migration in the embryo from the neural crest to the skin: piebaldism; Waardenburg syndrome 1–4 (WS1–WS4); dyschromatosis symmetrica hereditaria. (2) Disorders of melanosome formation in the melanocyte: Hermansky–Pudlak syndrome 1–7 (HPS1–7); Chediak–Higashi syndrome 1 (CHS1). (3) Disorders of melanin synthesis in the melanosome: oculocutaneous albinism 1–4 (OCA1–4). (4) Disorders of mature melanosome transfer to the tips of the dendrites Griscelli syndrome 1–3 (GS1–3). These disorders are presented and their gene mutations and pathogenesis are discussed. © 2004 Wiley-Liss, Inc.


title: Text 2 Onto A Framework for Ontology Learning and Data-driven Change Discovery

abstract: In this paper we present Text2Onto, a framework for ontology learning from textual resources. Three main features distinguish Text2Onto from our earlier framework TextToOnto as well as other state-of-the-art ontology learning frameworks. First, by representing the learned knowledge at a meta-level in the form of instantiated modeling primitives within a so called Probabilistic Ontology Model (POM), we remain independent of a concrete target language while being able to translate the instantiated primitives into any (reasonably expressive) knowledge representation formalism. Second, user interaction is a core aspect of Text2Onto and the fact that the system calculates a confidence for each learned object allows to design sophisticated visualizations of the POM. Third, by incorporating strategies for data-driven change discovery, we avoid processing the whole corpus from scratch each time it changes, only selectively updating the POM according to the corpus changes instead. Besides increasing efficiency in this way, it also allows a user to trace the evolution of the ontology with respect to the changes in the underlying corpus.


title: Mandibular intraosseous squamous cell carcinoma lesion associated with odontogenic keratocyst: a case report

abstract: Squamous cell carcinoma (SCC) is the most common malignant tumor in the oral cavity, and it accounts for about 90% of all oral cancers. Several risk factors for oral SCC have been identified; however, SCC associated with odontogenic keratocysts have rarely been reported. The present study describes the case of a 36-year-old man with SCC of the right ramus of the mandible, which was initially diagnosed as a benign odontogenic cyst. He underwent enucleation at another hospital followed by segmental mandibulectomy and fibular free flap reconstruction at our institution. In this case, we introduce a patient with oral cancer associated with odontogenic cyst on the mandible and report a satisfactory outcome with wide resection and immediate free flap reconstruction.


title: Anti-cropping digital image watermarking using Sudoku

abstract: Many digital image watermarking schemes have been developed to embed copyright information into an image. However, an attacker may reuse parts of a watermarked image by cropping out unwanted parts. Several techniques have been designed to overcome this attack but due to their limited redundancy approach, some section of the images can still be retrieved without detectable watermark. In this paper, a new watermarking scheme that is robust against severe cropping using Sudoku is proposed. It is based on Sudoku's permutation property that allows evenly distributed copies of watermark pieces in all parts of the cover image. A valid Sudoku solution is used during the embedding as well as during the detection of the watermark. Using classic 9 × 9 Sudoku, the scheme demonstrated robustness of up to 94% of random cropping.


title: Usability evaluation for history educational games

abstract: The potential for integration of digital games and learning becomes ever more significant recently. One of the goals in educational game design is to create engaging and immersive learning experiences for delivering specified learning goals, outcomes and experiences. However, there is limited number of research done on game usability or quality of game user interface. Failure to design usable game interfaces can interfere with the larger goal of creating a compelling experience for users and can have a negative effect on the overall quality and success of a game. In this paper, we review usability problems identified by previous researchers and propose a history educational game design which includes pedagogical and game design component. Some snapshots of our game module are also presented. Finally we present a usability evaluation method for history educational game design. From our critical literature reviews, we also proposed six constructs which are interface, mechanics, gameplay, playability, feedback and immersion for usability evaluation.


title: Optimized Zero False Positives Perceptron Training for Malware Detection

abstract: The increasing number of malware in the past 4 years has determined researchers to test different machine learning techniques to automate the detection system. But because of the large size of the dataset and the need of having a high detection rate, the resulted models have often produced many false positives. This paper proposes a modified version of the perceptron algorithm able to detect malware samples while training at a low rate (even zero) of false positives. A very low number of false positives is crucial because in a real life situation detecting a clean file as malware can destroy the operating system or render other programs unusable. We also provide a method of optimizing the training speed for the algorithm while maintaining the same accuracy. The resulted algorithm can be used in an ensemble or voting system to increase detection and eliminate false positives.


title: Study on the Space Interface Characteristics of Minimalism Architecture

abstract: By backward-reasoning and analyzing the features of spatial forms of contemporary minimalism architecture, an abstract concept, space interface, is introduced to restore the original elements, investigate the construction processes and explain the essential connotations of the spatial forms. On this basis, three different types of rational construction logics are summed up: the geometric forms of volume, the illumination manifestations of light and the folding appearances of surface, in order to interpret the inherent simplicity and purity of the space interface characteristics of minimalism architecture and provide a kind of realistic theories and methods of space interface manipulation for architectural practices.


title: ODE - Augmented Training Improves Anomaly Detection in Sensor Data from Machines

abstract: Machines of all kinds from vehicles to industrial equipment are increasingly instrumented with hundreds of sensors. Using such data to detect anomalous behaviour is critical for safety and efficient maintenance. However, anomalies occur rarely and with great variety in such systems, so there is often insufficient anomalous data to build reliable detectors. A standard approach to mitigate this problem is to use one class methods relying only on data from normal behaviour. Unfortunately, even these approaches are more likely to fail in the scenario of a dynamical system with manual control input(s). Normal behaviour in response to novel control input(s) might look very different to the learned detector which may be incorrectly detected as anomalous. In this paper, we address this issue by modelling time-series via Ordinary Differential Equations (ODE) and utilising such an ODE model to simulate the behaviour of dynamical systems under varying control inputs. The available data is then augmented with data generated from the ODE, and the anomaly detector is retrained on this augmented dataset. Experiments demonstrate that ODE-augmented training data allows better coverage of possible control input(s) and results in learning more accurate distinctions between normal and anomalous behaviour in time-series.


title: Privacy preserving data mining using image slicing and visual cryptography

abstract: Slicing geometry (image) into different frames and randomizing them can help in hiding a secret message (key) within the image and also preserve privacy. Authenticated users can use the relevant key and extract the original image at its destination. This field termed as visual cryptography or steganography is one of the most secure forms of message transfer available today and is highly suited for image files. The scope of this paper is to combine steganography and cryptography properties in such a way to make it harder to retrieve the image of the secret message. In this work, the image is sliced and randomized, the stored random slices upon match with a search string (query) the original image is retrieved. Multithread approach is combined in the searching process to reduce the time complexity of the search algorithm. This method is particularly advantageous to store large number of records, categorized into different groups, and each group indexed with specific keys and when used at the receiving side the particular group image(s) becomes accessible.


title: Effective Vehicle-Based Kangaroo Detection for Collision Warning Systems Using Region-Based Convolutional Networks

abstract: Traffic collisions between kangaroos and motorists are on the rise on Australian roads. According to a recent report, it was estimated that there were more than 20,000 kangaroo vehicle collisions that occurred only during the year 2015 in Australia. In this work, we are proposing a vehicle-based framework for kangaroo detection in urban and highway traffic environment that could be used for collision warning systems. Our proposed framework is based on region-based convolutional neural networks (RCNN). Given the scarcity of labeled data of kangaroos in traffic environments, we utilized our state-of-the-art data generation pipeline to generate 17,000 synthetic depth images of traffic scenes with kangaroo instances annotated in them. We trained our proposed RCNN-based framework on a subset of the generated synthetic depth images dataset. The proposed framework achieved a higher average precision (AP) score of 92% over all the testing synthetic depth image datasets. We compared our proposed framework against other baseline approaches and we outperformed it with more than 37% in AP score over all the testing datasets. Additionally, we evaluated the generalization performance of the proposed framework on real live data and we achieved a resilient detection accuracy without any further fine-tuning of our proposed RCNN-based framework.


title: Computerized screening of diabetic retinopathy employing blood vessel segmentation in retinal images

abstract: Abstract Diabetic retinopathy is a severe sight threatening disease which causes blindness among working age people. This research work presents a retinal vessel segmentation technique, which can be used in computer based retinal image analysis. This proposed method could be used as a prescreening system for the early detection of diabetic retinopathy. The algorithm implemented in this work can be effectively used for detection and analysis of vascular structures in retinal images. The retinal blood vessel morphology helps to classify the severity and identify the successive stages of a number of diseases. The changes in retinal vessel diameter are one of the symptoms for diseases based on vascular pathology. The size of typical retinal vessel is a few pixels wide and it becomes critical and challenging to obtain precise measurements using computer based automatic analysis of retinal images. This method classifies each image pixel as vessel or non-vessel and thereby produces the segmentation of vasculature in retinal images. Retinal blood vessels are identified and segmented by making use of a multilayer perceptron neural network, for which the inputs are derived from three primary colour components of the image, i.e., red, green and blue. Back propagation algorithm which provides a proficient technique to change the weights in a feed-forward network is employed. The performance of this method was evaluated and tested using the retinal images from the DRIVE database and has obtained illustrative results. The measured accuracy of the proposed system was 95.03% for the segmentation algorithm tested on this database.


title: Cascading Bandits for Large-Scale Recommendation Problems

abstract: Most recommender systems recommend a list of items. The user examines the list, from the first item to the last, and often chooses the first attractive item and does not examine the rest. This type of user behavior can be modeled by the cascade model. In this work, we study cascading bandits, an online learning variant of the cascade model where the goal is to recommend $K$ most attractive items from a large set of $L$ candidate items. We propose two algorithms for solving this problem, which are based on the idea of linear generalization. The key idea in our solutions is that we learn a predictor of the attraction probabilities of items from their features, as opposing to learning the attraction probability of each item independently as in the existing work. This results in practical learning algorithms whose regret does not depend on the number of items $L$. We bound the regret of one algorithm and comprehensively evaluate the other on a range of recommendation problems. The algorithm performs well and outperforms all baselines.


title: A CMOS fully differential W-band passive imager with <2 K NETD

abstract: This paper presents a fully differential W-band passive imager that integrates LNA, Dicke switch, detector and filter, with minimum NEPs of 21 ƒW/√Hz / 24 ƒW/√Hz for without/with switch cases and a peak responsivity of >1.8 MV/W. To authors' knowledge, this represents the largest responsivity to date from a CMOS radiometer chip. It achieves NETD = 1.94 K with 30ms integration time. The chip occupies 0.49 mm2 active area with 57 mW power consumption.


title: Multiple Sensor Fusion and Classification for Moving Object Detection and Tracking

abstract: The accurate detection and classification of moving objects is a critical aspect of advanced driver assistance systems. We believe that by including the object classification from multiple sensor detections as a key component of the object's representation and the perception process, we can improve the perceived model of the environment. First, we define a composite object representation to include class information in the core object's description. Second, we propose a complete perception fusion architecture based on the evidential framework to solve the detection and tracking of moving objects problem by integrating the composite representation and uncertainty management. Finally, we integrate our fusion approach in a real-time application inside a vehicle demonstrator from the interactIVe IP European project, which includes three main sensors: radar, lidar, and camera. We test our fusion approach using real data from different driving scenarios and focusing on four objects of interest: pedestrian, bike, car, and truck.


title: Automatic Bangla Text Summarization Using Term Frequency and Semantic Similarity Approach

abstract: With the increasing amount of data within the cloud, it is harder to get the expected one. This leads to the idea of text summarization. Automatic text summarization is a tool for summarizing textual data into a short and concise piece of information via which people can have the idea about the content. Several approaches are introduced but there are a little amount of work has been done on Bangla text summarizing techniques due to some different and multifaceted structure of Bangla language. This paper illustrates the implementation of term frequency and semantic sentence similarity based summarizing approaches to summarize a single Bangla document. Removing stopwords, noisy words, lemmatization, tokenization has been done beforehand. Both of these methods return a bunch of top-ranked sentences to create a summary. The rank of a sentence is determined by the term frequency for the first approach and the sentence similarity for the second approach. The experimental result shows a favorable outcome for both of the approaches. Further improvements of these approaches certainly will return an enchanting outcome.


title: Tensor Canonical Correlation Analysis for Multi-View Dimension Reduction

abstract: Canonical correlation analysis (CCA) has proven an effective tool for two-view dimension reduction due to its profound theoretical foundation and success in practical applications. In respect of multi-view learning, however, it is limited by its capability of only handling data represented by two-view features, while in many real-world applications, the number of views is frequently many more. Although the ad hoc way of simultaneously exploring all possible pairs of features can numerically deal with multi-view data, it ignores the high order statistics (correlation information) which can only be discovered by simultaneously exploring all features. Therefore, in this work, we develop tensor CCA (TCCA) which straightforwardly yet naturally generalizes CCA to handle the data of an arbitrary number of views by analyzing the covariance tensor of the different views. TCCA aims to directly maximize the canonical correlation of multiple (more than two) views. Crucially, we prove that the main problem of multiview canonical correlation maximization is equivalent to finding the best rank-1 approximation of the data covariance tensor, which can be solved efficiently using the well-known alternating least squares (ALS) algorithm. As a consequence, the high order correlation information contained in the different views is explored and thus a more reliable common subspace shared by all features can be obtained.


title: Dynamic Model Learning and Manipulation Planning for Objects in Hospitals Using a Patient Assistant Mobile (PAM)Robot

abstract: One of the most concerning and costly problems in hospitals is patients falls. We address this problem by introducing PAM, a patient assistant mobile robot, that maneuvers mobility aids to assist with fall prevention. Common objects found inside hospitals include objects with legs (i.e. walkers, tables, chairs, equipment stands). For a mobile robot operating in such environments, safely maneuvering these objects without collision is essential. Since providing the robot with dynamic models of all possible legged objects that may exist in such environments is not feasible, autonomous learning of an approximate dynamic model for these objects would significantly improve manipulation planning. We describe a probabilistic method to do this by fitting pre-categorized object models learned from minimal force and motion interactions with an object. In addition, we account for multiple manipulation strategies, which requires a hybrid control system comprised of discrete grasps on legs and continuous applied forces. To do this, we use a simple one-wheel point-mass model. A hybrid MPC-based manipulation planning algorithm was developed to compensate for modeling errors. While the proposed algorithm applies to a broad range of legged objects, we only show results for the case of a 2-wheel, 4-legged walker in this paper. Simulation and experimental tests show that the obtained dynamic model is sufficiently accurate for safe and collision-free manipulation. When combined with the proposed manipulation planning algorithm, the robot can successfully move the object to a desired position without collision.


title: LaserStroke: Mid-air Tactile Experiences on Contours Using Indirect Laser Radiation

abstract: This demonstration presents a novel form of mid-air tactile display, LaserStroke, that makes use of a laser irradiated on the elastic medium attached to the skin. LaserStroke extends a laser device with an orientation control platform and a magnetic tracker so that it can elicit tapping and stroking sensations to a user's palm from a distance. LaserStroke offers unique tactile experiences while a user freely moves his/her hand in midair.


title: Reconstructing Visual Experiences from Brain Activity Evoked by Natural Movies

abstract: Summary Quantitative modeling of human brain activity can provide crucial insights about cortical representations [1, 2] and can form the basis for brain decoding devices [3–5]. Recent functional magnetic resonance imaging (fMRI) studies have modeled brain activity elicited by static visual patterns and have reconstructed these patterns from brain activity [6–8]. However, blood oxygen level-dependent (BOLD) signals measured via fMRI are very slow [9], so it has been difficult to model brain activity elicited by dynamic stimuli such as natural movies. Here we present a new motion-energy [10, 11] encoding model that largely overcomes this limitation. The model describes fast visual information and slow hemodynamics by separate components. We recorded BOLD signals in occipitotemporal visual cortex of human subjects who watched natural movies and fit the model separately to individual voxels. Visualization of the fit models reveals how early visual areas represent the information in movies. To demonstrate the power of our approach, we also constructed a Bayesian decoder [8] by combining estimated encoding models with a sampled natural movie prior. The decoder provides remarkable reconstructions of the viewed movies. These results demonstrate that dynamic brain activity measured under naturalistic conditions can be decoded using current fMRI technology.


Categorizer ) : Neural Network for Text Categorization


title: Information-centric network function virtualization over 5g mobile wireless networks

abstract: Wireless network virtualization and information-centric networking (ICN) are two promising techniques in software-defined 5G mobile wireless networks. Traditionally, these two technologies have been addressed separately. In this paper we show that integrating wireless network virtualization with ICN techniques can significantly improve the end-to-end network performance. In particular, we propose an information- centric wireless network virtualization architecture for integrating wireless network virtualization with ICN. We develop the key components of this architecture: radio spectrum resource, wireless network infrastructure, virtual resources (including content-level slicing, network-level slicing, and flow-level slicing), and informationcentric wireless virtualization controller. Then we formulate the virtual resource allocation and in-network caching strategy as an optimization problem, considering the gain of not only virtualization but also in-network caching in our proposed information-centric wireless network virtualization architecture. The obtained simulation results show that our proposed information-centric wireless network virtualization architecture and the related schemes significantly outperform the other existing schemes.


title: Stream-based joint exploration-exploitation active learning

abstract: Learning from streams of evolving and unbounded data is an important problem, for example in visual surveillance or internet scale data. For such large and evolving real-world data, exhaustive supervision is impractical, particularly so when the full space of classes is not known in advance therefore joint class discovery (exploration) and boundary learning (exploitation) becomes critical. Active learning has shown promise in jointly optimising exploration-exploitation with minimal human supervision. However, existing active learning methods either rely on heuristic multi-criteria weighting or are limited to batch processing. In this paper, we present a new unified framework for joint exploration-exploitation active learning in streams without any heuristic weighting. Extensive evaluation on classification of various image and surveillance video datasets demonstrates the superiority of our framework over existing methods.


title: Knowledge sharing factors and innovation capability

abstract: In practicing knowledge management in organization has a potential in developing and nurture innovation culture and competitive advantages in manufacturing industry. The innovation culture required people in organization to collaborate and work together in exchange and share their knowledge, ideas and experience. Apparently, knowledge sharing is a pivotal process of enhance innovation through exchange their ideas, opinions and discuss among employees to come out with new ideas. Hence, the main purpose of this study is to determine the knowledge sharing factors such as technology, rewards, leadership, trust and organization in innovative capability. Survey was conducted in twenty three selected manufacturing companies in Selangor. Descriptive and correlational study was conducted and result showed that reward system, trust and organizational learning were significant in the developing innovation capability in manufacturing. Technology and leadership did not support for this study. The findings were contrast with the previous study as highlights the importance of technology usage was mainly contributed in innovation capability. The results of this study would give a notion that technology, reward system, leadership/management support, trust, and organizational learning may have an influence on innovation capability. Besides that, this study is significantly useful for manufacturing company especially the exporter companies. Companies that involve import export business worldwide particularly require continuous innovation to remain competitive in the market. However, before any innovation made, they should recognize the factors that can help them to do innovation. Therefore, knowledge sharing is a vital factor that can increase innovation in organization.


title: Wideband Microstrip 180 $^{\circ}$ Hybrid Utilizing Ground Slots

abstract: The letter describes the design of a wideband microstrip 180° hybrid, which makes use of two sides of a single-layer substrate. The device is formed by the out-of-phase and in-phase dividers which are suitably unified. By making use of ground slots and microstrip to slot transitions all four its ports are of microstrip type. The hybrid exhibits a well-balanced power division between the output ports and good quality return losses of the out-of-phase (difference) and in-phase (summation) ports across the band from 3 to 9 GHz. The isolation between the output ports is better than 10 dB. The isolation between the difference and summation ports is greater than 40 dB and the phase characteristics are close to the ideal 180° and 0°.