title: Fingerprint Extraction Using Smartphone Camera

abstract: In the previous decade, there has been a considerable rise in the usage of smartphones.Due to exorbitant advancement in technology, computational speed and quality of image capturing has increased considerably. With an increase in the need for remote fingerprint verification, smartphones can be used as a powerful alternative for fingerprint authentication instead of conventional optical sensors. In this research, wepropose a technique to capture finger-images from the smartphones and pre-process them in such a way that it can be easily matched with the optical sensor images.Effective finger-image capturing, image enhancement, fingerprint pattern extraction, core point detection and image alignment techniques have been discussed. The proposed approach has been validated on FVC 2004 DB1 & DB2 dataset and the results show the efficacy of the methodology proposed. The method can be deployed for real-time commercial usage.


title: Variational Network Quantization

abstract: In this paper, the preparation of a neural network for pruning and few-bit quantization is formulated as a variational inference problem. To this end a quantizing prior that leads to a multi-modal, sparse posterior distribution over weights is introduced and a differentiable Kullback-Leibler divergence approximation for this prior is derived. After training with Variational Network Quantization, weights can be replaced by deterministic quantization values with small to negligible loss of task accuracy (including pruning by setting weights to 0). The method does not require fine-tuning after quantization. Results are shown for ternary quantization on LeNet-5 (MNIST) and DenseNet-121 (CIFAR-10).


title: A gameplay definition through videogame classification

abstract: This paper is part of an experimental approach aimed to raise a videogames classification. Being inspired by the methodology that Propp used for the classification of Russian fairy tales, we have identified recurrent diagrams within rules of videogames, that we called "Gameplay Bricks". The combinations of these different bricks should allow us to represent a classification of all videogames in accordance with their rules. In this article, we will study the nature of these bricks, especially the link they seem to have with two types of game rules: the rules that allow the player to "manipulate" the elements of the game, and the rules defining the "goal" of the game. This study will lead to an hypothesis about the nature of gameplay.


title: Coreference Resolution Using Competition Learning Approach

abstract: In this paper we propose a competition learning approach to coreference resolution. Traditionally, supervised machine learning approaches adopt the single-candidate model. Nevertheless the preference relationship between the antecedent candidates cannot be determined accurately in this model. By contrast, our approach adopts a twin-candidate learning model. Such a model can present the competition criterion for antecedent candidates reliably, and ensure that the most preferred candidate is selected. Furthermore, our approach applies a candidate filter to reduce the computational cost and data noises during training and resolution. The experimental results on MUC-6 and MUC-7 data set show that our approach can outperform those based on the single-candidate model.


title: HARP: Hierarchical Representation Learning for Networks

abstract: We present HARP, a novel method for learning low dimensional embeddings of a graph's nodes which preserves higher-order structural features. Our proposed method achieves this by compressing the input graph prior to embedding it, effectively avoiding troublesome embedding configurations (i.e. local minima) which can pose problems to non-convex optimization. HARP works by finding a smaller graph which approximates the global structure of its input. This simplified graph is used to learn a set of initial representations, which serve as good initializations for learning representations in the original, detailed graph. We inductively extend this idea, by decomposing a graph in a series of levels, and then embed the hierarchy of graphs from the coarsest one to the original graph. HARP is a general meta-strategy to improve all of the state-of-the-art neural algorithms for embedding graphs, including DeepWalk, LINE, and Node2vec. Indeed, we demonstrate that applying HARP's hierarchical paradigm yields improved implementations for all three of these methods, as evaluated on both classification tasks on real-world graphs such as DBLP, BlogCatalog, CiteSeer, and Arxiv, where we achieve a performance gain over the original implementations by up to 14% Macro F1.


title: Passive UHF RFID in Paper Industry: Challenges, Benefits and the Application Environment

abstract: Radio frequency identification (RFID) systems for the paper industry is an emerging research topic due to the need for an automated identification system for the paper industry which would carry on the identification codes of paper and board reels throughout their life cycle. This paper discusses the application of passive ultra-high frequency (UHF) RFID systems to the paper industry. Challenges, benefits, and the application environment of using passive UHF RFID systems in the paper industry are presented and discussed. The major challenges are development of globally operable tag antenna designs and integration of reader units and reader antennas to paper handling machinery. To confront and solve these challenges, this paper presents novel tag antenna designs for paper and board reel identification and proposes solutions for reader and reader antenna integration to paper handling machinery. In addition, the identification locations within the paper reel supply chain and the effects of RFID systems to supply chain visibility are presented and discussed. In addition, test results of using passive UHF RFID systems in the paper industry environment are presented.


Tubelet Detector for Spatio-Temporal Action Localization


title: A Hybrid Switched Reactive-Based Visual Servo Control of 5-DOF Robot Manipulators for Pick-and-Place Tasks

abstract: This paper addresses the problem of visual servo control for a five-degree-of-freedom robot manipulator to perform pick-and-place tasks. A new hybrid switched reactive-based visual servo control structure, inspired from a sensing-and-reaction behavior without the inverse interaction matrix computing, is proposed to handle this problem efficiently. The proposed structure is similar to the logic-based approaches, but the requirements of the fuzzy modeling and/or the fuzzy-rule base learning are omitted. To achieve this, a novel hardware-aware reactive function approach is presented to directly map an image position error vector to a desired end-effector velocity vector under the consideration of hardware limitations. This approach helps to simplify the implementation of visual servo systems with improved reliability. Moreover, the proposed control system is a hybrid switching controller consisting of both image-based and position-based reactive planning schemes, which allows improving the robustness and effectiveness of the visual servo system. Experimental results validate the performance of the proposed visual servo control method in a realistic scenario setting.


title: Malicious Software Classification Using Transfer Learning of ResNet-50 Deep Neural Network

abstract: Malicious software (malware) has been extensively used for illegal activity and new malware variants are discovered at an alarmingly high rate. The ability to group malware variants into families with similar characteristics makes possible to create mitigation strategies that work for a whole class of programs. In this paper, we present a malware family classification approach using a deep neural network based on the ResNet-50 architecture. Malware samples are represented as byteplot grayscale images and a deep neural network is trained freezing the convolutional layers of ResNet-50 pre-trained on the ImageNet dataset and adapting the last layer to malware family classification. The experimental results on a dataset comprising 9,339 samples from 25 different families showed that our approach can effectively be used to classify malware families with an accuracy of 98.62%.


title: A dataset of stereoscopic images and ground-truth disparity mimicking human fixations in peripersonal space

abstract: Binocular stereopsis is the ability of a visual system, belonging to a live being or a machine, to interpret the different visual information deriving from two eyes/cameras for depth perception. From this perspective, the ground-truth information about three-dimensional visual space, which is hardly available, is an ideal tool both for evaluating human performance and for benchmarking machine vision algorithms. In the present work, we implemented a rendering methodology in which the camera pose mimics realistic eye pose for a fixating observer, thus including convergent eye geometry and cyclotorsion. The virtual environment we developed relies on highly accurate 3D virtual models, and its full controllability allows us to obtain the stereoscopic pairs together with the ground-truth depth and camera pose information. We thus created a stereoscopic dataset: GENUA PESTO—GENoa hUman Active fixation database: PEripersonal space STereoscopic images and grOund truth disparity. The dataset aims to provide a unified framework useful for a number of problems relevant to human and computer vision, from scene exploration and eye movement studies to 3D scene reconstruction. Machine-accessible metadata file describing the reported data (ISA-Tab format)


title: Vehicle detection and tracking in wide field-of-view aerial video

abstract: This paper presents a joint probabilistic relation graph approach to simultaneously detect and track a large number of vehicles in low frame rate aerial videos. Due to low frame rate, low spatial resolution and sheer number of moving objects, detection and tracking in wide area video poses unique challenges. In this paper, we explore vehicle behavior model from road structure and generate a set of constraints to regulate both object based vertex matching and pairwise edge matching schemes. The proposed relation graph approach then unifies these two matching schemes into a single cost minimization framework to produce a quadratic optimized association result. The experiments on hours of real videos demonstrate the graph matching framework with vehicle behavior model effectively improves tracking performance in large scale dense traffic scenarios.


title: High-Resolution Image Inpainting Using Multi-scale Neural Patch Synthesis

abstract: Recent advances in deep learning have shown exciting promise in filling large holes in natural images with semantically plausible and context aware details, impacting fundamental image manipulation tasks such as object removal. While these learning-based methods are significantly more effective in capturing high-level features than prior techniques, they can only handle very low-resolution inputs due to memory limitations and difficulty in training. Even for slightly larger images, the inpainted regions would appear blurry and unpleasant boundaries become visible. We propose a multi-scale neural patch synthesis approach based on joint optimization of image content and texture constraints, which not only preserves contextual structures but also produces high-frequency details by matching and adapting patches with the most similar mid-layer feature correlations of a deep classification network. We evaluate our method on the ImageNet and Paris Streetview datasets and achieved state-of-the-art inpainting accuracy. We show our approach produces sharper and more coherent results than prior methods, especially for high-resolution images.


title: A hybrid security algorithm for RSA cryptosystem

abstract: This paper presents a new Hybrid security algorithm for RSA cryptosystem named as Hybrid RSA (HRSA). Here computation of "public key" (P) as well as "private key" (Q) depends on the value of M, where M is the product of four prime numbers. So complexity involved in factorizing the variable M increases. Another interesting feature about this algorithm is that the computation of P and Q involves the computation of some more intermediate factors which makes the computation more complex. In other existing works, the variable x or M is transferred during encryption and decryption process. However, this approach eliminates the transfer of variable x and M, where x represents the multiplication of two prime numbers a and b. Thus the proposed approach provides more secure path for encryption and decryption process. To substantiate this fact, the "key generation time", "encryption speed" and "decryption speed" of the proposed HRSA have been compared with conventional RSA and ERSA algorithms.


title: Negotiating presence-in-absence: contact, content and context

abstract: On the basis of a longitudinal field study of domestic communication, we report some essential constituents of the user experience of awareness of others who are distant in space or time, i.e. presence-in-absence. We discuss presence-in-absence in terms of its social (Contact) and informational (Content) facets, and the circumstances of the experience (Context). The field evaluation of a prototype, 'The Cube', designed to support presence-in-absence, threw up issues in the interrelationships between contact, content and context; issues that the designers of similar social artifacts will need to address.


title: Development of a Multi-Channel Compact-Size Wireless Hybrid sEMG/NIRS Sensor System for Prosthetic Manipulation

abstract: It is evident that surface electromyography (sEMG)-based approaches have inherent difficulty in coping with modern dominant applications of clinical diagnosis and human machine interface such as prosthetic manipulation. This paper presents a hybrid sensor to attentively overcome the difficulty with a clinical purpose of simultaneously acquiring electrophysiological, hemodynamic, and oxidative metabolic information of muscle activity, also with front-end conditioning circuit and Bluetooth module integrated and packaged. A multi-channel compact-size wireless hybrid sEMG/near-infrared spectroscopy (NIRS) acquisition system is developed, forming a platform to demonstrate individual sEMG and NIRS measurement capabilities, and their combination. Extensive experiments are carried out to explore sensor functionality based on the sEMG, NIRS, and their combination, convincingly addressing the capabilities meeting their commercial or state-of-the-art counterparts. Future work is targeted to extract the sEMG/NIRS sensor-based muscular fatigue which plays a crucial role in biomedical and clinical applications.


title: Optimal sizing of stand-alone hybrid wind/PV system with battery storage

abstract: In this paper, a new methodology developed to design a hybrid wind/photovoltaic (wind/PV) system, is presented. Based on an optimization process using a deterministic algorithm, the developed methodology helps the authors to obtain the optimal number and type of PV panels, wind turbines and storage units ensuring that the system total cost is minimized while guaranteeing the permanent availability of energy to cover the load energy requirements. The hybrid system configuration and modeling are presented and the used deterministic algorithm (DIRECT algorithm) is described and implemented in MATLAB environment to optimize the hybrid wind/PV system.


title: Technical Section: 2D shape morphing via automatic feature matching and hierarchical interpolation

abstract: The paper presents a new method to interpolate a pair of 2D shapes that are represented by piecewise linear curves. The method addresses two key problems in 2D shape morphing process: feature correspondence and path interpolation. First, a robust feature metric is defined to measure the similarity of a pair of 2D shapes in terms of visual appearance, orientation and relative size. Based on the metric, an optimal problem is defined and solved to associate the features on the source shape with the corresponding ones on the target shape. Then, a two-level hierarchical approach is proposed to solve the corresponding features interpolation trajectory problem. The algorithm decomposes the input shapes into a pair of corresponding coarse polygons and several pairs of corresponding features. Then the corresponding coarse polygons are interpolated in an as-rigid-as-possible plausible way; meanwhile the corresponding features are interpolated using the intrinsic method. Thus interior distortions of the intermediate shapes could be avoided and the feature details on the input shapes could be well preserved. Experimental results show that the method can generate smooth, natural and visually pleasing 2D shape morphing effects.


title: Three- and four-dimensional HDlive rendering image of fetal acrania/exencephaly in early pregnancy

abstract: We present two cases of fetal acrania/exencephaly reconstructed employing the three- (3D) and four-dimensional (4D) HDlive rendering mode in early gestation. Two fetuses with acrania/exencephaly were studied with the 3D/4D HDlive rendering mode at 13 and 15 weeks, respectively. In Case 1, at 13 weeks' gestation, deformed and degenerated brain tissues were clearly shown using the 3D/4D HDlive rendering mode. In Case 2, at 15 weeks and 6 days of gestation, two amniotic bands and acrania/exencephaly were depicted with the 3D/4D HDlive rendering mode. Fragile brain hemispheres with amniotic bands were rocking with fetal movements. The 3D/4D HDlive rendering mode provides physicians, couples, and their families with important and additional information, and has the potential to supplement two-dimensional and conventional 3D ultrasound in diagnosing fetal acrania/exencephaly.


title: Development of a cost-effective ecg monitor for cardiac arrhythmia detection using heart rate variability

abstract: Ischemic Heart Disease (IHD) and stroke are statistically the leading causes of death world-wide. Both diseases deal with various types of cardiac arrhythmias, e.g. premature ventricular contractions (PVCs), ventricular and supra-ventricular tachycardia, atrial fibrillation. For monitoring and detecting such an irregular heart rhythm accurately, we are now developing a very cost-effective ECG monitor, which is implemented in 8-bit MCU with an efficient QRS detector using steep-slope algorithm and arrhythmia detection algorithm using a simple heart rate variability (HRV) parameter. This work shows the results of evaluating the real-time steep-slope algorithm using MIT-BIH Arrhythmia Database. The performance of this algorithm has 99.72% of sensitivity and 99.19% of positive predictivity. We then show the preliminary results of arrhythmia detection using various types of normal and abnormal ECGs from an ECG simulator. The result is, 18 of 20 ECG test signals were correctly detected.


title: Digital beamforming of smart antenna in millimeterwave communication

abstract: In this paper, a digital beamforming technique is studied which its transmitter model is designed for a 16-element linear phased array to a 256-element (16×16) rectangular phased array. First, its directivity is calculated. Base on the result, beam pattern characteristics are analyzed. Furthermore, the interference potential is compared by means the beamforming demo of millimeter waves against the microwave band used now. The simulation results demonstrate that the beam squint effect is relatively better at a higher frequency of millimeter wave band; the addition of directivity is in line to the increasing of the amount of the antenna array elements. Although, the potential of destructive interference is increasingly in the millimeters wave bands.


title: Exploring narrative interpretation and adaptation for interactive story creation

abstract: Adaptation of stories - as a translation between media, such as literature and film - is explored for genres of interactive storytelling that make use of highly-interactive and user-adaptive technology. A concrete case study of transforming and abstracting a Hemingway short story is discussed in detail. The conclusion is that even though Interactive Storytelling content has to follow formal models, these cannot be derived from a written narrative alone and need story creators' input in order to work for interactivity.


title: Accurate network anomaly classification with generalized entropy metrics

abstract: The accurate detection and classification of network anomalies based on traffic feature distributions is still a major challenge. Together with volume metrics, traffic feature distributions are the primary source of information of approaches scalable to high-speed and large scale networks. In previous work, we proposed to use the Tsallis entropy based traffic entropy spectrum (TES) to capture changes in specific activity regions, such as the region of heavy-hitters or rare elements. Our preliminary results suggested that the TES does not only provide more details about an anomaly but might also be better suited for detecting them than traditional approaches based on Shannon entropy. We refine the TES and propose a comprehensive anomaly detection and classification system called the entropy telescope. We analyze the importance of different entropy features and refute findings of previous work reporting a supposedly strong correlation between different feature entropies and provide an extensive evaluation of our entropy telescope. Our evaluation with three different detection methods (Kalman filter, PCA, KLE), one classification method (SVM) and a rich set of anomaly models and real backbone traffic demonstrates the superiority of the refined TES approach over TES and the classical Shannon-only approaches. For instance, we found that when switching from Shannon to the refined TES approach, the PCA method detects small to medium sized anomalies up to 20% more accurately. Classification accuracy is improved by up to 19% when switching from Shannon-only to TES and by another 8% when switching from TES to the refined TES approach. To complement our evaluation, we run the entropy telescope on one month of backbone traffic finding that most prevalent anomalies are different types of scanning (69-84%) and reflector DDoS attacks (15-29%).


Wheeled Rob 17 . Wheeled Robots


Design and Optimization a Circular Shape Network Antenna Micro Strip for Some Application


title: Name-face association with web facial image supervision

abstract: This paper describes methods for automatically associating faces detected from multimedia documents with their names presented in the surrounding metadata. We consider the task in the image matching (IM) framework, where external Web facial images are automatically retrieved as the gallery face set of the names in advance, and a detected face is assigned to one of the names, or none of them, according to the association score between the two kinds of faces and constraints. Several important issues are investigated within the IM framework. In collecting Web facial images, beyond the basic scheme that use a celebrity name purely as the query to crawl facial images, a context-assisted image search method is proposed to enhance the relevance and discriminability of the retrieved faces. In constraint formulation, we propose an assigning-thresholding (AT) pipeline to uniformly ensure that the name-face correspondence is strictly one-to-one, and set low confidence associations as null assignments. In association score computation, we propose methods that jointly consider IM with the well-established graph-based association (GA) method at different stages, aiming at producing more accurate scores to benefit the association. Based on these efforts, an Accu-IM method performing the association as accurate as possible and a Fast-IM method performing the association in real-time are respective proposed. Extensive experiments on datasets of captioned News images and Web videos both demonstrate the advantages of the proposed efforts individually and jointly, which consistently provide improvement gains under different settings when compared with state-of-the-art methods.


title: Estimating Human Movement Parameters Using a Software Radio-based Radar

abstract: Radar is an attractive technology for long term monitoring of human movement as it operates remotely, can be placed behind walls and is able to monitor a large area depending on its operating parameters. A radar signal reflected off a moving person carries rich information on his or her activity pattern in the form of a set of Doppler frequency signatures produced by the specific combination of limbs and torso movements. To enable classification and efficient storage and transmission of movement data, unique parameters have to be extracted from the Doppler signatures. Two of the most important human movement parameters for activity identification and classification are the velocity profile and the fundamental cadence frequency of the movement pattern. However, the complicated pattern of limbs and torso movement worsened by multipath propagation in indoor environment poses a challenge for the extraction of these human movement parameters. In this paper, three new approaches for the estimation of human walking velocity profile in indoor environment are proposed and discussed. The first two methods are based on spectrogram estimates whereas the third method is based on phase difference computation. In addition, a method to estimate the fundamental cadence frequency of the gait is suggested and discussed. The accuracy of the methods are evaluated and compared in an indoor experiment using a flexible and low-cost software defined radar platform. The results obtained indicate that the velocity estimation methods are able to estimate the velocity profile of the person’s translational motion with an error of less than 10%. The results also showed that the fundamental cadence is estimated with an error of 7%.


title: GNSS Spoofing Detection using High-Frequency Antenna Motion and Carrier-Phase Data

abstract: A method is developed that processes Global Navigation Satellite System (GNSS) beat carrier phase measurements from a single moving antenna in order to determine whether the GNSS signals are being spoofed. This technique allows a specially equipped GNSS receiver to detect sophisticated spoofing that cannot be detected using receiver autonomous integrity monitoring techniques. It works for both encrypted military signals and for unencrypted civilian signals. It does not require changes to the signal structure of unencrypted civilian GNSS signals. The method uses a short segment of beat carrier-phase time histories that are collected while the receiver's single antenna is undergoing a known, highfrequency motion profile, typically one pre-programmed into an antenna articulation system. The antenna also can be moving in an unknown way at lower frequencies, as might be the case if it were mounted on a ground vehicle, a ship, an airplane, or a spacecraft. The spoofing detection algorithm correlates high-pass-filtered versions of the known motion component with high-pass-filtered versions of the carrier phase variations. True signals produce a specific correlation pattern, and spoofed signals produce a recognizably different correlation pattern if the spoofer transmits its false signals from a single antenna. The most pronounced difference is that non-spoofed signals display variations between the beat carrier phase responses of multiple signals, but all signals' responses are identical in the spoofed case. These differing correlation characteristics are used to develop a hypothesis test in order to detect a spoofing attack or the lack thereof. For moving-base receivers, there is no need for prior knowledge of the vehicle's attitude. Instead, the detection calculations also provide a rough attitude measurement. Several versions of this spoofing detection system have been designed and tested. Some have been tested only with truth-model data, but one has been tested with actual live-signal data from the Global Positioning System (GPS) C/A code on the L1 frequency. The livedata tests correctly identified spoofing attacks in the 4 cases out of 8 trials that had actual attacks. These detections used worst-case false-alarm probabilities of 10 6 , and their worst-case probabilities of missed detection


title: Practical value of adequate marketing management theory

abstract: This is a critical discourse on marketing management textbooks and their presentations of general marketing theory. These books claim to be general, complete and up‐to‐date, although the base of “textbook theory” is consumer goods mass marketing, a minority of all marketing if compared to services and B‐to‐B marketing. Seminal developments over the past decades in services marketing, quality management, relationship marketing and CRM are treated as special cases although they intervene in all types of marketing. The article claims that marketing management has become stereotyped on a derelict foundation in commodity‐like textbooks. It ends with guidelines on how research in marketing could reinvent itself to the benefit of both academics and practitioners.


title: ROTH, VONA: MOVING VOLUME KINECTFUSION 1 Moving Volume KinectFusion

abstract: Newcombe and Izadi et al’s KinectFusion [5] is an impressive new algorithm for real-time dense 3D mapping using the Kinect. It is geared towards games and augmented reality, but could also be of great use for robot perception. However, the algorithm is currently limited to a relatively small volume fixed in the world at start up (typically a ∼ 3m cube). This limits applications for perception. Here we report moving volume KinectFusion with additional algorithms that allow the camera to roam freely. We are interested in perception in rough terrain, but the system would also be useful in other applications including free-roaming games and awareness aids for hazardous environments or the visually impaired. Our approach allows the algorithm to handle a volume that moves arbitrarily on-line (Figure 1).


title: CaDAR: An Efficient Routing Algorithm for Wireless-Optical Broadband Access Network

abstract: Hybrid wireless-optical broadband access network (WOBAN) is a combination of wireless and optical networks to optimize the cost and performance of an access network. Wireless nodes collect traffic from end users and carry them to the optical part of a WOBAN using multiple hops, accumulating delay at each wireless node. Moreover, the radio capacity on each wireless link limits the capacity on each outgoing link from the node in a single-radio wireless mesh network (WMN) of a WOBAN. Thus, delay and capacity limitation in the WMN of a WOBAN is a major bottleneck. We design a capacity and delay aware routing scheme, CaDAR, to minimize the delay and increase network support in the WMN of a WOBAN. Our analysis shows that CaDAR is an efficient routing scheme for a single-radio WMN for a WOBAN that can support much higher load and has lower system delay than other approaches because of better load balanced routing.


title: Emotional Development in Children with Different Attachment Histories: The First Three Years

abstract: The development of fear, anger, and joy was examined in 112 children using a longitudinal design. Children were observed at 9, 14, 22, and 33 months in standard laboratory episodes designed to elicit fear, anger, or joy. At 14 months, mother–child attachment was assessed in the Strange Situation. The attachment groups (avoidant, secure, resistant, and disorganized/unclassifiable) differed in the trajectories of emotional development, with the differences first apparent at 14 months of age. Resistant children were the most fearful and least joyful, and fear was their strongest emotion. More than secure children, they responded with distress even in episodes designed to elicit joy. When examined longitudinally, over the second and third years, secure children became significantly less angry. In contrast, insecure children’s negative emotions increased: Avoidant children became more fearful, resistant children became less joyful, and disorganized/unclassifiable children became more angry. Higher attachment security uniquely predicted that at 33 months, children would show less fear and anger in episodes designed to elicit fear and anger, and less distress in episodes designed to elicit joy, even in conservative regression analyses controlling for all the earlier emotion scores.


ON SCAN-ON-RECEIVE AND ADAPTIVE DIGITAL BEAMFORMING FOR HIGH-RESOLUTION WIDE-SWATH SYNTHETIC APERTURE RADAR


title: BioGlass: Physiological parameter estimation using a head-mounted wearable device

abstract: This work explores the feasibility of using sensors embedded in Google Glass, a head-mounted wearable device, to measure physiological signals of the wearer. In particular, we develop new methods to use Glass's accelerometer, gyroscope, and camera to extract pulse and respiratory rates of 12 participants during a controlled experiment. We show it is possible to achieve a mean absolute error of 0.83 beats per minute (STD: 2.02) for heart rate and 1.18 breaths per minute (STD: 2.04) for respiration rate when considering different combinations of sensors. These results included testing across sitting, supine, and standing still postures before and after physical exercise.


title: Sign Language Spotting with a Threshold Model Based on Conditional Random Fields

abstract: Sign language spotting is the task of detecting and recognizing signs in a signed utterance, in a set vocabulary. The difficulty of sign language spotting is that instances of signs vary in both motion and appearance. Moreover, signs appear within a continuous gesture stream, interspersed with transitional movements between signs in a vocabulary and nonsign patterns (which include out-of-vocabulary signs, epentheses, and other movements that do not correspond to signs). In this paper, a novel method for designing threshold models in a conditional random field (CRF) model is proposed which performs an adaptive threshold for distinguishing between signs in a vocabulary and nonsign patterns. A short-sign detector, a hand appearance-based sign verification method, and a subsign reasoning method are included to further improve sign language spotting accuracy. Experiments demonstrate that our system can spot signs from continuous data with an 87.0 percent spotting rate and can recognize signs from isolated data with a 93.5 percent recognition rate versus 73.5 percent and 85.4 percent, respectively, for CRFs without a threshold model, short-sign detection, subsign reasoning, and hand appearance-based sign verification. Our system can also achieve a 15.0 percent sign error rate (SER) from continuous data and a 6.4 percent SER from isolated data versus 76.2 percent and 14.5 percent, respectively, for conventional CRFs.


title: A MOSFET only, step-up DC-DC micro power converter, for solar energy harvesting applications

abstract: In this paper, a step-up micro power converter for solar energy harvesting applications is presented. The circuit is based on a switched-capacitor voltage doubler architecture with MOSFET capacitors, which results in an area approximately eight times smaller than using MiM capacitors for the 0.13µm CMOS technology. In order to compensate for the loss of efficiency, due to the larger parasitic capacitances, a charge reutilization scheme is employed. The circuit uses a phase controller, designed specifically to work with a solar cell, in order to regulate the output voltage to 1.2 V. Electrical simulations of the circuit, together with an equivalent electrical model of a solar cell, show that the circuit can deliver a power of 536.1 µW to the load, while drawing a power of 799.8 µW from the solar cell, corresponding to a maximum efficiency of 67%.


title: Total variation on a tree

abstract: We consider the problem of minimizing the continuous valued total variation subject to different unary terms on trees and propose fast direct algorithms based on dynamic programming to solve these problems. We treat both the convex and the nonconvex case and derive worst-case complexities that are equal to or better than existing methods. We show applications to total variation based two dimensional image processing and computer vision problems based on a Lagrangian decomposition approach. The resulting algorithms are very efficient, offer a high degree of parallelism, and come along with memory requirements which are only in the order of the number of image pixels.


title: Hierarchical internal representation of spectral features in deep convolutional networks trained for EEG decoding

abstract: Recently, there is increasing interest and research on the interpretability of machine learning models, for example how they transform and internally represent EEG signals in Brain-Computer Interface (BCI) applications. This can help to understand the limits of the model and how it may be improved, in addition to possibly provide insight about the data itself. Schirrmeister et al. (2017) have recently reported promising results for EEG decoding with deep convolutional neural networks (ConvNets) trained in an end-to-end manner and, with a causal visualization approach, showed that they learn to use spectral amplitude changes in the input. In this study, we investigate how ConvNets represent spectral features through the sequence of intermediate stages of the network. We show higher sensitivity to EEG phase features at earlier stages and higher sensitivity to EEG amplitude features at later stages. Intriguingly, we observed a specialization of individual stages of the network to the classical EEG frequency bands alpha, beta, and high gamma. Furthermore, we find first evidence that particularly in the last convolutional layer, the network learns to detect more complex oscillatory patterns beyond spectral phase and amplitude, reminiscent of the representation of complex visual features in later layers of ConvNets in computer vision tasks. Our findings thus provide insights into how ConvNets hierarchically represent spectral EEG features in their intermediate layers and suggest that ConvNets can exploit and might help to better understand the compositional structure of EEG time series.


title: Exploring Query Optimization Techniques in Relational Databases

abstract: In the modern era, digital data is considered as the more valuable asset of an organization, and the organizations assign more significance to it than the software and hardware assets. Database systems are computer-based record keeping systems, which have been developed to store data for efficient retrieval and processing. One particular approach is the relational databases in which all the information is stored in rows and columns in a series of interconnected tables, and a snippet of structured code called query is used to interact with these database tables. Database management involves indexing of data by tagging information based on some common factors and corresponding criteria. Performance of SQL query against a production database eventually becomes an issue sooner or later. The time-intensive queries not only degrade performance of servers and applications by consuming substantial system resources, but can also result in table locking and data corruption. Therefore, query optimization becomes necessary to prevent performance degradation. Query optimization primarily means selection, followed by sequencing in specific order, of the different SQL clauses to formulate an efficient query from the multiple query plans by drawing a comparison of the query plans based on the cost of the resources involved and the response time. The objective of query optimization is to provide minimum response time and maximum throughput (i.e., the efficient use of resources). This paper reviews different query optimization techniques and approaches discussed in the contemporary literature for both centralized and distributed databases. The paper also highlights merits of these techniques by critically analyzing them.


Perceived Factors Influencing Consumer Trust and Its Impact on Online Purchase Intention in Indonesia


title: Tag-aware recommender systems based on deep neural networks

abstract: Many researchers have introduced tag information to recommender systems to improve the performance of traditional recommendation techniques. However, user-defined tags will usually suffer from many problems, such as sparsity, redundancy, and ambiguity. To address these problems, we propose a new recommendation algorithm based on deep neural networks. In the proposed algorithm, users' profiles are initially represented by tags and then a deep neural network model is used to extract the in-depth features from tag space layer by layer. In this way, representations of the raw data will become more abstract and advanced, and therefore the unique structure of tag space will be revealed automatically. Based on those extracted abstract features, users' profiles are updated and used for making recommendations. The experimental results demonstrate the usefulness of the proposed algorithm and show its superior performance over the clustering based recommendation algorithms. In addition, the impact of network depth on the algorithm performance is also investigated.


Cleansing of acute traumatic wounds : tap water or normal saline ?


title: POMDP-based dialogue manager adaptation to extended domains

abstract: Existing spoken dialogue systems are typically designed to operate in a static and well-defined domain, and are not well suited to tasks in which the concepts and values change dynamically. To handle dynamically changing domains, techniques will be needed to transfer and reuse existing dialogue policies and rapidly adapt them using a small number of dialogues in the new domain. As a first step in this direction, this paper addresses the problem of automatically extending a dialogue system to include a new previously unseen concept (or slot) which can be then used as a search constraint in an information query. The paper shows that in the context of Gaussian process POMDP optimisation, a domain can be extended through a simple expansion of the kernel and then rapidly adapted. As well as being much quicker, adaptation rather than retraining from scratch is shown to avoid subjecting users to unacceptably poor performance during the learning stage.


title: A 256 kb 65 nm 8T Subthreshold SRAM Employing Sense-Amplifier Redundancy

abstract: Aggressively scaling the supply voltage of SRAMs greatly minimizes their active and leakage power, a dominating portion of the total power in modern ICs. Hence, energy constrained applications, where performance requirements are secondary, benefit significantly from an SRAM that offers read and write functionality at the lowest possible voltage. However, bit-cells and architectures achieving very high density conventionally fail to operate at low voltages. This paper describes a high density SRAM in 65 nm CMOS that uses an 8T bit-cell to achieve a minimum operating voltage of 350 mV. Buffered read is used to ensure read stability, and peripheral control of both the bit-cell supply voltage and the read-buffer's foot voltage enable sub-T4 write and read without degrading the bit-cell's density. The plaguing area-offset tradeoff in modern sense-amplifiers is alleviated using redundancy, which reduces read errors by a factor of five compared to device up-sizing. At its lowest operating voltage, the entire 256 kb SRAM consumes 2.2 muW in leakage power.


title: Design aspects of a 50hp 6-pole synchronous reluctance motor for electrified powertrain applications

abstract: In this paper, design aspects of a 6-pole synchronous reluctance traction motor are presented for improving the machine performance in electrified powertrain applications. An increase in the number of poles at the same current rating amplifies the output torque and reduces the torque pulsations. Moreover, in this machine, anisotropic structure of the rotor eliminates use of cage, windings, and magnets results in a lower cost. Combination of the aforementioned qualifies this machine as an important candidate for the traction motors in the near future. The rigid structure of rotor enables the machine to operate at high speed that leads to compactness and higher torque density. To achieve a proper design, the machine needs to meet a desired torque envelope in different modes of operation i.e., start-up, acceleration, and cruising that highlights the cross sectional effects of the magnetic and mechanical performances. In this work, the magnetic and mechanical performance analyses of a 6-pole machine with respect to the pole numbers, rotor multi-layer structure, rotor mechanical robustness, desired torque envelope, and the maximum operating speed are discussed and compared with a well-known 4-pole geometry in order to address the proposed machine perforce improvements.


title: Small-signal modeling and stability analysis of a bidirectional electric vehicle charger

abstract: This paper addresses the stability challenges that exist in a bidirectional grid connected electric vehicle (EV) charger. The EV charger under consideration comprises of an ac-dc single phase active rectifier stage followed by a dc-dc dual active bridge (DAB) stage. small-signal models suited for stability analysis of this charger are derived. Controller design considerations are discussed in order to ensure closed loop stability of individual stages and to prevent any instability due to interaction between the stages. Experimental results on an EV charger hardware prototype are presented to validate the proposed models and control design considerations.


title: AON: Towards Arbitrarily-Oriented Text Recognition

abstract: Recognizing text from natural images is a hot research topic in computer vision due to its various applications. Despite the enduring research of several decades on optical character recognition (OCR), recognizing texts from natural images is still a challenging task. This is because scene texts are often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted) arrangements, which have not yet been well addressed in the literature. Existing methods on text recognition mainly work with regular (horizontal and frontal) texts and cannot be trivially generalized to handle irregular texts. In this paper, we develop the arbitrary orientation network (AON) to directly capture the deep features of irregular texts, which are combined into an attention-based decoder to generate character sequence. The whole network can be trained end-to-end by using only images and word-level annotations. Extensive experiments on various benchmarks, including the CUTE80, SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed AON-based method achieves the-state-of-the-art performance in irregular datasets, and is comparable to major existing methods in regular datasets.


title: Diet, gut microbiota and cognition

abstract: The consumption of a diet high in fat and sugar can lead to the development of obesity, type 2 diabetes mellitus (T2DM), cardiovascular disease and cognitive decline. In the human gut, the trillions of harmless microorganisms harboured in the host's gastrointestinal tract are called the 'gut microbiota'. Consumption of a diet high in fat and sugar changes the healthy microbiota composition which leads to an imbalanced microbial population in the gut, a phenomenon known as "gut dysbiosis". It has been shown that certain types of gut microbiota are linked to the pathogenesis of obesity. In addition, long-term consumption of a high fat diet is associated with cognitive decline. It has recently been proposed that the gut microbiota is part of a mechanistic link between the consumption of a high fat diet and the impaired cognition of an individual, termed "microbiota-gut-brain axis". In this complex relationship between the gut, the brain and the gut microbiota, there are several types of gut microbiota and host mechanisms involved. Most of these mechanisms are still poorly understood. Therefore, this review comprehensively summarizes the current evidence from mainly in vivo (rodent and human) studies of the relationship between diet, gut microbiota and cognition. The possible mechanisms that the diet and the gut microbiota have on cognition are also presented and discussed.


title: A simple procedure for pruning back-propagation trained neural networks

abstract: The sensitivity of the global error (cost) function to the inclusion/exclusion of each synapse in the artificial neural network is estimated. Introduced are shadow arrays which keep track of the incremental changes to the synaptic weights during a single pass of back-propagating learning. The synapses are then ordered by decreasing sensitivity numbers so that the network can be efficiently pruned by discarding the last items of the sorted list. Unlike previous approaches, this simple procedure does not require a modification of the cost function, does not interfere with the learning process, and demands a negligible computational overhead. >


title: Semantic Foggy Scene Understanding with Synthetic Data

abstract: This work addresses the problem of semantic foggy scene understanding (SFSU). Although extensive research has been performed on image dehazing and on semantic scene understanding with clear-weather images, little attention has been paid to SFSU. Due to the difficulty of collecting and annotating foggy images, we choose to generate synthetic fog on real images that depict clear-weather outdoor scenes, and then leverage these partially synthetic data for SFSU by employing state-of-the-art convolutional neural networks (CNN). In particular, a complete pipeline to add synthetic fog to real, clear-weather images using incomplete depth information is developed. We apply our fog synthesis on the Cityscapes dataset and generate Foggy Cityscapes with 20,550 images. SFSU is tackled in two ways: (1) with typical supervised learning, and (2) with a novel type of semi-supervised learning, which combines (1) with an unsupervised supervision transfer from clear-weather images to their synthetic foggy counterparts. In addition, we carefully study the usefulness of image dehazing for SFSU. For evaluation, we present Foggy Driving, a dataset with 101 real-world images depicting foggy driving scenes, which come with ground truth annotations for semantic segmentation and object detection. Extensive experiments show that (1) supervised learning with our synthetic data significantly improves the performance of state-of-the-art CNN for SFSU on Foggy Driving; (2) our semi-supervised learning strategy further improves performance; and (3) image dehazing marginally advances SFSU with our learning strategy. The datasets, models and code are made publicly available.


title: An Influence Propagation View of PageRank

abstract: For a long time, PageRank has been widely used for authority computation and has been adopted as a solid baseline for evaluating social influence related applications. However, when measuring the authority of network nodes, the traditional PageRank method does not take the nodes’ prior knowledge into consideration. Also, the connection between PageRank and social influence modeling methods is not clearly established. To that end, this article provides a focused study on understanding PageRank as well as the relationship between PageRank and social influence analysis. Along this line, we first propose a linear social influence model and reveal that this model generalizes the PageRank-based authority computation by introducing some constraints. Then, we show that the authority computation by PageRank can be enhanced if exploiting more reasonable constraints (e.g., from prior knowledge). Next, to deal with the computational challenge of linear model with general constraints, we provide an upper bound for identifying nodes with top authorities. Moreover, we extend the proposed linear model for better measuring the authority of the given node sets, and we also demonstrate the way to quickly identify the top authoritative node sets. Finally, extensive experimental evaluations on four real-world networks validate the effectiveness of the proposed linear model with respect to different constraint settings. The results show that the methods with more reasonable constraints can lead to better ranking and recommendation performance. Meanwhile, the upper bounds formed by PageRank values could be used to quickly locate the nodes and node sets with the highest authorities.


Glabellar red dots in frontal fibrosing alopecia: a further clinical sign of vellus follicle involvement


title: QRS Template Matching for Recognition of Ventricular Ectopic Beats

abstract: We propose a quasi real-time method for discrimination of ventricular ectopic beats from both supraventricular and paced beats in the electrocardiogram (ECG). The heartbeat waveforms were evaluated within a fixed-length window around the fiducial points (100 ms before, 450 ms after). Our algorithm was designed to operate with minimal expert intervention and we define that the operator is required only to initially select up to three 'normal' heartbeats (the most frequently seen supraventricular or paced complexes). These were named original QRS templates and their copies were substituted continuously throughout the ECG analysis to capture slight variations in the heartbeat waveforms of the patient's sustained rhythm. The method is based on matching of the evaluated heartbeat with the QRS templates by a complex set of ECG descriptors, including maximal cross-correlation, area difference and frequency spectrum difference. Temporal features were added by analyzing the R-R intervals. The classification criteria were trained by statistical assessment of the ECG descriptors calculated for all heartbeats in MIT-BIH Supraventricular Arrhythmia Database. The performance of the classifiers was tested on the independent MIT-BIH Arrhythmia Database. The achieved unbiased accuracy is represented by sensitivity of 98.4% and specificity of 98.86%, both being competitive to other published studies. The provided computationally efficient techniques enable the fast post-recording analysis of lengthy Holter-monitor ECG recordings, as well as they can serve as a quasi real-time detection method embedded into surface ECG monitors.


title: Gender Recognition Based on Sift Features

abstract: This paper proposes a robust approach for face detection and gender classification in color images. Previous researches about gender recognition suppose an expensive computational and time-consuming pre-processing step in order to alignment in which face images are aligned so that facial landmarks like eyes, nose, lips, chin are placed in uniform locations in image. In this paper, a novel technique based on mathematical analysis is represented in three stages that eliminates alignment step. First, a new color based face detection method is represented with a better result and more robustness in complex backgrounds. Next, the features which are invariant to affine transformations are extracted from each face using scale invariant feature transform (SIFT) method. To evaluate the performance of the proposed algorithm, experiments have been conducted by employing a SVM classifier on a database of face images which contains 500 images from distinct people with equal ratio of male and female.


title: On blockchain-based authorization architecture for beyond-5G mobile services

abstract: This paper proposes a new conceptual architecture for authorization of mobile services based on blockchain technologies, and presents a design of procedures for heterogeneous mobile communication services. Furthermore, an extension of the procedures is considered in order to enhance privacy protection for users. The new architecture realizes the separation of mobile communication infrastructure and billing functions and multiple use of several mobile communication services under a single contract with a billing operator.


title: Millimeter-wave mobile communications for 5G: Challenges and opportunities

abstract: The bandwidth shortage experienced by wireless communication has motivated the use of the under-utilized millimeter-wave (mmWave) spectrum for future 5G broadband mobile communication networks. Successful deployment of mmWave mobile communication systems requires sufficient knowledge of the mmWave propagation channel. This paper discusses the characteristics of the mmWave propagation channel, and highlights the main challenges, solutions and benefits associated with the use of mmWaves. The paper also discusses some open research topics that are crucial for the development of 5G mmWave mobile communications.


title: Best Practices for Fine-Tuning Visual Classifiers to New Domains

abstract: Recent studies have shown that features from deep convolutional neural networks learned using large labeled datasets, like ImageNet, provide effective representations for a variety of visual recognition tasks. They achieve strong performance as generic features and are even more effective when fine-tuned to target datasets. However, details of the fine-tuning procedure across datasets and with different amount of labeled data are not well-studied and choosing the best fine-tuning method is often left to trial and error. In this work we systematically explore the design-space for fine-tuning and give recommendations based on two key characteristics of the target dataset: visual distance from source dataset and the amount of available training data. Through a comprehensive experimental analysis, we conclude, with a few exceptions, that it is best to copy as many layers of a pre-trained network as possible, and then adjust the level of fine-tuning based on the visual distance from source.


title: Detecting and Preventing Cyber Insider Threats: A Survey

abstract: Information communications technology systems are facing an increasing number of cyber security threats, the majority of which are originated by insiders. As insiders reside behind the enterprise-level security defence mechanisms and often have privileged access to the network, detecting and preventing insider threats is a complex and challenging problem. In fact, many schemes and systems have been proposed to address insider threats from different perspectives, such as intent, type of threat, or available audit data source. This survey attempts to line up these works together with only three most common types of insider namely traitor, masquerader, and unintentional perpetrator, while reviewing the countermeasures from a data analytics perspective. Uniquely, this survey takes into account the early stage threats which may lead to a malicious insider rising up. When direct and indirect threats are put on the same page, all the relevant works can be categorised as host, network, or contextual data-based according to audit data source and each work is reviewed for its capability against insider threats, how the information is extracted from the engaged data sources, and what the decision-making algorithm is. The works are also compared and contrasted. Finally, some issues are raised based on the observations from the reviewed works and new research gaps and challenges identified.


title: Modification of the Clinical Global Impressions (CGI) scale for use in bipolar illness (BP): the CGI-BP

abstract: The Clinical Global Impressions Scale (CGI) was modified specifically for use in assessing global illness severity and change in patients with bipolar disorder. Criticisms of the original CGI were addressed by correcting inconsistencies in scaling, identifying time frames for comparison, clarifying definitions of illness severity and change, and separating out assessment of treatment side effects from illness improvement during treatment. A Detailed User's Guide was developed to train clinicians in the use of the new CGI-Bipolar Version (CGI-BP) for rating severity of manic and depressive episodes and the degree of change from the immediately preceding phase and from the worst phase of illness. The revised scale and manual provide a focused set of instructions to facilitate the reliability of these ratings of mania, depression, and overall bipolar illness during treatment of an acute episode or in longer-term illness prophylaxis. Interrater reliability of the scale was demonstrated in preliminary analyses. Thus, the modified CGI-BP is anticipated to be more useful than the original CGI in studies of bipolar disorder.


title: Rapyuta: A Cloud Robotics Platform

abstract: In this paper, we present the design and implementation of Rapyuta, an open-source cloud robotics platform. Rapyuta helps robots to offload heavy computation by providing secured customizable computing environments in the cloud. The computing environments also allow the robots to easily access the RoboEarth knowledge repository. Furthermore, these computing environments are tightly interconnected, paving the way for deployment of robotic teams. We also describe three typical use cases, some benchmarking and performance results, and two proof-of-concept demonstrations.


title: Prediction of intent in robotics and multi-agent systems

abstract: Moving beyond the stimulus contained in observable agent behaviour, i.e. understanding the underlying intent of the observed agent is of immense interest in a variety of domains that involve collaborative and competitive scenarios, for example assistive robotics, computer games, robot–human interaction, decision support and intelligent tutoring. This review paper examines approaches for performing action recognition and prediction of intent from a multi-disciplinary perspective, in both single robot and multi-agent scenarios, and analyses the underlying challenges, focusing mainly on generative approaches.


title: Secure Key Distribution for the Smart Grid

abstract: To secure the network communication for the smart grid, it is important that a secure key management scheme is needed. The focus of this paper is on the secure key distribution for the smart grid. In this paper, we first overview the key management scheme recently proposed by Wu-Zhou and show it is vulnerable to the man-in-the-middle attack. Then we propose a new key distribution protocol and demonstrate it is secure and efficient for smart grid network. Applying traditional PKI to the smart grid requires significant work and maintenance of the public key. By using Kerberos to smart grid may lose authentication from the third party due to power outages. Therefore, we propose a scheme for a smart grid using a trusted third party which not only has no issue on key revocation, but also the third party can be easily duplicated in case power outages occur.


title: Image Restoration using Autoencoding Priors

abstract: We propose to leverage denoising autoencoder networks as priors to address image restoration problems. We build on the key observation that the output of an optimal denoising autoencoder is a local mean of the true data density, and the autoencoder error (the difference between the output and input of the trained autoencoder) is a mean shift vector. We use the magnitude of this mean shift vector, that is, the distance to the local mean, as the negative log likelihood of our natural image prior. For image restoration, we maximize the likelihood using gradient descent by backpropagating the autoencoder error. A key advantage of our approach is that we do not need to train separate networks for different image restoration tasks, such as non-blind deconvolution with different kernels, or super-resolution at different magnification factors. We demonstrate state of the art results for non-blind deconvolution and super-resolution using the same autoencoding prior.


title: Neurorehabilitation of stroke

abstract: Despite ongoing improvements in the acute treatment of cerebrovascular diseases and organization of stroke services, many stroke survivors are in need of neurorehabilitation, as more than two-thirds show persisting neurologic deficits. While early elements of neurorehabilitation are already taking place on the stroke unit, after the acute treatment, the patient with relevant neurologic deficits usually takes part in an organized inpatient multidisciplinary rehabilitation program and eventually continues with therapies in an ambulatory setting afterwards. A specialized multidisciplinary neurorehabilitation team with structured organization and processes provides a multimodal, intense treatment program for stroke patients which is adapted in detail to the individual goals of rehabilitation. There are many parallels between postlesional neuroplasticity (relearning) and learning in the development of individuals as well as task learning of healthy persons. One key principle of neurorehabilitation is the repetitive creation of specific learning situations to promote mechanisms of neural plasticity in stroke recovery. There is evidence of achieving a better outcome of neurorehabilitation with early initiation of treatment, high intensity, with specific goals and active therapies, and the coordinated work and multimodality of a specialized team. In this context, interdisciplinary goal-setting and regular assessments of the patient are important. Furthermore, several further potential enhancers of neural plasticity, e.g., peripheral and brain stimulation techniques, pharmacological augmentation, and use of robotics, are under evaluation.


title: A Pointwise Approach for Vietnamese Diacritics Restoration

abstract: The automatic insertion of diacritics in electronic texts is necessary for a number of languages, including French, Romanian, Croatian, Sindhi, Vietnamese, etc. When diacritics are removed from a word and the resulting string of characters is not a word, it is easy to recover the diacritics. However, sometimes the resulting string is also a word, possibly with different grammatical properties or a different meaning, and this makes recovery of the missing diacritics a difficult task for software as well as for human readers. This paper is the first to study automatic diacritic restoration in Vietnamese texts. Modern Vietnamese is a complex language with many diacritical marks, and white space does not always function as a word separator. This paper proposes a point wise approach for automatically recovering missing diacritics, using three features for classification: n-grams of syllables, n-grams of syllable types, and dictionary word features. Our experiments show that the proposed method can recover diacritics with a 94.7% accuracy rate.


title: A Brain–Computer Interface (BCI) for the Detection of Mine-Like Objects in Sidescan Sonar Imagery

abstract: Detection of mine-like objects (MLOs) in sidescan sonar imagery is a problem that affects our military in terms of safety and cost. The current process involves large amounts of time for subject matter experts to analyze sonar images searching for MLOs. The automation of the detection process has been heavily researched over the years and some of these computer vision approaches have improved dramatically, providing substantial processing speed benefits. However, the human visual system has an unmatched ability to recognize objects of interest. This paper posits a brain–computer interface (BCI) approach, that combines the complementary benefits of computer vision and human vision. The first stage of the BCI, a Haar-like feature classifier, is cascaded in to the second stage, rapid serial visual presentation (RSVP) of images chips. The RSVP paradigm maximizes throughput while allowing an electroencephalography (EEG) interest classifier to determine the human subjects' recognition of objects. In an additional proposed BCI system we add a third stage that uses a trained support vector machine (SVM) based on the Haar-like features of stage one and the EEG interest scores of stage two. We characterize and show performance improvements for subsets of these BCI systems over the computer vision and human vision capabilities alone.


title: ELF: An Extensive, Lightweight and Flexible Research Platform for Real-time Strategy Games

abstract: In this paper, we propose ELF, an Extensive, Lightweight and Flexible platform for fundamental reinforcement learning research. Using ELF, we implement a highly customizable real-time strategy (RTS) engine with three game environments (Mini-RTS, Capture the Flag and Tower Defense). Mini-RTS, as a miniature version of StarCraft, captures key game dynamics and runs at 40K frame-per-second (FPS) per core on a Macbook Pro notebook. When coupled with modern reinforcement learning methods, the system can train a full-game bot against built-in AIs end-to-end in one day with 6 CPUs and 1 GPU. In addition, our platform is flexible in terms of environment-agent communication topologies, choices of RL methods, changes in game parameters, and can host existing C/C++-based game environments like Arcade Learning Environment. Using ELF, we thoroughly explore training parameters and show that a network with Leaky ReLU and Batch Normalization coupled with long-horizon training and progressive curriculum beats the rule-based built-in AI more than $70\%$ of the time in the full game of Mini-RTS. Strong performance is also achieved on the other two games. In game replays, we show our agents learn interesting strategies. ELF, along with its RL platform, is open-sourced at this https URL


title: Stepping back to move forward: Expressive writing promotes self-distancing

abstract: Prior research indicates that expressive writing enhances well-being by leading people to construct meaningful narratives that explain distressing life experiences. But how does expressive writing facilitate meaning-making? We addressed this issue in 2 longitudinal studies by examining whether and how expressive writing promotes self-distancing, a process that facilitates meaning-making. At baseline in both studies, participants reflected on a distressing life experience. In Study 1 participants were then randomly assigned to write about their distressing experience or a non-emotional topic for 15 min on 3 consecutive days; in Study 2 participants were randomly assigned to write or think about their distressing experience or write about a non-emotional topic for the same amount of time. One day following the intervention, expressive writing participants in both studies self-distanced more when they reflected over their distressing experience compared with participants in the other conditions, which in turn led them to experience less emotional reactivity 1 month (Studies 1 and 2) and 6 months (Study 2) after the intervention. Analyses using data from both studies indicated that expressive writing reduced physical symptoms indirectly through its effects on self-distancing and emotional reactivity [that is, expressive writing group (vs. comparison groups) → greater self-distancing → less emotional reactivity → fewer physical symptoms]. Finally, linguistic analyses using essays from both studies indicated that increased use of causation words and decreased use of negative emotion words and first-person singular pronouns predicted increases in self-distancing over time. These findings demonstrate that expressive writing promotes self-distancing and illustrate how it does so.


title: Advanced PVD-TiAlN coatings on carbide and cermet cutting tools

abstract: Abstract Significant advances have been made in the process design and development of PVD-TiAlN coatings for carbide and cermet cutting tools. Higher plasma ionization in the TiAlN deposition process creates coatings with dense microstructure and excellent adhesion characteristics. The resulting new generation of PVD-TiAlN coatings provide increased productivity in a wide range of machining operations and work-piece materials. This paper discusses the characteristics of high-ionization TiAlN coatings and their performance in metal-cutting applications.


Hypothyroidism and Ayurveda


title: Fashion Is Taking Shape: Understanding Clothing Preference Based on Body Shape From Online Sources

abstract: To study the correlation between clothing garments and body shape, we collected a new dataset (Fashion Takes Shape), which includes images of female users with clothing category annotations. Despite the progress in body shape estimation from images, it turns out to be challenging to infer body shape from such diverse, real-world photos. Hence, we propose a novel and robust multi-photo approach to estimate body shapes of each user and build a conditional model of clothing categories given body-shape. We demonstrate that in real-world data, clothing categories and body-shapes are correlated and show that our multi-photo approach leads to a better predictive model for clothing categories compared to models based on single-view shape estimates or manually annotated body types. We see our method as the first step towards the large-scale understanding of clothing preferences from body shape.


title: Spatio-Temporal Instance Learning: Action Tubes from Class Supervision

abstract: The goal of this work is spatio-temporal action localization in videos, using only the supervision from video-level class labels. The state-of-the-art casts this weakly-supervised action localization regime as a Multiple Instance Learning problem, where instances are a priori computed spatio-temporal proposals. Rather than disconnecting the spatio-temporal learning from the training, we propose Spatio-Temporal Instance Learning, which enables action localization directly from box proposals in video frames. We outline the assumptions of our model and propose a max-margin objective and optimization with latent variables that enable spatio-temporal learning of actions from video labels. We also provide an efficient linking algorithm and two reranking strategies to facilitate and further improve the action localization. Experimental evaluation on four action datasets demonstrate the effectiveness of our approach for localization from weak supervision. Moreover, we show how to incorporate other supervision levels and mixtures, as a step towards determining optimal supervision strategies for action localization.


title: Verisimilar Image Synthesis for Accurate Detection and Recognition of Texts in Scenes

abstract: The requirement of large amounts of annotated images has become one grand challenge while training deep neural network models for various visual detection and recognition tasks. This paper presents a novel image synthesis technique that aims to generate a large amount of annotated scene text images for training accurate and robust scene text detection and recognition models. The proposed technique consists of three innovative designs. First, it realizes "semantic coherent" synthesis by embedding texts at semantically sensible regions within the background image, where the semantic coherence is achieved by leveraging the semantic annotations of objects and image regions that have been created in the prior semantic segmentation research. Second, it exploits visual saliency to determine the embedding locations within each semantic sensible region, which coincides with the fact that texts are often placed around homogeneous regions for better visibility in scenes. Third, it designs an adaptive text appearance model that determines the color and brightness of embedded texts by learning from the feature of real scene text images adaptively. The proposed technique has been evaluated over five public datasets and the experiments show its superior performance in training accurate and robust scene text detection and recognition models.


title: Visualising the digital twin using web services and augmented reality

abstract: As the number of network connected devices in an industrial system increases, the management and handling of all the information generated become a challenge. In this work we explore concepts of Cyber-Physical System model the virtual part of industrial devices (sensor, machines, CLPs) using the Digital Twin concept and propose an architecture based on web services for accessing their data. We present a case study where an Augmented Reality system access the Twin Model data via web services and display real-time information to the user. Moreover, we present a review of how the involved concepts, which have a strong computational background, relate to industrial applications and how they can expand the possibility of services and business models.


title: A single camera eye-gaze tracking system with free head motion

abstract: Eye-gaze as a form of human machine interface holds great promise for improving the way we interact with machines. Eye-gaze tracking devices that are non-contact, non-restrictive, accurate and easy to use will increase the appeal for including eye-gaze information in future applications. The system we have developed and which we describe in this paper achieves these goals using a single high resolution camera with a fixed field of view. The single camera system has no moving parts which results in rapid reacquisition of the eye after loss of tracking. Free head motion is achieved using multiple glints and 3D modeling techniques. Accuracies of under 1° of visual angle are achieved over a field of view of 14x12x20 cm and over various hardware configurations, camera resolutions and frame rates.


title: Design and optimization on ESD self-protection schemes for 700V LDMOS in high voltage power IC

abstract: This paper presents an ESD self-protection scheme for a 700V high-voltage laterally diffused metal-oxide-semiconductor (LDMOS) field effect transistor. The safe operating area (SOA) and breakdown failure mechanism of 700V LDMOS are analyzed using simulations and experimental results. The scalability of thermal failure current with LDMOS width is also demonstrated.


title: Minimal Sufficient Explanations for Factored Markov Decision Processes

abstract: Explaining policies of Markov Decision Processes (MDPs) is complicated due to their probabilistic and sequential nature. We present a technique to explain policies for factored MDP by populating a set of domain-independent templates. We also present a mechanism to determine a minimal set of templates that, viewed together, completely justify the policy. Our explanations can be generated automatically at run-time with no additional effort required from the MDP designer. We demonstrate our technique using the problems of advising undergraduate students in their course selection and assisting people with dementia in completing the task of handwashing. We also evaluate our explanations for course-advising through a user study involving students.


Children's time use in the Longitudinal Study of Australian Children: Data quality and analytical issues in the 4-year cohort


title: Dual-polarized one-dimensional leaky wave antenna

abstract: In this abstract we present a one-dimensional leaky-wave antenna that is fed from two ports and that provides a dual-polarized radiation pattern from a single radiating aperture. This simple and robust antenna provides high gain in both horizontal and vertical linear polarization with low cross-polarization levels (below −30dB), which makes it potentially suitable for applications such as polarimetric radars.


title: Hybrid fur rendering: combining volumetric fur with explicit hair strands

abstract: Hair is typically modeled and rendered using either explicitly defined hair strand geometry or a volume texture of hair densities. Taken each on their own, these two hair representations have difficulties in the case of animal fur as it consists of very dense and thin undercoat hairs in combination with coarse guard hairs. Explicit hair strand geometry is not well-suited for the undercoat hairs, while volume textures are not well-suited for the guard hairs. To efficiently model and render both guard hairs and undercoat hairs, we present a hybrid technique that combines rasterization of explicitly defined guard hairs with ray marching of a prismatic shell volume with dynamic resolution. The latter is the key to practical combination of the two techniques, and it also enables a high degree of detail in the undercoat. We demonstrate that our hybrid technique creates a more detailed and soft fur appearance as compared with renderings that only use explicitly defined hair strands. Finally, our rasterization approach is based on order-independent transparency and renders high-quality fur images in seconds.


Deep Learning of Spatial and Temporal Features for Automotive Prediction


title: Representations of Space and Time in the Maximization of Information Flow in the Perception-Action Loop

abstract: Sensor evolution in nature aims at improving the acquisition of information from the environment and is intimately related with selection pressure toward adaptivity and robustness. Our work in the area indicates that information theory can be applied to the perception-action loop. ::: ::: This letter studies the perception-action loop of agents, which is modeled as a causal Bayesian network. Finite state automata are evolved as agent controllers in a simple virtual world to maximize information flow through the perception-action loop. The information flow maximization organizes the agent's behavior as well as its information processing. To gain more insight into the results, the evolved implicit representations of space and time are analyzed in an information-theoretic manner, which paves the way toward a principled and general understanding of the mechanisms guiding the evolution of sensors in nature and provides insights into the design of mechanisms for artificial sensor evolution.


title: The Fear Factor: How It Affects Students Learning to Program in a Tertiary Environment

abstract: Introduction "Learning and teaching are central activities in the increasingly complex and multi-facetted educational enterprises that are a prominent feature of post-industrial societies" (Hager, 2005, p. 633). Research into educational methods and theories is an ongoing process that covers many disciplines. In the Computer Science and Information Systems fields, there has been much concern regarding both the learning and teaching of computer programming and the high attrition rates associated with these courses (Robins, Rountree & Rountree, 2003). In spite of many years of research, it would appear that there is still a global problem within the educational environment relating to computer programming (Mead et al., 2006). The focus of many studies has been on improving the quality of teaching, aiming to make it more efficient and effective. However, it appears that less attention has been paid to the experience of learning to program from the students' perspective, and understanding what, in their perception, are the difficulties presented by the nature of programming. In the words of Jenkins (2002, p. 53), "If students struggle to learn something, it follows that this thing is for some reason difficult to learn." It would seem that in order to teach more effectively, it is essential for educators to have an understanding of what it is about programming that makes learning it so troublesome for many students worldwide. This paper reports on a study that was conducted to understand and describe how students personally experience fear whilst learning programming and to investigate whether this fear affects other aspects related to their studies, such as self-confidence, time management, and problem solving skills, all of which are needed in order to succeed (Scott, 2008; Simon et al., 2006). Narratives of their experiences were analyzed and relationships between the units of meaning were elicited to formulate and propose a model as a concrete tool for educators interested in studying how the fears that beset programming students affect their learning. Fear, programming, and phenomenology were core concepts used in this study. "Fear" may be considered an overly emotional word, but for this study it should be regarded as a descriptor for denoting a lack of interest in, or lack of appreciation of, programming as a discipline. In other words, some students may be experiencing a lack of confidence or apprehension regarding their ability to code or program, rather than actual fear. "Programming", as used in this study, refers to the full systems development cycle. It includes learning the basic theoretical concepts, as well as the practical act of coding, and having a final product ready for implementation. "Phenomenology" is an interpretive research approach used to study human experiences (Bruce et al., 2004). It does not aim to categorize individuals but rather to detail the number of different ways people can experience a single phenomenon, which, in this study, is learning to program. It is anticipated that this research into the challenges and difficulties of learning to program, as encountered and narrated by the students themselves, will contribute to a deeper understanding of this area of concern for educationalists in general and tertiary programming educators in particular. It is also hoped that the research findings will provide useful insight for students who are also experiencing anxiety as a result of their struggle with learning programming. The Act of Learning to Program The teaching and learning of programming appears to be a subject of worldwide, historic concern. "Learning to program is notoriously considered difficult. In spite of more than forty years of experience, teaching programming is still considered a major challenge" (Caspersen & Bennedsen, 2007, p. 111). Many researchers regard programming courses as extremely demanding and the associated attrition rates high (Bergin & Reilly, 2005a, 2005b; Boyle, Carter, & Clark 2002); Gomes & Mendes, 2007a, 2007b; Jenkins, 2002; Simon et al. …


title: iCu□e — An IoT application for Indian currency recognition in vernacular languages for visually challenged people

abstract: God created this universe along with all living and non-living beings. Human is one of the best among his creations and in Human beings, e yes are the gift of God to see His creations. As of now, humans are considered as the only developed creatures among His creations and have developed themselves from Stone Age to the Computing Era. As the human civilizations grew up, the transactions have moved from barter system to currency. Every country has its own currency in terms of coins and paper notes. Each of the currency of Individual County has its unique features, colors, denominations and international value. We, all, having been given two beautiful eyes could recognize the currency easily. Yet, many a times, it is not as easy as it seems to recognize a currency. The denomination can easily be recognized for a currency but it becomes difficult to identify a counterfeit currency from the real one. Especially for the blind people, it is a herculean task like finding a needle from haystack. The main motive of the work is to develop a robust image processing algorithm to identify the Indian currency, both paper-based and coins, in Indian Regional languages and convert it into a working prototype in order to provide a tool to the blind people for the same purpose.


title: Millimeter Wave Channel Measurements and Implications for PHY Layer Design

abstract: There has been an increasing interest in the millimeter wave (mmW) frequency regime in the design of next-generation wireless systems. The focus of this work is on understanding mmW channel properties that have an important bearing on the feasibility of mmW systems in practice and have a significant impact on physical (PHY) layer design. In this direction, simultaneous channel sounding measurements at 2.9, 29 and 61 GHz are performed at a number of transmit-receive location pairs in indoor office, shopping mall and outdoor environments. Based on these measurements, this paper first studies large-scale properties such as path loss and delay spread across different carrier frequencies in these scenarios. Towards the goal of understanding the feasibility of outdoor-to-indoor coverage, material measurements corresponding to mmW reflection and penetration are studied and significant notches in signal reception spread over a few GHz are reported. Finally, implications of these measurements on system design are discussed and multiple solutions are proposed to overcome these impairments.


title: Arnold Quellin's Statues at Glamis Castle

abstract: On 23rd December 1685 Arnold Quellin, Carver, signed an agreement with Patrick, Earl of Strathmore and Kinghorne, to provide statues of the four Stuart kings and a bust of the Earl himself, to be completed by 1st June the following year for a fee of £160. Although Quellin died in September 1686, the contract was evidently completed since all four statues and the bust are recorded at Glamis Castle in the seventeenth and eighteenth centuries. Two of the statues, the James I and Charles I, as well as the bust are still at the castle today. Of the missing statues, one, the James II, is known from an engraving to resemble closely the James attributed to Grinling Gibbons which now stands in front of the National Gallery in Trafalgar Square, itself one of a series of statues of monarchs depicted as Roman conquerors. The other, the Charles II, may possibly have been similar to the Quellin Charles now at the Guildhall. The document and statues provide new evidence of a sculptor popular in his day, whose reputation has been largely obscured by the fame of his master, Grinling Gibbons.


Green Vehicle Routing Problem with Time-Varying Tra ffic Congestion


title: Viewport-Adaptive Navigable 360-Degree Video Delivery

abstract: The delivery and display of 360-degree videos on Head-Mounted Displays (HMDs) presents many technical challenges. 360-degree videos are ultra high resolution spherical videos, which contain an omnidirectional view of the scene. However only a portion of this scene is displayed on the HMD. Moreover, HMD need to respond in 10 ms to head movements, which prevents the server to send only the displayed video part based on client feedback. To reduce the bandwidth waste, while still providing an immersive experience, a viewport-adaptive 360-degree video streaming system is proposed. The server prepares multiple video representations, which differ not only by their bit-rate, but also by the qualities of different scene regions. The client chooses a representation for the next segment such that its bit-rate fits the available throughput and a full quality region matches its viewing. We investigate the impact of various spherical-to-plane projections and quality arrangements on the video quality displayed to the user, showing that the cube map layout offers the best quality for the given bit-rate budget. An evaluation with a dataset of users navigating 360-degree videos demonstrates that segments need to be short enough to enable frequent view switches.


title: “Strong beats skinny every time”: Disordered eating and compulsive exercise in women who post fitspiration on Instagram

abstract: OBJECTIVE ::: Fitspiration is a recent Internet trend designed to motivate people to eat healthily and to exercise. The aim of the study was to investigate disordered eating and exercise in women who post fitspiration on Instagram. ::: ::: ::: METHOD ::: Participants were 101 women who post fitspiration images on Instagram and a comparison group of 102 women who post travel images. Both groups completed measures of disordered eating and compulsive exercise. ::: ::: ::: RESULTS ::: Women who post fitspiration images scored significantly higher on drive for thinness, bulimia, drive for muscularity, and compulsive exercise. Almost a fifth (17.5%) of these women were at risk for diagnosis of a clinical eating disorder, compared to 4.3% of the travel group. Compulsive exercise was related to disordered eating in both groups, but the relationship was significantly stronger for women who post fitspiration images. ::: ::: ::: DISCUSSION ::: For some women, posting fitspiration images on Instagram may signify maladaptive eating and exercise behaviors. © 2016 Wiley Periodicals, Inc. (Int J Eat Disord 2017; 50:76-79).


title: Empowering personalized medicine with big data and semantic web technology: Promises, challenges, and use cases

abstract: In healthcare, big data tools and technologies have the potential to create significant value by improving outcomes while lowering costs for each individual patient. Diagnostic images, genetic test results and biometric information are increasingly generated and stored in electronic health records presenting us with challenges in data that is by nature high volume, variety and velocity, thereby necessitating novel ways to store, manage and process big data. This presents an urgent need to develop new, scalable and expandable big data infrastructure and analytical methods that can enable healthcare providers access knowledge for the individual patient, yielding better decisions and outcomes. In this paper, we briefly discuss the nature of big data and the role of semantic web and data analysis for generating “smart data” which offer actionable information that supports better decision for personalized medicine. In our view, the biggest challenge is to create a system that makes big data robust and smart for healthcare providers and patients that can lead to more effective clinical decision-making, improved health outcomes, and ultimately, managing the healthcare costs. We highlight some of the challenges in using big data and propose the need for a semantic data-driven environment to address them. We illustrate our vision with practical use cases, and discuss a path for empowering personalized medicine using big data and semantic web technology.


title: Polar-Grid Representation and Kriging-Based 2.5D Interpolation for Urban Environment Modelling

abstract: In this paper a spatial interpolation approach, based on polar-grid representation and Kriging predictor, is proposed for 3D point cloud sampling. Discrete grid representation is a widely used technique because of its simplicity and capacity of providing an efficient and compact representation, allowing subsequent applications such as artificial perception and autonomous navigation. Two-dimensional occupancy grid representations have been studied extensively in the past two decades, and recently 2.5D and 3D grid-based approaches dominate current applications. A key challenge in perception systems for vehicular applications is to balance low computational complexity and reliable data interpretation. To this end, this paper contributes with a discrete 2.5D polar-grid that upsamples the input data, ie sparse 3D point cloud, by means of a deformable kriging-based interpolation strategy. Experiments carried out on the KITTI dataset, using data from a LIDAR, demonstrate that the approach proposed in this work allows a proper representation of urban environments.


title: Estimating Vehicle Movement Direction from Smartphone Accelerometers Using Deep Neural Networks

abstract: Characterization of driving maneuvers or driving styles through motion sensors has become a field of great interest. Before now, this characterization used to be carried out with signals coming from extra equipment installed inside the vehicle, such as On-Board Diagnostic (OBD) devices or sensors in pedals. Nowadays, with the evolution and scope of smartphones, these have become the devices for recording mobile signals in many driving characterization applications. Normally multiple available sensors are used, such as accelerometers, gyroscopes, magnetometers or the Global Positioning System (GPS). However, using sensors such as GPS increase significantly battery consumption and, additionally, many current phones do not include gyroscopes. Therefore, we propose the characterization of driving style through only the use of smartphone accelerometers. We propose a deep neural network (DNN) architecture that combines convolutional and recurrent networks to estimate the vehicle movement direction (VMD), which is the forward movement directional vector captured in a phone's coordinates. Once VMD is obtained, multiple applications such as characterizing driving styles or detecting dangerous events can be developed. In the development of the proposed DNN architecture, two different methods are compared. The first one is based on the detection and classification of significant acceleration driving forces, while the second one relies on longitudinal and transversal signals derived from the raw accelerometers. The final success rate of VMD estimation for the best method is of 90.07%.


title: How Can Edge Computing Benefit From Software-Defined Networking: A Survey, Use Cases, and Future Directions

abstract: A novel paradigm that changes the scene for the modern communication and computation systems is the Edge Computing. It is not a coincidence that terms like Mobile Cloud Computing, Cloudlets, Fog Computing, and Mobile-Edge Computing are gaining popularity both in academia and industry. In this paper, we embrace all these terms under the umbrella concept of “Edge Computing” to name the trend where computational infrastructures hence the services themselves are getting closer to the end user. However, we observe that bringing computational infrastructures to the proximity of the user does not magically solve all technical challenges. Moreover, it creates complexities of its own when not carefully handled. In this paper, these challenges are discussed in depth and categorically analyzed. As a solution direction, we propose that another major trend in networking, namely software-defined networking (SDN), should be taken into account. SDN, which is not proposed specifically for Edge Computing, can in fact serve as an enabler to lower the complexity barriers involved and let the real potential of Edge Computing be achieved. To fully demonstrate our ideas, initially, we put forward a clear collaboration model for the SDN-Edge Computing interaction through practical architectures and show that SDN related mechanisms can feasibly operate within the Edge Computing infrastructures. Then, we provide a detailed survey of the approaches that comprise the Edge Computing domain. A comparative discussion elaborates on where these technologies meet as well as how they differ. Later, we discuss the capabilities of SDN and align them with the technical shortcomings of Edge Computing implementations. We thoroughly investigate the possible modes of operation and interaction between the aforementioned technologies in all directions and technically deduce a set of “Benefit Areas” which is discussed in detail. Lastly, as SDN is an evolving technology, we give the future directions for enhancing the SDN development so that it can take this collaboration to a further level.