title: Interference Management in 5G Reverse TDD HetNets with Wireless Backhaul: A Large System Analysis

abstract: This work analyzes a heterogeneous network (HetNet), which comprises a macro base station (BS) equipped with a large number of antennas and an overlaid dense tier of small cell access points (SCAs) using a wireless backhaul for data traffic. The static and low mobility user equipment terminals (UEs) are associated with the SCAs while those with medium-to-high mobility are served by the macro BS. A reverse time division duplexing (TDD) protocol is used by the two tiers, which allows the BS to locally estimate both the intra-tier and inter-tier channels. This knowledge is then used at the BS either in the uplink (UL) or in the downlink (DL) to simultaneously serve the macro UEs (MUEs) and to provide the wireless backhaul to SCAs. A concatenated linear precoding technique employing either zero-forcing (ZF) or regularized ZF is used at the BS to simultaneously serve MUEs and SCAs in DL while nulling interference toward those SCAs in UL. We evaluate and characterize the performance of the system through the power consumption of UL and DL transmissions under the assumption that target rates must be satisfied and imperfect channel state information is available for MUEs. The analysis is conducted in the asymptotic regime where the number of BS antennas and the network size (MUEs and SCAs) grow large with fixed ratios. Results from large system analysis are used to provide concise formulae for the asymptotic UL and DL transmit powers and precoding vectors under the above assumptions. Numerical results are used to validate the analysis in different settings and to make comparisons with alternative network architectures.


title: Damage Assessment from Social Media Imagery Data During Disasters

abstract: Rapid access to situation-sensitive data through social media networks creates new opportunities to address a number of real-world problems. Damage assessment during disasters is a core situational awareness task for many humanitarian organizations that traditionally takes weeks and months. In this work, we analyze images posted on social media platforms during natural disasters to determine the level of damage caused by the disasters. We employ state-of-the-art machine learning techniques to perform an extensive experimentation of damage assessment using images from four major natural disasters. We show that the domain-specific fine-tuning of deep Convolutional Neural Networks (CNN) outperforms other state-of-the-art techniques such as Bag-of-Visual-Words (BoVW). High classification accuracy under both event-specific and cross-event test settings demonstrate that the proposed approach can effectively adapt deep-CNN features to identify the severity of destruction from social media images taken after a disaster strikes.


title: GCD Computation of n Integers

abstract: Greatest Common Divisor (GCD) computation is one of the most important operation of algorithmic number theory. In this paper we present the algorithms for GCD computation of $n$ integers. We extend the Euclid's algorithm and binary GCD algorithm to compute the GCD of more than two integers.


title: Asymptotic results for eigenvector methods

abstract: Asymptotic results are presented for the MUSIC and Kumaresan and Tufts spectral estimators. The paper makes use of results obtained by Sugiura, which are more accurate than those of Anderson. It is shown that the predictions are in good agreement with simulated results.


title: A deep learning approach to the classification of 3D CAD models

abstract: Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD models. In this paper, we propose an automatic 3D CAD model classification approach based on deep neural networks. According to prior knowledge of the CAD domain, features are selected and extracted from 3D CAD models first, and then preprocessed as high dimensional input vectors for category recognition. By analogy with the thinking process of engineers, a deep neural network classifier for 3D CAD models is constructed with the aid of deep learning techniques. To obtain an optimal solution, multiple strategies are appropriately chosen and applied in the training phase, which makes our classifier achieve better performance. We demonstrate the efficiency and effectiveness of our approach through experiments on 3D CAD model datasets.


title: Equivalence in two-stage DEA approaches

abstract: Data envelopment analysis (DEA) is a linear programming problem approach for evaluating the relative efficiency of peer decision making units (DMUs) that have multiple inputs and outputs. DMUs can have a two-stage structure where all the outputs from the first stage are the only inputs to the second stage, in addition to the inputs to the first stage and the outputs from the second stage. The outputs from the first stage to the second stage are called intermediate measures. This paper examines relations and equivalence between two existing DEA approaches that address measuring the performance of two-stage processes.


ECNDT 2006- We.2.5.3 Defect Detection Method in Digital Radiography for Porosity in Magnesium


title: Blockchains from a distributed computing perspective

abstract: The roots of blockchain technologies are deeply interwoven in distributed computing.


Principes de Géométrie Analytique


title: Re-usable features in a hierarchical concept network for autonomous learning in complex games

abstract: The use of re-usable features to define conceptual elements is a recognised trait in many models of semantic memory, and provides advantages in efficiency of representation, and a manner to preserve links between related concepts. In order to form scalable and generalisable representations, autonomous systems are advantaged by the ability to re-use features, and to develop such a network of features autonomously. Existing learning systems that build knowledge structures in a reinforcement based environment tend to use separately defined rules, rather than re-use of shared features. The system described is a form of Learning Classifier System, based on the Activation-Reinforcement Classifier System that reinforces rules according to separate properties of expected reward and accessibility. This provides a useful platform for examining the construction of rules from re-used features. An implementation is described that constructs a network of features, that are used to define rules. This is able to operate successfully on the game of Dots and Boxes, providing stable operation and the ability to activate rules from a body of 4000 autonomously developed features. Examining the network produced shows a scale-free connectivity distribution, which is a property common in human semantic networks.


title: The Impacts of Carbon Tariff on Green Supply Chain Design

abstract: With the growing awareness of global warming, many companies worldwide are improving their supply chain sustainability, under pressure from the government or their own shareholders. However, not all countries around the world equally emphasize the threat. For example, some countries still have not implemented any carbon dioxide emission regulations to address this problem. Carbon regulations in only subglobal areas may result in even higher global emissions because of carbon leakage. One possible approach to cope with the carbon leakage problem is to impose carbon tariffs on the goods from unregulated countries. In this paper, a mathematical model is built to explore the impacts of carbon tariff imposition on the supply chain network design, where the carbon tariff is imposed when the goods flow from unregulated countries to regulated countries. Moreover, our paper provides a complete experimental study by applying this model in a real case study, Company G, a major Taiwan-based multinational company in the electronic products industry. The outcomes demonstrate the conditions where the introduction of carbon tariffs forces firms from unregulated countries to take actions to reduce carbon emissions.


title: A Comparison of Rhythmic Similarity Measures

abstract: Measuring the similarity between rhythms is a fundamental problem in computational music theory, with many applications such as music information retrieval and copyright infringement resolution. A common way to represent a rhythm is as a binary sequence where a zero denotes a rest (silence) and a one represents a beat or note onset. This paper compares various measures of rhythm similarity including the Hamming distance, the Euclidean interval-vector distance, the interval-dierence distance measure of Coyle and Shmulevich, the swap distance, and the chronotonic distance measures of Gustafson and Hofmann-Engl. Traditionally, rhythmic similarity measures are compared according to how well rhythms may be recognized with them, how ecien tly they can be retrieved from a data base, or how well they model human perception and cognition of rhythms. In contrast, here similarity measures are compared on the basis of how much insight they provide about the structural inter-relationships that exist within families of rhythms, when phylogenetic trees and graphs are computed from the distance matrices determined by these similarity measures. For two collections of rhythms, namely the 4/4 time and 12/8 time clave-bell time lines used in traditional African and Afro-American music, the chronotonic and swap distances appear to be superior to the other measures, and each has its own atractive features. The similarity measures are also compared according to their computational complexity.


title: Self-organizing maps with information theoretic learning

abstract: Abstract The self-organizing map (SOM) is one of the popular clustering and data visualization algorithms and has evolved as a useful tool in pattern recognition, data mining since it was first introduced by Kohonen. However, it is observed that the magnification factor for such mappings deviates from the information-theoretically optimal value of 1 (for the SOM it is 2/3). This can be attributed to the use of the mean square error to adapt the system, which distorts the mapping by oversampling the low probability regions. In this work, we first discuss the kernel SOM in terms of a similarity measure called correntropy induced metric (CIM) and empirically show that this can enhance the magnification of the mapping without much increase in the computational complexity of the algorithm. We also show that adapting the SOM in the CIM sense is equivalent to reducing the localized cross information potential, an information-theoretic function that quantifies the similarity between two probability distributions. Using this property we propose a kernel bandwidth adaptation algorithm for Gaussian kernels, with both homoscedastic and heteroscedastic components. We show that the proposed model can achieve a mapping with optimal magnification and can automatically adapt the parameters of the kernel function.


title: Kriging metamodeling in simulation: A review

abstract: This article reviews Kriging (also called spatial correlation modeling). It presents the basic Kriging assumptions and formulas contrasting Kriging and classic linear regression metamodels. Furthermore, it extends Kriging to random simulation, and discusses bootstrapping to estimate the variance of the Kriging predictor. Besides classic one-shot statistical designs such as Latin Hypercube Sampling, it reviews sequentialized and customized designs. It ends with topics for future research.


title: Occlusion-Free Face Alignment: Deep Regression Networks Coupled with De-Corrupt AutoEncoders

abstract: Face alignment or facial landmark detection plays an important role in many computer vision applications, e.g., face recognition, facial expression recognition, face animation, etc. However, the performance of face alignment system degenerates severely when occlusions occur. In this work, we propose a novel face alignment method, which cascades several Deep Regression networks coupled with De-corrupt Autoencoders (denoted as DRDA) to explicitly handle partial occlusion problem. Different from the previous works that can only detect occlusions and discard the occluded parts, our proposed de-corrupt autoencoder network can automatically recover the genuine appearance for the occluded parts and the recovered parts can be leveraged together with those non-occluded parts for more accurate alignment. By coupling de-corrupt autoencoders with deep regression networks, a deep alignment model robust to partial occlusions is achieved. Besides, our method can localize occluded regions rather than merely predict whether the landmarks are occluded. Experiments on two challenging occluded face datasets demonstrate that our method significantly outperforms the state-of-the-art methods.


title: Learning Sparsifying Transforms

abstract: The sparsity of signals and images in a certain transform domain or dictionary has been exploited in many applications in signal and image processing. Analytical sparsifying transforms such as Wavelets and DCT have been widely used in compression standards. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular especially in applications such as image denoising, inpainting, and medical image reconstruction. While there has been extensive research on learning synthesis dictionaries and some recent work on learning analysis dictionaries, the idea of learning sparsifying transforms has received no attention. In this work, we propose novel problem formulations for learning sparsifying transforms from data. The proposed alternating minimization algorithms give rise to well-conditioned square transforms. We show the superiority of our approach over analytical sparsifying transforms such as the DCT for signal and image representation. We also show promising performance in signal denoising using the learnt sparsifying transforms. The proposed approach is much faster than previous approaches involving learnt synthesis, or analysis dictionaries.


title: Coordination of Pharyngeal and Laryngeal Swallowing Events During Single Liquid Swallows After Oral Endotracheal Intubation for Patients with Acute Respiratory Distress Syndrome

abstract: To evaluate timing and duration differences in airway protection and esophageal opening after oral intubation and mechanical ventilation for acute respiratory distress syndrome (ARDS) survivors versus age-matched healthy volunteers. Orally intubated adult (≥ 18 years old) patients receiving mechanical ventilation for ARDS were evaluated for swallowing impairments via a videofluoroscopic swallow study (VFSS) during usual care. Exclusion criteria were tracheostomy, neurological impairment, and head and neck cancer. Previously recruited healthy volunteers (n = 56) served as age-matched controls. All subjects were evaluated using 5-ml thin liquid barium boluses. VFSS recordings were reviewed frame-by-frame for the onsets of 9 pharyngeal and laryngeal events during swallowing. Eleven patients met inclusion criteria, with a median (interquartile range [IQR]) intubation duration of 14 (9, 16) days, and VFSSs completed a median of 5 (4, 13) days post-extubation. After arrival of the bolus in the pharynx, ARDS patients achieved maximum laryngeal closure a median (IQR) of 184 (158, 351) ms later than age-matched, healthy volunteers (p < 0.001) and it took longer to achieve laryngeal closure with a median (IQR) difference of 151 (103, 217) ms (p < 0.001), although there was no significant difference in duration of laryngeal closure. Pharyngoesophageal segment opening was a median (IQR) of − 116 (− 183, 1) ms (p = 0.004) shorter than in age-matched, healthy controls. Evaluation of swallowing physiology after oral endotracheal intubation in ARDS patients demonstrates slowed pharyngeal and laryngeal swallowing timing, suggesting swallow-related muscle weakness. These findings may highlight specific areas for further evaluation and potential therapeutic intervention to reduce post-extubation aspiration.


title: An Experiment in Virtual Reality to Measure Daylight-Driven Interest in Rendered Architectural Scenes

abstract: This paper introduces an experiment using a virtual reality headset to collect subjective evaluations of rendered daylit architectural scenes. By varying sky conditions and view directions from a fixed position in architectural renderings, the authors collected subjective perceptual ratings and compared them to image-based measures related to impressions of visual interest. The use of virtual reality allowed for the extraction of headtracking data, providing additional insight on how people perceived the immersive scenes. Findings reveal a dependency between visual interest impressions and quantitative predictors, both of which vary with sky conditions and view directions within the scene.


title: Warmth and competence as distinct dimensions of value in social emotions

abstract: Gervais & Fessler's analysis collapses across two orthogonal dimensions of social value to explain contempt: relational value, predicted by cooperation, and agentic value, predicted by status. These dimensions interact to potentiate specific social emotions and behaviors in intergroup contexts. By neglecting the unique roles of these dimensions - and their associated attributes: warmth and competence - the sentiment framework cedes predictive precision.


title: Oscillations in Notch Signaling Regulate Maintenance of Neural Progenitors

abstract: Summary Expression of the Notch effector gene Hes1 is required for maintenance of neural progenitors in the embryonic brain, but persistent and high levels of Hes1 expression inhibit proliferation and differentiation of these cells. Here, by using a real-time imaging method, we found that Hes1 expression dynamically oscillates in neural progenitors. Furthermore, sustained overexpression of Hes1 downregulates expression of proneural genes, Notch ligands, and cell cycle regulators, suggesting that their proper expression depends on Hes1 oscillation. Surprisingly, the proneural gene Neurogenin2 ( Ngn2 ) and the Notch ligand Delta-like1 ( Dll1 ) are also expressed in an oscillatory manner by neural progenitors, and inhibition of Notch signaling, a condition known to induce neuronal differentiation, leads to downregulation of Hes1 and sustained upregulation of Ngn2 and Dll1 . These results suggest that Hes1 oscillation regulates Ngn2 and Dll1 oscillations, which in turn lead to maintenance of neural progenitors by mutual activation of Notch signaling.


title: Situated agents can have goals

abstract: This paper discusses the problem of action selection for an autonomous agent. We argue that the so-called ''situated agents'', which have been built in response to the limitations observed with classical planners, do not provide a satisfactory solution to this problem because of their lack of goals and run-time arbitration. We present a novel action selection theory which allows arbitration among goals and actions while producing fast and robust activity in a tight interaction loop with the environment. The theory models action slection as an emergent property of an activation/inhibition dynamics among the actions the agent can select and between the actions and the environment. A handful of global parameters make it possible to smoothly mediate between several action selection criteria. For example, one can balance goal-orientedness against situation-orientedness, bias towards ongoing plans (inertia) against adaptivity, thoughtfulness against speed, and adjust sensitivity to goal conflicts.


title: An overview to Software Architecture in Intrusion Detection System

abstract: Today by growing network systems, security is a key feature of each network infrastructure. Network Intrusion Detection Systems (IDS) provide defense model for all security threats which are harmful to any network. The IDS could detect and block attack-related network traffic. The network control is a complex model. Implementation of an IDS could make delay in the network. Several software-based network intrusion detection systems are developed. However, the model has a problem with high speed traffic. This paper reviews of many type of software architecture in intrusion detection systems and describes the design and implementation of a high-performance network intrusion detection system that combines the use of software-based network intrusion detection sensors and a network processor board. The network processor which is a hardware-based model could acts as a customized load balancing splitter. This model cooperates with a set of modified content-based network intrusion detection sensors rather than IDS in processing network traffic and controls the high-speed.


title: Weakly-Supervised Video Scene Co-parsing

abstract: In this paper, we propose a scene co-parsing framework to assign pixel-wise semantic labels in weakly-labeled videos, i.e., only video-level category labels are given. To exploit rich semantic information, we first collect all videos that share the same video-level labels and segment them into supervoxels. We then select representative supervoxels for each category via a supervoxel ranking process. This ranking problem is formulated with a submodular objective function and a scene-object classifier is incorporated to distinguish scenes and objects. To assign each supervoxel a semantic label, we match each supervoxel to these selected representatives in the feature domain. Each supervoxel is then associated with a series of category potentials and assigned to a semantic label with the maximum one. The proposed co-parsing framework extends scene parsing from single images to videos and exploits mutual information among a video collection. Experimental results on the Wild-8 and SUNY-24 datasets show that the proposed algorithm performs favorably against the state-of-the-art approaches.


title: Crystallization and preliminary crystallographic data of purple acid phosphatase from red kidney bean.

abstract: Purple acid phosphatase from red kidney bean has been crystallized from ammonium sulfate solutions in the pH range from 3.5 to 5.5. The crystal form is tetragonal bipyramidal and the largest crystals grew up to 2.0 mm long. Systematic absences indicate one of the enantiomorphic space groups P4(1)2(1)2 (92) or P4(3)2(1)2 (96) with cell dimensions a = b = 104.1(1) A and c = 308.7(2) A. The asymmetric unit contains one dimer with Mr of 110,700, determined by ultraviolet-laser desorption mass spectrometry. The crystals, with a salt-free density of 1.12 g/cm3 and a water content of 67%, diffract to 3.5 A.


title: Children (and adults) benefit from visual feedback during gesture interaction on mobile touchscreen devices

abstract: Surface-gesture interaction styles used on mobile touchscreen devices often depend on the platform and application. Some applications show a visual trace of gesture input being made by the user, whereas others do not. Little work has been done examining the usability of visual feedback for surface-gestures, especially for children. In this paper, we extend our previous work on an empirical study conducted with children, teens, and adults to explore characteristics of gesture interaction with and without visual feedback. We analyze 9 simple and 7 complex gesture features to determine whether differences exist between users of different age groups when completing surface-gestures with and without visual feedback. We find that the gestures generated diverge significantly in ways that make them difficult to interpret by some recognizers. For example, users tend to make gestures with fewer strokes in the absence of visual feedback, and tend to make shorter, more compact gestures using straighter lines in the presence of visual feedback. In addition, users prefer to see visual feedback. Based on these findings, we present design recommendations for surface-gesture interfaces for children, teens, and adults on mobile touchscreen devices. We recommend providing visual feedback, especially for children, wherever possible.


title: Line detection in images through regularized hough transform

abstract: The problem of determining the location and orientation of straight lines in images is often encountered in the fields of computer vision and image processing. Traditionally the Hough transform has been widely used to solve this problem for binary images, due to it's simplicity and effectiveness. In this paper we pose the problem of detecting straight lines in gray-scale images as an inverse problem. We treat the input image as noisy observations, which are related to the underlying transform domain image through the inverse Hough transform operator. We then regularize this inverse problem using constraints that accentuate peaks in the Hough parameter space. We present four different forms of such constraints and demonstrate their effectiveness. Finally we show how our scheme can be alternatively viewed as one of finding an optimal representation of the image in terms of elements chosen from a redundant dictionary of lines, and thus is a form of adaptive signal representation.


title: Injecting Comments to Detect JavaScript Code Injection Attacks

abstract: Most web programs are vulnerable to cross site scripting (XSS) that can be exploited by injecting JavaScript code. Unfortunately, injected JavaScript code is difficult to distinguish from the legitimate code at the client side. Given that, server side detection of injected JavaScript code can be a layer of defense. Existing server side approaches rely on identifying legitimate script code, and an attacker can circumvent the technique by injecting legitimate JavaScript code. Moreover, these approaches assume that no JavaScript code is downloaded from third party websites. To address these limitations, we develop a server side approach that distinguishes injected JavaScript code from legitimate JavaScript code. Our approach is based on the concept of injecting comment statements containing random tokens and features of legitimate JavaScript code. When a response page is generated, JavaScript code without or incorrect comment is considered as injected code. Moreover, the valid comments are checked for duplicity. Any presence of duplicate comments or a mismatch between expected code features and actually observed features represents JavaScript code as injected. We implement a prototype tool that automatically injects JavaScript comments and deploy injected JavaScript code detector as a server side filter. We evaluate our approach with three JSP programs. The evaluation results indicate that our approach detects a wide range of code injection attacks.


title: Detection of Measles Virus RNA in Urine Specimens from Vaccine Recipients

abstract: Analysis of urine specimens by using reverse transcriptase-PCR was evaluated as a rapid assay to identify individuals infected with measles virus. For the study, daily urine samples were obtained from either 15-month-old children or young adults following measles immunization. Overall, measles virus RNA was detected in 10 of 12 children during the 2-week sampling period. In some cases, measles virus RNA was detected as early as 1 day or as late as 14 days after vaccination. Measles virus RNA was also detected in the urine samples from all four of the young adults between 1 and 13 days after vaccination. This assay will enable continued studies of the shedding and transmission of measles virus and, it is hoped, will provide a rapid means to identify measles infection, especially in mild or asymptomatic cases.


title: Learning Structural Node Embeddings via Diffusion Wavelets

abstract: Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can be used for a variety of machine learning tasks. However, learning structural representations of nodes is a challenging problem, and it has typically involved manually specifying and tailoring topological features for each node. In this paper, we develop GraphWave, a method that represents each node's network neighborhood via a low-dimensional embedding by leveraging heat wavelet diffusion patterns. Instead of training on hand-selected features, GraphWave learns these embeddings in an unsupervised way. We mathematically prove that nodes with similar network neighborhoods will have similar GraphWave embeddings even though these nodes may reside in very different parts of the network, and our method scales linearly with the number of edges. Experiments in a variety of different settings demonstrate GraphWave's real-world potential for capturing structural roles in networks, and our approach outperforms existing state-of-the-art baselines in every experiment, by as much as 137%.


title: A Review of Simulators with Haptic Devices for Medical Training

abstract: Medical procedures often involve the use of the tactile sense to manipulate organs or tissues by using special tools. Doctors require extensive preparation in order to perform them successfully; for example, research shows that a minimum of 750 operations are needed to acquire sufficient experience to perform medical procedures correctly. Haptic devices have become an important training alternative and they have been considered to improve medical training because they let users interact with virtual environments by adding the sense of touch to the simulation. Previous articles in the field state that haptic devices enhance the learning of surgeons compared to current training environments used in medical schools (corpses, animals, or synthetic skin and organs). Consequently, virtual environments use haptic devices to improve realism. The goal of this paper is to provide a state of the art review of recent medical simulators that use haptic devices. In particular we focus on stitching, palpation, dental procedures, endoscopy, laparoscopy, and orthopaedics. These simulators are reviewed and compared from the viewpoint of used technology, the number of degrees of freedom, degrees of force feedback, perceived realism, immersion, and feedback provided to the user. In the conclusion, several observations per area and suggestions for future work are provided.


Short sleep duration is associated with increased obesity markers in European adolescents: effect of physical activity and dietary habits. The HELENA study


title: MeshEye: a hybrid-resolution smart camera mote for applications in distributed intelligent surveillance

abstract: Surveillance is one of the promising applications to which smart camera motes forming a vision-enabled network can add increasing levels of intelligence. We see a high degree of in-node processing in combination with distributed reasoning algorithms as the key enablers for such intelligent surveillance systems. To put these systems into practice still requires a considerable amount of research ranging from mote architectures, pixel-processing algorithms, up to distributed reasoning engines. This paper introduces MeshEye, an energy-efficient smart camera mote architecture that has been designed with intelligent surveillance as the target application in mind. Special attention is given to MeshEye's unique vision system: a low-resolution stereo vision system continuously determines position, range, and size of moving objects entering its field of view. This information triggers a color camera module to acquire a high-resolution image sub-array containing the object, which can be efficiently processed in subsequent stages. It offers reduced complexity, response time, and power consumption over conventional solutions. Basic vision algorithms for object detection, acquisition, and tracking are described and illustrated on real- world data. The paper also presents a basic power model that estimates lifetime of our smart camera mote in battery-powered operation for intelligent surveillance event processing.


title: Learning an appearance-based gaze estimator from one million synthesised images

abstract: Learning-based methods for appearance-based gaze estimation achieve state-of-the-art performance in challenging real-world settings but require large amounts of labelled training data. Learning-by-synthesis was proposed as a promising solution to this problem but current methods are limited with respect to speed, appearance variability, and the head pose and gaze angle distribution they can synthesize. We present UnityEyes, a novel method to rapidly synthesize large amounts of variable eye region images as training data. Our method combines a novel generative 3D model of the human eye region with a real-time rendering framework. The model is based on high-resolution 3D face scans and uses real-time approximations for complex eyeball materials and structures as well as anatomically inspired procedural geometry methods for eyelid animation. We show that these synthesized images can be used to estimate gaze in difficult in-the-wild scenarios, even for extreme gaze angles or in cases in which the pupil is fully occluded. We also demonstrate competitive gaze estimation results on a benchmark in-the-wild dataset, despite only using a light-weight nearest-neighbor algorithm. We are making our UnityEyes synthesis framework available online for the benefit of the research community.


title: Business process management: a systemic approach?

abstract: Purpose – The purpose of this paper is to assess whether the business process management (BPM) approach contributes to applying systemic characteristics in organisations.Design/methodology/approach – This is a theoretical and descriptive work based on a review of the literature on BPM and systemic approach.Findings – From the analysis of its stages, it was possible to find a strong correspondence between BPM and the systemic characteristics found in the literature.Practical implications – The paper presents practical implications to professionals as well as academics. The contribution to the body of knowledge on BPM derives from the identification of systemic characteristics in it, thus justifying its practical application to organisations in order to ensure better systemicity and adaptability. As processes are directed to the same goal, unnecessary and misdirected steps are redesigned or eliminated, concentrating resources on core processes and improving the organisation's performance. The paper also con...


title: Automated grading of breast cancer histopathology using cascaded ensemble with combination of multi-level image features

abstract: We present a novel image-analysis based method for automatically distinguishing low, intermediate, and high grades of breast cancer in digitized histopathology. A multiple level feature set, including pixel-, object-, and semantic-level features derived from convolutional neural networks (CNN), is extracted from 106 hematoxylin and eosin stained breast biopsy tissue studies from 106 women patients. These multi-level features allow not only characterization of cancer morphology, but also extraction of structural and interpretable information within the histopathological images. In this study, an improved hybrid active contour model based segmentation method was used to segment nuclei from the images. The semantic-level features were extracted by a CNN approach, which described the proportions of nuclei belonging to the different grades, in conjunction with pixel-level (texture) and object-level (architecture) features, to create an integrated set of image attributes that can potentially outperform either subtype of features individually. We utilized a cascaded approach to train multiple support vector machine (SVM) classifiers using combinations of feature subtypes to enable the possibility of maximizing the performance by leveraging different feature sets extracted from multiple levels. Final class (cancer grade) was determined by combining the scores produced by the individual SVM classifiers. By employing a light (three-layer) CNN model and parallel computing, the presented approach is computationally efficient and applicable to large-scale datasets. The method achieved an accuracy of 0.92 for low versus high, 0.77 for low versus intermediate, and 0.76 for intermediate versus high, and an overall accuracy of 0.69 when discriminating low, intermediate, and high grades of histopathological breast cancer images. This suggested that our grading method could be useful in developing a computational diagnostic tool for differentiating breast cancer grades, which might enable objective and reproducible alternative for diagnosis. HighlightsAn automated breast cancer grading method in histopathology is presented.Multi-level features are extracted to capture histomorphometric attributes in histology.The grading method greatly improves the breast cancer grading performance.


Estimation of Tree Count from Satellite Imagery through Mathematical Morphology


title: Advantages of a variable DC-link voltage by using a DC-DC converter in hybrid-electric vehicles

abstract: In electric and hybrid-electric vehicle applications, power electronic converters are used to couple the traction machine with the battery stack. The simplest topology consists of an inverter between the battery stack and the machine. In an often discussed alternative, a DC-DC converter is used to feed the traction machine with a higher and stable voltage level. This paper deals with the differences which arise in terms of the applied control strategy and identifies operating regions of improved efficiency.


title: Consciousness in dreams.

abstract: Abstract This chapter argues that dreaming is an important state of consciousness and that it has many features that complement consciousness in the wake state. The chapter discusses consciousness in dreams and how it comes about. It discusses the changes that occur in the neuromodulatory environment and in the neuronal connectivity of the brain as we fall asleep and begin our night journeys. Dreams evolve from internal sources though the dream may look different than any one of these since something entirely new may emerge through self-organizing processes. The chapter also explores characteristics of dreaming consciousness such as acceptance of implausibility and how that might lead to creative insight. Examples of studies, which have shown creativity in dream sleep, are provided to illustrate important characteristics of dreaming consciousness. The chapter also discusses the dream body and how it relates to our consciousness while dreaming. Differences and similarities between wake, lucid, non-lucid and day dreaming are explored and the chapter concludes with a discussion on what we can learn from each of these expressions of consciousness.


title: VAiRoma: A Visual Analytics System for Making Sense of Places, Times, and Events in Roman History

abstract: Learning and gaining knowledge of Roman history is an area of interest for students and citizens at large. This is an example of a subject with great sweep (with many interrelated sub-topics over, in this case, a 3,000 year history) that is hard to grasp by any individual and, in its full detail, is not available as a coherent story. In this paper, we propose a visual analytics approach to construct a data driven view of Roman history based on a large collection of Wikipedia articles. Extracting and enabling the discovery of useful knowledge on events, places, times, and their connections from large amounts of textual data has always been a challenging task. To this aim, we introduce VAiRoma, a visual analytics system that couples state-of-the-art text analysis methods with an intuitive visual interface to help users make sense of events, places, times, and more importantly, the relationships between them. VAiRoma goes beyond textual content exploration, as it permits users to compare, make connections, and externalize the findings all within the visual interface. As a result, VAiRoma allows users to learn and create new knowledge regarding Roman history in an informed way. We evaluated VAiRoma with 16 participants through a user study, with the task being to learn about roman piazzas through finding relevant articles and new relationships. Our study results showed that the VAiRoma system enables the participants to find more relevant articles and connections compared to Web searches and literature search conducted in a roman library. Subjective feedback on VAiRoma was also very positive. In addition, we ran two case studies that demonstrate how VAiRoma can be used for deeper analysis, permitting the rapid discovery and analysis of a small number of key documents even when the original collection contains hundreds of thousands of documents.


title: Evaluating Intelligibility Usage and Usefulness in a Context-Aware Application

abstract: Intelligibility has been proposed to help end-users understand context-aware applications with their complex inference and implicit sensing. Usable explanations can be generated and designed to improve user understanding. However, will users want to use these intelligibility features? How much intelligibility will they use, and will this be sufficient to improve their understanding? We present a quasi-field experiment of how participants used the intelligibility features of a context-aware application. We investigated how many explanations they viewed, how that affected their understanding of the application’s behavior, and suggestions they had for improving its behavior. We discuss what constitutes successful intelligibility usage, and provide recommendations for designing intelligibility to promote its effective use.


title: A model-driven framework for ETL process development

abstract: ETL processes are the backbone component of a data warehouse, since they supply the data warehouse with the necessary integrated and reconciled data from heterogeneous and distributed data sources. However, the ETL process development, and particularly its design phase, is still perceived as a time-consuming task. This is mainly due to the fact that ETL processes are typically designed by considering a specific technology from the very beginning of the development process. Thus, it is difficult to share and reuse methodologies and best practices among projects implemented with different technologies. To the best of our knowledge, no attempt has been yet dedicated to harmonize the ETL process development by proposing a common and integrated development strategy. To overcome this drawback, in this paper, a framework for model-driven development of ETL processes is introduced. The benefit of our framework is twofold: (i) using vendor-independent models for a unified design of ETL processes, based on the expressive and well-known standard for modeling business processes, the Business Process Modeling Notation (BPMN), and (ii) automatically transforming these models into the required vendor-specific code to execute the ETL process into a concrete platform.


The Open Ophthalmology Journal, 2008, 2, 77-90 77 High Resolution Implantable Microsystem and Probe Design for Retinal


Cryptography And Network Security Principles And Practices


title: In situ steam generation (ISSG) versus standard steam technology: impact on oxide reliability

abstract: This work studies the reliability behaviour of gate oxides grown by in situ steam generation technology. A comparison with standard steam oxides is performed, investigating interface and bulk properties. A reduced conduction at low fields and an improved reliability is found for ISSG oxide. The initial lower bulk trapping, but with similar degradation rate with respect to standard oxides, explains the improved reliability results.


title: Deep Supervision for Pancreatic Cyst Segmentation in Abdominal CT Scans

abstract: Automatic segmentation of an organ and its cystic region is a prerequisite of computer-aided diagnosis. In this paper, we focus on pancreatic cyst segmentation in abdominal CT scan. This task is important and very useful in clinical practice yet challenging due to the low contrast in boundary, the variability in location, shape and the different stages of the pancreatic cancer. Inspired by the high relevance between the location of a pancreas and its cystic region, we introduce extra deep supervision into the segmentation network, so that cyst segmentation can be improved with the help of relatively easier pancreas segmentation. Under a reasonable transformation function, our approach can be factorized into two stages, and each stage can be efficiently optimized via gradient back-propagation throughout the deep networks. We collect a new dataset with 131 pathological samples, which, to the best of our knowledge, is the largest set for pancreatic cyst segmentation. Without human assistance, our approach reports a \(63.44\%\) average accuracy, measured by the Dice-Sorensen coefficient (DSC), which is higher than the number (\(60.46\%\)) without deep supervision.


title: Chaos and Cryptography: Block Encryption Ciphers Based on Chaotic Maps

abstract: This paper is devoted to the analysis of the impact of chaos-based techniques on block encryption ciphers. We present several chaos based ciphers. Using the well-known principles in the cryptanalysis we show that these ciphers do not behave worse than the standard ones, opening in this way a novel approach to the design of block encryption ciphers.


title: Zero-Voltage-Switching PWM Full-Bridge Converter Employing Auxiliary Transformer to Reset the Clamping Diode Current

abstract: Introducing clamping diodes into the zero-voltage-switching (ZVS) pulsewidth-modulation (PWM) full-bridge (FB) converters can eliminate the voltage oscillation across the output rectifier diodes; however, the clamping diodes result in increase in conduction loss and suffer from serious reverse recovery at light load. In order to solve these problems, the clamping diode current should be reset rapidly. This paper proposes a scheme of resetting the clamping diode current over the full-load range by introducing an auxiliary transformer into the ZVS PWM FB converter. The operation principles of the proposed scheme under heavy-load and light-load conditions are analyzed in details. The design considerations of the introduced auxiliary transformer are also given. A performance comparison among the proposed scheme of resetting the clamping diode current and other ones are addressed. Experimental results verify the effectiveness of the analysis and prove that the proposed converter has improved reliability and reduced conduction loss compared with the classical one.


title: Deriving Boolean structures from distributional vectors

abstract: Corpus-based distributional semantic models capture degrees of semantic relatedness among the words of very large vocabularies, but have problems with logical phenomena such as entailment, that are instead elegantly handled by model-theoretic approaches, which, in turn, do not scale up. We combine the advantages of the two views by inducing a mapping from distributional vectors of words (or sentences) into a Boolean structure of the kind in which natural language terms are assumed to denote. We evaluate this Boolean Distributional Semantic Model (BDSM) on recognizing entailment between words and sentences. The method achieves results comparable to a state-of-the art SVM, degrades more gracefully when less training data are available and displays interesting qualitative properties.


A Comparison of Empirical Models for Predicting Student Retention


title: Camera egomotion estimation in the ADAS context

abstract: Camera-based Advanced Driver Assistance Systems (ADAS) have concentrated many research efforts in the last decades. Proposals based on monocular cameras require the knowledge of the camera pose with respect to the environment, in order to reach an efficient and robust performance. A common assumption in such systems is considering the road as planar, and the camera pose with respect to it as approximately known. However, in real situations, the camera pose varies along time due to the vehicle movement, the road slope, and irregularities on the road surface. Thus, the changes in the camera position and orientation (i.e., the egomotion) are critical information that must be estimated at every frame to avoid poor performances. This work focuses on egomotion estimation from a monocular camera under the ADAS context. We review and compare egomotion methods with simulated and real ADAS-like sequences. Basing on the results of our experiments, we show which of the considered nonlinear and linear algorithms have the best performance in this domain.


title: Asymmetric development of dorsal and ventral attention networks in the human brain

abstract: ABSTRACT Two neural systems for goal-directed and stimulus-driven attention have been described in the adult human brain; the dorsal attention network (DAN) centered in the frontal eye fields (FEF) and intraparietal sulcus (IPS), and the ventral attention network (VAN) anchored in the temporoparietal junction (TPJ) and ventral frontal cortex (VFC). Little is known regarding the processes governing typical development of these attention networks in the brain. Here we use resting state functional MRI data collected from thirty 7 to 12 year-old children and thirty 18 to 31 year-old adults to examine two key regions of interest from the dorsal and ventral attention networks. We found that for the DAN nodes (IPS and FEF), children showed greater functional connectivity with regions within the network compared with adults, whereas adults showed greater functional connectivity between the FEF and extra-network regions including the posterior cingulate cortex. For the VAN nodes (TPJ and VFC), adults showed greater functional connectivity with regions within the network compared with children. Children showed greater functional connectivity between VFC and nodes of the salience network. This asymmetric pattern of development of attention networks may be a neural signature of the shift from over-representation of bottom-up attention mechanisms to greater top-down attentional capacities with development.


Toward a Universal Underspecified Semantic Representation


title: Parenting stress and psychological functioning among mothers of preschool children with autism and developmental delay

abstract: Parents of children with developmental disabilities, particularly autism spectrum disorders (ASDs), are at risk for high levels of distress. The factors contributing to this are unclear. This study investigated how child characteristics influence maternal parenting stress and psychological distress. Participants consisted of mothers and developmental-age matched preschool-aged children with ASD (N = 51) and developmental delay without autism (DD) ( N = 22). Evidence for higher levels of parenting stress and psychological distress was found in mothers in the ASD group compared to the DD group. Children's problem behavior was associated with increased parenting stress and psychological distress in mothers in the ASD and DD groups. This relationship was stronger in the DD group. Daily living skills were not related to parenting stress or psychological distress. Results suggest clinical services aiming to support parents should include a focus on reducing problem behaviors in children with developmental disab...


title: Lightweight Secure RPL: A Need in IoT

abstract: The Internet of Things (IoT) is a term which indicates the accessibility of everyday objects or things over the internet. These objects can be sensors, actuators etc. IoT supports various applications like Building & home automation, Smart cities, Smart energy, Wearables, Health care etc. but these applications are suffering from the security issues as these are accessible over the internet. In IoT, 6LowPAN is the layer which takes care of routing and uses the RPL (Routing for Low Power and Lossy Network) protocol. Current specification of RPL has many vulnerabilities which in turn causes attacks. So there is a need for improvement of this protocol. In this paper we have discussed gaps in the implementation of RPL protocol which causes security attacks and also proposed the solutions to overcome these issues.


Mammogram of Breast Cancer detection Based using Image Enhancement Algorithm


title: First- and second-order optimality conditions for piecewise smooth objective functions

abstract: Any piecewise smooth function that is specified by an evaluation procedure involving smooth elemental functions and piecewise linear functions like and can be represented in the so-called abs-normal form. By an extension of algorithmic, or automatic, differentiation, one can then compute certain first-and second-order derivative vectors and matrices that represent a local piecewise linearization and provide additional curvature information. On the basis of these quantities, we characterize local optimality by first-and second-order necessary and sufficient conditions, which generalize the corresponding Kuhn-Tucker-Karush KKT theory for smooth problems. The key assumption is the linear independence kink qualification, a generalization of Linear Independence Constraint Qualification LICQ familiar from nonlinear optimization. It implies that the objective has locally a so-called decomposition and renders everything tractable in terms of matrix factorizations and other simple linear algebra operations. By yielding descent directions, whenever they are violated the new optimality conditions point the way to a superlinearly convergent generalized Quadratic Program solver, which is currently under development. We exemplify the theory on two nonsmooth examples of Nesterov.


title: ROBOT-CENTRIC ELEVATION MAPPING WITH UNCERTAINTY ESTIMATES

abstract: This paper addresses the local terrain mapping process for an autonomous robot. Building upon an onboard range measurement sensor and an existing robot pose estimation, we formulate a novel elevation mapping method from a robot-centric perspective. This formulation can explicitly handle drift of the robot pose estimation which occurs for many autonomous robots. Our mapping approach fully incorporates the distance sensor measurement uncertainties and the six-dimensional pose covariance of the robot. We introduce a computationally efficient formulation of the map fusion process, which allows for mapping a terrain at high update rates. Finally, our approach is demonstrated on a quadrupedal robot walking over obstacles.


title: Culture and Development of Self-Regulation

abstract: In the present paper, I aim to both contribute to a culture based theory of self regulation and clarify some mutually constitutive functions of psychological and sociocultural phenomena. I examine the relations between self and culture and the role of these relations in the develop ment of self regulation. Self regulation is based on the intention to modify internal processes and behavior to reach one’s goals. Because the development of self regulation is embedded in a cultural context that gives priority to a specific model of agency, processes of self regulation are assumed to differ cross culturally. First, I give a brief overview on self regulation research. Fur ther, I discuss the contribution of a culture informed perspective to the study of self regulation and its development, taking into account interpersonal self regulation. Finally, I present a ‘Cul tural Model of Agency and Self Regulation,’ which suggests how to link culture and individual agency. Self regulation has recently become a debated topic in other disciplines such as behavior economics and sociology. In behavior economics, researchers have regarded delay of gratification or the regulation of risk taking behavior as preconditions for rational behav ior. In psychology, self regulation is usually conceived of as a complex, universal human ability that structures goal directed behavior and increases the likelihood of fulfilling a variety of individual needs. This and individual differences in self regulation underline the necessity to investigate its components, its developmental conditions, and its activa tion in different contexts. Although investigators have long noted that standards for self regulation and related strategies differ for people from different cultural backgrounds (e.g., Eisenberg & Zhou, 2000), researchers have neglected the role of culture in most studies on self regulation. In only a few studies have researchers investigated the relations between the individual person (self system) and culture (sociocultural system), and how these relations become effective in the development of self regulation. In line with culture psychological studies on socialization, I assume here that the dominant cultural values and goals and the related cultural model of agency affect socialization conditions for the child and the respective developmental outcomes for self regulation. The trans mission and development of self regulation in the socialization of children is fostered by internalization processes (Tomasello, Carpenter, Call, Behne, & Moll, 2005). According to Grusec and Goodnow (1994), the internalization process is based on a positive parent–child relationship. The expectations of significant others become part of the child’s own personal value system, especially when the other persons belong to the child’s in group (Trommsdorff, 2009). The child’s internalization of social norms and of perceived expectations of others who are close develops into a self regulatory system fostering cultural fit.


title: Urban Water Flow and Water Level Prediction Based on Deep Learning

abstract: The future planning, management and prediction of water demand and usage should be preceded by long-term variation analysis for related parameters in order to enhance the process of developing new scenarios whether for surface-water or ground-water resources. This paper aims to provide an appropriate methodology for long-term prediction for the water flow and water level parameters of the Shannon river in Ireland over a 30-year period from 1983–2013 through a framework that is composed of three phases: city wide scale analytics, data fusion, and domain knowledge data analytics phase which is the main focus of the paper that employs a machine learning model based on deep convolutional neural networks (DeepCNNs). We test our proposed deep learning model on three different water stations across the Shannon river and show it out-performs four well-known time-series forecasting models. We finally show how the proposed model simulate the predicted water flow and water level from 2013–2080. Our proposed solution can be very useful for the water authorities for better planning the future allocation of water resources among competing users such as agriculture, demotic and power stations. In addition, it can be used for capturing abnormalities by setting and comparing thresholds to the predicted water flow and water level.


title: Pattern Recognition Through PerceptuallyImportant Points In Financial Time Series

abstract: Technical Analysis is a financial risk management practice that has been in use since the advent of the stock market and Pattern Recognition is an indivisible part of it. There has been a lot of research into pattern recognition in time series. Existing pattern recognition techniques lack dynamic extensibility. They do not provide any interfaces in order to include new patterns for recognition dynamically. This limits the operability of these techniques to a particular domain. This research devises a new technique for domain independent pattern recognition while giving sufficient speed and accuracy. This enables it to be used by critical Decision Support Systems for time series of different domains. The system emulates the human visual cognition process by implementing the concept of Perceptually Important Points Identification (PIPI). Perceptually Important Points (PIP) represents the minimal set of data points which are necessary to form a pattern. For dynamic inclusion of patterns a Pattern Definition Language (PDL) has been conceptualized for defining patterns in time series by using a declarative programming paradigm. This also results in domain


title: Glaserian and Straussian grounded theory: similar or completely different?

abstract: Grounded Theory is growing in popularity as a research method in ICT research areas such as Information Systems and Software Engineering. Although there are two distinct methods, namely the Glaserian and the Straussian versions, a substantial number of research articles tend to ignore the difference and just claim that they are using grounded theory. To a researcher new to the grounded theory method, the two methods look very similar. Because the Straussian method is more prescriptive, most opt to follow this method, without investigating the Glaserian version. The few who try to use a hybrid of the two methods (not appreciating that the two methods are substantially different), only realize after a significant investment in time that the methods are not reconcilable and that either the one or the other should be followed. To contribute towards eliminating the confusion, this paper investigates the differences between the two methods. This will, hopefully, enable ICT researchers to make a more informed decision on which method to follow.


title: The berlin sparql benchmark

abstract: The SPARQL Query Language for RDF and the SPARQL Protocol for RDF are implemented by a growing number of storage systems and are used within enterprise and open Web settings. As SPARQL is taken up by the community, there is a growing need for benchmarks to compare the performance of storage systems that expose SPARQL endpoints via the SPARQL protocol. Such systems include native RDF stores as well as systems that rewrite SPARQL queries to SQL queries against non-RDF relational databases. This article introduces the Berlin SPARQL Benchmark (BSBM) for comparing the performance of native RDF stores with the performance of SPARQL-to-SQL rewriters across architectures. The benchmark is built around an e-commerce use case in which a set of products is offered by different vendors and consumers have posted reviews about products. The benchmark query mix emulates the search and navigation pattern of a consumer looking for a product. The article discusses the design of the BSBM benchmark and presents the results of a benchmark experiment comparing the performance of four popular RDF stores (Sesame, Virtuoso, Jena TDB, and Jena SDB) with the performance of two SPARQL-to-SQL rewriters (D2R Server and Virtuoso RDF Views) as well as the performance of two relational database management systems (MySQL and Virtuoso RDBMS).


title: Network-Aware Operator Placement for Stream-Processing Systems

abstract: To use their pool of resources efficiently, distributed stream-processing systems push query operators to nodes within the network. Currently, these operators, ranging from simple filters to custom business logic, are placed manually at intermediate nodes along the transmission path to meet application-specific performance goals. Determining placement locations is challenging because network and node conditions change over time and because streams may interact with each other, opening venues for reuse and repositioning of operators. This paper describes a stream-based overlay network (SBON), a layer between a stream-processing system and the physical network that manages operator placement for stream-processing systems. Our design is based on a cost space, an abstract representation of the network and on-going streams, which permits decentralized, large-scale multi-query optimization decisions. We present an evaluation of the SBON approach through simulation, experiments on PlanetLab, and an integration with Borealis, an existing stream-processing engine. Our results show that an SBON consistently improves network utilization, provides low stream latency, and enables dynamic optimization at low engineering cost.


title: Playful palette: an interactive parametric color mixer for artists

abstract: We present Playful Palette, a color picker interface for digital paint programs that derives intuition from oil paint and watercolor palettes, but extends them with digital features. A Playful Palette is a set of blobs of color that blend together to create gradients and gamuts. They can be directly manipulated to explore arrangements and harmonies. All edits are non-destructive, and an infinite history allows previous palettes to be revisited and modified, recoloring the painting. The Playful Palette design is motivated by a pilot study of how artists use paint palettes, and we evaluate the final design with a set of traditional and digital media painters to demonstrate that Playful Palette is effective both at enabling artists' color tasks, and at amplifying their creativity.


title: SmartTactMaps: a smartphone-based approach to support blind persons in exploring tactile maps

abstract: Despite increasing digitalization of our society many blind persons still have very limited access to predominantly pictorial information such as maps. In this paper we introduce a novel approach to improve the accessibility of maps for blind users by utilizing the abilities of standard smartphones. A major issue of tactile maps is the limited discriminability of the humans' tactile sense. Textual annotation of maps is crucial, but adds much complexity to tactile maps. Additionally, only few Braille labels can be accommodated to maintain legibility. In our approach we link smartphones with adapted tactile maps which transforms the physical maps into interactive surfaces using both the tactile and the auditory modality. We integrate machine readable metadata into these maps which can be recognized by the smartphones' camera to immediately obtain detailed map descriptions from a free global database. During tactile exploration of the map, blind users can request auditory explanations by interacting with the mobile application. An experimental application and a user study demonstrate the feasibility of our approach.


title: Basic structural change in Vehicular Adhoc Networks

abstract: With the increase in population, vehicle traffic has also increased on roads; which have caused an increase in accidents, due to which many people have lost their lives and millions are injured annually. Hence, a foolproof and secure Vehicular Adhoc Network (VANET) structure is required to reduce the number of accidents considerably. In this paper, a VANET structure has been proposed for highways and urban environments. In the Highway model, separate Road Side Units have been provided for the traffic on each side. In this way group formation for localized traffic will be easy on highways. Simulation results show that by using proposed model, average Throughput and End-to-End delay are improved considerably while packet loss has also been reduced.


title: Deep reinforcement learning for partial differential equation control

abstract: This paper develops a data-driven method for control of partial differential equations (PDE) based on deep reinforcement learning (RL) techniques. We design a Deep Fitted Q-Iteration (DFQI) algorithm that works directly with a high-dimensional representation of the state of PDE, thus allowing us to avoid the model order reduction step common in the conventional PDE control design approaches. We apply the DFQI algorithm to the problem of flow control for time-varying 2D convection-diffusion PDE, as a simplified model for heating, ventilating, air conditioning (HVAC) control design in a room. We also study the transfer learning of a policy learned for a PDE to another one.


title: Development of Implantable Wireless Sensor Nodes for Animal Husbandry and MedTech Innovation

abstract: In this paper, we report the development, evaluation, and application of ultra-small low-power wireless sensor nodes for advancing animal husbandry, as well as for innovation of medical technologies. A radio frequency identification (RFID) chip with hybrid interface and neglectable power consumption was introduced to enable switching of ON/OFF and measurement mode after implantation. A wireless power transmission system with a maximum efficiency of 70% and an access distance of up to 5 cm was developed to allow the sensor node to survive for a duration of several weeks from a few minutes’ remote charge. The results of field tests using laboratory mice and a cow indicated the high accuracy of the collected biological data and bio-compatibility of the package. As a result of extensive application of the above technologies, a fully solid wireless pH sensor and a surgical navigation system using artificial magnetic field and a 3D MEMS magnetic sensor are introduced in this paper, and the preliminary experimental results are presented and discussed.


title: Skinput: appropriating the skin as an interactive canvas

abstract: Skinput is a technology that appropriates the skin as an input surface by analyzing mechanical vibrations that propagate through the body. Specifically, we resolve the location of finger taps on the arm and hand using a novel sensor array, worn as an armband. This approach provides an on-body finger input system that is always available, naturally portable, and minimally invasive. When coupled with a pico-projector, a fully interactive graphical interface can be rendered directly on the body. To view video of Skinput, visit http://cacm.acm.org.


title: The Matrix of Change: A Tool for Business Process Reengineering

abstract: Business process reengineering efforts suffer from low success rates, due in part to a lack of tools for managing the change process. The Matrix of Change can help managers identify critical interactions among processes. In particular, this tool helps managers deal with issues such as how quickly change should proceed, the order in which changes should take place, whether to start at a new site, and whether the proposed systems are stable and coherent. When applied at a medical products manufacturer, the Matrix of Change provided unique and useful guidelines for change management.


title: Design and dynamic analysis of a Transformable Hovering Rotorcraft (THOR)

abstract: This paper describes the Transformable HOvering Rotorcraft (THOR), a prototype Unmanned Aerial Vehicle (UAV) that explores a novel approach in combining the range and speed of a horizontal flying platform with the hovering and maneuverability of a rotor-wing. This is achieved by integrating a tailless flying wing configuration with a single-axis rotor, or monocopter. By maintaining full utilization of all aerodynamic surfaces and propulsion sources in both flight modes, this method represents the most structurally efficient approach to achieving a cruising mode and a hovering mode on the same frame. Using a dual servo and motor configuration, we propose an under-actuated system that is able to achieve controllability in 4 degrees of freedom while in its horizontal cruising mode and in 5 degrees of freedom while in its hovering mode. In both indoor and outdoor experiments, the UAV is able to transition between either flight modes seamlessly and repeatedly without the need for any additional mechanisms and actuators.


title: Virtual dog head: using 3D models to teach complex veterinary anatomy

abstract: Oregon State University veterinary medicine faculty recently observed that fourth year veterinary medical students consistently struggled during their clinical rotations in cases that required accurate and detailed recall of the complex anatomical relationships in the head region of a dog. These structures were difficult to see and understand since they were hidden behind muscle and tissue and often destroyed or damaged during dissection.


title: Augmented Reality, Virtual Learning Environment and Mobile Learning in education: A comparison

abstract: This paper discusses the trends of technology in education and comparison between three types of technology: Augmented Reality (AR), Virtual Learning Environment (VLE) and Mobile Learning (ML). This paper aims to encourage educators and learners to incorporate the technologies in the teaching and learning process. The integration of these technologies in the teaching and learning process is able to provide new learning environment and improve the teaching and learning quality. The learning process becomes enjoyable and interesting with technologies. Although technology has a lot of benefit to the education field, educators must be creative and innovative to implement technology in the teaching and learning process. Therefore, educators and learners need to select the appropriate technology according to the lesson taught.


title: Efficacy and safety of the glucagon-like peptide-1 receptor agonist liraglutide added to insulin therapy in poorly regulated patients with type 1 diabetes—a protocol for a randomised, double-blind, placebo-controlled study: The Lira-1 study

abstract: Introduction Intensive insulin therapy is recommended for the treatment of type 1 diabetes (T1D). Hypoglycaemia and weight gain are the common side effects of insulin treatment and may reduce compliance. In patients with insulin-treated type 2 diabetes, the addition of glucagon-like peptide-1 receptor agonist (GLP-1RA) therapy has proven effective in reducing weight gain and insulin dose. The present publication describes a protocol for a study evaluating the efficacy and safety of adding a GLP-1RA to insulin treatment in overweight patients with T1D in a randomised, double-blinded, controlled design. Methods and analysis In total, 100 patients with type 1 diabetes, poor glycaemic control (glycated haemoglobin (HbA1c) >8%) and overweight (body mass index >25 kg/m 2 ) will be randomised to either liraglutide 1.8 mg once daily or placebo as an add-on to intensive insulin therapy in this investigator initiated, double-blinded, placebo-controlled parallel study. The primary end point is glycaemic control as measured by changes in HbA1c. Secondary end points include changes in the insulin dose, hypoglyacemic events, body weight, lean body mass, fat mass, food preferences and adverse events. Glycaemic excursions, postprandial glucagon levels and gastric emptying rate during a standardised liquid meal test will also be studied. Ethics and dissemination The study is approved by the Danish Medicines Authority, the Regional Scientific-Ethical Committee of the Capital Region of Denmark and the Data Protection Agency. The study will be carried out under the surveillance and guidance of the good clinical practice (GCP) unit at Copenhagen University Hospital Bispebjerg in accordance with the ICH-GCP guidelines and the Helsinki Declaration. Trial registration number NCT01612468.


title: AdROSA – Adaptive personalization of web advertising

abstract: One of the greatest and most recent challenges for online advertising is the use of adaptive personalization at the same time that the Internet continues to grow as a global market. Most existing solutions to online advertising placement are based on demographic targeting or on information gained directly from the user. The AdROSA system for automatic web banner personalization, which integrates web usage and content mining techniques to reduce user input and to respect users' privacy, is presented in the paper. Furthermore, certain advertising policies, important factors for both publishers and advertisers, are taken into consideration. The integration of all the relevant information is accomplished in one vector space to enable online and fully personalized advertising.


title: Regulation of parkinsonian motor behaviours by optogenetic control of basal ganglia circuitry

abstract: Neural circuits of the basal ganglia are critical for motor planning and action selection. Two parallel basal ganglia pathways have been described, and have been proposed to exert opposing influences on motor function. According to this classical model, activation of the 'direct' pathway facilitates movement and activation of the 'indirect' pathway inhibits movement. However, more recent anatomical and functional evidence has called into question the validity of this hypothesis. Because this model has never been empirically tested, the specific function of these circuits in behaving animals remains unknown. Here we report direct activation of basal ganglia circuitry in vivo, using optogenetic control of direct- and indirect-pathway medium spiny projection neurons (MSNs), achieved through Cre-dependent viral expression of channelrhodopsin-2 in the striatum of bacterial artificial chromosome transgenic mice expressing Cre recombinase under control of regulatory elements for the dopamine D1 or D2 receptor. Bilateral excitation of indirect-pathway MSNs elicited a parkinsonian state, distinguished by increased freezing, bradykinesia and decreased locomotor initiations. In contrast, activation of direct-pathway MSNs reduced freezing and increased locomotion. In a mouse model of Parkinson's disease, direct-pathway activation completely rescued deficits in freezing, bradykinesia and locomotor initiation. Taken together, our findings establish a critical role for basal ganglia circuitry in the bidirectional regulation of motor behaviour and indicate that modulation of direct-pathway circuitry may represent an effective therapeutic strategy for ameliorating parkinsonian motor deficits.


title: SAR Image Classification via Deep Recurrent Encoding Neural Networks

abstract: Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. With the advancement of imaging techniques, it permits to produce higher resolution SAR data and extend data amount. Therefore, intelligent algorithms for high-resolution SAR image classification are demanded. Inspired by deep learning technology, an end-to-end classification model from the original SAR image to final classification map is developed to automatically extract features and conduct classification, which is named deep recurrent encoding neural networks (DRENNs). In our proposed framework, a spatial feature learning network based on long–short-term memory (LSTM) is developed to extract contextual dependencies of SAR images, where 2-D image patches are transformed into 1-D sequences and imported into LSTM to learn the latent spatial correlations. After LSTM, nonnegative and Fisher constrained autoencoders (NFCAEs) are proposed to improve the discrimination of features and conduct final classification, where nonnegative constraint and Fisher constraint are developed in each autoencoder to restrict the training of the network. The whole DRENN not only combines the spatial feature learning power of LSTM but also utilizes the discriminative representation ability of our NFCAE to improve the classification performance. The experimental results tested on three SAR images demonstrate that the proposed DRENN is able to learn effective feature representations from SAR images and produce competitive classification accuracies to other related approaches.


title: What can N-grams learn for malware detection?

abstract: Recent work has shown that byte n-grams learn mostly low entropy features, such as function imports and strings, which has brought into question whether byte n-grams can learn information corresponding to higher entropy levels, such as binary code. We investigate that hypothesis in this work by performing byte n-gram analysis on only specific sub-sections of the binary file, and compare to results obtained by n-gram analysis on assembly code generated from disassembled binaries. We do this by leveraging the change in model performance and ensembles to glean insights about the data. In doing so we discover that byte n-grams can learn from the code regions, but do not necessarily learn any new information. We also discover that assembly n-grams may not be as effective as previously thought and that disambiguating instructions by their binary opcode, an approach not previously used for malware detection, is critical for model generalization.


IS UTAUT REALLY USED OR JUST CITED FOR THE SAKE OF IT? A SYSTEMATIC REVIEW OF CITATIONS OF UTAUT’s ORIGINATING ARTICLE


title: Gene expression profiling predicts clinical outcome of breast cancer

abstract: Breast cancer patients with the same stage of disease can have markedly different treatment responses and overall outcome. The strongest predictors for metastases (for example, lymph node status and histological grade) fail to classify accurately breast tumours according to their clinical behaviour1,2,3. Chemotherapy or hormonal therapy reduces the risk of distant metastases by approximately one-third; however, 70–80% of patients receiving this treatment would have survived without it4,5. None of the signatures of breast cancer gene expression reported to date6,7,8,9,10,11,12 allow for patient-tailored therapy strategies. Here we used DNA microarray analysis on primary breast tumours of 117 young patients, and applied supervised classification to identify a gene expression signature strongly predictive of a short interval to distant metastases (‘poor prognosis’ signature) in patients without tumour cells in local lymph nodes at diagnosis (lymph node negative). In addition, we established a signature that identifies tumours of BRCA1 carriers. The poor prognosis signature consists of genes regulating cell cycle, invasion, metastasis and angiogenesis. This gene expression profile will outperform all currently used clinical parameters in predicting disease outcome. Our findings provide a strategy to select patients who would benefit from adjuvant therapy.


Sentence Weighting for Neural Machine Translation Domain Adaptation


title: Cognitive strategies to regulate emotions—current evidence and future directions

abstract: Emotions are important and basic in human experience, and are comprised of different components, such as subjective feelings, cognitive appraisal, physiological response and action tendencies (Kleinginna and Kleinginna, 1981). Emotions become dysfunctional when they interfere with one's ability to behave adaptively, and therefore successful emotion regulation (ER), when necessary, is crucial for psychological health. Difficulties in adaptive ER are related to different psychopathologies such as depression and anxiety disorders [for a review see Aldao et al. (2010)]. A well-known model that describes the process of ER was suggested by Gross (1998). The model describes five processes of ER that occur at different time points in the course of emotional processing and regulation. In this paper we focus on two ER strategies included in the process model of Gross—distraction and reappraisal—and also on labeling, an ER strategy that is not part of Gross' model. Reappraisal, distraction and labeling are cognitive strategies used to regulate emotions [for a review on the reciprocal relationship between emotion and cognition see Dolcos et al. (2011)]. We start with definitions and findings regarding distraction, reappraisal and labeling. Subsequently, we present the process model of Gross, with labeling as an additional form of ER. Finally, we suggest that labeling might mediate the effectiveness of reappraisal in clinical populations with deficits in emotion recognition and ER, and discuss the clinical implications of this suggestion and future directions of research and conceptualization.


title: On being on the same page: organizational communication and the user-centred development of a digital library collection

abstract: In a world of rapid technological change, how do organizations understand and make sense of technology? How do organizational members share their understandings of technology with other organizational members? Existing research into organizational responses to new technologies has described how these responses can be complex, unpredictable, and sometimes conflictual, and how remedies for such conflicts are often framed in terms of the need for improved communication amongst organizational members regarding the ways in which they define technologies. I argue that such approaches simplify and elide both the complex nature of perceiving technology, and also the ways in which such perceptions are explained. ::: This thesis describes the organizational communication of an NSF-funded project as it designs a complex technical artifact, the Digital Water Education Library (DWEL). Drawing on Anthony Giddens' theory of structuration, and in particular his model of consciousness, I suggest that new technologies (such as digital libraries) can be understood by different organizational members in different ways that are ontologically driven and at times incommensurate. Referring to Pelle Ehn's theory of work-oriented design and Wittgenstein's notion of language games, I suggest that the amelioration of such ontological differences amongst organizational members involves those members not just talking about these technologies but also engaging in various forms of reflexive practice associated with them. ::: I illustrate my claims with an account of the DWEL project that describes how while various project members appeared to be in discursive agreement regarding the issues involved in the design of DWEL, at the same time they embraced different ontological understandings of what they thought DWEL as a digital library was. I will describe the problems that this caused the project; how a range of qualitative and quantitative theories and methods were used to analyse this situation, to identify the ontologies involved, and to provide feedback to DWEL's project management; and how the subsequent organizational communication interventions based on these analyses affected the project in positive ways.


title: Primed in U.S.A. OPTIMAL AUCTION DESIGN*t

abstract: This paper considers the problem faced by a seller who has a single object to sell to one of several possible buyers, when the seller has imperfect information about how much the buyers might be willing to pay for the object. The seller's problem is to design an auction game which has a Nash equilibrium giving him the highest possible expected utility. Optimal auctions are derived in this paper for a wide class of auction design problems.


title: Acceleration of Ungapped Extension in Mercury BLAST, 7th workshop on media and streaming processors

abstract: The amount of biosequence data being produced each year is growing exponentially. Extracting useful information from this massive amount of data efficiently is becoming an increasingly difficult task. There are many available software tools that molecular biologists use for comparing genomic data. This paper focuses on accelerating the most widely used such tool, BLAST. Mercury BLAST takes a streaming approach to the BLAST computation by offloading the performance-critical sections to specialized hardware. This hardware is then used in combination with the processor of the host system to deliver BLAST results in a fraction of the time of the general-purpose processor alone. This paper presents the design of the ungapped extension stage of Mercury BLAST. The architecture of the ungapped extension stage is described along with the context of this stage within the Mercury BLAST system. The design is compact and runs at 100MHz on available FPGAs, making it an effective and powerful component for accelerating biosequence comparisons. The performance of this stage is 25x that of the standard software distribution, yielding close to 50x performance improvement on the complete BLAST application. The sensitivity is essentially equivalent to that of the standard distribution.


title: Anna: A KVS for Any Scale

abstract: Modern cloud providers offer dense hardware with multiple cores and large memories, hosted in global platforms. This raises the challenge of implementing high-performance software systems that can effectively scale from a single core to multicore to the globe. Conventional wisdom says that software designed for one scale point needs to be rewritten when scaling up by 10-100X. In contrast, we explore how a system can be architected to scale across many orders of magnitude by design. We explore this challenge in the context of a new key-value store system called Anna: a partitioned, multi-mastered system that achieves high performance and elasticity via wait-free execution and coordination-free consistency. Our design rests on a simple architecture of coordination-free actors that perform state update via merge of lattice-based composite data structures. We demonstrate that a wide variety of consistency models can be elegantly implemented in this architecture with unprecedented consistency, smooth fine-grained elasticity, and performance that far exceeds the state of the art.


title: Characterization of 1.2 kV SiC super-junction SBD implemented by trench and implantation technique

abstract: This paper presents the characterizations of SiC Super-Junction SBD, which can break the theoretical one dimensional limit of the SiC unipolar devices. The Super-Junction structure is implemented by trench and implantation technique. The mesa width of Super-Junction device is a key structure parameter, therefore its impacts on device forward and reverse characteristics are analyzed by measurement and numeric simulations. The temperature dependence of the specific on-resistance (R sp, on ) of this device is measured and compared with that of a conventional SiC SBD. SiC Super-Junction SBD shows a slower increase in R sp, on with temperature. The switching capability of SiC Super-Junction device is demonstrated for the first time by double pulse tester.


title: Integrated double sided cooling packaging of planar SiC power modules

abstract: A packaging scheme for the integrated power electronics building block is developed, that incorporates double sided direct cooling (forced air or liquid) into assembly comprised of multiple planar packaged power silicon carbide (SiC) phase leg modules. The power modules feature three-dimensional (3-D) planar electrical interconnection and dual exposed cooling surfaces through bonding of power dies between two sandwiched substrates with attached pin-fin cold plates. The unique coolant manifolds, providing matched cooling passages to the modules, were designed and fabricated by using newly developed additive manufacture (three dimensional, 3-D printing) technology. By combining these packaging attributes, advanced SiC power converters integrating both functions of electrical interconnection and cooling that achieve 75% reduction in parasitic electrical impedance and 40% reduction in thermal resistance have been produced. The advantages of this SiC packaging scheme in cost-effectiveness, power conversion efficiency, power density of power electronics systems in modern electric drive vehicles are demonstrated by evaluating the 100A, 1200V SiC half-bridge power module assemblies.


title: Automated Generation of Attack Trees

abstract: Attack trees are widely used to represent threat scenarios in a succinct and intuitive manner, suitable for conveying security information to non-experts. The manual construction of such objects relies on the creativity and experience of specialists, and therefore it is error-prone and impracticable for large systems. Nonetheless, the automated generation of attack trees has only been explored in connection to computer networks and levering rich models, whose analysis typically leads to an exponential blow-up of the state space. We propose a static analysis approach where attack trees are automatically inferred from a process algebraic specification in a syntax-directed fashion, encompassing a great many application domains and avoiding incurring systematically an exponential explosion. Moreover, we show how the standard propositional denotation of an attack tree can be used to phrase interesting quantitative problems, that can be solved through an encoding into Satisfiability Modulo Theories. The flexibility and effectiveness of the approach is demonstrated on the study of a national-scale authentication system, whose attack tree is computed thanks to a Java implementation of the framework.


title: LocLess: Do you Really Care Where Your Cloud Files Are?

abstract: Physical location of data in cloud storage is a problem that gains a lot of attention not only from the actual cloud providers but also from the end users' who lately raise many concerns regarding the privacy of their data. It is a common practice that cloud service providers create replicate users' data across multiple physical locations. However, moving data in different countries means that basically the access rights are transferred based on the local laws of the corresponding country. In other words, when a cloud service provider stores users' data in a different country then the transferred data is subject to the data protection laws of the country where the servers are located. In this paper, we propose LocLess, a protocol which is based on a symmetric searchable encryption scheme for protecting users' data from unauthorized access even if the data is transferred to different locations. The idea behind LocLess is that "Once data is placed on the cloud in an unencrypted form or encrypted with a key that is known to the cloud service provider, data privacy becomes an illusion". Hence, the proposed solution is solely based on encrypting data with a key that is only known to the data owner.


title: An Improved SDA Based Defect Prediction Framework for Both Within-Project and Cross-Project Class-Imbalance Problems

abstract: Background. Solving the class-imbalance problem of within-project software defect prediction (SDP) is an important research topic. Although some class-imbalance learning methods have been presented, there exists room for improvement. For cross-project SDP, we found that the class-imbalanced source usually leads to misclassification of defective instances. However, only one work has paid attention to this cross-project class-imbalance problem. Objective. We aim to provide effective solutions for both within-project and cross-project class-imbalance problems. Method. Subclass discriminant analysis (SDA), an effective feature learning method, is introduced to solve the problems. It can learn features with more powerful classification ability from original metrics. For within-project prediction, we improve SDA for achieving balanced subclasses and propose the improved SDA (ISDA) approach. For cross-project prediction, we employ the semi-supervised transfer component analysis (SSTCA) method to make the distributions of source and target data consistent, and propose the SSTCA+ISDA prediction approach. Results . Extensive experiments on four widely used datasets indicate that: 1) ISDA-based solution performs better than other state-of-the-art methods for within-project class-imbalance problem; 2) SSTCA+ISDA proposed for cross-project class-imbalance problem significantly outperforms related methods. Conclusion . Within-project and cross-project class-imbalance problems greatly affect prediction performance, and we provide a unified and effective prediction framework for both problems.


title: Modeling of failure probability and statistical design of spin-torque transfer magnetic random access memory (STT MRAM) array for yield enhancement

abstract: Spin-torque transfer magnetic RAM (STT MRAM) is a promising candidate for future universal memory. It combines the desirable attributes of current memory technologies such as SRAM, DRAM and flash memories. It also solves the key drawbacks of conventional MRAM technology: poor scalability and high write current. In this paper, we analyzed and modeled the failure probabilities of STT MRAM cells due to parameter variations. Based on the model, we developed an efficient simulation tool to capture the coupled electro/magnetic dynamics of spintronic device, leading to effective prediction for memory yield. We also developed a statistical optimization methodology to minimize the memory failure probability. The proposed methodology can be used at an early stage of the design cycle to enhance memory yield.


title: A Security and Privacy Review of VANETs

abstract: Vehicular ad hoc networks (VANETs) have stimulated interest in both academic and industry settings because, once deployed, they would bring a new driving experience to drivers. However, communicating in an open-access environment makes security and privacy issues a real challenge, which may affect the large-scale deployment of VANETs. Researchers have proposed many solutions to these issues. We start this paper by providing background information of VANETs and classifying security threats that challenge VANETs. After clarifying the requirements that the proposed solutions to security and privacy problems in VANETs should meet, on the one hand, we present the general secure process and point out authentication methods involved in these processes. Detailed survey of these authentication algorithms followed by discussions comes afterward. On the other hand, privacy preserving methods are reviewed, and the tradeoff between security and privacy is discussed. Finally, we provide an outlook on how to detect and revoke malicious nodes more efficiently and challenges that have yet been solved.


title: A high-speed BCI based on code modulation VEP

abstract: Recently, electroencephalogram-based brain?computer interfaces (BCIs) have attracted much attention in the fields of neural engineering and rehabilitation due to their noninvasiveness. However, the low communication speed of current BCI systems greatly limits their practical application. In this paper, we present a high-speed BCI based on code modulation of visual evoked potentials (c-VEP). Thirty-two target stimuli were modulated by a time-shifted binary pseudorandom sequence. A multichannel identification method based on canonical correlation analysis (CCA) was used for target identification. The online system achieved an average information transfer rate (ITR) of 108 ? 12 bits min?1 on five subjects with a maximum ITR of 123 bits min?1 for a single subject.