title: G.fast Technology and the FTTdp Network

abstract: This white paper gives an overview of use cases and applications of G.fast transmission technology that is currently under development. The goal of G.fast is to deliver data at fiber speed to the customers using telephony copper wires. This solution shall provide high speed services at reasonable deployment cost for many subscribers. Low power consumption and ease of use for the customers are the main goals of the technology. Hardware-based simulations shall demonstrate the capabilities of G.fast for different applications. The channel models used to build different network topologies are based on a joint work of Deutsche Telekom and Lantiq. Lantiq has provided solutions for specific core features of G.fast which are presented in this paper.


title: ChaLearn Looking at People RGB-D Isolated and Continuous Datasets for Gesture Recognition

abstract: In this paper, we present two large video multi-modal datasets for RGB and RGB-D gesture recognition: the ChaLearn LAP RGB-D Isolated Gesture Dataset (IsoGD) and the Continuous Gesture Dataset (ConGD). Both datasets are derived from the ChaLearn Gesture Dataset (CGD) that has a total of more than 50000 gestures for the "one-shot-learning" competition. To increase the potential of the old dataset, we designed new well curated datasets composed of 249 gesture labels, and including 47933 gestures manually labeled the begin and end frames in sequences. Using these datasets we will open two competitions on the CodaLab platform so that researchers can test and compare their methods for "user independent" gesture recognition. The first challenge is designed for gesture spotting and recognition in continuous sequences of gestures while the second one is designed for gesture classification from segmented data. The baseline method based on the bag of visual words model is also presented.


title: Learning Feature Embedding with Strong Neural Activations for Fine-Grained Retrieval

abstract: Fine-grained object retrieval, which aims at finding objects belonging to the same sub-category as the probe object from a large database, is becoming increasingly popular because of its research and application significance. Recently, convolutional neural network (CNN) based deep learning models have achieved promising retrieval performance, as they can learn both feature representations and discriminative distance metrics jointly. Specifically, a generic method is to extract activations of the fully-connected layer as feature descriptors and simultaneously optimize classification constraints (e.g., softmax loss) and similarity constraints (e.g., triplet loss) to improve the representative capability of the features. However, the typical fully-connected layer activations are more focused on representing global attributes of the corresponding image, thus relatively less sensitive to specific local characteristics. Therefore, the features learned through these approaches in general are not sufficiently capable for retrieving fine-grained objects. To attack this issue, we propose an effective feature embedding by simultaneously encoding original global features and discriminative local features, in which the local features are extracted by exploiting strong neural activations on the last convolutional layer. We present that the novel feature embedding can dramatically enlarge the gap between inter-class variance and intra-class variance, which is the key factor to improve retrieval precision. In addition, we show our architecture can also be applied in person re-identification. Experimental results on multiple challenging benchmarks demonstrate that our method outperforms the current state-of-the-art approaches by large margins.


title: A 2D broadband, printed Luneburg lens antenna

abstract: This paper details the design of a printed-circuit-board implementation of a 2D Luneburg lens. The refractive index of the lens is controlled through a combination of meandering crossed microstrip lines and varying their widths. The 12.4⋋￮ diameter lens is designed to operate in the Transverse Electromagnetic (TEM) mode at 13 GHz. The lens was designed, fabricated, and measured. The measured half power beamwidth of the experimental lens is 5.26°.


title: User generated soundscapes activating museum visitors

abstract: Museums are seeking various ways to attract and engage audience. Activities like virtual museums, mobile audio guides, augmented reality as well as interactive digital stories aim to provide interactive experiences for an audience with familiarity of digital interaction. Our target is to enable visitor's role change from a passive audience to an active participator as content creator. We concentrate on audio augmentation for museum context because audio provides another perspective into the content in question. In this paper, we discuss our experiences of bringing user generated soundscapes as a part of museums' activities by developing a soundscape platform and a number of mobile applications running on it. Most of the functionality is distributed as ease of use and intuitive mobile applications, which have been evaluated in real context in two workshops. The workshops had different user groups and goals. The first one evaluated ease of use and feelings evoked of the young audiences, and the second one ease of use and acceptance of use for other museums than the Museum of Technology, which was our primary design environment.


title: A dual polarized all metal wideband vivaldi array with wide scan ability

abstract: A wideband dual-polarized Vivaldi antenna with 60 degree scan capability is presented with full wave analysis results. Several mechanical criteria are satisfied in order to make the antenna fabrication very easy and affordable using EDM technique in 3 axes. The antenna active input impedance and active polarization isolation are also optimized to prevent losses due to polarization to polarization coupling.


title: Remote Triggered Single-Axis Solar PV Tracking System with Varying Angle of Incidence

abstract: With the increasing number of remotely accessible laboratories, technical education is reaching student communities in more versatile and flexile ways. This work describes the design and implementation of a remotely operable, single-axis photovoltaic tracking system with two degree resolution through an online, real-time, Virtual Laboratories environment. The tracking system is shown to accurately calculate the sun’s position and orient the attached photovoltaic (PV) module to face the sun either directly or at an angle of incidence selected by the user. The laboratory experiment covers topics such as the efficiency of the PV panel, the angle of incidence and how these specific variables contribute to the power extracted from the panel. In particular, the remote experiment teaches how to calculate cosine losses, which are of fundamental importance to the efficient and cost effective implementation of solar installations, and the calculation of the variation in power production as a function of the angle of incidence. The graphical user interface (GUI) allows users to experimentally determine the angle at which the maximum power is generated. The novelty of this work includes teaching power extraction from renewable, solar photovoltaic energy sources without confining the students or instrumentation to classroom walls or physical laboratories.


title: Continual Learning with Neural Networks: A Review

abstract: Continual learning broadly refers to the algorithms which aim to learn continuously over time across varying domains, tasks or data distributions. This is in contrast to algorithms restricted to learning a fixed number of tasks in a given domain, assuming a static data distribution. In this survey we aim to discuss a wide breadth of challenges faced in a continual learning setup and review existing work in the area. We discuss parameter regularization techniques to avoid catastrophic forgetting in neural networks followed by memory based approaches and the role of generative models in assisting continual learning algorithms. We discuss how dynamic neural networks assist continual learning by endowing neural networks with a new capacity to learn further. We conclude by discussing possible future directions.


Support for Mobile Learners in Distributed Space


title: SRFeat: Single Image Super-Resolution with Feature Discrimination

abstract: Generative adversarial networks (GANs) have recently been adopted to single image super-resolution (SISR) and showed impressive results with realistically synthesized high-frequency textures. However, the results of such GAN-based approaches tend to include less meaningful high-frequency noise that is irrelevant to the input image. In this paper, we propose a novel GAN-based SISR method that overcomes the limitation and produces more realistic results by attaching an additional discriminator that works in the feature domain. Our additional discriminator encourages the generator to produce structural high-frequency features rather than noisy artifacts as it distinguishes synthetic and real images in terms of features. We also design a new generator that utilizes long-range skip connections so that information between distant layers can be transferred more effectively. Experiments show that our method achieves the state-of-the-art performance in terms of both PSNR and perceptual quality compared to recent GAN-based methods.


title: Electronic Commerce: Definition, Theory, and Context

abstract: Electronic commerce is a relatively new concept that crept into the business vocabulary during the 1970s. A picture of electronic commerce is emerging in which the Internet will become the essential dial-tone for conducting business by the year 2000. This contribution addresses definitional, theoretical and contextual issues including the nature, drivers, enablers, and the magnitude of electronic commerce. The author discusses the role of electronic markets, the effects of information technology on electronic commerce, interactivity, and the evolution of disintermediation to reintermediation. A definition and typology of electronic commerce are offered. Theoretical and conceptual approaches to electronic commerce are advanced in terms of (1) transaction cost theory, (2) marketing, (3) diffusion, (4) information retrieval, and (5) strategic networking. Lastly, the author poses the question of how electronic commerce adds value.


title: Approximate SRAM for Energy-Efficient, Privacy-Preserving Convolutional Neural Networks

abstract: Convolutional neural networks (ConvNets) achieve remarkable accuracy in a variety of classification domains. Unfortunately, deployment of these algorithms in embedded systems and mobile devices is limited by the high memory power consumption caused by network storage requirements and memory access intensity. Our work studies the tradeoff between energy (required memory supply voltage), accuracy (classification error rate), and privacy (noise injection mechanisms) in hardware implementation of ConvNets. Our implementation and experiments based on measurements from a 28nm SRAM test chip demonstrate supply voltage reduction of 300mV at 99% of floating-point classification accuracy, under a modest (9, 10-5)-differential privacy budget. This corresponds to 5.0x leakage power reduction and 2.8x memory access power savings, with minimal hardware overhead.


title: Selection and Fusion of Color Models for Image Feature Detection

abstract: The choice of a color model is of great importance for many computer vision algorithms (e.g., feature detection, object recognition, and tracking) as the chosen color model induces the equivalence classes to the actual algorithms. As there are many color models available, the inherent difficulty is how to automatically select a single color model or, alternatively, a weighted subset of color models producing the best result for a particular task. The subsequent hurdle is how to obtain a proper fusion scheme for the algorithms so that the results are combined in an optimal setting. To achieve proper color model selection and fusion of feature detection algorithms, in this paper, we propose a method that exploits nonperfect correlation between color models or feature detection algorithms derived from the principles of diversification. As a consequence, a proper balance is obtained between repeatability and distinctiveness. The result is a weighting scheme which yields maximal feature discrimination. The method is verified experimentally for three different image feature detectors. The experimental results show that the fusion method provides feature detection results having a higher discriminative power than the standard weighting scheme. Further, it is experimentally shown that the color model selection scheme provides a proper balance between color invariance (repeatability) and discriminative power (distinctiveness)


title: Struggling to Excel: A Field Study of Challenges Faced by Spreadsheet Users

abstract: Spreadsheets have become one of the most widely-adopted software technologies. They have proven useful for performing numeric computations as well as for organizing, manipulating, exploring, and visualizing data. Yet only one aspect of spreadsheets, formulas, has received extensive attention in field studies to date. In this paper, we describe a three-part field study that widens this focus to uncover a broader range of challenges that people encounter when creating and using spreadsheets. This study has revealed several opportunities to improve spreadsheet editors, including developing different modes for spreadsheet creation, improving support for spreadsheet reuse, and helping users to find and use features.


title: An Atomatic Fundus Image Analysis System for Clinical Diagnosis of Glaucoma

abstract: Glaucoma is a serious ocular disease and leads blindness if it couldni¦t be detected and treated in proper way. The diagnostic criteria for glaucoma include intraocular pressure measurement, optic nerve head evaluation, retinal nerve fiber layer and visual field defect. The observation of optic nerve head, cup to disc ratio and neural rim configuration are important for early detecting glaucoma in clinical practice. However, the broad range of cup to disc ratio is difficult to identify early changes of optic nerve head, and different ethnic groups possess various features in optic nerve head structures. Hence, it is still important to develop various detection techniques to assist clinicians to diagnose glaucoma at early stages. In this study, we developed an automatic detection system which contains two major phases: the first phase performs a series modules of digital fundus retinal image analysis including vessel detection, vessel in painting, cup to disc ratio calculation, and neuro-retinal rim for ISNT rule, the second phase determines the abnormal status of retinal blood vessels from different aspect of view. In this study, the novel idea of integrating image in painting and active contour model techniques successfully assisted the correct identification of cup and disk regions. Several clinical fundus retinal images containing normal and glaucoma images were applied to the proposed system for demonstration.


title: MantaRay: A novel autonomous sampling instrument for in situ measurements of environmental microplastic particle concentrations

abstract: Presented here is the initial hardware and software design of a prototype autonomous microplastic sampling instrument. Microplastics are defined as particles of plastic < 5 mm greatest dimension. They are becoming pervasive in the world ocean due to anthropogenic pollution. The ocean has spatially variable concentrations of surface microplastics, so attempting to identify trends in global dispersal patterns is difficult and expensive using current research techniques. Understanding the global dispersion patterns and degradation rates of microplastics will help to uncover the associated human and ecosystem impacts. A novel low-cost oceanographic sensor has been developed that can determine the concentration of marine microplastics over large spatial areas. This sensor can remove plastic particulates from seawater and archive them for later analysis, determine microplastic concentrations for 28 discrete samples recording GPS position, and simultaneously measure salinity and water temperature. This sensor has been designed around the open-source Arduino platform, allowing for maximum implementation of additional sensors and systems in future prototypes. The MantaRay sensor can be implemented on a drifter, mooring, or Autonomous Underwater Vehicle to gather diverse data on the dispersion of microplastics. This sensor could drastically cut research costs associated with studying deep-sea microplastic concentrations and increase our understanding of plastic dispersion and degradation rates in marine ecosystems.


Social Data Integration and Analytics for Health Intelligence


title: Classification of orchid species using Neural Network

abstract: Orchid species have a largest families among the botanical plant. Basically, a species of orchid are visually recognizing from its color, root, petal shape or even the size. However, there are several orchid species that really look alike and the type could be falsely classified. The aim of this paper is to classify two species of orchids which are physically look identical, i.e. Dendrobium Madame Pampadour and Dendrobium Cqompactum using image processing techniques. Using Neural Network, the classification rate is 85.7%.


title: Sub-Image Homomorphic Filtering Technique for Improving Facial Identification under Difficult Illumination Conditions

abstract: In this paper we will propose a simple modification of standard homomorphic filtering technique and thus significantly improve face recognition performance on images with difficult illumination conditions. We will also give a detailed theoretical description of a homomorphic filter and compare our proposed method to common illumination compensation techniques used in face recognition literature. The comparisons will be performed on standard grayscale FERET database and this will, in addition, be the first evaluation of homomorphic filter on this database. Results will show that our method yields significantly better identification results than standard illumination compensation methods currently used in face recognition.


abstract: A new soft switching technique for buck, boost and buck-boost converters using a coupled inductor is proposed in this paper. The principles of operation of these converters are analyzed in detail. An additional winding is added on the same core of the main inductor for the purpose of commutation. By using hysteresis current control, zero voltage switching (ZVS) conditions are ensured over wide load range. The main inductor current is kept in continuous conduction mode (CCM) with small ripple, which allows high output power and small filter parameters. Also, the switching frequency is kept constant when load changes. Prototypes of buck, boost, and buck-boost converters have been built to verify the proposed concept. The experimental results are presented and they verify the analysis.


title: Deep Saliency with Encoded Low Level Distance Map and High Level Features

abstract: Recent advances in saliency detection have utilized deep learning to obtain high level features to detect salient regions in a scene. These advances have demonstrated superior results over previous works that utilize hand-crafted low level features for saliency detection. In this paper, we demonstrate that hand-crafted features can provide complementary information to enhance performance of saliency detection that utilizes only high level features. Our method utilizes both high level and low level features for saliency detection under a unified deep learning framework. The high level features are extracted using the VGG-net, and the low level features are compared with other parts of an image to form a low level distance map. The low level distance map is then encoded using a convolutional neural network(CNN) with multiple 1 1 convolutional and ReLU layers. We concatenate the encoded low level distance map and the high level features, and connect them to a fully connected neural network classifier to evaluate the saliency of a query region. Our experiments show that our method can further improve the performance of stateof-the-art deep learning-based saliency detection methods.


title: Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data

abstract: Fraud, waste, and abuse in the U.S. healthcare system are estimated at $700 billion annually. Predictive analytics offers government and private payers the opportunity to identify and prevent or recover such billings. This paper proposes a data-driven method for fraud detection based on comparative research, fraud cases, and literature review. Unsupervised data mining techniques such as outlier detection are suggested as effective predictors for fraud. Based on a multi-dimensional data model developed for Medicaid claim data, specific metrics for dental providers were developed and evaluated in analytical experiments using outlier detection applied to claim, provider, and patient data in a state Medicaid program. The proposed methodology enabled successful identification of fraudulent activity, with 12 of the top 17 suspicious providers (71%) referred to officials for investigation with clearly anomalous and inappropriate activity. Future research is underway to extend the method to other specialties and enable its use by fraud analysts.


Nonlinear current control of an inverter-fed induction machine


title: Visual stability prediction for robotic manipulation

abstract: Understanding physical phenomena is a key competence that enables humans and animals to act and interact under uncertain perception in previously unseen environments containing novel objects and their configurations. Developmental psychology has shown that such skills are acquired by infants from observations at a very early stage. In this paper, we contrast a more traditional approach of taking a model-based route with explicit 3D representations and physical simulation by an end-to-end approach that directly predicts stability from appearance. We ask the question if and to what extent and quality such a skill can directly be acquired in a data-driven way — bypassing the need for an explicit simulation at run-time. We present a learning-based approach based on simulated data that predicts stability of towers comprised of wooden blocks under different conditions and quantities related to the potential fall of the towers. We first evaluate the approach on synthetic data and compared the results to human judgments on the same stimuli. Further, we extend this approach to reason about future states of such towers that in return enables successful stacking.


title: 4D imaging through spray-on optics

abstract: Light fields are a powerful concept in computational imaging and a mainstay in image-based rendering; however, so far their acquisition required either carefully designed and calibrated optical systems (micro-lens arrays), or multi-camera/multi-shot settings. Here, we show that fully calibrated light field data can be obtained from a single ordinary photograph taken through a partially wetted window. Each drop of water produces a distorted view on the scene, and the challenge of recovering the unknown mapping from pixel coordinates to refracted rays in space is a severely underconstrained problem. The key idea behind our solution is to combine ray tracing and low-level image analysis techniques (extraction of 2D drop contours and locations of scene features seen through drops) with state-of-the-art drop shape simulation and an iterative refinement scheme to enforce photo-consistency across features that are seen in multiple views. This novel approach not only recovers a dense pixel-to-ray mapping, but also the refractive geometry through which the scene is observed, to high accuracy. We therefore anticipate that our inherently self-calibrating scheme might also find applications in other fields, for instance in materials science where the wetting properties of liquids on surfaces are investigated.


A 2 Scattering Theory: Born Series 1


title: Internal Uniplanar Antenna for LTE/WWAN/GPS/GLONASS Applications in Tablet/Laptop Computers

abstract: This letter presents an internal uniplanar small size multiband antenna for tablet/laptop computer applications. The proposed antenna in addition to common LTE/WWAN channels covers commercial GPS/GLONASS frequency bands. The antenna is comprised of three sections: coupled-fed, shorting and low frequency spiral strips with the size of $50 \times 11 \times 0.8~\hbox{mm}^2$ . With the aid of spiral strip, lower band operation at 900 MHz is achieved. Two operating frequency bands cover 870–965 and 1556–2480 MHz. In order to validate simulation results, a prototype of the proposed printed antenna is fabricated and tested. Good agreement between the simulation and measurement results is obtained.


title: Radar Detection of Moving Targets Behind Corners

abstract: Detection of moving objects concealed behind a concrete wall corner has been demonstrated, using Doppler-based techniques with a stepped-frequency radar centered at 10 GHz, in a reduced-scale model of a street scenario. Micro-Doppler signatures have been traced in the return from a human target, both for walking and for breathing. Separate material measurements of the reflection and transmission of the concrete in the wall have showed that wall reflections are the dominating wave propagation mechanism for producing target detections, while wave components transmitted through the walls could be neglected. Weaker detections have been made of target returns via diffraction in the wall corner. A simple and fast algorithm for the detection and generation of detection tracks in down range has been developed, based on moving target indication technique.


title: How to make secure email easier to use

abstract: Cryptographically protected email has a justly deserved reputation of being difficult to use. Based on an analysis of the PEM, PGP and S/MIME standards and a survey of 470 merchants who sell products on Amazon.com, we argue that the vast majority of Internet users can start enjoying digitally signed email today. We present suggestions for the use of digitally signed mail in e-commerce and simple modifications to webmail systems that would significantly increase integrity, privacy and authorship guarantees that those systems make. We then show how to use the S/MIME standard to extend such protections Internet-wide. Finally, we argue that software vendors must make minor changes to the way that mail clients store email before unsophisticated users can safely handle mail that is sealed with encryption.


title: Robust lane detection & tracking based on novel feature extraction and lane categorization

abstract: In this paper, we introduce a robust lane detection and tracking algorithm to cope with complex scenarios and to decrease the effect of thresholds. For lane feature extraction, an extension to the symmetrical local threshold (SLT) is proposed to improve the feature map and obtain orientation information. Then, while creating a Hough accumulator, obtained orientation information is used to decrease computational complexity (≈ 60 times) and acquire a clearer accumulator. The left and right lanes are categorized by applying a mask on the Hough accumulator, which leads to low computational complexity and reduced sensitivity to thresholding. To quantify the new feature map, we used ground truth lane markings from the RoMa Datasets and the optimum true positive (TP) to positive (P) ratio increased from 69% to 86% on average, compared to the SLT. The successful lane detection rate calculated from more than 10K frames is, 96.2%, demonstrating the robustness of the system.


title: A Data Imputation Method Based on Deep Belief Network

abstract: Accurately filling missing values is an important step to enhance the usability of Big Data. However current incomplete data imputation algorithms are of high time complexity and low accuracy. To address this problem, we propose a novel algorithm to impute incomplete data. Firstly, a deep belief network model with denoising is designed to remove the noise brought by incomplete data and extract high quality features. Then, we utilize softmax for data classification. Finally, according to the classification results, partial distance and sequence imputation strategies are proposed to measure the correlation between records and improve filling accuracy, respectively. Compared with different algorithms, the experimental results confirm the effectiveness and efficiency of the proposed method in data imputation.


title: Adaptive Submodularity: Theory and Applications in Active Learning and Stochastic Optimization

abstract: Solving stochastic optimization problems under partial observability, where one needs to adaptively make decisions with uncertain outcomes, is a fundamental but notoriously difficult challenge. In this paper, we introduce the concept of adaptive submodularity, generalizing submodular set functions to adaptive policies. We prove that if a problem satisfies this property, a simple adaptive greedy algorithm is guaranteed to be competitive with the optimal policy. In addition to providing performance guarantees for both stochastic maximization and coverage, adaptive submodularity can be exploited to drastically speed up the greedy algorithm by using lazy evaluations. We illustrate the usefulness of the concept by giving several examples of adaptive submodular objectives arising in diverse applications including sensor placement, viral marketing and active learning. Proving adaptive submodularity for these problems allows us to recover existing results in these applications as special cases, improve approximation guarantees and handle natural generalizations.


title: Motion doodles: an interface for sketching character motion

abstract: In this paper we present a novel system for sketching the motion of a character. The process begins by sketching a character to be animated. An animated motion is then created for the character by drawing a continuous sequence of lines, arcs, and loops. These are parsed and mapped to a parameterized set of output motions that further reflect the location and timing of the input sketch. The current system supports a repertoire of 18 different types of motions in 2D and a subset of these in 3D. The system is unique in its use of a cursive motion specification, its ability to allow for fast experimentation, and its ease of use for non-experts.


title: Intelligent PHEV charging and discharging strategy in smart grid

abstract: The anticipation of a large penetration of Plug-in Hybrid Electric Vehicles (PHEVs) and Plug-in Electric Vehicles (PEVs) into the market brings up many new technical problems that need to be addressed. In the near future, a large number of PHEVs/PEVs connected to power grids will add a large-scale energy load, as well as add substantial energy resources that can be utilized. Vehicle-to-Grid (V2G) technology is a most promising opportunity in PHEV/PEV adoption. In this paper, the authors propose an intelligent method for optimally managing a large number of PHEVs/PEVs (e.g., 3,000) charging/discharging at a municipal parking deck. The authors used the Estimation of Distribution Algorithm (EDA) to determine the optimal charging/discharging times and patterns over a period of 24 hours. A mathematical framework for the objective function (i.e., maximizing the overall profit on a vehicle fleet base) is also given in detail. The authors characterized the performance of EDA-based energy management using a Matlab simulation, and compared it with other optimization techniques.


title: Annotation-based video enrichment for blind people: a pilot study on the use of earcons and speech synthesis

abstract: Our approach to address the question of online video accessibility for people with sensory disabilities is based on video annotations that are rendered as video enrichments during the playing of the video. We present an exploratory work that focuses on video accessibility for blind people with audio enrichments composed of speech synthesis and earcons (i.e. nonverbal audio messages). Our main results are that earcons can be used together with speech synthesis to enhance understanding of videos; that earcons should be accompanied with explanations; and that a potential side effect of earcons is related to video rhythm perception.


title: Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums

abstract: Alzheimer disease (AD) and related dementias share numerous mechanistic and epidemiological similarities with type 2 diabetes mellitus (T2DM). However, whether these associations result from synergistic interactions between the disorders is unclear. This Review summarizes our current knowledge regarding insulin signalling and resistance in the brain and discusses current evidence concerning neurological impairment in T2DM and insulin resistance in AD.


title: Neural decoding of attentional selection in multi-speaker environments without access to clean sources

abstract: OBJECTIVE ::: People who suffer from hearing impairments can find it difficult to follow a conversation in a multi-speaker environment. Current hearing aids can suppress background noise; however, there is little that can be done to help a user attend to a single conversation amongst many without knowing which speaker the user is attending to. Cognitively controlled hearing aids that use auditory attention decoding (AAD) methods are the next step in offering help. Translating the successes in AAD research to real-world applications poses a number of challenges, including the lack of access to the clean sound sources in the environment with which to compare with the neural signals. We propose a novel framework that combines single-channel speech separation algorithms with AAD. ::: ::: ::: APPROACH ::: We present an end-to-end system that (1) receives a single audio channel containing a mixture of speakers that is heard by a listener along with the listener's neural signals, (2) automatically separates the individual speakers in the mixture, (3) determines the attended speaker, and (4) amplifies the attended speaker's voice to assist the listener. ::: ::: ::: MAIN RESULTS ::: Using invasive electrophysiology recordings, we identified the regions of the auditory cortex that contribute to AAD. Given appropriate electrode locations, our system is able to decode the attention of subjects and amplify the attended speaker using only the mixed audio. Our quality assessment of the modified audio demonstrates a significant improvement in both subjective and objective speech quality measures. ::: ::: ::: SIGNIFICANCE ::: Our novel framework for AAD bridges the gap between the most recent advancements in speech processing technologies and speech prosthesis research and moves us closer to the development of cognitively controlled hearable devices for the hearing impaired.


title: Clinical and symptomatological reflections: the fascial system

abstract: Every body structure is wrapped in connective tissue, or fascia, creating a structural continuity that gives form and function to every tissue and organ. Currently, there is still little information on the functions and interactions between the fascial continuum and the body system; unfortunately, in medical literature there are few texts explaining how fascial stasis or altered movement of the various connective layers can generate a clinical problem. Certainly, the fascia plays a significant role in conveying mechanical tension, in order to control an inflammatory environment. The fascial continuum is essential for transmitting muscle force, for correct motor coordination, and for preserving the organs in their site; the fascia is a vital instrument that enables the individual to communicate and live independently. This article considers what the literature offers on symptoms related to the fascial system, trying to connect the existing information on the continuity of the connective tissue and symptoms that are not always clearly defined. In our opinion, knowing and understanding this complex system of fascial layers is essential for the clinician and other health practitioners in finding the best treatment strategy for the patient.


title: A Deep Metric for Multimodal Registration

abstract: Multimodal registration is a challenging problem in medical imaging due the high variability of tissue appearance under different imaging modalities. The crucial component here is the choice of the right similarity measure. We make a step towards a general learning-based solution that can be adapted to specific situations and present a metric based on a convolutional neural network. Our network can be trained from scratch even from a few aligned image pairs. The metric is validated on intersubject deformable registration on a dataset different from the one used for training, demonstrating good generalization. In this task, we outperform mutual information by a significant margin.


title: Recognition of Pornographic Web Pages by Classifying Texts and Images

abstract: With the rapid development of the World Wide Web, people benefit more and more from the sharing of information. However, Web pages with obscene, harmful, or illegal content can be easily accessed. It is important to recognize such unsuitable, offensive, or pornographic Web pages. In this paper, a novel framework for recognizing pornographic Web pages is described. A C4.5 decision tree is used to divide Web pages, according to content representations, into continuous text pages, discrete text pages, and image pages. These three categories of Web pages are handled, respectively, by a continuous text classifier, a discrete text classifier, and an algorithm that fuses the results from the image classifier and the discrete text classifier. In the continuous text classifier, statistical and semantic features are used to recognize pornographic texts. In the discrete text classifier, the naive Bayes rule is used to calculate the probability that a discrete text is pornographic. In the image classifier, the object's contour-based features are extracted to recognize pornographic images. In the text and image fusion algorithm, the Bayes theory is used to combine the recognition results from images and texts. Experimental results demonstrate that the continuous text classifier outperforms the traditional keyword-statistics-based classifier, the contour-based image classifier outperforms the traditional skin-region-based image classifier, the results obtained by our fusion algorithm outperform those by either of the individual classifiers, and our framework can be adapted to different categories of Web pages


title: Cache Games -- Bringing Access-Based Cache Attacks on AES to Practice

abstract: Side channel attacks on cryptographic systems exploit information gained from physical implementations rather than theoretical weaknesses of a scheme. In recent years, major achievements were made for the class of so called access-driven cache attacks. Such attacks exploit the leakage of the memory locations accessed by a victim process. In this paper we consider the AES block cipher and present an attack which is capable of recovering the full secret key in almost real time for AES-128, requiring only a very limited number of observed encryptions. Unlike previous attacks, we do not require any information about the plaintext (such as its distribution, etc.). Moreover, for the first time, we also show how the plaintext can be recovered without having access to the cipher text at all. It is the first working attack on AES implementations using compressed tables. There, no efficient techniques to identify the beginning of AES rounds is known, which is the fundamental assumption underlying previous attacks. We have a fully working implementation of our attack which is able to recover AES keys after observing as little as 100 encryptions. It works against the OpenS SL 0.9.8n implementation of AES on Linux systems. Our spy process does not require any special privileges beyond those of a standard Linux user. A contribution of probably independent interest is a denial of service attack on the task scheduler of current Linux systems (CFS), which allows one to observe (on average) every single memory access of a victim process.


title: Fast intra-encoding algorithm for High Efficiency Video Coding

abstract: The emerging High Efficiency Video Coding (HEVC) standard provides equivalent subjective quality with about 50% bit rate reduction compared to the H.264/AVC High profile. However, the improvement of coding efficiency is obtained at the expense of increased computational complexity. This paper presents a fast intra-encoding algorithm for HEVC, which is composed of the following four techniques. Firstly, an early termination technique for coding unit (CU) depth decision is proposed based on the depth of neighboring CUs and the comparison results of rate distortion (RD) costs between the parent CU and part of its child CUs. Secondly, the correlation of intra-prediction modes between neighboring PUs is exploited to accelerate the intra-prediction mode decision for HEVC intra-coding and the impact of the number of mode candidates after the rough mode decision (RMD) process in HM is studied in our work. Thirdly, the TU depth range is restricted based on the probability of each TU depth and one redundant process is removed in the TU depth selection process based on the analysis of the HEVC reference software. Finally, the probability of each case for the intra-transform skip mode is studied to accelerate the intra-transform skip mode decision. Experimental results show that the proposed algorithm can provide about 50% time savings with only 0.5% BD-rate loss on average when compared to HM 11.0 for the Main profile all-intra-configuration. Parts of these techniques have been adopted into the HEVC reference software. HighlightsAn early termination technique for coding unit (CU) depth decision.The correlation of intra-prediction modes between neighboring PUs is exploited to accelerate the intra-mode decision.The TU depth range is restricted based on the probability of each TU depth.The probability of each case for the intra-transform skip mode is firstly studied.


title: The finite element method in electromagnetics

abstract: This Key Note presents a summary of the development of the Finite Element Method in the field of Electromagnet ic Engineering, together with a description of several contributions of the authors to the Finite Element Method and its application to the solution of electromagnetic problems. First, a self-adaptive mesh scheme is presented in the context of the quasi-static and full-wave analysis of general anisotropic multiconductor arbitrary shaped waveguiding structures. A comparison between two a posteriori error estimates is done. The first one is based on the complete residual of the differential equations defining the problem. The second one is based on a recovery or smoothing technique of the electromagnetic field. Next, an implementation of the first family of Nedelec's curl-conforming elements done by the authors is outlined. Its features are highlighted and compared with other curl-conforming elements. A presentation of an iterative procedure using a numerically exact radiation condition for the analysis of open (scattering and radiation) problems follows. Other contributions of the authors, like the use of wavelet like basis functions and an implementation of a Time Domain Finite Element Method in the context of two-dimensional scattering problems are only mentioned due to the lack of space.


title: High School Classmates and College Success

abstract: This paper uses administrative data from the University of Texas-Austin to examine whether the number of same high school classmates at college entry influences college achievement, measured by grade point average (GPA) and persistence. For each freshman cohort from 1993 through 2003 we calculate the number and ethnic makeup of college freshmen from each Texas high school. Empirical specifications include high school fixed effects to control for unobservable differences across schools that influence both college enrollment behavior and academic performance. Using an instrumental variables/fixed effects estimation strategy, we also evaluate whether “marginal” increases in the number of high school classmates influence college grades. Results show that students who arrive on campus with a larger number of high school classmates outperform their counterparts from smaller high school cohorts. Average effects of larger high school cohorts on college achievement are small, but a marginal increase in the number of same-race classmates raises GPA by 0.1 point. Results provide suggestive evidence that minority academic benefits from larger high school cohorts are greater for minority compared with white students.


title: Hydrogen peroxide as a biodegradation stimulator in remediation processes of soils heavily contaminated with petrochemicals

abstract: Abstract The soil contaminated with petroleum products must be excluded from the crops and treated to reclamation processes. Natural processes of decomposition of hydrocarbon compounds go very slow, so it is necessary to use bioaugumentation or stimulation in order to accelerate the return of the soil to high culture. In this study the effect of hydrogen peroxide on the process of cleaning soil strongly contaminated with pertochemicals was investigated. For this purpose, a pot experiment lasting 60 days was carried. The dynamics of changes in the population of filamentous fungi, yeasts and bacteria were examined and also content of aliphatic hydrocarbons (n-alkanes), monoaromatic and polycyclic aromatic hydrocarbons (PAHs). Experimental use of hydrogen peroxide in the process of biodegradation of petroleum compounds assisted in the analyzed soil led to an increase of the number of grampositive bacteria during the test. Stimulation of oil products biodegradation by hydrogen peroxide also increased by 35% decomposition efficiency of aliphatic hydrocarbons (C8-C40) and about 50% PAH’s in comparison to control samples without hydrogen peroxide. There was no influence of hydrogen peroxide on the content of monoaromatic hydrocarbons (BTEX) with respect to controls, although in the end of experiment, the total concentration decreased by about 50% compared to the initial content.


title: Complexity: A Guided Tour

abstract: What enables individually simple insects like ants to act with such precision and purpose as a group? How do trillions of individual neurons produce something as extraordinarily complex as consciousness? What is it that guides self-organizing structures like the immune system, the World Wide Web, the global economy, and the human genome? These are just a few of the fascinating and elusive questions that the science of complexity seeks to answer. In this remarkably accessible and companionable book, leading complex systems scientist Melanie Mitchell provides an intimate, detailed tour of the sciences of complexity, a broad set of efforts that seek to explain how large-scale complex, organized, and adaptive behavior can emerge from simple interactions among myriad individuals. Comprehending such systems requires a wholly new approach, one that goes beyond traditional scientific reductionism and that re-maps long-standing disciplinary boundaries. Based on her work at the Santa Fe Institute and drawing on its interdisciplinary strategies, Mitchell brings clarity to the workings of complexity across a broad range of biological, technological, and social phenomena, seeking out the general principles or laws that apply to all of them. She explores as well the relationship between complexity and evolution, artificial intelligence, computation, genetics, information processing, and many other fields. Richly illustrated and vividly written, Complexity: A Guided Tour offers a comprehensive and eminently comprehensible overview of the ideas underlying complex systems science, the current research at the forefront of this field, and the prospects for the field's contribution to solving some of the most important scientific questions of our time.


title: Chipless RFID tag with integrated sensor

abstract: A chipless tag based on multiresonators with integrated temperature sensor is presented. The tag consists of a UWB monopole antenna loaded with multiple cascaded spiral EBGs which have a 1∶1 correspondence with data bits. The data bits are encoded into the spectrum hence the tag has a unique spectral signature. The chipless tag is less sensitive to tag orientation and has smaller size than earlier generations of the tag. The temperature sensor is a NTC thermistor which changes the radar cross-section of the tag antenna. The change in radar cross-section is used to determine the temperature while the spectral signature is used to identify the tag's unique ID. A proof-of-concept tag is developed and tested which has 6-bits and operates between 2 – 2.6 GHz. The chipless RFID tag is designed for ultra-low cost sensing applications.


title: Satirical News Detection and Analysis using Attention Mechanism and Linguistic Features

abstract: Satirical news is considered to be entertainment, but it is potentially deceptive and harmful. Despite the embedded genre in the article, not everyone can recognize the satirical cues and therefore believe the news as true news. We observe that satirical cues are often reflected in certain paragraphs rather than the whole document. Existing works only consider document-level features to detect the satire, which could be limited. We consider paragraph-level linguistic features to unveil the satire by incorporating neural network and attention mechanism. We investigate the difference between paragraph-level features and document-level features, and analyze them on a large satirical news dataset. The evaluation shows that the proposed model detects satirical news effectively and reveals what features are important at which level.


title: Gabor filter based image representation for object classification

abstract: Data representation plays an important role in a classifier's accuracy. A given dataset may lead to better results by simply applying a change of basis while keeping the original number of parameters. In this paper, Gabor Filter based image representation has been exploited for object classification. First, Gabor filter based convolution is computed for features extraction, then down-sampling is performed and features are normalized to zero mean and unit variance. This image representation having discriminative visual patterns is used for training of object classifier in Matlab Neural Toolbox. Performance of this proposed image representation is examined on two real world image datasets CIFAR and MNIST and results show that data representation using Gabor can provide good classification without increasing the number of trainable parameters. Finally, this approach is compared to different configurations of Convolutional Neural Network having trainable parameters to verify the validity of proposed image representation.


title: A Random Matrix Approach to Differential Privacy and Structure Preserved Social Network Graph Publishing

abstract: Online social networks are being increasingly used for analyzing various societal phenomena such as epidemiology, information dissemination, marketing and sentiment flow. Popular analysis techniques such as clustering and influential node analysis, require the computation of eigenvectors of the real graph's adjacency matrix. Recent de-anonymization attacks on Netflix and AOL datasets show that an open access to such graphs pose privacy threats. Among the various privacy preserving models, Differential privacy provides the strongest privacy guarantees. ::: In this paper we propose a privacy preserving mechanism for publishing social network graph data, which satisfies differential privacy guarantees by utilizing a combination of theory of random matrix and that of differential privacy. The key idea is to project each row of an adjacency matrix to a low dimensional space using the random projection approach and then perturb the projected matrix with random noise. We show that as compared to existing approaches for differential private approximation of eigenvectors, our approach is computationally efficient, preserves the utility and satisfies differential privacy. We evaluate our approach on social network graphs of Facebook, Live Journal and Pokec. The results show that even for high values of noise variance sigma=1 the clustering quality given by normalized mutual information gain is as low as 0.74. For influential node discovery, the propose approach is able to correctly recover 80 of the most influential nodes. We also compare our results with an approach presented in [43], which directly perturbs the eigenvector of the original data by a Laplacian noise. The results show that this approach requires a large random perturbation in order to preserve the differential privacy, which leads to a poor estimation of eigenvectors for large social networks.


LfO ' f fyper-responsrvrty to Touch and Vestibular Stimuli as a Predictor of Positive Response to Sensory Integration Procedures by Autistic Children ( autism , vestibular , touch , sensory integration )


title: Errors and Complications in SQL Query Formulation

abstract: SQL is taught in almost all university level database courses, yet SQL has received relatively little attention in educational research. In this study, we present a database management system independent categorization of SQL query errors that students make in an introductory database course. We base the categorization on previous literature, present a class of logical errors that has not been studied in detail, and review and complement these findings by analyzing over 33,000 SQL queries submitted by students. Our analysis verifies error findings presented in previous literature and reveals new types of errors, namely logical errors recurring in similar manners among different students. We present a listing of fundamental SQL query concepts we have identified and based our exercises on, a categorization of different errors and complications, and an operational model for designing SQL exercises.


Privacy-Preserving Sensor Data Analysis for Edge Computing


title: AMAI: Adaptive Music for Affect Improvement

abstract: Adaptive Music for Affect Improvement (AMAI) is a music generation and playback system whose goal is to steer the listener towards a state of more positive affect. AMAI utilizes techniques from game music in order to adjust elements of the music being heard; such adjustments are made adaptively in response to valence levels of the listener, measured via facial expression and emotion detection. A user study involving AMAI was conducted, with N=19 participants across three groups, one for each strategy of Discharge, Diversion, and Discharge -> Diversion. Significant differences in valence between music-related stages of the study were found between the groups, with Discharge -> Diversion exhibiting the greatest increase in valence, followed by Diversion and Discharge. Significant differences in affect between groups were also found in one pair of self-reported surveys, with Discharge -> Diversion exhibiting the greatest decrease in positive affect, followed by Diversion and Discharge.


title: Characterizing user behavior in online social networks

abstract: Understanding how users behave when they connect to social networking sites creates opportunities for better interface design, richer studies of social interactions, and improved design of content distribution systems. In this paper, we present a first of a kind analysis of user workloads in online social networks. Our study is based on detailed clickstream data, collected over a 12-day period, summarizing HTTP sessions of 37,024 users who accessed four popular social networks: Orkut, MySpace, Hi5, and LinkedIn. The data were collected from a social network aggregator website in Brazil, which enables users to connect to multiple social networks with a single authentication. Our analysis of the clickstream data reveals key features of the social network workloads, such as how frequently people connect to social networks and for how long, as well as the types and sequences of activities that users conduct on these sites. Additionally, we crawled the social network topology of Orkut, so that we could analyze user interaction data in light of the social graph. Our data analysis suggests insights into how users interact with friends in Orkut, such as how frequently users visit their friends' or non-immediate friends' pages. In summary, our analysis demonstrates the power of using clickstream data in identifying patterns in social network workloads and social interactions. Our analysis shows that browsing, which cannot be inferred from crawling publicly available data, accounts for 92% of all user activities. Consequently, compared to using only crawled data, considering silent interactions like browsing friends' pages increases the measured level of interaction among users.


title: Gender Representation in Cinematic Content: A Multimodal Approach

abstract: The goal of this paper is to enable an objective understanding of gender portrayals in popular films and media through multimodal content analysis. An automated system for analyzing gender representation in terms of screen presence and speaking time is developed. First, we perform independent processing of the video and the audio content to estimate gender distribution of screen presence at shot level, and of speech at utterance level. A measure of the movie's excitement or intensity is computed using audiovisual features for every scene. This measure is used as a weighting function to combine the gender-based screen/speaking time information at shot/utterance level to compute gender representation for the entire movie. Detailed results and analyses are presented on seventeen full length Hollywood movies.


title: A Randomized, Double-Blind, Multicenter Comparison of the Efficacy and Tolerability of Restylane Versus Zyplast for the Correction of Nasolabial Folds

abstract: BACKGROUND. Bovine collagen is extensively used for facial soft tissue augmentation but provides only temporary correction and can cause hypersensitivity reactions. Hyaluronic acid derivatives potentially offer improved longevity of correction and a reduced risk of immunogenicity and hypersensitivity. OBJECTIVE. To compare the efficacy and safety of nonanimal stabilized hyaluronic acid gel (Restylane; Q-Med, Uppsala, Sweden) with that of bovine collagen (Zyplast) for treatment of nasolabial folds. METHODS. One hundred thirty-eight patients with prominent nasolabial folds were randomized to treatment with hyaluronic acid gel and bovine collagen on contralateral sides of the face. Treatments were repeated at 2-week intervals, as required, to achieve ‘‘optimal cosmetic result’’ (baseline). Outcomes were evaluated by a blinded investigator at 2, 4, and 6 months after baseline. RESULTS. Less injection volume was required for ‘‘optimal cosmetic result’’ with hyaluronic acid gel than with bovine collagen, and patients and investigators judged hyaluronic acid gel to be more effective in maintaining cosmetic correction. The investigator-based Wrinkle Severity Rating Scale and Global Aesthetic Improvement Scale assessments at 6 months after baseline indicated that hyaluronic acid gel was superior in 56.9% and 62.0% of patients, respectively, whereas bovine collagen was superior in 9.5% and 8.0% of patients, respectively. The frequency, intensity, and duration of local injection-site reactions were similar for the two products. CONCLUSION. Nonanimal stabilized hyaluronic acid provides a more durable aesthetic improvement than bovine collagen and is well tolerated.


title: Familial filicide and filicide classification

abstract: Filicide is the killing of a child by his or her parent. Despite the disturbing nature of these crimes, a study of filicide classification can provide insight into their causes. Furthermore, a study of filicide classification provides information essential to accurate death certification. We report


title: Bariatric Surgery Evolution from the Malabsorptive to the Hormonal Era

abstract: While bariatric procedures continued to evolve and develop since the 1950s, their classification has not matched this evolution. The procedures are commonly classified into restrictive, malabsorptive, or combined. In this day and age, we recognize different mechanisms of action of the bariatric procedures. This article aims to review and update the old classifications based on our current understanding of the hormonal aspects of the various bariatric procedures and the role of gut hormones in weight loss and treatment of the associated metabolic comorbidities. The article suggests the need for a new classification of the bariatric procedures, based on the mechanism of action, involving the hormonal aspects of the procedure.


title: An 8T Low-Voltage and Low-Leakage Half-Selection Disturb-Free SRAM Using Bulk-CMOS and FinFETs

abstract: In this paper, we present a new 8T design for static random access memory (SRAM) cell that is based on traditional Si technology and reduces leakage power considerably compared with a conventional design. Proposed design can be fully functional at smaller supply voltages over the conventional 6T SRAM cell. To verify the proposed design, a 32 kb SRAM is designed and simulated in 90 nm CMOS technology using the proposed 8T and conventional 6T SRAM cells. Operating at their VDD \(_{\min }\) , simulations show improvement of 58% and 67% for write and read power per operation, respectively, for our design. To address the challenge of half-selection during write operation, a new low-power internal write-back scheme is presented. Finally, designing proposed cell using fin-shaped field effect transistors shows less sensitivity to variations and also improvement of \(2.08\times \) in read static noise margin at \({\rm VDD}=1.0\) V over bulk-CMOS-based SRAM cell.


title: Failure Analysis for Distributed Computing Environments

abstract: Distributed computing systems cover a broad range of computing infrastructures, which are heterogeneous, inter-connected and architected around stack-based deployments. Failure occurrences within such tightly-coupled systems while are expected, do not easily lend to predictive modeling due to the complex interactions between interconnected service layers. This work examines service level instabilities, occurring within data centers, participating in (HEP) scientific research. We present a stability measure based on which a failure event selection process is deployed to detect periods of instability within individual data centers. Experts recognize that understanding conditions for failure is crucial when designing recovery procedures. For distributed computing systems risk and failure analysis facilitates implementation of measures for service availability, subsystem recovery and network redundancy.


title: Bilinear classifiers for visual recognition

abstract: We describe an algorithm for learning bilinear SVMs. Bilinear classifiers are a discriminative variant of bilinear models, which capture the dependence of data on multiple factors. Such models are particularly appropriate for visual data that is better represented as a matrix or tensor, rather than a vector. Matrix encodings allow for more natural regularization through rank restriction. For example, a rank-one scanning-window classifier yields a separable filter. Low-rank models have fewer parameters and so are easier to regularize and faster to score at run-time. We learn low-rank models with bilinear classifiers. We also use bilinear classifiers for transfer learning by sharing linear factors between different classification tasks. Bilinear classifiers are trained with biconvex programs. Such programs are optimized with coordinate descent, where each coordinate step requires solving a convex program - in our case, we use a standard off-the-shelf SVM solver. We demonstrate bilinear SVMs on difficult problems of people detection in video sequences and action classification of video sequences, achieving state-of-the-art results in both.


title: Overview: Narcissistic personality disorder.

abstract: The authors trace the evolution of narcissistic personality disorder as a nosological entity in a critical survey of the literature, considering and comparing differing theoretical viewpoints regarding the genesis of this disorder. They review its various descriptions, including the one in DSM-III, and develop a composite picture of the syndrome. The disorder consists of characteristic deficits in six broad areas of functioning: 1) self-concept, 2) interpersonal relationships, 3) social adaptation, 4) ethics, standards, and ideals, 5) love and sexuality, and 6) cognitive style. The authors identify guidelines for distinguishing the narcissistic personality from other personality disorders as well as areas needing continued research.


title: DYNAMIC ASSET ALLOCATION WITH EVENT RISK

abstract: An inherent risk facing investors in financial markets is that a major event may trigger a large abrupt change in stock prices and market volatility. This paper studies the implications of jumps in prices and volatility on investment strategies. Using the event-risk framework of Duffie, Pan, and Singleton, we provide an analytical solution to the optimal portfolio problem. We find that event risk dramatically affects the optimal strategy. An investor facing event risk is less willing to take leveraged or short positions. In addition, the investor acts as if some portion of his wealth may become illiquid and the optimal strategy blends elements of both dynamic and buy-and-hold portfolio strategies. Jumps in prices and volatility both have an important influence on the optimal strategy.


title: Eye tracking in the wild

abstract: An active contour tracker is presented which can be used for gaze-based interaction with off-the-shelf components. The underlying contour model is based on image statistics and avoids explicit feature detection. The tracker combines particle filtering with the EM algorithm. The method exhibits robustness to light changes and camera defocusing; consequently, the model is well suited for use in systems using off-the-shelf hardware, but may equally well be used in controlled environments, such as in IR-based settings. The method is even capable of handling sudden changes between IR and non-IR light conditions, without changing parameters. For the purpose of determining where the user is looking, calibration is usually needed. The number of calibration points used in different methods varies from a few to several thousands, depending on the prior knowledge used on the setup and equipment. We examine basic properties of gaze determination when the geometry of the camera, screen, and user is unknown. In particular we present a lower bound on the number of calibration points needed for gaze determination on planar objects, and we examine degenerate configurations. Based on this lower bound we apply a simple calibration procedure, to facilitate gaze estimation.


title: What Is eSports and Why Do People Watch It

abstract: Purpose: In this study we investigate why do people spectate eSports on the internet. We define eSports (electronic sports) as a form of sports where the primary aspects of the sport are facilitated by electronic systems; the input of players and teams as well as the output of the eSports system are mediated by human-computer interfaces. In more practical terms, eSports refer to competitive video gaming (broadcasted on the internet).Methodology: We employed the MSSC (Motivations Scale for Sports Consumption) which is one of the most widely applied measurement instruments for sports consumption in general. The questionnaire was designed and pre-tested before distributing to target respondents (N=888). The reliability and validity of the instrument both met the commonly accepted guidelines. The model was assessed first by examining its measurement model and then the structural model.Findings: The results indicate that escapism, acquiring knowledge about the games being played, novelty and eSports athlete aggressiveness were found to positively predict eSport spectating frequency.Originality: During recent years, eSports (electronic sports) and video game streaming have become rapidly growing forms of new media in the internet driven by the growing provenance of (online) games and online broadcasting technologies. Today, hundreds of millions of people spectate eSports. The present investigation presents a large study on gratification-related determinants of why people spectate eSports on the internet. Moreover, the study proposes a definition for eSports and further discusses how eSports can be seen as a form of sports.


title: Cognitive radio for disaster response networks: survey, potential, and challenges

abstract: In the wake of a natural or man-made disaster, restoration of telecommunications is essential. First responders must coordinate their responses, immediate casualties require assistance, and all affected citizens may need to access information and contact friends and relatives. Existing access and core infrastructure may be damaged or destroyed, so to support the required services, new infrastructure must be rapidly deployed and integrated with undamaged resources still in place. This new equipment should be flexible enough to interoperate with legacy systems and heterogeneous technologies. The ability to selforganize is essential in order to minimize any delays associated with manual configuration. Finally, it must be robust and reliable enough to support mission-critical applications. Wireless systems can be more easily reconfigured than wired solutions to adapt to the various changes in the operating environment that can occur in a disaster scenario. A cognitive radio is one that can observe its operating environment, make decisions and reconfigure in response to these observations, and learn from experience. This article examines the use of cognitive radio technologies for disaster response networks and shows that they are ideally suited to fulfill the unique requirements of these networks. Key enabling technologies for realizing real-world cognitive radio networks for disaster response are discussed and core challenges are examined.


A Comparison of Features for the Automatic Labeling of Student answers to Open-ended Questions


title: Communicating Thematic Data Quality with Web Map Services

abstract: Geospatial information of many kinds, from topographic maps to scientific data, is increasingly being made available through web mapping services. These allow georeferenced map images to be served from data stores and displayed in websites and geographic information systems, where they can be integrated with other geographic information. The Open Geospatial Consortium’s Web Map Service (WMS) standard has been widely adopted in diverse communities for sharing data in this way. However, current services typically provide little or no information about the quality or accuracy of the data they serve. In this paper we will describe the design and implementation of a new “quality-enabled” profile of WMS, which we call “WMS-Q”. This describes how information about data quality can be transmitted to the user through WMS. Such information can exist at many levels, from entire datasets to individual measurements, and includes the many different ways in which data uncertainty can be expressed. We also describe proposed extensions to the Symbology Encoding specification, which include provision for visualizing uncertainty in raster data in a number of different ways, including contours, shading and bivariate colour maps. We shall also describe new open-source implementations of the new specifications, which include both clients and servers.


title: The Secretion of Protein Material in the Parasympathetic Submaxillary Saliva

abstract: This paper deals with a study of the secretion of protein material by the submaxillary gland of the cat in response to electrical stimulation of the chorda tympani nerve. In the attempt to make a more generous use of certain advantages offered by physical methods in research on the digestive glands, the submaxillary gland was chosen for the initial investigation because of the relative ease of physiological experimentation. The study is characterized by (1) the use of quantitative spectroscopic methods of analysis which permit an extension in the range of the investigation over that possible with a purely chemical procedure, and (2) the application of mathematical analysis in the interpretation of the results. The work of previous investigators (Heidenhain 1868, 1878, 1883; Werther 1886; Langley and Fletcher 1890; Gregerson and Ingalls 1931) has shown that with increasing intensity of the stimulus the para-sympathetic submaxillary saliva becomes more concentrated in (1) inorganic salts which are presumably derived from the tissue fluids and ultimately from the blood, and (2) organic material, provided that the stores in the gland are not too much depleted by previous stimulation. The concentration changes in the former group of substances are presumably connected with phenomena occurring at the glandular membranes; those in the latter group obviously cannot be accounted for by a simple washing out of stored material from the gland, but must result from the operation of a more complicated secretion mechanism. In the attempt to gain a better understanding of the fundamental nature of the secretion processes, we have determined spectroscopically the relative concentrations of protein material, and the Na and K concentrations in samples of saliva obtained in response to various intensities of stimulation of the chorda tympani nerve. Many of the samples were also analysed chemically for protein and non-protein nitrogen, chlorides, and acid-combining power. These substances, along with water, are the main constituents of the submaxillary saliva as obtained in our experiments. A long series of relatively small samples of secretion was taken from each animal; this feature of the experimental technique is considered to be important, viz. under such conditions one may best determine the changes in composition resulting from the variation of one factor only—the intensity of the stimulus—since changes in other factors which are not directly controlled are small from sample to sample.


title: Do points, levels and leaderboards harm intrinsic motivation?: an empirical analysis of common gamification elements

abstract: It is heavily debated within the gamification community whether specific game elements may actually undermine users' intrinsic motivation. This online experiment examined the effects of three commonly employed game design elements -- points, leaderboard, levels -- on users' performance, intrinsic motivation, perceived autonomy and competence in an image annotation task. Implementation of these game elements significantly increased performance, but did not affect perceived autonomy, competence or intrinsic motivation. Our findings suggest that points, levels and leaderboards by themselves neither make nor break users' intrinsic motivation in non-game contexts. Instead, it is assumed that they act as progress indicators, guiding and enhancing user performance. While more research on the contextual factors that may potentially mediate the effects of game elements on intrinsic motivation is required, it seems that the implementation of points, levels, and leaderboards is a viable means to promote specific user behavior in non-game contexts.


title: Unfriending on Facebook: Friend Request and Online/Offline Behavior Analysis

abstract: Objectives: Determine the role of the friend request in unfriending decisions. Find factors in unfriending decisions and find differences in the perception of online and offline behaviors that vary depending on the unfriending decision. Method: Survey research conducted online. 690 surveys about unfriending were analyzed using exploratory statistical techniques. Results: The research results show that the initiator of the friend request has more than their expected share of unfriends compared to those who receive the friend request. There are online and offline factors for unfriending decisions; the research identified six constructs to evaluate unfriending decisions. There are 4 components for online behaviors (unimportant/frequent posts, polarizing posts, inappropriate posts and everyday life posts) and 2 offline components (disliked behavior and changes in the relationship). Survey respondents who said they unfriend for online reasons were more likely to agree that the person posted too frequently about unimportant topics, polarizing topics, and inappropriate topics compared to those who unfriended for offline reasons.


abstract: Manchester code is widely used in industrial control applications as a method of communication. Its encoder and decoder is usually designed based on PLL, which make the circuit complicated. This paper introduces a novel Manchester encoder and decoder based on CPLD. The experimental results proved that, the design is correct, easy to be implemented and easy to be integrated in the existing system.


title: A Deep Hybrid Model for Weather Forecasting

abstract: Weather forecasting is a canonical predictive challenge that has depended primarily on model-based methods. We explore new directions with forecasting weather as a data-intensive challenge that involves inferences across space and time. We study specifically the power of making predictions via a hybrid approach that combines discriminatively trained predictive models with a deep neural network that models the joint statistics of a set of weather-related variables. We show how the base model can be enhanced with spatial interpolation that uses learned long-range spatial dependencies. We also derive an efficient learning and inference procedure that allows for large scale optimization of the model parameters. We evaluate the methods with experiments on real-world meteorological data that highlight the promise of the approach.


title: Boosting with Early Stopping: Convergence and Consistency

abstract: Boosting is one of the most significant advances in machine learning for classification and regression. In its original and computationally flexible version, boosting seeks to minimize empirically a loss function in a greedy fashion. The resulting estimator takes an additive function form and is built iteratively by applying a base estimator (or learner) to updated samples depending on the previous iterations. An unusual regularization technique, early stopping, is employed based on CV or a test set. This paper studies numerical convergence, consistency and statistical rates of convergence of boosting with early stopping, when it is carried out over the linear span of a family of basis functions. For general loss functions, we prove the convergence of boosting's greedy optimization to the infinimum of the loss function over the linear span. Using the numerical convergence result, we find early-stopping strategies under which boosting is shown to be consistent based on i.i.d. samples, and we obtain bounds on the rates of convergence for boosting estimators. Simulation studies are also presented to illustrate the relevance of our theoretical results for providing insights to practical aspects of boosting. As a side product, these results also reveal the importance of restricting the greedy search step-sizes. as known in practice through the work of Friedman and others. Moreover, our results lead to a rigorous proof that for a linearly separable problem, AdaBoost with E → 0 step-size becomes an L 1 -margin maximizer when left to run to convergence.


title: Memetic algorithm with simulated annealing strategy and tightness greedy optimization for community detection in networks

abstract: A Memetic algorithm (MA-SAT) is proposed for community detection in networks.Simulated annealing (SA) and tightness greedy optimization (TGO) are used.TGO is designed to improve diversity, increasing little computation cost.The balance between SA strategy and genetic algorithm is analyzed.The abundant results show that the MA-SAT is very efficient and competitive. Community structure is one of the most important properties in complex networks, and the problem of community detection in the networks has been investigated extensively in recent years. In this paper, a Memetic algorithm (MA) based on genetic algorithm with two different local search strategies is proposed to maximize the modularity density, and a more general version of the objective function is used with a tunable parameter λ which can resolve the resolution limit. One local search strategy is simulated annealing (SA), and the other one is tightness greedy optimization (TGO). SA is employed to find individuals with higher modularity density, which helps to enhance the convergence speed of the MA and avoid being trapped into local optima. TGO adopts the local tightness function which makes full use of local structural information to generate neighbor partition, which increases very little computation cost and benefits the diversity of the population of MA. Experiments on the computer-generated networks, LFR Benchmark networks, and real-world networks show that compared with several state-of-the-art methods, our algorithm (named as MA-SAT) is very efficient and competitive.


title: Mining the Web's Link Structure

abstract: The Web is a hypertext body of approximately 300 million pages that continues to grow at roughly a million pages per day. Page variation is more prodigious than the data's raw scale: taken as a whole, the set of Web pages lacks a unifying structure and shows far more authoring style and content variation than that seen in traditional text document collections. This level of complexity makes an "off-the-shelf" database management and information retrieval solution impossible. To date, index based search engines for the Web have been the primary tool by which users search for information. Such engines can build giant indices that let you quickly retrieve the set of all Web pages containing a given word or string. Experienced users can make effective use of such engines for tasks that can be solved by searching for tightly constrained key words and phrases. These search engines are, however, unsuited for a wide range of equally important tasks. In particular, a topic of any breadth will typically contain several thousand or million relevant Web pages. How then, from this sea of pages, should a search engine select the correct ones-those of most value to the user? Clever is a search engine that analyzes hyperlinks to uncover two types of pages: authorities, which provide the best source of information on a given topic; and hubs, which provide collections of links to authorities. We outline the thinking that went into Clever's design, report briefly on a study that compared Clever's performance to that of Yahoo and AltaVista, and examine how our system is being extended and updated.


title: Defensiveness Versus Remediation: Self-Theories and Modes of Self-Esteem Maintenance

abstract: How people maintain and repair their self-esteem has been a topic of widespread interest. In this article, the authors ask, What determines whether people will use direct, remedial actions, or defensive actions? In three studies, they tested the hypothesis that a belief in fixed intelligence (entity theory) would produce defensiveness, whereas a belief in improvable intelligence (incremental theory) would foster remediation. In each study, participants assigned to the entity condition opted for defensive self-esteem repair (downward comparison in Studies 1 and 3; a tutorial on already mastered material in Study 2), but those in the incremental condition opted for self-improvement (upward comparison in Studies 1 and 3; a tutorial on unmastered material in Study 2). Experiment 3 also linked these strategies to self-esteem repair; remedial strategies were the most effective in recovering lost self-esteem for those in the incremental condition, whereas defensive strategies were most effective for those in the...


title: Qualitative Case Study Methodology: Study Design and Implementation for Novice Researchers

abstract: Qualitative case study methodology provides tools for researchers to study complex phenomena within their contexts. When the approach is applied correctly, it becomes a valuable method for health science research to develop theory, evaluate programs, and develop interventions. The purpose of this paper is to guide the novice researcher in identifying the key elements for designing and implementing qualitative case study research projects. An overview of the types of case study designs is provided along with general recommendations for writing the research questions, developing propositions, determining the “case” under study, binding the case and a discussion of data sources and triangulation. To facilitate application of these principles, clear examples of research questions, study propositions and the different types of case study designs


title: Gender Asymmetries in Reality and Fiction: The Bechdel Test of Social Media

abstract: The subjective nature of gender inequality motivates the analysis and comparison of data from real and fictional human interaction. We present a computational extension of the Bechdel test: A popular tool to assess if a movie contains a male gender bias, by looking for two female characters who discuss about something besides a man. We provide the tools to quantify Bechdel scores for both genders, and we measure them in movie scripts and large datasets of dialogues between users of MySpace and Twitter. Comparing movies and users of social media, we find that movies and Twitter conversations have a consistent male bias, which does not appear when analyzing MySpace. Furthermore, the narrative of Twitter is closer to the movies that do not pass the Bechdel test than to those that pass it. ::: We link the properties of movies and the users that share trailers of those movies. Our analysis reveals some particularities of movies that pass the Bechdel test: Their trailers are less popular, female users are more likely to share them than male users, and users that share them tend to interact less with male users. Based on our datasets, we define gender independence measurements to analyze the gender biases of a society, as manifested through digital traces of online behavior. Using the profile information of Twitter users, we find larger gender independence for urban users in comparison to rural ones. Additionally, the asymmetry between genders is larger for parents and lower for students. Gender asymmetry varies across US states, increasing with higher average income and latitude. This points to the relation between gender inequality and social, economical, and cultural factors of a society, and how gender roles exist in both fictional narratives and public online dialogues.


title: On the implementation of an efficient FPGA-based CFAR processor for target detection

abstract: Real-time performance of adaptive digital signal processing algorithms is required in many applications but it often means a high computational load for many conventional processors. In this paper, we present a configurable hardware architecture for adaptive processing of noisy signals for target detection based on Constant False Alarm Rate (CFAR) algorithms. The architecture has been designed to deal with parallel/pipeline processing and to be configured for three version of CFAR algorithms, the Cell-Average, the Max and the Min CFAR. The proposed architecture has been implemented on a Field Programmable Gate Array (FPGA) device providing good performance improvements over software implementations. FPGA implementation results are presented and discussed.


title: Image processing based data extraction from graphical representation

abstract: Raw data is commonly presented in graphical form in published literature. Often authors may be in need to source data from such publications. The raw research data is normally not available to fellow researchers due to non availability of data itself or lack of interest of the author to share the data. Presently many software tools are available for extracting the data from the graphs. But the user has to manually select the points where the data is to be extracted. Few automation features provided in the tools are time consuming and need post processing to eliminate the errors in data extraction. An image processing based algorithm is developed to automatically extract the data from the graphs with minimal inputs by the user. The tests on the developed tool shows that the extracted data is close to the raw data with error less than 1%. The results are motivating enough to upgrade the developed algorithm with more features.


title: Neural Network Recognizer for Hand-Written Zip Code Digits

abstract: This paper describes the construction of a system that recognizes handprinted digits, using a combination of classical techniques and neural-net methods. The system has been trained and tested on real-world data, derived from zip codes seen on actual U.S. Mail. The system rejects a small percentage of the examples as unclassifiable, and achieves a very low error rate on the remaining examples. The system compares favorably with other state-of-the art recognizers. While some of the methods are specific to this task, it is hoped that many of the techniques will be applicable to a wide range of recognition tasks.


title: Bridging the Robot Perception Gap with Mid-Level Vision

abstract: The practical application of machine perception to support physical manipulation in unstructured environments remains a barrier to the development of intelligent robotic systems. Recently, great progress has been made by the large-scale machine perception community, but these methods have made few contributions to the applied robotic perception. This is in part because such large-scale systems are designed to recognize category labels of large numbers of objects from a single image, rather than highly accurate, efficient, and robust pose estimation in environments for which a robot has reliable prior knowledge. In this paper, we illustrate the potential for synergistic integration of modern computer vision methods into robotics by augmenting a RANSAC-based registration method with a state-of-the art semantic segmentation algorithm. We detail a convolutional architecture for semantic labeling of the scene, modified to operate efficiently using integral images. We combine this labeling with two novel scene parsing variants of RANSAC, and show, on a new RGB-D dataset that contains complex configurations of textureless and highly specular objects, that our method demonstrates improved performance of pose estimation over the unaugmented algorithms.


title: Multimodal approaches for emotion recognition: a survey

abstract: Recent technological advances have enabled human users to interact with computers in ways previously unimaginable. Beyond the confines of the keyboard and mouse, new modalities for human-computer interaction such as voice, gesture, and force-feedback are emerging. Despite important advances, one necessary ingredient for natural ::: interaction is still missing-emotions. Emotions play an important role in human-to-human communication and interaction, allowing people to express themselves beyond the verbal domain. The ability to understand human emotions is desirable for the computer in several applications. This paper explores new ways of human-computer ::: interaction that enable the computer to be more aware of the user's emotional and attentional expressions. We present the basic research in the field and the recent advances into the emotion recognition from facial, voice, and physiological signals, where the different modalities are treated independently. We then describe the challenging problem of multimodal emotion recognition and we advocate the use of probabilistic graphical models when fusing the different modalities. We also discuss the difficult issues of obtaining reliable affective data, obtaining ground truth for emotion recognition, and the use of unlabeled data.


title: Rethinking Success in Software Projects: Looking Beyond the Failure Factors

abstract: The notions of success and failure in software projects are confusing. Failure is often considered in the context of the iron triangle as the inability to meet time, cost, and performance constraints. While there is a consensus around the prevalence of project failure, new projects seem destined to repeat past mistakes. This chapter tries to advance the discussion by offering a new perspective for reasoning about the meaning of success and the different types of software project failures.


title: A folded dipole antenna for RFID

abstract: Radio frequency identification (RFID) of objects or people has become very popular in many services in industry, distribution logistics, manufacturing companies and goods flow systems. When RFID frequency rises into the microwave region, the tag antenna must be carefully designed to match the free space and to the following ASIC. In this paper, we present a novel folded dipole antenna with a very simple configuration. The required input impedance can be achieved easily by choosing suitable geometry parameters.


title: Predicting MicroRNA-Disease Associations Using Network Topological Similarity Based on DeepWalk

abstract: Recently, increasing experimental studies have shown that microRNAs (miRNAs) involved in multiple physiological processes are connected with several complex human diseases. Identifying human disease-related miRNAs will be useful in uncovering novel prognostic markers for cancer. Currently, several computational approaches have been developed for miRNA-disease association prediction based on the integration of additional biological information of diseases and miRNAs, such as disease semantic similarity and miRNA functional similarity. However, these methods do not work well when this information is unavailable. In this paper, we present a similarity-based miRNA-disease prediction method that enhances the existing association discovery methods through a topology-based similarity measure. DeepWalk, a deep learning method, is utilized in this paper to calculate similarities within a miRNA-disease association network. It shows superior predictive performance for 22 complex diseases, with area under the ROC curve scores ranging from 0.805 to 0.937 by using five-fold cross-validation. In addition, case studies on breast cancer, lung cancer, and prostatic cancer further justify the use of our method to discover latent miRNA-disease pairs.


title: Pattern-Reconfigurable Microstrip Patch Antenna With Multidirectional Beam for WiMAX Application

abstract: A parasitic planar patch antenna capable of multi directional pattern reconfiguration is presented. The antenna structure consists of a driven element surrounded by four parasitic elements that act either as reflector(s) or director(s) depending on the switching arrangement. Beam reconfiguration is achieved by using four p-i-n diode switches where the effect of switching technique on the overall element performance is investigated numerically and experimentally. The proposed antenna achieves nine distinguished main beam angular positions. Moreover, the proposed antenna has achieved excellent realized gain levels at all configuration scenarios with a minimum value of 7 dBi. Simulation and measurement results show good agreement and promising applications of such antenna structure in enhanced WiMAX systems.


title: Dietary Broccoli Alters Rat Cecal Microbiota to Improve Glucoraphanin Hydrolysis to Bioactive Isothiocyanates

abstract: Broccoli consumption brings many health benefits, including reducing the risk of cancer and inflammatory diseases. The objectives of this study were to identify global alterations in the cecal microbiota composition using 16S rRNA sequencing analysis and glucoraphanin (GRP) hydrolysis to isothiocyanates ex vivo by the cecal microbiota, following different broccoli diets. Rats were randomized to consume AIN93G (control) or different broccoli diets; AIN93G plus cooked broccoli, a GRP-rich powder, raw broccoli, or myrosinase-treated cooked broccoli. Feeding raw or cooked broccoli for four days or longer both changed the cecal microbiota composition and caused a greater production of isothiocyanates ex vivo. A more than two-fold increase in NAD(P)H: quinone oxidoreductase 1 activity of the host colon mucosa after feeding cooked broccoli for seven days confirmed the positive health benefits. Further studies revealed that dietary GRP was specifically responsible for the increased microbial GRP hydrolysis ex vivo, whereas changes in the cecal microbial communities were attributed to other broccoli components. Interestingly, a three-day withdrawal from a raw broccoli diet reversed the increased microbial GRP hydrolysis ex vivo. Findings suggest that enhanced conversion of GRP to bioactive isothiocyanates by the cecal microbiota requires four or more days of broccoli consumption and is reversible.


title: Is data clustering in adversarial settings secure?

abstract: Clustering algorithms have been increasingly adopted in security applications to spot dangerous or illicit activities. However, they have not been originally devised to deal with deliberate attack attempts that may aim to subvert the clustering process itself. Whether clustering can be safely adopted in such settings remains thus questionable. In this work we propose a general framework that allows one to identify potential attacks against clustering algorithms, and to evaluate their impact, by making specific assumptions on the adversary's goal, knowledge of the attacked system, and capabilities of manipulating the input data. We show that an attacker may significantly poison the whole clustering process by adding a relatively small percentage of attack samples to the input data, and that some attack samples may be obfuscated to be hidden within some existing clusters. We present a case study on single-linkage hierarchical clustering, and report experiments on clustering of malware samples and handwritten digits.