title: Object stereo — Joint stereo matching and object segmentation

abstract: This paper presents a method for joint stereo matching and object segmentation. In our approach a 3D scene is represented as a collection of visually distinct and spatially coherent objects. Each object is characterized by three different aspects: a color model, a 3D plane that approximates the object's disparity distribution, and a novel 3D connectivity property. Inspired by Markov Random Field models of image segmentation, we employ object-level color models as a soft constraint, which can aid depth estimation in powerful ways. In particular, our method is able to recover the depth of regions that are fully occluded in one input view, which to our knowledge is new for stereo matching. Our model is formulated as an energy function that is optimized via fusion moves. We show high-quality disparity and object segmentation results on challenging image pairs as well as standard benchmarks. We believe our work not only demonstrates a novel synergy between the areas of image segmentation and stereo matching, but may also inspire new work in the domain of automatic and interactive object-level scene manipulation.


title: From the decoding of cortical activities to the control of a JACO robotic arm: a whole processing chain

abstract: This paper presents a complete processing chain for decoding intracranial data recorded in the cortex of a monkey and replicates the associated movements on a JACO robotic arm by Kinova. We developed specific modules inside the OpenViBE platform in order to build a Brain-Machine Interface able to read the data, compute the position of the robotic finger and send this position to the robotic arm. More pre- cisely, two client/server protocols have been tested to transfer the finger positions: VRPN and a light protocol based on TCP/IP sockets. According to the requested finger position, the server calls the associ- ated functions of an API by Kinova to move the fin- gers properly. Finally, we monitor the gap between the requested and actual fingers positions. This chain can be generalized to any movement of the arm or wrist.


title: Automated video looping with progressive dynamism

abstract: Given a short video we create a representation that captures a spectrum of looping videos with varying levels of dynamism, ranging from a static image to a highly animated loop. In such a progressively dynamic video, scene liveliness can be adjusted interactively using a slider control. Applications include background images and slideshows, where the desired level of activity may depend on personal taste or mood. The representation also provides a segmentation of the scene into independently looping regions, enabling interactive local adjustment over dynamism. For a landscape scene, this control might correspond to selective animation and deanimation of grass motion, water ripples, and swaying trees. Converting arbitrary video to looping content is a challenging research problem. Unlike prior work, we explore an optimization in which each pixel automatically determines its own looping period. The resulting nested segmentation of static and dynamic scene regions forms an extremely compact representation.


title: Advanced deep learning for blood vessel segmentation in retinal fundus images

abstract: Rising of deep learning methodologies draws huge attention to their application in image processing and classification. Catching up the trends, this study briefly presents state-of-the-art of deep learning applications in medical imaging interfered with achievements of blood vessel segmentation methods in neurosensory retinal fundus images. Successful segmentation based on deep learning offers advantage in diagnosing ophthalmological disease or pathology.


title: Supervised Descent Method for Solving Nonlinear Least Squares Problems in Computer Vision

abstract: Many computer vision problems (e.g., camera calibration, image alignment, structure from motion) are solved with nonlinear optimization methods. It is generally accepted that second order descent methods are the most robust, fast, and reliable approaches for nonlinear optimization of a general smooth function. However, in the context of computer vision, second order descent methods have two main drawbacks: (1) the function might not be analytically differentiable and numerical approximations are impractical, and (2) the Hessian may be large and not positive definite. To address these issues, this paper proposes generic descent maps, which are average "descent directions" and rescaling factors learned in a supervised fashion. Using generic descent maps, we derive a practical algorithm - Supervised Descent Method (SDM) - for minimizing Nonlinear Least Squares (NLS) problems. During training, SDM learns a sequence of decent maps that minimize the NLS. In testing, SDM minimizes the NLS objective using the learned descent maps without computing the Jacobian or the Hessian. We prove the conditions under which the SDM is guaranteed to converge. We illustrate the effectiveness and accuracy of SDM in three computer vision problems: rigid image alignment, non-rigid image alignment, and 3D pose estimation. In particular, we show how SDM achieves state-of-the-art performance in the problem of facial feature detection. The code has been made available at www.humansensing.cs.cmu.edu/intraface.


title: Human Brain: Left-Right Asymmetries in Temporal Speech Region

abstract: At the time Walter Levitsky and I published this paper the prevailing view was that there were no significant anatomical asymmetries between the hemispheres and that the cause of cerebral dominance would have to be sought in purely physiological or in subtle anatomical differences between the two sides. Since the publication of this paper the results reported here have been replicated by several groups. Juhn Wada’s report at the International Neurological Congress in 1969 not only confirmed our findings in adults, but also reported the presence of these asymmetries in the brains of fetuses and new-borns. More recently Teszner (1972) in Paris and Witelson and Pallie (1973) in Canada have added further confirmations. The statistics in all these series have been very close in value. Dr. William Sheremata and I have as yet unpublished data showing a correlation between the larger left temporal plane and the longer left occipital horn described by McRae et at. (1968). As a result of this correlation it will be possible to make a rough prediction as to the presence of anatomical asymmetry on the basis of pneumoencephalographic findings. The development of elaborate computerized scanning devices should make it possible to determine the size of the left occipital horn non-invasively. In fact it is possible that by suitable programming it will be possible to determine directly the size of the temporal plane on the two sides.


May I take your order? A Neural Model for Extracting Structured Information from Conversations


title: Real-Time Self-Adaptive Deep Stereo

abstract: Deep convolutional neural networks trained end-to-end are the state-of-the-art methods to regress dense disparity maps from stereo pairs. These models, however, suffer from a notable decrease in accuracy when exposed to scenarios significantly different from the training set (e.g., real vs synthetic images, etc.). We argue that it is extremely unlikely to gather enough samples to achieve effective training/tuning in any target domain, thus making this setup impractical for many applications. Instead, we propose to perform unsupervised and continuous online adaptation of a deep stereo network, which allows for preserving its accuracy in any environment. However, this strategy is extremely computationally demanding and thus prevents real-time inference. We address this issue introducing a new lightweight, yet effective, deep stereo architecture, Modularly ADaptive Network(MADNet), and developing a Modular ADaptation (MAD) algorithm, which independently trains sub-portions of the network. By deploying MADNet together with MAD we introduce the first real-time self-adaptive deep stereo system enabling competitive performance on heterogeneous datasets. Our code is publicly available at https://github.com/CVLAB-Unibo/Real-time-self-adaptive-deep-stereo.


title: Data-driven Methods for the Study of Food Perception, Preparation, Consumption, and Culture

abstract: The study of food consumption, in the broadest sense, intersects with a wide range of academic disciplines. The perception of food involves molecular biology, chemistry, soft-matter physics, neuroscience, psychology, physiology, and even machine learning. Our choices and preparation of food are of interest to anthropologists, social scientists, historians, philosophers, and linguists. The advent of information technology has enabled the accumulation and analysis of large amounts of food-related data, from the interactions of biomolecules and the chemical properties of aroma compounds to online recipes and food-related social media postings. In this perspective article, we outline several areas in which the availability and analysis of data can inform us about general principles that may underlie the perception of food and the diversity of culinary practice. One of these areas is the study of umami taste through a combination of chemical analysis and quantitative sensory evaluation for a wide range of fermented products. Another is the mapping of global culinary diversity by mining online recipes and the analysis of culinary habits recorded by individuals on social media. A third area is the study of the properties of flavour compounds, and the application of these insights in the context of high-end gastronomy. These examples illustrate that large-scale data analysis is already transforming our understanding of food perception and consumption, and that it is likely to fundamentally influence our food choices and habits in the future.


Body Sensor Network - A Wireless Sensor Platform for Pervasive Healthcare Monitoring


title: Optimization Criteria, Sensitivity and Robustness of Motion and Structure Estimation

abstract: The prevailing efforts to study the standard formulation of motion and structure recovery have been recently focused on issues of sensitivity and robustness of existing techniques. While many cogent observations have been made and verified experimentally, many statements do not hold in general settings and make a comparison of existing techniques difficult. With an ultimate goal of clarifying these issues we study the main aspects of the problem: the choice of objective functions, optimization techniques and the sensitivity and robustness issues in the presence of noise. We clearly reveal the relationship among different objective functions, such as (normalized) epipolar constraints, reprojection error or triangulation, which can all be be unified in a new optimal triangulation procedure formulated as a constrained optimization problem. Regardless of various choices of the objective function, the optimization problems all inherit the same unknown parameter space, the so called essential manifold, making the new optimization techniques on Riemanian manifolds directly applicable. Using these analytical results we provide a clear account of sensitivity and robustness of the proposed linear and nonlinear optimization techniques and study the analytical and practical equivalence of different objective functions. The geometric characterization of critical points of a function defined on essential manifold and the simulation results clarify the difference between the effect of bas relief ambiguity and other types of local minima leading to a consistent interpretations of simulation results over large range of signal-to-noise ratio and variety of configurations.


The use of heparin and of dextran in arterial reconstruction.


title: A Smart Approach for Matching, Learning and Querying Information from the Human Resources Domain

abstract: We face the complex problem of timely, accurate and mutually satisfactory mediation between job offers and suitable applicant profiles by means of semantic processing techniques. In fact, this problem has become a major challenge for all public and private recruitment agencies around the world as well as for employers and job seekers. It is widely agreed that smart algorithms for automatically matching, learning, and querying job offers and candidate profiles will provide a key technology of high importance and impact and will help to counter the lack of skilled labor and/or appropriate job positions for unemployed people. Additionally, such a framework can support global matching aiming at finding an optimal allocation of job seekers to available jobs, which is relevant for independent employment agencies, e.g. in order to reduce unemployment.


title: Food Image Recognition via Superpixel Based Low-Level and Mid-Level Distance Coding for Smart Home Applications

abstract: Food image recognition is a key enabler for many smart home applications such as smart kitchen and smart personal nutrition log. In order to improve living experience and life quality, smart home systems collect valuable insights of users’ preferences, nutrition intake and health conditions via accurate and robust food image recognition. In addition, efficiency is also a major concern since many smart home applications are deployed on mobile devices where high-end GPUs are not available. In this paper, we investigate compact and efficient food image recognition methods, namely low-level and mid-level approaches. Considering the real application scenario where only limited and noisy data are available, we first proposed a superpixel based Linear Distance Coding (LDC) framework where distinctive low-level food image features are extracted to improve performance. On a challenging small food image dataset where only 12 training images are available per category, our framework has shown superior performance in both accuracy and robustness. In addition, to better model deformable food part distribution, we extend LDC’s feature-to-class distance idea and propose a mid-level superpixel food parts-to-class distance mining framework. The proposed framework show superior performance on a benchmark food image datasets compared to other low-level and mid-level approaches in the literature.


title: Face Recognitio Point

abstract: In this paper, a novel regularized nearest points (RNP) method is proposed for image sets based face recognition. By modeling an image set as a regularized affine hull (RAH), two regularized nearest points (RNP), one on each image set's RAH, are automatically determined by an efficient iterative solver. The between-set distance of RNP is then defined by considering both the distance between the RNPs and the structure of image sets. Compared with the recently developed sparse approximated nearest points (SANP) method, RNP has a more concise formulation, less variables and lower time complexity. Extensive experiments on benchmark databases (e.g., Honda/UCSD, CMU Mobo and YouTube databases) clearly show that our proposed RNP consistently outperforms state-of-the-art methods in both accuracy and efficiency.


title: Adaptive Visualization of Search Results: Bringing User Models to Visual Analytics

abstract: Adaptive visualization is a new approach at the crossroads of user modeling and information visualization. Taking into account information about a user, adaptive visualization attempts to provide user-adapted visual presentation of information. This paper proposes Adaptive VIBE, an approach for adaptive visualization of search results in an intelligence analysis context. Adaptive VIBE extends the popular VIBE visualization framework by infusing user model terms as reference points for spatial document arrangement and manipulation. We explored the value of the proposed approach using data obtained from a user study. The result demonstrated that user modeling and spatial visualization technologies are able to reinforce each other, creating an enhanced level of user support. Spatial visualization amplifies the user model's ability to separate relevant and non-relevant documents, whereas user modeling adds valuable reference points to relevance-based spatial visualization.


title: When Acquisition Spoils Retention: Direct Selling vs. Delegation Under CRM

abstract: The widespread implementation of customer relationship management technologies in business has allowed companies to increasingly focus on both acquiring and retaining customers. The challenge of designing incentive mechanisms that simultaneously focus on customer acquisition and customer retention comes from the fact that customer acquisition and customer retention are usually separate but intertwined tasks that make providing proper incentives more difficult. The present study develops incentive mechanisms that simultaneously address acquisition and retention of customers with an emphasis on the interactions between them. The main focus of this study is to examine the impact of the negative effect of acquisition on retention, i.e., the spoiling effect, on firm performance under direct selling and delegation of customer acquisition. Our main finding is that the negative effect of acquisition on retention has a significant impact on acquisition and retention efforts and firm profit. In particular, when the customer acquisition and retention are independent, the firm's profit is higher under direct selling than under delegation; however, when acquisition spoils retention, interestingly, the firm's profit may be higher under delegation. Our analysis also finds that the spoiling effect not only reduces the optimal acquisition effort but may also reduce retention effort under both direct selling and delegation. Comparing the optimal efforts under direct selling and delegation, the acquisition effort is always lower under delegation regardless of the spoiling effect, but the retention effort may be higher under delegation with the spoiling effect. Furthermore, when the customer antagonism effect from price promotions is considered, our main results hold regarding the firm's preferences between direct selling and delegation, which demonstrates the robustness of our model. ::: ::: This paper was accepted by Pradeep Chintagunta, marketing.


title: A novel approach for trajectory feature representation and anomalous trajectory detection

abstract: Trajectories obtained from low level tracking algorithm provide an opportunity for us to analyze meaningful behaviors and monitor adverse or malicious events. How to abstract meaningful features from the raw data of trajectories is a challenge due to the high dimensionality and noise. In this paper, a novel approach, stacked denoising autoencoder(SDA) is applied to address this problem. This method can reduce the dimensionality of the trajectories significantly, so that they can be handled easily. More importantly, the denoising process of the SDA can capture the structure of the raw data, so the features they producing generalize well for detecting anomalous trajectories. The results of the numerical experiments prove the validity of the proposed approach.


title: Movie Recommender system using the user's psychological profile

abstract: In this paper we present Movie Recommender, a system which provides movie recommendations based on the information known about the users. These recommendations are done using the analysis of the users' psychological profile, their watching history and the movies scores from other websites. They are based on aggregate similarity calculation. The system uses both collaborative filtering and content filtering (using an approach based on different features of the movies from the database). Although there are similar applications available, they tend to ignore the data specific to the user, which in our opinion is essential for his/her behavior.


title: APE: A Data-Driven, Behavioral Model-Based Anti-Poaching Engine

abstract: We consider the problem of protecting a set of animals such as rhinos and elephants in a game park using $D$ drones and $R$ ranger patrols (on the ground) with $R\geq D$ . Using two years of data about animal movements in a game park, we propose the probabilistic spatio-temporal graph (pSTG) model of animal movement behaviors and show how we can learn it from the movement data. Using 17 months of data about poacher behavior, we also learn the probability that a region in the game park will be targeted by poachers. We formalize the anti-poaching problem as that of finding a coordinated route for the drones and ranger patrols that maximize the expected number of animals that are protected, given these two models as input and show that it is NP-complete. Because of this, we fine tune classical local search and genetic algorithms to the case of anti-poaching by taking specific advantage of the nature of the anti-poaching problem and its objective function. We develop a measure of the quality of an algorithm to route the drones and ranger patrols called “improvement ratio.” We develop a dynamic programming based $\mathsf{APE\_Coord\_Route}$ algorithm and show that it performs very well in practice, achieving an improvement ratio over 90%.


title: Spatio-temporal Action Recognition: A Survey

abstract: The task of action recognition or action detection involves analyzing videos and determining what action or motion is being performed. The primary subject of these videos are predominantly humans performing some action. However, this requirement can be relaxed to generalize over other subjects such as animals or robots. The applications can range from anywhere between human-computer inter-action to automated video editing proposals. When we consider spatiotemporal action recognition, we deal with action localization. This task not only involves determining what action is being performed but also when and where itis being performed in said video. This paper aims to survey the plethora of approaches and algorithms attempted to solve this task, give a comprehensive comparison between them, explore various datasets available for the problem, and determine the most promising approaches.


title: Moving Deep Learning into Web Browser: How Far Can We Go?

abstract: Recently, several JavaScript-based deep learning frameworks have emerged, making it possible to perform deep learning tasks directly in browsers. However, little is known on what and how well we can do with these frameworks for deep learning in browsers. To bridge the knowledge gap, in this paper, we conduct the first empirical study of deep learning in browsers. We survey 7 most popular JavaScript-based deep learning frameworks, investigating to what extent deep learning tasks have been supported in browsers so far. Then we measure the performance of different frameworks when running different deep learning tasks. Finally, we dig out the performance gap between deep learning in browsers and on native platforms by comparing the performance of TensorFlow.js and TensorFlow in Python. Our findings could help application developers, deep-learning framework vendors and browser vendors to improve the efficiency of deep learning in browsers.


title: Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications

abstract: Part II of the tutorial has summarized the remaining building blocks of the VO pipeline: specifically, how to detect and match salient and repeatable features across frames and robust estimation in the presence of outliers and bundle adjustment. In addition, error propagation, applications, and links to publicly available code are included. VO is a well understood and established part of robotics. VO has reached a maturity that has allowed us to successfully use it for certain classes of applications: space, ground, aerial, and underwater. In the presence of loop closures, VO can be used as a building block for a complete SLAM algorithm to reduce motion drift. Challenges that still remain are to develop and demonstrate large-scale and long-term implementations, such as driving autonomous cars for hundreds of miles. Such systems have recently been demonstrated using Lidar and Radar sensors [86]. However, for VO to be used in such systems, technical issues regarding robustness and, especially, long-term stability have to be resolved. Eventually, VO has the potential to replace Lidar-based systems for egomotion estimation, which are currently leading the state of the art in accuracy, robustness, and reliability. VO offers a cheaper and mechanically easier-to-manufacture solution for egomotion estimation, while, additionally, being fully passive. Furthermore, the ongoing miniaturization of digital cameras offers the possibility to develop smaller and smaller robotic systems capable of ego-motion estimation.


title: Elleanthus ollgaardii (Orchidaceae), a New Species from Ecuador

abstract: In 1984, Benjamin Ollgaard and his colleagues during their expedition to Ecuador discovered a new species of Elleanthus (Orchidaceae). They deposited the original material in the Missouri Botanical Garden Herbarium but they failed to publish a description of their discovery. A few years later in 1991, Calaway H. Dodson named it Elleanthus ollgardii on the herbarium sheet but again no description was published. Based on our studies and comparisons with other herbarium materials we validate and illustrate E. ollgaardii Dudek & Szlach. as a species new to science.


title: SentiBank: large-scale ontology and classifiers for detecting sentiment and emotions in visual content

abstract: A picture is worth one thousand words, but what words should be used to describe the sentiment and emotions conveyed in the increasingly popular social multimedia? We demonstrate a novel system which combines sound structures from psychology and the folksonomy extracted from social multimedia to develop a large visual sentiment ontology consisting of 1,200 concepts and associated classifiers called SentiBank. Each concept, defined as an Adjective Noun Pair (ANP), is made of an adjective strongly indicating emotions and a noun corresponding to objects or scenes that have a reasonable prospect of automatic detection. We believe such large-scale visual classifiers offer a powerful mid-level semantic representation enabling high-level sentiment analysis of social multimedia. We demonstrate novel applications made possible by SentiBank including live sentiment prediction of social media and visualization of visual content in a rich intuitive semantic space.


title: Primal least squares twin support vector regression

abstract: The training algorithm of classical twin support vector regression (TSVR) can be attributed to the solution of a pair of quadratic programming problems (QPPs) with inequality constraints in the dual space. However, this solution is affected by time and memory constraints when dealing with large datasets. In this paper, we present a least squares version for TSVR in the primal space, termed primal least squares TSVR (PLSTSVR). By introducing the least squares method, the inequality constraints of TSVR are transformed into equality constraints. Furthermore, we attempt to directly solve the two QPPs with equality constraints in the primal space instead of the dual space; thus, we need only to solve two systems of linear equations instead of two QPPs. Experimental results on artificial and benchmark datasets show that PLSTSVR has comparable accuracy to TSVR but with con- siderably less computational time. We further investigate its validity in predicting the opening price of stock.


title: Unified Programmability of Virtualized Network Functions and Software-Defined Wireless Networks

abstract: The quickly growing demand for wireless networks and the numerous application-specific requirements stand in stark contrast to today’s inflexible management and operation of wireless networks. While most research focuses on mobile networks, WiFi is often left out of the purview. In this paper, we present and evaluate OpenSDWN, a novel WiFi architecture based on a joint software-defined network and network functions virtualization approach. OpenSDWN exploits virtualization across the wired and wireless network and introduces datapath programmability to enable service differentiation and fine-grained transmission control, facilitating the prioritization of critical applications. OpenSDWN implements per-client virtual access points and per-client virtual middleboxes, to render network functions more flexible and support mobility and seamless migration. Moreover, OpenSDWN also increases the security of upcoming WiFi HotSpot architectures by following a functional split approach. Finally, OpenSDWN can also be used to out-source the control over the home network to a participatory interface or to an Internet service provider.


title: Comparison of Three Different CNN Architectures for Age Classification

abstract: As one of the powerful tools of machine learning, Convolutional Neural Network (CNN) architectures are used tosolve complex problems like image recognition, video analysisand natural language processing. In this paper, three differentCNN architectures for age classification using face images arecompared. The Morph dataset containing over 55k images isused in experiments and success of a 6-layer CNN and 2 variantsof ResNet with different depths are compared. The images in thedataset are divided into 6 different age classes. While 80% of theimages are used in training of the networks, the rest of the 20% isused for testing. The performance of the networks are comparedaccording to two different criteria namely, the ability to makethe estimation pointing the exact age classes of test images andthe ability to make the estimation pointing the exact age classesor at most neighboring classes of the images. According to theperformance results obtained, with 6-layer network, it is possibleto estimate the exact or neighboring classes of the images withless than 5% error. It is shown that for a 6 class age classificationproblem 6-layer network is more successful than the deeperResNet counterparts since 6-layer network is less susceptible tooverfitting for this problem.


Access to Information: The Dilemma for Rural Community Development


title: Imaging brain amyloid in Alzheimer's disease with Pittsburgh Compound-B

abstract: This report describes the first human study of a novel amyloid-imaging positron emission tomography (PET) tracer, termed Pittsburgh Compound-B (PIB), in 16 patients with diagnosed mild AD and 9 controls. Compared with controls, AD patients typically showed marked retention of PIB in areas of association cortex known to contain large amounts of amyloid deposits in AD. In the AD patient group, PIB retention was increased most prominently in frontal cortex (1.94-fold, p 0.0001). Large increases also were observed in parietal (1.71-fold, p 0.0002), temporal (1.52-fold, p 0.002), and occipital (1.54-fold, p 0.002) cortex and the striatum (1.76-fold, p 0.0001). PIB retention was equivalent in AD patients and controls in areas known to be relatively unaffected by amyloid deposition (such as subcortical white matter, pons, and cerebellum). Studies in three young (21 years) and six older healthy controls (69.5 11 years) showed low PIB retention in cortical areas and no significant group differences between young and older controls. In cortical areas, PIB retention correlated inversely with cerebral glucose metabolism determined with 18Ffluorodeoxyglucose. This relationship was most robust in the parietal cortex (r 0.72; p 0.0001). The results suggest that PET imaging with the novel tracer, PIB, can provide quantitative information on amyloid deposits in living subjects. Ann Neurol 2004;55:306 –319


title: A Supervised Method for Constructing Sentiment Lexicon in Persian Language

abstract: Due to the increasing growth of digital content on the internet and social media, sentiment analysis problem is one of the emerging fields. This problem deals with information extraction and knowledge discovery from textual data using natural language processing has attracted the attention of many researchers. Construction of sentiment lexicon as a valuable language resource is a one of the important fields of study in this domain. The main researches in the area of sentiment analysis have focused on English language and few works considered the sentiment analysis in Persian language due to the lack of resources. This paper aims to introduce a supervised method for creating a sentiment dictionary in Persian language with extracting linguistic features in reviews and statistical mutual information to determine the sentiment orientation and sentistrength of words. To evaluate the proposed method, a set of existing reviews in the online retail site is used in various domains and the present dictionary is compared with Sentiwordnet. The results show the proposed method achieves an accuracy of 80% in determining the orientation of sentiment word.


title: SAR image formation toolbox for MATLAB

abstract: While many synthetic aperture radar (SAR) image formation techniques exist, two of the most intuitive methods ::: for implementation by SAR novices are the matched filter and backprojection algorithms. The matched filter and ::: (non-optimized) backprojection algorithms are undeniably computationally complex. However, the backprojection ::: algorithm may be successfully employed for many SAR research endeavors not involving considerably large ::: data sets and not requiring time-critical image formation. Execution of both image reconstruction algorithms ::: in MATLAB is explicitly addressed. In particular, a manipulation of the backprojection imaging equations is ::: supplied to show how common MATLAB functions, ifft and interp1, may be used for straight-forward SAR ::: image formation. In addition, limits for scene size and pixel spacing are derived to aid in the selection of an ::: appropriate imaging grid to avoid aliasing. Example SAR images generated though use of the backprojection ::: algorithm are provided given four publicly available SAR datasets. Finally, MATLAB code for SAR image ::: reconstruction using the matched filter and backprojection algorithms is provided.


title: Cache augmented database management systems

abstract: Cache Augmented Database Management Systems, CADBMSs, enhance the velocity of simple operations that read and write a small amount of data from big data. They are most suitable for those applications with workloads that exhibit a high read to write ratio, e.g., interactive social networking actions. This study surveys state of the art with CADBMSs and presents physical data independence as the next step in their evolution. We detail the requirements of this evolution, technological trends and software practices, and our research efforts in this area.


title: Improved CEEMDAN and PSO-SVR Modeling for Near-Infrared Noninvasive Glucose Detection

abstract: Diabetes is a serious threat to human health. Thus, research on noninvasive blood glucose detection has become crucial locally and abroad. Near-infrared transmission spectroscopy has important applications in noninvasive glucose detection. Extracting useful information and selecting appropriate modeling methods can improve the robustness and accuracy of models for predicting blood glucose concentrations. Therefore, an improved signal reconstruction and calibration modeling method is proposed in this study. On the basis of improved complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and correlative coefficient, the sensitive intrinsic mode functions are selected to reconstruct spectroscopy signals for developing the calibration model using the support vector regression (SVR) method. The radial basis function kernel is selected for SVR, and three parameters, namely, insensitive loss coefficient , penalty parameter , and width coefficient , are identified beforehand for the corresponding model. Particle swarm optimization (PSO) is employed to optimize the simultaneous selection of the three parameters. Results of the comparison experiments using PSO-SVR and partial least squares show that the proposed signal reconstitution method is feasible and can eliminate noise in spectroscopy signals. The prediction accuracy of model using PSO-SVR method is also found to be better than that of other methods for near-infrared noninvasive glucose detection.


title: Ultra-Wideband Bandpass Filter Using Multilayer Liquid-Crystal-Polymer Technology

abstract: Novel ultra-wideband (UWB) bandpass filters are proposed based on quasi-lumped-element prototypes and implemented with multilayer liquid-crystal-polymer (LCP) technology. In this study, the broadside-coupled microstrip radial stubs and high-impedance microstrip lines are adopted as quasi-lumped elements for realizing compact UWB bandpass filters. By introducing a short-circuited high-impedance microstrip line as a shunt inductor and suitably designing quasi-lumped-element capacitors, a compact six-pole bandpass filter is implemented with the Federal Communications Commission (FCC) defined UWB specifications. To further improve the selectivity and wideband performance, an eight-pole filter of this type is developed by adding two shunt short-circuited microstrip stubs, which introduce a transmission zero at the upper passband edge. The proposed filters are fabricated using multilayer LCP technology. Good agreement between simulated and measured results of these filters are observed. The measured results show that the fabricated six-pole filter has good specifications for the FCC-defined UWB system. The fabricated eight-pole filter has an ultra-wide fractional bandwidth (139%) and a good stopband rejection level, which is higher than 38.1 dB from 10.57 to 18.0 GHz. The proposed filters are attractive for UWB communications and radar systems.


title: Adaptive Loss Minimization for Semi-Supervised Elastic Embedding

abstract: The semi-supervised learning usually only predict labels for unlabeled data appearing in training data, and cannot effectively predict labels for testing data never appearing in training set. To handle this out-of-sample problem, many inductive methods make a constraint such that the predicted label matrix should be exactly equal to a linear model. In practice, this constraint is too rigid to capture the manifold structure of data. Motivated by this deficiency, we relax the rigid linear embedding constraint and propose to use an elastic embedding constraint on the predicted label matrix such that the manifold structure can be better explored. To solve our new objective and also a more general optimization problem, we study a novel adaptive loss with efficient optimization algorithm. Our new adaptive loss minimization method takes the advantages of both L1 norm and L2 norm, and is robust to the data outlier under Laplacian distribution and can efficiently learn the normal data under Gaussian distribution. Experiments have been performed on image classification tasks and our approach outperforms other state-of-the-art methods.


title: The App Sampling Problem for App Store Mining

abstract: Many papers on App Store Mining are susceptible to the App Sampling Problem, which exists when only a subset of apps are studied, resulting in potential sampling bias. We introduce the App Sampling Problem, and study its effects on sets of user review data. We investigate the effects of sampling bias, and techniques for its amelioration in App Store Mining and Analysis, where sampling bias is often unavoidable. We mine 106,891 requests from 2,729,103 user reviews and investigate the properties of apps and reviews from 3 different partitions: the sets with fully complete review data, partially complete review data, and no review data at all. We find that app metrics such as price, rating, and download rank are significantly different between the three completeness levels. We show that correlation analysis can find trends in the data that prevail across the partitions, offering one possible approach to App Store Analysis in the presence of sampling bias.


title: The effect of glycolic acid on the treatment of acne in Asian skin.

abstract: BACKGROUND ::: Glycolic acid has become important and popular for treating acne. ::: ::: ::: OBJECTIVE ::: To evaluate the efficacy and safety of serial glycolic acid peels with glycolic acid home care products on facial acne lesions and other associated skin problems. ::: ::: ::: METHODS ::: We collected 40 Asian candidates with moderate to moderately severe acne. They were divided into two groups according to the degree of greasiness of their facial skin. The two groups' members were treated with four series of 35% and 50% glycolic acid peels, respectively. They also used 15% glycolic acid home care products during this study period. The improvement of acne as well as other associated problems were assessed by both the physicians and the patient themselves. ::: ::: ::: RESULTS ::: Significant resolution of comedones, papules, and pustules was found. The skin texture of each candidate was dramatically rejuvenated. Consistent and repetitive treatment with glycolic acid was needed for the apparent improvement of acne scars and cystic lesions. The follicular pores also became comparatively smaller. Furthermore, most of the candidates had much brighter and lighter looking skin. Only small percentage of patients (5.6%) developed side effects. ::: ::: ::: CONCLUSION ::: Glycolic acid has considerable therapeutic value for acne with minimal side effects even in Asian skin. It may be an ideal adjunctive treatment of acne.


title: Software Industry Business Models

abstract: Software companies can leverage successful firms' business and revenue models to create a competitive advantage.


title: Path Planning of Mobile Robot Based on Modification of Vector Field Histogram using Neuro-Fuzzy Algorithm

abstract: In this paper, a neuro-fuzzy algorithm has been implemented to improve the path planning of a mobile robot based on modification of vector field histogram (VFH) approach using neuro-fuzzy algorithm. A back propagation neural network has been used for detection of unknown obstacle to avoid collision. A neural network model is used to learn (off line) many critical status of obstacle topology. Fuzzy rules were applied to steer the mobile robot to the target direction. The candidate valleys generated by neural network from local sensor data in the robot would be fuzzified to select a winning valley that is nearest to the target direction. The simulation result of the proposed algorithm shows good navigation properties and ability to overcome the limitation of traditional vector field histogram approach. This work was implemented using Matlab® and Mobotsim®


title: How to Build a Highly Available System Using Consensus

abstract: Lamport showed that a replicated deterministic state machine is a general way to implement a highly available system, given a consensus algorithm that the replicas can use to agree on each input. His Paxos algorithm is the most fault-tolerant way to get consensus without real-time guarantees. Because general consensus is expensive, practical systems reserve it for emergencies and use leases (locks that time out) for most of the computing. This paper explains the general scheme for efficient highly available computing, gives a general method for understanding concurrent and fault-tolerant programs, and derives the Paxos algorithm as an example of the method.


title: Constraint Functional Logic Programming for

abstract: We describe origami programming methodology based on constraint functional logic programming. The basic operations of origami are reduced to solving systems of equations which describe the geometric properties of paper folds. We developed two software components: one that provides primitives to construct, manipulate and visualize paper folds and the other that solves the systems of equations. Using these components, we illustrate computer-supported origami construction and show the significance of the constraint functional logic programming paradigm in the program development.


title: Cheddar: a flexible real time scheduling framework

abstract: This paper describes an Ada framework called Cheddar which provides tools to check if a real time application meets its temporal constraints. The framework is based on the real time scheduling theory and is mostly written for educational purposes. With Cheddar, an application is defined by a set of processors, tasks, buffers, shared resources and messages. Cheddar provides feasibility tests in the cases of monoprocessor, multiprocessor and distributed systems. It also provides a flexible simulation engine which allows the designer to describe and run simulations of specific systems. The framework is open and has been designed to be easily connected to CASE tools such as editors, design tools, simulators, ...


title: Density and Guard Band in Migration Scenarios to Coherent Ultra-Dense WDM

abstract: In this work the maximum density is numerically investigated giving the compromise of physical impairments and spectral efficiency on coherent ultra-dense wavelength division multiplexing passive optical networks (UDWDM-PON). The required guard band to legacy 1.25-2.5 Gb/s intensity modulation direct detection (IMDD) systems specified closely to Gigabit-capable PON equipments is also analyzed in order to mitigate linear and nonlinear crosstalk. Using 3 GHz frequency grid guaranteed the best performance of the system (32x1.25 Gb/s quadrature phase-shift keying with coherent detection) with respect to the 10% Error Vector Magnitude limit; 12.5 GHz guard band to legacy IMDD system was sufficient to minimize strong linear and nonlinear crosstalk (mostly four-wave mixing) on both systems.


title: Poverty and the Developing Brain: Insights from Neuroimaging

abstract: Poverty is a significant social problem, affecting how individuals live and the resources available to them. For children, poverty represents a chronically suboptimal developmental environment as much as it reflects a state of economic stress. Neuroimaging techniques permit the non-invasive investigation of human brain development in adverse conditions such as poverty. Brain imaging has contributed to the understanding of economic disparity by identifying changes in the brain’s structure and function associated with poverty. Noninvasive imaging of the developing human brain enables the testing of hypotheses about links between economic disadvantage and neural development. Due to the plasticity of neural pathways, some of the effects of poverty on the brain may be reversible. Empirical research investigating the neural correlates of income disparity will continue to enable the design of targeted interventions to prevent and ameliorate the effects of


title: Attentive Semantic Role Labeling with Boundary Indicator

abstract: The goal of semantic role labeling (SRL) is to discover the predicate-argument structure of a sentence, which plays a critical role in deep processing of natural language. This paper introduces simple yet effective auxiliary tags for dependency-based SRL to enhance a syntax-agnostic model with multi-hop self-attention. Our syntax-agnostic model achieves competitive performance with state-of-the-art models on the CoNLL-2009 benchmarks both for English and Chinese.


title: Comparison between horn and bow-tie antennas for Ground Penetrating Radar

abstract: Many different antennas are proposed and tested for GPR, but bow-tie or horn antennas are doubtless the most used. Their simple and robust design makes them effective in most applications. The aim of this paper is a direct comparison between a horn and a bow-tie antenna, used in the same operative conditions, i.e. in the same band, with the same transceiver and on the same ground. Although this is a preliminary study based on a very limited set of experimental data, horn antenna appears to have advantages with respect to bow-tie antenna in GPR applications.


title: The case for dumb requirements engineering tools

abstract: [Context and Motivation] This paper notes the advanced state of the natural language (NL) processing art and considers four broad categories of tools for processing NL requirements documents. These tools are used in a variety of scenarios. The strength of a tool for a NL processing task is measured by its recall and precision. [Question/Problem] In some scenarios, for some tasks, any tool with less than 100% recall is not helpful and the user may be better off doing the task entirely manually. [Principal Ideas/Results] The paper suggests that perhaps a dumb tool doing an identifiable part of such a task may be better than an intelligent tool trying but failing in unidentifiable ways to do the entire task. [Contribution] Perhaps a new direction is needed in research for RE tools.


title: Development and experimental verification of a hybrid vertical take-off and landing (VTOL) unmanned aerial vehicle(UAV)

abstract: In this paper, we present the design and verification of a hybrid vertical takeoff and landing (VTOL) unmanned aerial vehicles (UAV) of the type named dualsystem or extra propulsion VTOL UAV. This paper features the comprehensive system development of such VTOL UAVs from all aspects, including the aircraft design and implementation, onboard devices integration, ground station support, and long distance communication. We proceed with aerodynamic analysis, mechanical design, and controller development. Finally, we verify by experiment that this hybrid VTOL UAV has the desired aerodynamic performance, flight stability, endurance and range. In addition, with the designed flight controller, the VTOL UAV can achieve full autonomous flight in a real outdoor environment. It serves a good platform for future research, such as vision-based precise landing, motion planing and quick 3D mapping, as well as service applications, such as medicine delivery.


title: A 6.66-kHz, 940-nW, 56ppm/°C, fully on-chip PVT variation tolerant CMOS relaxation oscillator

abstract: A fully on-chip CMOS relaxation oscillator (ROSC) with a PVT variation compensation circuit is proposed in this paper. The circuit is based on a conventional ROSC and has a distinctive feature in the compensation circuit that compensates for comparator's non-idealities caused by offset voltage and delay time. We also developed a bias circuit consisting of positive and negative temperature coefficient resistors to obtain the temperature compensated clock frequency. Measurement results demonstrated that the circuit can generate a stable clock frequency of 6.66 kHz. The power dissipation was 940 nW. The measured line regulation and temperature coefficient were 0.98%/V and 56ppm/°C, respectively.


title: Temperature Rise Estimation of Induction Motor Drives Based on Loadability Curves to Facilitate Design of Electric Powertrains

abstract: Thermal protection limits are equally important as mechanical specifications when designing electric drivetrains. However, properties of motor drives like mass/length of copper winding or heat dissipation factor are not available in producers’ catalogs. The lack of this essential data prevents the effective selection of drivetrain components and makes it necessary to consult critical design decisions with equipment's suppliers. Therefore, in this paper, the popular loadability curves that are available in catalogs become a basis to formulate a method that allows to estimate temperature rise of motor drives. The current technique allows for evaluating a temperature rise of a motor drive for any overload magnitude, duty cycle, and ambient temperature, contrary to using a discrete set of permissible overload conditions that are provided by manufacturers. The proposed approach is based on industrially adopted practices, greatly improves flexibility of a design process, and facilitates communication in a supplier–customer dialog.


title: Operating an unknown drawer using an aerial manipulator

abstract: This paper is about opening and closing an unknown drawer using an aerial manipulator. To accommodate practical applications, it is assumed that the direction of motion and mechanical properties of the drawer are not given beforehand. A multirotor combined with a robotic arm is used for the manipulation task. Typical drawers are allowed to move in only one direction, which constrains the motion of the aerial manipulator while operating a drawer. To analyze this interaction, the dynamic characteristics of the aerial manipulator are modeled. Also, configuration of the aerial manipulator for exerting the desired force to a drawer is presented. To handle the uncertainties associated with the mechanism of a drawer, strategies exploiting velocity of the end effector are employed. The proposed approach is validated with experiments including opening and closing a common drawer, which is detected by a camera mounted in the palm of the end effector.


title: 2-Dimensional singular value decomposition for 2D maps and images

abstract: For a set of 1D vectors, standard singular value decomposition (SVD) is frequently applied. For a set of 2D objects such as images or weather maps, we form 2dSVD, which computes principal eigenvectors of rowrow and column-column covariance matrices, exactly as in the standard SVD. We study optimality properties of 2dSVD as low-rank approximation and show that it provides a framework unifying two recent approaches. Experiments on images and weather maps illustrate the usefulness of 2dSVD.


title: Flight demonstrations of unmanned aerial vehicle swarming concepts

abstract: A has been engaged in a number of independent research and development PL has been engaged in a number of independent research and development projects over the past 5 years intended to demonstrate the cooperative behaviors of swarms of small, autonomous unmanned aerial vehicles (UAVs). Swarm members cooperate to accomplish complex mission goals with no human in the loop. These projects represent a variety of approaches to UAV swarming, including teaming, consensus variables, and stigmergic potential fields. A series of experiments was conducted from 2001 through 2005 to demonstrate these concepts, and research in this area is ongoing. As a result of these efforts, APL has developed autonomy frameworks, hardware architectures, and communications concepts that are applicable across a broad range of small, autonomous aerial vehicles.


discovRE: Efficient Cross-Architecture Identification of Bugs in Binary Code


title: Teaching English Idioms as Metaphors through Cognitive-Oriented Methods: A Case in an EFL Writing Class

abstract: Idioms have long been regarded as problematic for L2 learners due to the arbitrariness of their meanings and forms. Traditional methods of teaching idioms focus on rote learning and memorization. Recent developments in cognitive linguistics research have considered idioms as analyzable expressions which are motivated by conceptual metaphors and whose meanings can derive from associations between source and target concepts. Believed to be imageable and comprehensive, idioms should be learned through the process of raising L2 learners’ awareness of conceptual metaphors behind these expressions. Nevertheless, these methods fail to notice culture entailments embedded in conceptual metaphors. Especially for FL learners who share neither common cultural background nor living environment with the target language users, difficulties resulting from transferability between L1 and L2 idioms owing to cultural similarities and differences may be serious. To bridge the gap between idioms and conceptual metaphors caused by cross-cultural differences, this study suggests a teaching method by incorporating the idea of metaphoric mappings. A case study was conducted in an EFL writing class to investigate the effect of the methods. Essays written by the students before and after the instruction were analyzed. Results showed that the students increased frequencies of using not only common idiomatic expressions but also creative analogies comprising vivid images based on the conceptual metaphors taught. Additionally, they used L2 expressions whose conceptual metaphors also existed in L1 more often then expressions whose conceptual metaphors were exclusive in L2. Such findings affirm the importance of culture and provide valuable insight to EFL teachers in adopting cognitive-oriented method to teach English idioms.


title: Diagnosis of heart disease patients using fuzzy classification technique

abstract: Data mining technique in the history of medical data found with enormous investigations found that the prediction of heart disease is very important in medical science. In medical history it is observed that the unstructured data as heterogeneous data and it is observed that the data formed with different attributes should be analyzed to predict and provide information for making diagnosis of a heart patient. Various techniques in Data Mining have been applied to predict the heart disease patients. But, the uncertainty in data was not removed with the techniques available in data mining and implemented by various authors. To remove uncertainty of unstructured data, an attempt was made by introducing fuzziness in the measured data. A membership function was designed and incorporated with the measured value to remove uncertainty and fuzzified data was used to predict the heart disease patients.. Further, an attempt was made to classify the patients based on the attributes collected from medical field. Minimum Euclidean distance Fuzzy K-NN classifier was designed to classify the training and testing data belonging to different classes. It was found that Fuzzy K-NN classifier suits well as compared with other classifiers of parametric techniques.


Measuring religious constructs: A hierarchical approach to construct organization and scale selection.


Systematic pattern selection using pattern language grammars and design space analysis


title: A practical distortion correcting method from fisheye image to perspective projection image

abstract: Fisheye camera is finding increasing number of applications in robotic vision, visual surveillance, and other fields. But the use of super-wide angle fish-eye lenses causes strong distortions in the resulting images. A method for the correction of fisheye distortion using only single images and linearity of imaged objects is presented. Spherical projection is a simple and effective method to transform a fisheye image to a perspective projection image. To use this method, the optic center of the fisheye image and the radius of the projecting sphere must be known in advance. The proposed algorithm is based on ellipse fitting. It requires only the possibility of the extraction of distorted image points from straight lines in the 3D scene. The mathematical model is introduced for the transformation. The method for finding out the optic center and the radius of the projection sphere is discussed. Contrary to most former algorithms, the algorithm discussed here does not depend on information about the real world coordinates of matching points. The correctness and effectiveness of the proposed method are demonstrated with real image.


Fake News Stance Detection


title: EFFECTS OF VIRTUAL REALITY-ENHANCED EXERCISE EQUIPMENT ON ADHERENCE AND EXERCISE-INDUCED FEELING STATES

abstract: A field study was conducted to test the effectiveness of virtual reality-enhanced cardiovascular exercise equipment for increasing adherence and attendance in a mixed-sex adult sample. Attendance was significantly higher in the virtual reality-enhanced condition than in the conditions without virtual reality over the 14-wk. period. Adherence was also highest (83.33%) in the virtual-reality bicycle group. Postexercise feelings of positive engagement, revitalization, tranquility, and physical exhaustion, as measured by the Exercise-induced Feeling Inventory, did not differ among groups. Contrary to previous findings, Self-motivation Inventory scores were not associated with either attendance or adherence. While findings suggest that virtual-reality features may promote exercise adherence or attendance, it is not yet known what psychological variables they affect. Implications were drawn regarding the practical possibilities for exercise promotion.


title: Multilingual OCR (MOCR): An Approach to Classify Words to Languages

abstract: are immense efforts to design a complete OCR for most of the world's leading languages, however, multilingual documents either of handwritten or of printed form. As a united attempt, Unicode based OCRs were studied mostly with some positive outcomes, despite the fact that a large character set slows down the recognition significantly. In this paper, we come out with a method to classify words to a language as the word segmentation is complete. For the purpose, we identified the characteristics of writings of several languages and utilized projecting method combined with some other feature extraction methods. In addition, this paper intends a modified statistical approach to correct the skewness before processing a segmented document. The proposed procedure, evaluated for a collection of both handwritten and printed documents, came with excellent outcomes in assigning words to languages.


title: Disease Mechanisms in ALS: Misfolded SOD1 Transferred Through Exosome-Dependent and Exosome-Independent Pathways

abstract: Amyotrophic lateral sclerosis (ALS) is a fatal adult-onset neuromuscular degenerative disorder with a poorly defined etiology. ALS patients experience motor weakness, which starts focally and spreads throughout the nervous system, culminating in paralysis and death within a few years of diagnosis. While the vast majority of clinical ALS is sporadic with no known cause, mutations in human copper-zinc superoxide dismutase 1 (SOD1) cause about 20 % of inherited cases of ALS. ALS with SOD1 mutations is caused by a toxic gain of function associated with the propensity of mutant SOD1 to misfold, presenting a non-native structure. The mechanisms responsible for the progressive spreading of ALS pathology have been the focus of intense study. We have shown that misfolded SOD1 protein can seed misfolding and aggregation of endogenous wild-type SOD1 similar to amyloid-β and prion protein seeding. Our recent observations demonstrate a transfer of the misfolded SOD1 species from cell to cell, modeling the intercellular transmission of disease through the neuroaxis. We have shown that both mutant and misfolded wild-type SOD1 can traverse cell-to-cell, either as protein aggregates that are released from dying cells and taken up by neighboring cells via macropinocytosis, or in association with vesicles which are released into the extracellular environment. Furthermore, once misfolding of wild-type SOD1 has been initiated in a human cell culture, it can induce misfolding in naive cell cultures over multiple passages of media transfer long after the initial misfolding template is degraded. Herein we review the data on mechanisms of intercellular transmission of misfolded SOD1.


title: Histogram of oriented gradients for human detection in video

abstract: Currently, Computer Vision (CV) is one of the most popular research topics in the world. This is because it can support the human daily life. Moreover, CV can also apply to various theories and researches. Human Detection is one of the most popular research topics in Computer Vision. In this paper, we present a study of technique for human detection from video, which is the Histograms of Oriented Gradients or HOG by developing a piece of application to import and detect the human from the video. We use the HOG Algorithm to analyze every frame from the video to find and count people. After analyzing video from starting to the end, the program generate histogram to show the number of detected people versus playing period of the video. As a result, the expected results are obtained, including the detection of people in the video and the histogram generation to show the appearance of human detected in the video file.


title: Ultrasonic navigation based blind aid for the visually impaired

abstract: Ultrasonic navigation based blind aid is built to guide the visually impaired and blind to walk easily in urban areas. The proposed system operates in two modes namely hurdle detection mode and fixed mode. In the hurdle detection mode, we have used ultrasonic sensor and water sensor to avoid obstacles using arduino. In this mode, the system detects solid and liquid obstacle sending respective instruction to the blind person through voice message via Bluetooth. The fixed mode provides the information and guidance to move from one place to another safely by setting a fixed route in blind stick from source to destination location. In addition to the above features, the system provides instructions to the blind to migrate to various places using the GPS system. An android device application is used to send messages via Bluetooth. The proposed system provides complete guidance and protection to a blind person under various circumstances.


title: How it works: a field study of non-technical users interacting with an intelligent system

abstract: In order to develop intelligent systems that attain the trust of their users, it is important to understand how users perceive such systems and develop those perceptions over time. We present an investigation into how users come to understand an intelligent system as they use it in their daily work. During a six-week field study, we interviewed eight office workers regarding the operation of a system that predicted their managers' interruptibility, comparing their mental models to the actual system model. Our results show that by the end of the study, participants were able to discount some of their initial misconceptions about what information the system used for reasoning about interruptibility. However, the overarching structures of their mental models stayed relatively stable over the course of the study. Lastly, we found that participants were able to give lay descriptions attributing simple machine learning concepts to the system despite their lack of technical knowledge. Our findings suggest an appropriate level of feedback for user interfaces of intelligent systems, provide a baseline level of complexity for user understanding, and highlight the challenges of making users aware of sensed inputs for such systems.


Dell fourth-quarter revenue, earnings will be lower than expected


title: A compact filtering power divider based on SIW triangular cavities

abstract: A compact substrate-integrated waveguide (SIW) filtering power divider with high isolation is presented in this paper based on SIW triangular cavities. An isolation resistance is exploited to achieve high isolation between two output ports. The design parameters can be realized by determining proper offset positions of the feeding ports and coupling windows, which can be fixed by the synthesized Qe and M ij . By using three triangular cavities as the basic elements, a filtering power divider is designed and fabricated on a single-layer substrate with the standard printed circuit boards (PCB) process. This filtering power divider operates at the center frequency of 11.4 GHz with the bandwidths of 0.75 GHz. There are good agreements in the simulated and measured results.


title: Multi-level Lasso for Sparse Multi-task Regression

abstract: We present a flexible formulation for variable selection in multi-task regression to allow for discrepancies in the estimated sparsity patterns accross the multiple tasks, while leveraging the common structure among them. Our approach is based on an intuitive decomposition of the regression coe_cients into a product between a component that is common to all tasks and another component that captures task-specificity. This decomposition yields the Multi-level Lasso objective that can be solved efficiently via alternating optimization. The analysis of the "orthonormal design" case reveals some interesting insights on the nature of the shrinkage performed by our method, compared to that of related work. Theoretical guarantees are provided on the consistency of Multi-level Lasso. Simulations and empirical study of micro-array data further demonstrate the value of our framework.


Change Impact Analysis in Agile Development


title: Queuing Theory Accurately Models the Need for Critical Care Resources

abstract: BACKGROUND ::: Allocation of scarce resources presents an increasing challenge to hospital administrators and health policy makers. Intensive care units can present bottlenecks within busy hospitals, but their expansion is costly and difficult to gauge. Although mathematical tools have been suggested for determining the proper number of intensive care beds necessary to serve a given demand, the performance of such models has not been prospectively evaluated over significant periods. ::: ::: ::: METHODS ::: The authors prospectively collected 2 years' admission, discharge, and turn-away data in a busy, urban intensive care unit. Using queuing theory, they then constructed a mathematical model of patient flow, compared predictions from the model to observed performance of the unit, and explored the sensitivity of the model to changes in unit size. ::: ::: ::: RESULTS ::: The queuing model proved to be very accurate, with predicted admission turn-away rates correlating highly with those actually observed (correlation coefficient = 0.89). The model was useful in predicting both monthly responsiveness to changing demand (mean monthly difference between observed and predicted values, 0.4+/-2.3%; range, 0-13%) and the overall 2-yr turn-away rate for the unit (21%vs. 22%). Both in practice and in simulation, turn-away rates increased exponentially when utilization exceeded 80-85%. Sensitivity analysis using the model revealed rapid and severe degradation of system performance with even the small changes in bed availability that might result from sudden staffing shortages or admission of patients with very long stays. ::: ::: ::: CONCLUSIONS ::: The stochastic nature of patient flow may falsely lead health planners to underestimate resource needs in busy intensive care units. Although the nature of arrivals for intensive care deserves further study, when demand is random, queuing theory provides an accurate means of determining the appropriate supply of beds.


title: Computer-Based Laboratory for Data Communications and Computer Networking

abstract: ANDRIKOPOULOS I, 1999, IEEE COMMUNICATIONS; COMER DE, 2002, HANDS ON NETWORKING; El-Kharashi MW, 2002, IEEE T EDUC, V45, P276, DOI 10.1109-TE.2002.1024621; GRODZINSKY F, 1999, NETWORKING DATA COMM; HAVILAND K, 1999, UNIX SYSTEM PROGRAMM; KATZELA I, 1999, MODELING SIMULATING; *PHP DEV TEAM, PHP MAN; ROMKEY J, 1055 RFC; Schneier Bruce, 1994, APPL CRYPTOGRAPHY PR; SCOTT R, REV NEWDES; Stallings W, 2000, DATA COMPUTER COMMUN; Tanenbaum A. S., 1996, COMPUTER NETWORKS; WILLIAMS R, PAINLESS GUIDE CRC E; ZIMMERMANN H, 1980, IEEE T COMMUN, V28, P425, DOI 10.1109-TCOM.1980.1094702


title: A dynamic risk-based access control architecture for cloud computing

abstract: Cloud computing is a distributed computing model that still faces problems. New ideas emerge to take advantage of its features and among the research challenges found in the cloud, we can highlight Identity and Access Management. The main problems of the application of access control in the cloud are the necessary flexibility and scalability to support a large number of users and resources in a dynamic and heterogeneous environment, with collaboration and information sharing needs. This paper proposes the use of risk-based dynamic access control for cloud computing. The proposal is presented as an access control model based on an extension of the XACML standard with three new components: the Risk Engine, the Risk Quantification Web Services and the Risk Policies. The risk policies present a method to describe risk metrics and their quantification, using local or remote functions. The risk policies allow users and cloud service providers to define how to handle risk-based access control for their resources, using different quantification and aggregation methods. The model reaches the access decision based on a combination of XACML decisions and risk analysis. A prototype of the model is implemented, showing it has enough expressivity to describe the models of related work. In the experimental results, the prototype takes between 2 and 6 milliseconds to reach access decisions using a risk policy. A discussion on the security aspects of the model is also presented.


Gestational Gigantomastia Complicating Pregnancy: A Case Report and Review of the Literature


title: Cybersecurity Attacks in Vehicle-to-Infrastructure (V2I) Applications and their Prevention

abstract: A connected vehicle (CV) environment is composed of a diverse data collection, data communication and dissemination, and computing infrastructure systems that are vulnerable to the same cyberattacks as all traditional computing environments. Cyberattacks can jeopardize the expected safety, mobility, energy, and environmental benefits from connected vehicle applications. As cyberattacks can lead to severe traffic incidents, it has become one of the primary concerns in connected vehicle applications. In this paper, we investigate the impact of cyberattacks on the vehicle-to-infrastructure (V2I) network from a V2I application point of view. Then, we develop a novel V2I cybersecurity architecture, named CVGuard, which can detect and prevent cyberattacks on the V2I environment. In designing CVGuard, key challenges, such as scalability, resiliency and future usability were considered. A case study using a distributed denial of service (DDoS) on a V2I application, i.e., the Stop Sign Gap Assist (SSGA) application, shows that CVGuard was effective in mitigating the adverse effects created by a DDoS attack. In our case study, because of the DDoS attack, conflicts between the minor and major road vehicles occurred in an unsignalized intersection, which could have caused potential crashes. A reduction of conflicts between vehicles occurred because CVGuard was in operation. The reduction of conflicts was compared based on the number of conflicts before and after the implementation and operation of the CVGuard security platform. Analysis revealed that the strategies adopted by the CVGuard were successful in reducing the inter-vehicle conflicts by 60% where a DDoS attack compromised the SSGA application at an unsignalized intersection.


title: The speaker-independent lipreading play-off; a survey of lipreading machines

abstract: Lipreading is a difficult gesture classification task. One problem in computer lipreading is speaker-independence. Speaker-independence means to achieve the same accuracy on test speakers not included in the training set as speakers within the training set. Current literature is limited on speaker-independent lipreading, the few independent test speaker accuracy scores are usually aggregated within dependent test speaker accuracies for an averaged performance. This leads to unclear independent results. Here we undertake a systematic survey of experiments with the TCD-TIMIT dataset using both conventional approaches and deep learning methods to provide a series of wholly speaker-independent benchmarks and show that the best speaker-independent machine scores 69.58% accuracy with CNN features and an SVM classifier. This is less than state-of-the-art speaker-dependent lipreading machines, but greater than previously reported in independence experiments.


title: Uncertainty Quantification in the Classification of High Dimensional Data

abstract: Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty. The methods are all based around the graph formulation of semi-supervised learning. We provide a unified framework which brings together a variety of methods which have been introduced in different communities within the mathematical sciences. We study probit classification, generalize the level-set method for Bayesian inverse problems to the classification setting, and generalize the Ginzburg-Landau optimization-based classifier to a Bayesian setting; we also show that the probit and level set approaches are natural relaxations of the harmonic function approach. We introduce efficient numerical methods, suited to large data-sets, for both MCMC-based sampling as well as gradient-based MAP estimation. Through numerical experiments we study classification accuracy and uncertainty quantification for our models; these experiments showcase a suite of datasets commonly used to evaluate graph-based semi-supervised learning algorithms.


title: Brain-computer interface-based robotic end effector system for wrist and hand rehabilitation: results of a three-armed randomized controlled trial for chronic stroke

abstract: The objective of this study was to investigate the efficacy of an Electroencephalography (EEG)-based Motor Imagery (MI) Brain-Computer Interface (BCI) coupled with a Haptic Knob (HK) robot for arm rehabilitation in stroke patients. In this three-arm, single-blind, randomized controlled trial; 21 chronic hemiplegic stroke patients (Fugl-Meyer Motor Assessment (FMMA) score 10-50), recruited after pre-screening for MI BCI ability, were randomly allocated to BCI-HK, HK or Standard Arm Therapy (SAT) groups. All groups received 18 sessions of intervention over 6 weeks, 3 sessions per week, 90 minutes per session. The BCI-HK group received 1 hour of BCI coupled with HK intervention, and the HK group received 1 hour of HK intervention per session. Both BCI-HK and HK groups received 120 trials of robot-assisted hand grasping and knob manipulation followed by 30 minutes of therapist-assisted arm mobilization. The SAT group received 1.5 hours of therapist-assisted arm mobilization and forearm pronation-supination movements incorporating wrist control and grasp-release functions. In all, 14 males, 7 females, mean age 54.2 years, mean stroke duration 385.1 days, with baseline FMMA score 27.0 were recruited. The primary outcome measure was upper-extremity FMMA scores measured mid-intervention at week 3, end-intervention at week 6, and follow-up at weeks 12 and 24. Seven, 8 and 7 subjects underwent BCI-HK, HK and SAT interventions respectively. FMMA score improved in all groups, but no intergroup differences were found at any time points. Significantly larger motor gains were observed in the BCI-HK group compared to the SAT group at weeks 3, 12 and 24, but motor gains in the HK group did not differ from the SAT group at any time point. In conclusion, BCI-HK is effective, safe, and may have the potential for enhancing motor recovery in chronic stroke when combined with therapist-assisted arm mobilization.


title: Design and optimization of a permanent magnet axial flux wheel motors for electric vehicle

abstract: Axial flux motor with two flat PM rotors and one coreless stator is designed and manufactured for the propulsion of an electric vehicle supplied by a Fuel cell. The originality of the architecture consists in the stator winding which is manufactured as a printed circuit with thick copper lamination. The second originality in this paper consists on the use of the duality between magnetic vector potential and flux density in a magneto-static problem. It allows using standard 2D finite element code for the computation of the fluxes and electromotive forces of an axial flux PM motor. The model is then used to optimize the winding topology in order to increase the efficiency of the propulsion system of electric vehicle powered by a fuel cell.


title: Contraceptive use patterns among Spanish single youth

abstract: OBJECTIVE ::: To examine sexual and contraceptive use patterns, including method choice, among unmarried youth in Spain. ::: ::: ::: METHOD ::: The analysis is based on the 1999 Youth Survey. The analytical sample comprised 696 never-married sexually experienced women aged 15-24 and 1070 men. Logistic regression was used to assess the influence of various socio-demographic factors on contraceptive use, and multinomial logistic regression was used to examine the determinants of method choice. ::: ::: ::: RESULTS ::: Given the trend towards earlier sexual initiation and increasing marriage postponement, sexual activity prior to marriage has become the norm in Spain. Nine out of 10 sexually active single youth reported contraceptive use at their last intercourse. Differentials across social subgroups were small, with the exception of those based on educational level. Young women and men with more than one partner in the previous year and those not currently in a stable relationship were more likely to engage in unprotected intercourse. Regarding the choice of method, the analysis revealed that, although condom is the dominant method, there is a tendency to shift to the pill with increasing age and relationship stability. ::: ::: ::: CONCLUSION ::: The level of contraceptive protection among unmarried youth is reasonably high, but a nontrivial proportion of youth engage in unprotected sexual activity.


title: Design and Evaluation of Multiple Valued Logic Gates Using Pseudo N-Type Carbon Nanotube FETs

abstract: Multiple valued logic (MVL) circuits are particularly attractive for nanoscale implementation as advantages in information density and operating speed can be harvested using emerging technologies. In this paper, a new family of MVL gates is proposed for implementation using carbon nanotube field-effect transistors (CNTFETs). The proposed designs use pseudo N-type CNTFETs and no resistor is utilized for their operation. This approach exploits threshold voltage control of the P-type and N-type transistors, while ensuring correct MVL operation for both ternary and quaternary logic gates. This paper provides a detailed assessment of several figures of merit, such as static power consumption, switching power consumption, propagation delay and the power-delay product (PDP). Compared with resistor-loaded designs, the proposed pseudo-NCNTFET MVL gates show advantages in circuit area, power consumption and energy efficiency, while still incurring a comparable propagation delay. Compared to a complementary logic family, the pseudo-NCNTFET MVL logic family requires a smaller circuit area with a similar propagation delay on average, albeit with a larger PDP and static power consumption. A design methodology and a discussion of issues related to leakage and yield are also provided for the proposed MVL logic family.


title: Reducing medical claims cost to Ghana’s National Health Insurance scheme: a cross-sectional comparative assessment of the paper- and electronic-based claims reviews

abstract: A robust medical claims review system is crucial for addressing fraud and abuse and ensuring financial viability of health insurance organisations. This paper assesses claims adjustment rate of the paper- and electronic-based claims reviews of the National Health Insurance Scheme (NHIS) in Ghana. The study was a cross-sectional comparative assessment of paper- and electronic-based claims reviews of the NHIS. Medical claims of subscribers for the year, 2014 were requested from the claims directorate and analysed. Proportions of claims adjusted by the paper- and electronic-based claims reviews were determined for each type of healthcare facility. Bivariate analyses were also conducted to test for differences in claims adjustments between healthcare facility types, and between the two claims reviews. The electronic-based review made overall adjustment of 17.0% from GHS10.09 million (USD2.64 m) claims cost whilst the paper-based review adjusted 4.9% from a total of GHS57.50 million (USD15.09 m) claims cost received, and the difference was significant (p < 0.001). However, there were no significant differences in claims cost adjustment rate between healthcare facility types by the electronic-based (p = 0.0656) and by the paper-based reviews (p = 0.6484). The electronic-based review adjusted significantly higher claims cost than the paper-based claims review. Scaling up the electronic-based review to cover claims from all accredited care providers could reduce spurious claims cost to the scheme and ensure long term financial sustainability.


title: Generating Animatable 3D Virtual Humans from Photographs

abstract: We present an easy, practical and efficient full body cloningmethodology. This system utilizes photos taken from the front, side and back of a person in any given imaging envir onment without requiring a special background or a controlled illuminating condition. A seamless generic bo dy specified in the VRML H-Anim 1.1 format is used to generate an individualized virtual human. The system is c of two major components: face-cloning and body-cloning. The face-cloning component uses feature poi nts on front and side images and then applies DFFD for shape modification. Next a fully automatic seamless text ure mapping is generated for 360 o coloring on a 3D polygonal model. The body-cloning component has two step s: (i) feature points specification, which enables automatic silhouette detection in an arbitrary background(ii) two-stage body modification by using feature points and body silhouette respectively. The final integrated huma n model has photo-realistic animatable face, hands, feet and body. The result can be visualized in any VRML compli ant browser.


title: Computer-Aided Bleeding Detection in WCE Video

abstract: Wireless capsule endoscopy (WCE) can directly take digital images in the gastrointestinal tract of a patient. It has opened a new chapter in small intestine examination. However, a major problem associated with this technology is that too many images need to be manually examined by clinicians. Currently, there is no standard for capsule endoscopy image interpretation and classification. Most state-of-the-art CAD methods often suffer from poor performance, high computational cost, or multiple empirical thresholds. In this paper, a new method for rapid bleeding detection in the WCE video is proposed. We group pixels through superpixel segmentation to reduce the computational complexity while maintaining high diagnostic accuracy. Feature of each superpixel is extracted using the red ratio in RGB space and fed into support vector machine for classification. Also, the influence of edge pixels has been removed in this paper. Comparative experiments show that our algorithm is superior to the existing methods in terms of sensitivity, specificity, and accuracy.


Bayesian Mixed-Effects Models for Recommender Systems


title: A Low Complexity Data Detection Algorithm for Uplink Multiuser Massive MIMO Systems

abstract: A major challenge for uplink multiuser massive multiple-input and multiple-output (MIMO) systems is the data detection problem at the receiver due to the substantial increase in the dimensions of MIMO systems. The optimal maximum likelihood detector is impractical for such large wireless systems, because it suffers from exponential complexity in terms of the number of users. Therefore, suboptimal alternatives with reduced complexity, such as the linear minimum mean square error (LMMSE) detector, are necessary. However, the LMMSE detector still introduces high computational complexity, mainly caused by the computation of the Gram matrix and matrix inversion. To reduce the computational complexity of data detection while achieving satisfactory bit error rate (BER) performance, we initially proposed an iterative data detection algorithm that exploits the coordinate descent method (CDM)-based algorithmic framework for uplink multiuser massive MIMO systems. We then developed a reduced-complexity hardware implementation algorithm by leveraging the “one-at-a-time” update property of the CDM-based algorithmic framework. Simulation results revealed that the proposed CDM-based detector provides the same or improved BER performance than the classical LMMSE algorithm at a lower complexity for different test scenarios.


title: Recognition of traffic signs based on their colour and shape features extracted using human vision models

abstract: Colour and shape are basic characteristics of traffic signs which are used both by the driver and to develop artificial traffic sign recognition systems. However, these sign features have not been represented robustly in the earlier developed recognition systems, especially in disturbed viewing conditions. In this study, this information is represented by using a human vision colour appearance model and by further developing existing behaviour model of visions. Colour appearance model CIECAM97 has been applied to extract colour information and to segment and classify traffic signs. Whilst shape features are extracted by the development of FOSTS model, the extension of behaviour model of visions. Recognition rate is very high for signs under artificial transformations that imitate possible real world sign distortion (up to 50% for noise level, 50 m for distances to signs, and 5° for perspective disturbances) for still images. For British traffic signs (n = 98) obtained under various viewing conditions, the recognition rate is up to 95%.


title: Consistent query answers in inconsistent probabilistic databases

abstract: Efficient and effective manipulation of probabilistic data has become increasingly important recently due to many real applications that involve the data uncertainty. This is especially crucial when probabilistic data collected from different sources disagree with each other and incur inconsistencies. In order to accommodate such inconsistencies and enable consistent query answering (CQA), in this paper, we propose the all-possible-repair semantics in the context of inconsistent probabilistic databases, which formalize the repairs on the database as repair worlds via a graph representation. In turn, the CQA problem can be converted into one in the so-called repaired possible worlds (w.r.t. both repair worlds and possible worlds). We investigate a series of consistent queries in inconsistent probabilistic databases, including consistent range queries, join, and top-k queries, which, however, need to deal with an exponential number of the repaired possible worlds at high cost. To tackle the efficiency problem of CQA, in this paper, we propose efficient approaches for retrieving consistent query answers, including effective pruning methods to filter out false positives. Extensive experiments have been conducted to demonstrate the efficiency and effectiveness of our approaches.


Random subset feature selection in automatic recognition of developmental disorders, affective states, and level of conflict from speech


title: Optimal Design Method for Large-Scale YASA Machines

abstract: The objective of this paper is to develop an analytical optimal design tool to determine a megawatt-scale yokelss and segmented armature (YASA) machine design that fulfills the application requirements and constraints. This analytical tool considers both electromagnetic and structural designs. Different designs that provide similar performance will have emerged from this analytical process. A design reference map that graphically shows the relationships and tradeoffs between each objective function is introduced. A multicriteria optimization process is applied to determine a design optimum. In the optimization process, the design objectives considered in this study are to minimize the outer diameter, to minimize the structural mass of the machine, to minimize the copper and iron losses, and to minimize the active materials cost. Three variables considered in calculating the objective functions are the air-gap flux density, the ratio of outer-to-inner machine diameter, and the current loading. The optimization method uses a pseudoweight vector to provide the flexibility to prioritize one or more objective functions, dependant on the specific application requirements.


Research Issues on Uncertainty in Geographic Data and GIS-Based Analysis


title: Real time facial expression recognition in video using support vector machines

abstract: Enabling computer systems to recognize facial expressions and infer emotions from them in real time presents a challenging research topic. In this paper, we present a real time approach to emotion recognition through facial expression in live video. We employ an automatic facial feature tracker to perform face localization and feature extraction. The facial feature displacements in the video stream are used as input to a Support Vector Machine classifier. We evaluate our method in terms of recognition accuracy for a variety of interaction and classification scenarios. Our person-dependent and person-independent experiments demonstrate the effectiveness of a support vector machine and feature tracking approach to fully automatic, unobtrusive expression recognition in live video. We conclude by discussing the relevance of our work to affective and intelligent man-machine interfaces and exploring further improvements.


title: Genetic Algorithm for Mixed Integer Nonlinear Programming Problems Using Separate Constraint Approximations

abstract: This paper describes a new approach for reducing the number of the fitness and constraint function evaluations required by a genetic algorithm (GA) for optimization problems with mixed continuous and discrete design variables. The proposed additions to the GA make the search more effective and rapidly improve the fitness value from generation to generation.The additions involve memory as a function of both discrete and continuous design variables, and multivariate approximation of the individual functions' responses in terms of several continuous design variables. The approximation is demonstrated for the minimum weight design of a composite cylindrical shell with grid stiffeners.


AODM as a framework and model for characterising learner experiences with technology


Effectiveness of Adaptive Passivity-based Trajectory Tracking Control Of a Cable-suspended Robot


title: Decomposing Global Light Transport Using Time of Flight Imaging

abstract: Global light transport is composed of direct and indirect components. In this paper, we take the first steps toward analyzing light transport using the high temporal resolution information of time of flight (ToF) images. With pulsed scene illumination, the time profile at each pixel of these images separates different illumination components by their finite travel time and encodes complex interactions between the incident light and the scene geometry with spatially-varying material properties. We exploit the time profile to decompose light transport into its constituent direct, subsurface scattering, and interreflection components. We show that the time profile is well modelled using a Gaussian function for the direct and interreflection components, and a decaying exponential function for the subsurface scattering component. We use our direct, subsurface scattering, and interreflection separation algorithm for five computer vision applications: recovering projective depth maps, identifying subsurface scattering objects, measuring parameters of analytical subsurface scattering models, performing edge detection using ToF images and rendering novel images of the captured scene with adjusted amounts of subsurface scattering.


title: The volume and evolution of web page templates

abstract: Web pages contain a combination of unique content and template material, which is present across multiple pages and used primarily for formatting, navigation, and branding. We study the nature, evolution, and prevalence of these templates on the web. As part of this work, we develop new randomized algorithms for template extraction that perform approximately twenty times faster than existing approaches with similar quality. Our results show that 40--50% of the content on the web is template content. Over the last eight years, the fraction of template content has doubled, and the growth shows no sign of abating. Text, links, and total HTML bytes within templates are all growing as a fraction of total content at a rate of between 6 and 8% per year. We discuss the deleterious implications of this growth for information retrieval and ranking, classification, and link analysis.


title: Spatial Multiplexing in Fiber Optics: The 10X Scaling of Metro/Core Capacities

abstract: Optical networks have economically enabled an unabated exponential traffic growth over the past decades, with commercial wavelength-division multiplexed (WDM) systems carrying over 20 Tb/s of traffic per fiber, closely approaching the nonlinear Shannon limit. Looking at all physical dimensions available for further capacity scaling, it becomes clear that tapping into the spatial dimension is the only long-term scalable solution to overcome the looming optical network capacity crunch. As such, space-division multiplexing (SDM) is bound to become an important optical networking technology over the next decade. Integration of SDM components will be key in reducing cost, footprint, and energy consumption, while integration-induced crosstalk may be compensated through the use of digital signal processing similar to that of vectoring used in digital subscriber lines (DSL) or multiple-input-multiple-output (MIMO) techniques used in long-term evolution (LTE) wireless systems.


title: A Taxonomy of Blockchain Technologies: Principles of Identification and Classification

abstract: A comparative study across the most widely known blockchain technologies is conducted with a bottom-up approach. Blockchains are deconstructed into their building blocks. Each building block is then hierarchically classified into main and subcomponents. Then, varieties of the subcomponents are identified and compared. A taxonomy tree is used to summarise the study and provide a navigation tool across different blockchain architectural configurations.