Query: GeoDa web: enhancing web-based mapping with spatial analytics

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GeoDa: An Introduction to Spatial Data Analysis <=SEP=> This article presents an overview of GeoDa, a free software program intended to serve as a user-friendly and graphical introduction to spatial analysis for nongeographic information systems (GIS) specialists. It includes functionality ranging from simple mapping to exploratory data analysis, the visualization of global and local spatial autocorrelation, and spatial regression. A key feature of GeoDa is an interactive environment that combines maps with statistical graphics, using the technology of dynamically linked windows. A brief review of the software design is given, as well as some illustrative examples that highlight distinctive features of the program in applications dealing with public health, economic development, real estate analysis, and criminology.

Exploratory analysis of spatial and temporal data - a systematic approach <=SEP=> To open the e-book, you will require Adobe Reader software program. You can download the installer and instructions free from the Adobe Web site if you do not have Adobe Reader already installed on your computer. You might download and conserve it to your PC for later read through. Be sure to follow the download button above to download the PDF file. Review s Review s This ebook may be worth purchasing. it absolutely was writtern quite flawlessly and beneficial. I discovered this ebook from my dad and i suggested this pdf to discover.-Ma ximil ia n Wil kin so n DDS-Ma ximil ia n Wil kin so n DDS This is actually the very best publication i have read through till now. It is definitely simplistic but unexpected situations in the 50 % in the pdf. You can expect to like just how the article writer compose this pdf. A new electronic book with a new perspective. Better then never, though i am quite late in start reading this one. Your life period will be change the instant you comprehensive looking at this pdf.

How Maps Work - Representation, Visualization, and Design <=SEP=> how maps work representation visualization and design. Book lovers, when you need a new book to read, find the book here. Never worry not to find what you need. Is the how maps work representation visualization and design your needed book now? That's true; you are really a good reader. This is a perfect book that comes from great author to share with you. The book offers the best experience and lesson to take, not only take, but also learn.

Local Indicators of Spatial Association <=SEP=> The capabilities for visualization, rapid data retrieval, and manipulation in geographic information systems (GIS) have created the need for new techniques of exploratory data analysis that focus on the "spatial" aspects of the data. The identification of local patterns of spatial association is an important concern in this respect. In this paper, I outline a new general class of local indicators of spatial association (LISA) and show how they allow for the decomposition of global indicators, such as Moran's I, into the contribution of each observation. The LISA statistics serve two purposes. On one hand, they may be interpreted as indicators of local pockets of nonstationarity, or hot spots, similar to the Gi and G; statistics of Getis and Ord (1992). On the other hand, they may be used to assess the influence of individual locations on the magnitude of the global statistic and to identify "outliers," as in Anselin's Moran scatterplot (1993a). An initial evaluation of the properties of a LISA statistic is carried out for the local Moran, which is applied in a study of the spatial pattern of conflict for African countries and in a number of Monte Carlo simulations.

Detection of Influential Observation in Linear Regression <=SEP=> Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use.

Clause Restructuring for Statistical Machine Translation <=SEP=> We describe a method for incorporating syntactic information in statistical machine translation systems. The first step of the method is to parse the source language string that is being translated. The second step is to apply a series of transformations to the parse tree, effectively reordering the surface string on the source language side of the translation system. The goal of this step is to recover an underlying word order that is closer to the target language word-order than the original string. The reordering approach is applied as a pre-processing step in both the training and decoding phases of a phrase-based statistical MT system. We describe experiments on translation from German to English, showing an improvement from 25.2% Bleu score for a baseline system to 26.8% Bleu score for the system with reordering, a statistically significant improvement.

Temporal Convolutional Neural Networks for Diagnosis from Lab Tests <=SEP=> Early diagnosis of treatable diseases is essential for improving healthcare, and many diseases’ onsets are predictable from annual lab tests and their temporal trends. We introduce a multi-resolution convolutional neural network for early detection of multiple diseases from irregularly measured sparse lab values. Our novel architecture takes as input both an imputed version of the data and a binary observation matrix. For imputing the temporal sparse observations, we develop a flexible, fast to train method for differentiable multivariate kernel regression. Our experiments on data from 298K individuals over 8 years, 18 common lab measurements, and 171 diseases show that the temporal signatures learned via convolution are significantly more predictive than baselines commonly used for early disease diagnosis.

Automatic Generation of Visual-Textual Presentation Layout <=SEP=> Visual-textual presentation layout (e.g., digital magazine cover, poster, Power Point slides, and any other rich media), which combines beautiful image and overlaid readable texts, can result in an eye candy touch to attract users’ attention. The designing of visual-textual presentation layout is therefore becoming ubiquitous in both commercially printed publications and online digital magazines. However, handcrafting aesthetically compelling layouts still remains challenging for many small businesses and amateur users. This article presents a system to automatically generate visual-textual presentation layouts by investigating a set of aesthetic design principles, through which an average user can easily create visually appealing layouts. The system is attributed with a set of topic-dependent layout templates and a computational framework integrating high-level aesthetic principles (in a top-down manner) and low-level image features (in a bottom-up manner). The layout templates, designed with prior knowledge from domain experts, define spatial layouts, semantic colors, harmonic color models, and font emotion and size constraints. We formulate the typography as an energy optimization problem by minimizing the cost of text intrusion, the utility of visual space, and the mismatch of information importance in perception and semantics, constrained by the automatically selected template and further preserving color harmonization. We demonstrate that our designs achieve the best reading experience compared with the reimplementation of parts of existing state-of-the-art designs through a series of user studies.

Disability studies as a source of critical inquiry for the field of assistive technology <=SEP=> Disability studies and assistive technology are two related fields that have long shared common goals - understanding the experience of disability and identifying and addressing relevant issues. Despite these common goals, there are some important differences in what professionals in these fields consider problems, perhaps related to the lack of connection between the fields. To help bridge this gap, we review some of the key literature in disability studies. We present case studies of two research projects in assistive technology and discuss how the field of disability studies influenced that work, led us to identify new or different problems relevant to the field of assistive technology, and helped us to think in new ways about the research process and its impact on the experiences of individuals who live with disability. We also discuss how the field of disability studies has influenced our teaching and highlight some of the key publications and publication venues from which our community may want to draw more deeply in the future.

Innovation Engines: Automated Creativity and Improved Stochastic Optimization via Deep Learning <=SEP=> The Achilles Heel of stochastic optimization algorithms is getting trapped on local optima. Novelty Search avoids this problem by encouraging a search in all interesting directions. That occurs by replacing a performance objective with a reward for novel behaviors, as defined by a human-crafted, and often simple, behavioral distance function. While Novelty Search is a major conceptual breakthrough and outperforms traditional stochastic optimization on certain problems, it is not clear how to apply it to challenging, high-dimensional problems where specifying a useful behavioral distance function is difficult. For example, in the space of images, how do you encourage novelty to produce hawks and heroes instead of endless pixel static? Here we propose a new algorithm, the Innovation Engine, that builds on Novelty Search by replacing the human-crafted behavioral distance with a Deep Neural Network (DNN) that can recognize interesting differences between phenotypes. The key insight is that DNNs can recognize similarities and differences between phenotypes at an abstract level, wherein novelty means interesting novelty. For example, a novelty pressure in image space does not explore in the low-level pixel space, but instead creates a pressure to create new types of images (e.g. churches, mosques, obelisks, etc.). Here we describe the long-term vision for the Innovation Engine algorithm, which involves many technical challenges that remain to be solved. We then implement a simplified version of the algorithm that enables us to explore some of the algorithm's key motivations. Our initial results, in the domain of images, suggest that Innovation Engines could ultimately automate the production of endless streams of interesting solutions in any domain: e.g. producing intelligent software, robot controllers, optimized physical components, and art.

Being Bayesian About Network Structure. A Bayesian Approach to Structure Discovery in Bayesian Networks <=SEP=> In many multivariate domains, we are interested in analyzing the dependency structure of the underlying distribution, e.g., whether two variables are in direct interaction. We can represent dependency structures using Bayesian network models. To analyze a given data set, Bayesian model selection attempts to find the most likely (MAP) model, and uses its structure to answer these questions. However, when the amount of available data is modest, there might be many models that have non-negligible posterior. Thus, we want compute the Bayesian posterior of a feature, i.e., the total posterior probability of all models that contain it. In this paper, we propose a new approach for this task. We first show how to efficiently compute a sum over the exponential number of networks that are consistent with a fixed order over network variables. This allows us to compute, for a given order, both the marginal probability of the data and the posterior of a feature. We then use this result as the basis for an algorithm that approximates the Bayesian posterior of a feature. Our approach uses a Markov Chain Monte Carlo (MCMC) method, but over orders rather than over network structures. The space of orders is smaller and more regular than the space of structures, and has much a smoother posterior “landscape”. We present empirical results on synthetic and real-life datasets that compare our approach to full model averaging (when possible), to MCMC over network structures, and to a non-Bayesian bootstrap approach.

Design Thinking Methods and Tools for Innovation in Multidisciplinary Teams <=SEP=> Design Thinking (DT), a human-centered approach to innovation, is regarded as a system of overlapping spaces of viability, desirability and feasibility. Innovation increases when these three perspectives are addressed. This position paper proposes DT methods and tools to foster innovation in a multidisciplinary team by facilitating decision-making processes. We discuss how DT methods and tools reflect one or more DT perspectives, namely, the human, business and technology perspectives. We also discuss how these DT methods and tools can support decision-making processes, collaboration and engagement in a multidisciplinary team. Author

Exfiltration of information from air-gapped machines using monitor's LED indicator <=SEP=> In this paper we present a new approach for data exfiltration by leaking data from monitor's LED to Smartphone's camera. The new approach may be used by attackers to leak valuable information from the organization as part of an Advanced Persistent Threat (APT). The proof of concept that was developed is described in the paper followed by a description of an experiment that demonstrates that practically people are not aware of the attack. We propose ways that will facilitate the detection of such threats and some possible countermeasures.

Unsupervised Learning without Overfitting: Empirical Risk Approximation as an Induction Principle for Reliable Clustering <=SEP=> Unsupervised learning algorithms are designed to extract structure from data s amples on the basis of a cost function for structures. For a reliable and robust inference proce ss, th unsupervised learning algorithm has to guarantee that the extracted structures ar e typical for the data source. In particular, it has to reject all structures where the infe renc is dominated by the arbitrariness of the sample noise and which, consequently, can be characterize d as overfitting in unsupervised learning. This paper summarizes an inference principle called Empirical Risk Approximationwhich allows us to quantitatively measure the overfitting effect and to derive a criterion as a saveguard against it. The crucial condition for learning is me t if (i) the empirical risk of learning uniformly converges towards the expected risk and if ( ii) the hypothesis class retains a minimal variety for consistent inference. Parameter s l ction of learnable data structures is demonstrated for the case of k-means clustering and Monte Carlo simulations are presented to support the selection principle. 1 PHILOSOPHY OF UNSUPERVISED LEARNING Learning algorithms are designed to extract structure from data. Two class es of algorithms have been widely discussed in the literature – supervised andunsupervised learning. The distinction between the two classes depends on supervision or teacher informati on which is either available to the learning algorithm or missing in the learning process. T hi paper describes a statistical learning theory of unsupervised learning. We summarize an induct ion principle for unsupervised learning which is refered to as Empirical Risk Approximation[1]. This principle is based on the optimization of a quality functional for structures in data and, most i portantly, it contains a safeguard against overfitting of structure to the noise in a sampl e set. The extracted structure of the data is encoded by a loss function and it is assumed to produce a lear ning risk below a predefined risk threshold. This induction principle is summarized by the fol lowing two inference steps: 1. Define a hypothesis class containing loss functions which evaluate candidate structures of the data and which measure their quality. Control the complexity of the hypothesis class by an upper bound on the costs. 2. Select an arbitrary loss function from the smallest subset which still guarantees c onsistent learning (in the sense of Statistical Learning Theory). The reader should note that the learning algorithm has to return a structure with c ostsbounded by a preselected cost threshold, but it is not required to return the structure w ith minimal empirical risk as in Vapnik’s “Empirical Risk Minimization” induction principle for classi fication and regression [2]. All structures in the data with risk smaller than the sel cted risk bound are considered to be equivalent in the approximation process without further distinction. Various cases of unsupervised learning algorithms are discussed in the literature wi h mphasis on the optimization aspect, i.e., data clustering or vector quantization, self-organi zing maps, principal or independent component analysis and principal curves, projection pursuit or algorithms for relational data as multidimensional scaling and pairwise clustering and histogram based clustering methods. How can we select a loss function from a hypothesis class with desired approxim ation quality or, equivalently, find a structure with bounded costs? The nested sequence of hypothesis classe suggests a tracking strategy of solutions which are elements of smaller and sm ller hypothesis classes in that sequence. The sequential sampling can be implemented by soluti on tracking, i.e., admissible solutions are incrementally improved by gradient descent if t he process samples from a subset with smaller risk bound than in the previous sampling step. Candidate procedure for the sampling of structures from the hypothesis class are stochastic search t chniques or continuation methods like simulated or deterministic annealing, although the theory does not refer to any particular learning algorithm. The general framework of Empirical Risk Approximationand its mathematical formulation is presented in Sec. 2, whereas a more detailed discussion can be found in [1]. Thi s theory of unsupervised learning is applied to the case of central clustering in Sec. 3. 2 MATHEMATICAL FRAMEWORK FOR UNSUPERVISED LEARNING The data samples X = xi 2 : IRd; 1 i l which have to be analysed by the unsupervised learning algorithm are elements of a suitable d-dimensional Euclidian space. The data are distributed according to a measure which is assumed to be known for the analysis. A mathematically precise statement of the Empirical Risk Approximationinduction principle requires several definitions which formalize the notion of searching for struct ure in the data. The quality of structures extracted from the data set X is evaluated by a learning cost function R̂( ;X ) = 1l l Xi=1 h(xi; ): (1) R̂( ;X ) denotes theempirical risk of learning a structure for an i.i.d. sample set X . The functionh(x; ) is known asloss function in statistics. It measures the costs for processing a generic datumx and often corresponds to the negative log-likelihood of a stochastic model. For example in vector quantization, the loss function quantifies what the costs are t o ssign a data pointx to a particular prototype or codebook vector. Each value 2 parametrizes an individual loss functions with denoting the parameter set. The parameter characterizes the different structures of the data set which are hypothetically considered candida te structures in the learning process and which have to be validated. Statistical learning t heory distinguishes between different classes of loss functions, i.e., 0 1 functions in classification problems, bounded functions and unbounded non-negative functions in regression problems. This paper is concerned with the class of unbounded non-negative functions since we are particularly interested in polynomially increasing loss function ( O kxkp)) as they occur in vector quantization. Note that the quality measurê R( ;X ) depends on the i.i.d. data set X and, therefore, is itself a random variable. The relevant quality measure for unsupervised learning, howev er, is the expectation value of this random variable, known as the expected risk of learning R( ) = Z h(x; ) d (x): (2) While minima ofR̂( ;X ) or solutions with bounded empirical risk are influenced by fluctuations in the samples, it is the expected risk R( ) which completely assesses the quality of learning results in a sound probabilistic fashion. The distribution is assumed to be known in the following analysis and it has to decay sufficiently fast such that all rth moments ( r > 2) of the loss functionh(x; ) are bounded byE fjh(x; ) R( )jrg r! r 2V fh(x; )g; 8 2 . E f:g andV f:g denote expectation and variance of a random variable, respectively. is a distribution dependent constant. The moment constraint on the distribution h lds for all distributions with exponentially fast decaying tails which include all distri butions with finite support. Empirical Risk Approximationis an induction principle which requires the learning algorithm to sample from the smallest consistently learnable subset of the hypothesis cla s . In the following, the hypothesis class H contains all loss functions h(x; ); 2 and the subsets of risk bounded loss functions HR are defined as HR = fh(x; ) : 2 ^ R( ) R g : (3) The subsetsHR are obviously nested since HR 1 HR 2 H forR 1 R 2 1 andH = limR !1HR . R induces a structure on the hypothesis class in the sense of Vapnik’s “Structural Risk” and essentially controls the complexity of the hypothesis clas . TheEmpirical Risk Approximationinduction principle requires to define a nested sequence of hypothesis classes with bounded expected risk and to sample from the hypothesis clas s with the desired approximation quality. Algorithmically, however, we select t he loss function according to the bounded empirical risk̂ R( ;X ) R . This induction principle is consistent if a bounded empirical risk implies in the asymptotic limit that the loss functi o has bounded expected riskR( ) R and, therefore, is an element of the hypothesis class HR , i.e., 8 2 lim l!1 R̂( ;X ) R =) h(x; ) 2 HR : (4) This implication essentially states that the expected risk asymptotica lly does not exceed the risk bound (R( ) R ) and, therefore, the inferred structure is an approximation of the optimal data structure with risk not worse than R . The consistency assumption (4) for Empirical Risk Approximationholds if the empirical risk uniformly converges towards the expected risk lim l!1P(sup 2 jR( ) R̂( ;X )j pV fh(x; )g > ) = 0; 8 > 0; (5) where denotes the hypothesis set of distinguishable structures. defines an -netH ;R on the set of loss functions HR or coarsens the hypothesis class with an -separated set if there does not exist a finite -net forHR . Using results from the theory of empirical processes the probability of an –deviation of empirical risk from expected risk can be bounded by Bernstein’s inequality ([3], Lemma 2.2.11) P( sup 2 jR( ) R̂( ;X )j pV fh(x; )g > ) 2jH j exp l 2 2(1 + = min) : (6) The minimal variance of all loss functions is denoted by 2 min = inf 2 V fh(x; )g. jH j denotes the cardinality of an -net constructed for the hypothesis class HR under the assumption of the measure . The confidence level limits the probability of large deviations [1]. The large deviation inequality weighs two competing effects in the learning probl em, i.e., the probability of a large deviation exponentially decreases with growing sample si ze l, whereas a large deviation becomes increasingly likely with growing cardinality of the hypothesis class. A compromise between both effects determines how reliable an estimate ac tually is for a given data set X . The sample complexity l0( ; ) is defined as the nec

Paper windows: interaction techniques for digital paper <=SEP=> In this paper, we present Paper Windows, a prototype windowing environment that simulates the use of digital paper displays. By projecting windows on physical paper, Paper Windows allows the capturing of physical affordances of paper in a digital world. The system uses paper as an input device by tracking its motion and shape with a Vicon Motion Capturing System. We discuss the design of a number of interaction techniques for manipulating information on paper displays.

Deep Convolutional Neural Network for 6-DOF Image Localization <=SEP=> Wee present an accurate and robust method for six degree of freedom image localization. There are two key-points of our method, 1). automatic immense photo synthesis and labeling from point cloud model and, 2). pose estimation with deep convolutional neural networks (ConvNets) regression. Our model can directly regresses 6-DOF camera poses from images, accurately describing where and how it was captured. We achieved an accuracy within 1 meters and 1 degree on our outdoor dataset, which covers about 20, 000m on our school campus. Unlike previous point cloud registration solutions, our model supports low resolution images (i.e. 224×224 in our settings), and is tiny in size when finished training. Moreover, in pose estimation, our model uses O(1) time & space complexity as trainset grows. We will show the importance to localization using hundreds of thousands of generated and self-labeled ”photos” came from a short video. We will show our model’s robustness despite of illumination and seasonal variances, which usually fails methods that leverage image feature descriptors like SIFT. Furthermore, we will show the ability of transfer our model trained on one scene to another, and the gains in accuracy and efficiency.

Characterizing in-text citations in scientific articles: A large-scale analysis <=SEP=> We report characteristics of in-text citations in over five million full text articles from two large databases – the PubMed Central Open Access subset and Elsevier journals – as functions of time, textual progression, and scientific field. The purpose of this study is to understand the characteristics of in-text citations in a detailed way prior to pursuing other studies focused on answering more substantive research questions. As such, we have analyzed in-text citations in several ways and report many findings here. Perhaps most significantly, we find that there are large field-level differences that are reflected in position within the text, citation interval (or reference age), and citation counts of references. In general, the fields of Biomedical and Health Sciences, Life and Earth Sciences, and Physical Sciences and Engineering have similar reference distributions, although they vary in their specifics. The two remaining fields, Mathematics and Computer Science and Social Science and Humanities, have different reference distributions from the other three fields and between themselves. We also show that in all fields the numbers of sentences, references, and in-text mentions per article have increased over time, and that there are field-level and temporal differences in the numbers of in-text mentions per reference. A final finding is that references mentioned only once tend to be much more highly cited than those mentioned multiple times.

Determined to Die! Ability to Act Following Multiple Self-inflicted Gunshot Wounds to the Head. The Cook County Office of Medical Examiner Experience (2005-2012) and Review of Literature. <=SEP=> Cases of multiple (considered 2+) self-inflicted gunshot wounds are a rarity and require careful examination of the scene of occurrence; thorough consideration of the decedent's psychiatric, medical, and social histories; and accurate postmortem documentation of the gunshot wounds. We present a series of four cases of multiple self-inflicted gunshot wounds to the head from the Cook County Medical Examiner's Office between 2005 and 2012 including the first case report of suicide involving eight gunshot wounds to the head. In addition, a review of the literature concerning multiple self-inflicted gunshot wounds to the head is performed. The majority of reported cases document two gunshot entrance wound defects. Temporal regions are the most common affected regions (especially the right and left temples). Determining the capability to act following a gunshot wound to the head is necessary in crime scene reconstruction and in differentiation between homicide and suicide.

Definition and Multidimensionality of Security Awareness: Close Encounters of the Second Order <=SEP=> This study proposes and examines a multidimensional definition of information security awareness. We also investigate its antecedents and analyze its effects on compliance with organizational information security policies. The above research goals are tested through the theoretical lens of technology threat avoidance theory and protection motivation theory. Information security awareness is defined as a second-order construct composed of the elements of threat and coping appraisals supplemented by the responsibilities construct to account for organizational environment. The study was executed in two stages. First, the participants (employees of a municipality) were exposed to a series of phishing messages. Second, the same individuals were asked to participate in a survey designed to examine their security awareness. The research model was tested using PLS-SEM approach. The results indicate that security awareness is in fact a second-order formative construct composed of six components. There are significant differences in security awareness levels between the victims of the phishing experiment and the employees who maintain compliance with security policies. Our study extends the theory by proposing and validating a general, yet practical definition of security awareness. It also bridges the gap between theory and practice - our contextualization of security awareness draws heavily on both fields.

Knowledge discovery and data mining in toxicology. <=SEP=> Knowledge discovery and data mining tools are gaining increasing importance for the analysis of toxicological databases. This paper gives a survey of algorithms, capable to derive interpretable models from toxicological data, and presents the most important application areas. The majority of techniques in this area were derived from symbolic machine learning, one commercial product was developed especially for toxicological applications. The main application area is presently the detection of structure-activity relationships, very few authors have used these techniques to solve problems in epidemiological and clinical toxicology. Although the discussed algorithms are very flexible and powerful, further research is required to adopt the algorithms to the specific learning problems in this area, to develop improved representations of chemical and biological data and to enhance the interpretability of the derived models for toxicological experts.

Toward autonomous mapping and exploration for mobile robots through deep supervised learning <=SEP=> We consider an autonomous mapping and exploration problem in which a range-sensing mobile robot is guided by an information-based controller through an a priori unknown environment, choosing to collect its next measurement at the location estimated to yield the maximum information gain within its current field of view. We propose a novel and time-efficient approach to predict the most informative sensing action using a deep neural network. After training the deep neural network on a series of thousands of randomly-generated “dungeon maps”, the predicted optimal sensing action can be computed in constant time, with prospects for appealing scalability in the testing phase to higher dimensional systems. We evaluated the performance of deep neural networks on the autonomous exploration of two-dimensional workspaces, comparing several different neural networks that were selected due to their success in recent ImageNet challenges. Our computational results demonstrate that the proposed method provides high efficiency as well as accuracy in selecting informative sensing actions that support autonomous mobile robot exploration.

Analysing the use of interactive technology to implement interactive teaching <=SEP=> Recent policy initiatives in England have focused on promoting ‘interactive’ teaching in schools, with a clear expectation that this will lead to improvements in learning. This expectation is based on the perceived success of such approaches in other parts of the world.At the same time, there has been a large investment in Information and Communication Technology (ICT) resources, and particularly in interactive whiteboard technology. This paper explores the idea of interactive teaching in relation to the interactive technology which might be used to support it. It explains the development of a framework for the detailed analysis of teaching and learning in activity settings which is designed to represent the features and relationships involved in interactivity. When applied to a case study of interactive teaching during a lesson involving a variety of technology-based activities, the framework reveals a confusion of purpose in students’ use of an ICT resource that limits the potential for learning when students are working independently. Discussion of relationships between technical and pedagogical interactivity points a way forward concerning greater focus on learning goals during activity in order to enable learners to be more autonomous in exploiting ICT’s affordances, and the conclusion identifies the variables and issues which need to be considered in future research which will illuminate this path.

Reducing Multiclass to Binary: A Unifying Approach for Margin Classifiers <=SEP=> We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including support-vector machines, AdaBoost, regression, logistic regression and decision-tree algorithms. We also give a multiclass generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms.

Facial Expression Recognition From Image Sequence Based on LBP and Taylor Expansion <=SEP=> The aim of an automatic video-based facial expression recognition system is to detect and classify human facial expressions from image sequence. An integrated automatic system often involves two components: 1) peak expression frame detection and 2) expression feature extraction. In comparison with the image-based expression recognition system, the video-based recognition system often performs online detection, which prefers low-dimensional feature representation for cost-effectiveness. Moreover, effective feature extraction is needed for classification. Many recent recognition systems often incorporate rich additional subjective information and thus become less efficient for real-time application. In our facial expression recognition system, first, we propose the double local binary pattern (DLBP) to detect the peak expression frame from the video. The proposed DLBP method has a much lower-dimensional size and can successfully reduce detection time. Besides, to handle the illumination variations in LBP, logarithm-laplace (LL) domain is further proposed to get a more robust facial feature for detection. Finally, the Taylor expansion theorem is employed in our system for the first time to extract facial expression feature. We propose the Taylor feature pattern (TFP) based on the LBP and Taylor expansion to obtain an effective facial feature from the Taylor feature map. Experimental results on the JAFFE and Cohn-Kanade data sets show that the proposed TFP method outperforms some state-of-the-art LBP-based feature extraction methods for facial expression feature extraction and can be suited for real-time applications.

A 1.2V 64Gb 341GB/S HBM2 stacked DRAM with spiral point-to-point TSV structure and improved bank group data control <=SEP=> With the recent increasing interest in big data and artificial intelligence, there is an emerging demand for high-performance memory system with large density and high data-bandwidth. However, conventional DIMM-type memory has difficulty achieving more than 50GB/s due to its limited pin count and signal integrity issues. High-bandwidth memory (HBM) DRAM, with TSV technology and wide IOs, is a prominent solution to this problem, but it still has many limitations: including power consumption and reliability. This paper presents a power-efficient structure of TSVs with reliability and a cost-effective HBM DRAM core architecture.

Parallel tree-ensemble algorithms for GPUs using CUDA <=SEP=> We present two new parallel implementations of the tree-ensemble algorithms Random Forest (RF) and Extremely randomized trees (ERT) for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). Random Forest and Extremely randomized trees are ensemble learners for classification and regression. They operate by constructing a multitude of decision trees at training time and outputting a prediction by comparing the outputs of the individual trees. Thanks to the inherent parallelism of the task, an obvious platform for its computation is to employ contemporary GPUs with a large number of processing cores. Previous parallel algorithms for Random Forests in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler hardware architecture and relatively few number of cores. The new parallel algorithms are designed for contemporary GPUs with a large number of cores and take into account aspects of the newer hardware architectures as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface for best possible performance on NVidia-based GPUs. An experimental study comparing with the most important previous solutions for CPU and GPU platforms shows significant improvement for the new implementations, often with several magnitudes.

The Role of Network Analysis in Industrial and Applied Mathematics <=SEP=> Many problems in industry — and in the social, natural, information, and medical sciences — involve discrete data and benefit from approaches from subjects such as network science, information theory, optimization, probability, and statistics. Because the study of networks is concerned explicitly with connectivity between different entities, it has become very prominent in industrial settings, and this importance has been accentuated further amidst the modern data deluge. In this commentary, we discuss the role of network analysis in industrial and applied mathematics, and we give several examples of network science in industry. We focus, in particular, on discussing a physical-applied-mathematics approach to the study of networks. 1 ar X iv :1 70 3. 06 84 3v 2 [ cs .S I] 1 4 Se p 20 17 The Role of Network Analysis in Industrial and Applied Mathematics: A Physical-Applied-Mathematics Perspective Mason A. Porter and Sam D. Howison Department of Mathematics, University of California, Los Angeles, Los Angeles, California 90095, USA Mathematical Institute, University of Oxford, Oxford OX2 6GG, UK CABDyN Complexity Centre, University of Oxford, Oxford OX1 1HP, UK

Measuring retail company performance using credit scoring techniques <=SEP=> This paper proposes a theoretical framework for predicting financial distress based on Hunt’s (2000) ‘Resource-Advantage (R-A) Theory of Competition’. The study focuses on the US retail market. Five credit scoring methodologies –Naïve Bayes, Logistic Regression, Recursive Partition, Artificial Neural Network, and Sequential Minimal Optimization— are used on a sample of 195 healthy companies and 51 distressed firms over different time periods from 1994 to 2002. Analyses provide sufficient evidence that the five credit scoring methodologies have sound classification ability. In the time period of one year before financial distress, logistic regression model shows identical performance with neural network model based on the accuracy rate and shows the best performance in terms of AUROC value. Other models are slightly worse for predicting financial distress, but still present high accuracy rate and AUROC value. Moreover, the methodologies remain sound even five years prior to financial distress with classification accuracy rates above 85% and AUROC values above 0.85 for all five methodologies. This paper also shows external environment influences exist based on the naïve bayes, logistic, recursive partition and SMO models, but these influences are weak. With regards to the model applicability, a subset of the different models is compared with Moody’s rankings. It is found that both SMO and Logistic models are better than the Neural Network model in terms of similarity with Moody’s ranking, with SMO being slightly better than the Logistic Model.

Are we dependent upon coffee and caffeine? A review on human and animal data <=SEP=> Caffeine is the most widely used psychoactive substance and has been considered occasionally as a drug of abuse. The present paper reviews available data on caffeine dependence, tolerance, reinforcement and withdrawal. After sudden caffeine cessation, withdrawal symptoms develop in a small portion of the population but are moderate and transient. Tolerance to caffeine-induced stimulation of locomotor activity has been shown in animals. In humans, tolerance to some subjective effects of caffeine seems to occur, but most of the time complete tolerance to many effects of caffeine on the central nervous system does not occur. In animals, caffeine can act as a reinforcer, but only in a more limited range of conditions than with classical drugs of dependence. In humans, the reinforcing stimuli functions of caffeine are limited to low or rather moderate doses while high doses are usually avoided. The classical drugs of abuse lead to quite specific increases in cerebral functional activity and dopamine release in the shell of the nucleus accumbens, the key structure for reward, motivation and addiction. However, caffeine doses that reflect the daily human consumption, do not induce a release of dopamine in the shell of the nucleus accumbens but lead to a release of dopamine in the prefrontal cortex, which is consistent with caffeine reinforcing properties. Moreover, caffeine increases glucose utilization in the shell of the nucleus accumbens only at rather high doses that stimulate most brain structures, non-specifically, and likely reflect the side effects linked to high caffeine ingestion. That dose is also 5-10-fold higher than the one necessary to stimulate the caudate nucleus, which mediates motor activity and the structures regulating the sleep-wake cycle, the two functions the most sensitive to caffeine. In conclusion, it appears that although caffeine fulfils some of the criteria for drug dependence and shares with amphetamines and cocaine a certain specificity of action on the cerebral dopaminergic system, the methylxanthine does not act on the dopaminergic structures related to reward, motivation and addiction.

Approximation algorithms for data placement in arbitrary networks <=SEP=> We study approximation algorithms for placing replicated data in arbitrary networks. Consider a network of nodes with individual storage capacities and a metric communication cost function, in which each node periodically issues a request for an object drawn from a collection of uniform-length objects. We consider the problem of placing copies of the objects among the nodes such that the average access cost is minimized. Our main result is a polynomial-time constant-factor approximation algorithm for this placement problem. Our algorithm is based on a careful rounding of a linear programming relaxation of the problem. We also show that the data placement problem is MAXSNP-hard.
We extend our approximation result to a generalization of the data placement problem that models additional costs such as the cost of realizing the placement. We also show that when object lengths are non-uniform, a constant-factor approximation is achievable if the capacity at each node in the approximate solution is allowed to exceed that in the optimal solution by the length of the largest object.