Image Based Camera Localization: an Overview

Virtual reality, augmented reality, robotics, and autonomous driving, have recently attracted much attention from both academic and industrial communities, in which image-based camera localization is a key task. However, there has not been a complete review on image-based camera localization. It is urgent to map this topic to enable individuals enter the field quickly. In this paper, an overview of image-based camera localization is presented. A new and complete classification of image-based camera localization approaches is provided and the related techniques are introduced. Trends for future development are also discussed. This will be useful not only to researchers, but also to engineers and other individuals interested in this field.


The peculiarities of knowledge management processes in SMEs: the case of Singapore

Purpose – The objectives of this study are two-fold. The first is to examine the peculiarities of KM processes that are unique in SMEs from three perspectives, namely knowledge creation, knowledge sharing and knowledge reuse. Secondly, to identify enablers and impediments of these KM processes that influence the competitiveness of SMEs. Design/methodology/approach – The study adopted a case study approach involving 21 participants comprising management staff and front-line employees from four Singaporean SMEs. Findings – The SME owner, rather than the employees, was found to be the key source and creator of knowledge and the sole driver of the KM processes. In SMEs, knowledge creation takes the form of innovative customized solutions to meet customers’ needs; knowledge sharing occurs through cross functionality, overlapping roles, and facilitated by close physical proximity in open workspaces; and knowledge reuse is often made tacitly, where common knowledge is prevalently embedded within the KM processes of SMEs. The enablers of knowledge creation process rested largely on the owner’s innovativeness, creativity and ability to acquire knowledge of the industry. Knowledge sharing processes are enabled by the awareness of roles, mutual respect and the level of trust among employees within the SME while knowledge reuse is fostered by close proximity of employees and the willingness and openness of the owner to impart his knowledge. The lack of the above enablement factors mentioned will hinder these KM processes. Research limitations/implications – The study is limited by the fact that data was collected from four SMEs in Singapore. Furthermore, only a small sample of staff from these SMEs was interviewed. Hence the findings need to be interpreted in light of such a scope. Practical implications – For SMEs, this research provides perspectives on the factors influencing KM processes, in particular, the importance of the owners’ knowledge and leadership, the flexibility and adaptability of the organization, and open culture to enable the capitalization of its knowledge assets to survive and stay competitive. For practitioners, this paper reinforces the importance of the management owners’ innovativeness, initiatives and support, and the level of social interaction and high level of trusts among employees in the SMEs to as enablers to effective KM processes in SMEs. Originality/value – To deepen on-going knowledge management research on SMEs, this paper provides insights and rich context to the distinctness of KM processes in SMEs.


An Analytical Solution to the Stance Dynamics of Passive Spring-Loaded Inverted Pendulum with Damping

The Spring-Loaded Inverted Pendulum (SLIP) model has been established both as a very accurate descriptive tool as well as a good basis for the design and control of running robots. In particular, approximate analytic solutions to the otherwise nonintegrable dynamics of this model provide principled ways in which gait controllers can be built, yielding invaluable insight into their stability properties. However, most existing work on the SLIP model completely disregards the effects of damping, which often cannot be neglected for physical robot platforms. In this paper, we introduce a new approximate analytical solution to the dynamics of this system that also takes into account viscous damping in the leg. We compare both the predictive performance of our approximation as well as the tracking performance of an associated deadbeat gait controller to similar existing methods in the literature and show that it significantly outperforms them in the presence of damping in the leg.


Smart foot device for women safety

In this paper, an attempt has been made to develop a smart device that can assist women when they feel unsafe. This smart device will be clipped to the footwear of the user and can be triggered discreetly. On tapping one foot behind the other four times, an alert is sent via Bluetooth Low Energy communication to an application on the victim's phone, programmed to generate a message seeking help with the location of the device attached. The results obtained were analysed using Naïve Bayes classifier and this low cost device showed an overall accuracy of 97.5%.


Informal social communication.


Stuttering in relation to anxiety, temperament, and personality: review and analysis with focus on causality.

UNLABELLED
Anxiety and emotional reactions have a central role in many theories of stuttering, for example that persons who stutter would tend to have an emotionally sensitive temperament. The possible relation between stuttering and certain traits of temperament or personality were reviewed and analyzed, with focus on temporal relations (i.e., what comes first). It was consistently found that preschool children who stutter (as a group) do not show any tendencies toward elevated temperamental traits of shyness or social anxiety compared with children who do not stutter. Significant group differences were, however, repeatedly reported for traits associated with inattention and hyperactivity/impulsivity, which is likely to reflect a subgroup of children who stutter. Available data is not consistent with the proposal that the risk for persistent stuttering is increased by an emotionally reactive temperament in children who stutter. Speech-related social anxiety develops in many cases of stuttering, before adulthood. Reduction of social anxiety in adults who stutter does not in itself appear to result in significant improvement of speech fluency. Studies have not revealed any relation between the severity of the motor symptoms of stuttering and temperamental traits. It is proposed that situational variability of stuttering, related to social complexity, is an effect of interference from social cognition and not directly from the emotions of social anxiety. In summary, the studies in this review provide strong evidence that persons who stutter are not characterized by constitutional traits of anxiety or similar constructs.

EDUCATIONAL OBJECTIVES
This paper provides a review and analysis of studies of anxiety, temperament, and personality, organized with the objective to clarify cause and effect relations. Readers will be able to (a) understand the importance of effect size and distribution of data for interpretation of group differences; (b) understand the role of temporal relations for interpretation of cause and effect; (c) discuss the results of studies of anxiety, temperament and personality in relation to stuttering; and (d) discuss situational variations of stuttering and the possible role of social cognition.


A quad 25Gb/s 270mW TIA in 0.13µm BiCMOS with <0.15dB crosstalk penalty

The push for 100Gb/s optical transport and beyond necessitates electronic components at higher speed and integration level in order to drive down cost, complexity and size of transceivers [1-2]. This requires parallel multi-channel optical transceivers each operating at 25Gb/s and beyond. Due to variations in the output power of transmitters and in some cases different optical paths the parallel receivers have to operate at different input optical power levels. This trend places increasing strain to the acceptable inter-channel crosstalk in integrated multi-channel receivers [3]. Minimizing this cross-talk penalty when all channels are operational is becoming increasingly important in ultra-high throughput optical links.


Learning from Multiple Partially Observed Views - an Application to Multilingual Text Categorization

We address the problem of learning classifiers when observat ions have multiple views, some of which may not be observed for all examples. We a ssume the existence of view generating functions which may complete t he missing views in an approximate way. This situation corresponds for examp le to learning text classifiers from multilingual collections where documents are not available in all languages. In that case, Machine Translation (MT) systems m ay be used to translate each document in the missing languages. We derive a gene ralization error bound for classifiers learned on examples with multiple arti ficially created views. Our result uncovers a trade-off between the size of the train ing set, the number of views, and the quality of the view generating functions. A s a consequence, we identify situations where it is more interesting to use mu ltiple views for learning instead of classical single view learning. An extension of this framework is a natural way to leverage unlabeled multi-view data in semisupervised learning. Experimental results on a subset of the Reuters RCV1/RCV2 co llecti ns support our findings by showing that additional views obtained from M T may significantly improve the classification performance in the cases identifi d by our trade-off.


Bilateral Back-Projection for Single Image Super Resolution

In this paper, a novel algorithm for single image super resolution is proposed. Back-projection [1] can minimize the reconstruction error with an efficient iterative procedure. Although it can produce visually appealing result, this method suffers from the chessboard effect and ringing effect, especially along strong edges. The underlining reason is that there is no edge guidance in the error correction process. Bilateral filtering can achieve edge-preserving image smoothing by adding the extra information from the feature domain. The basic idea is to do the smoothing on the pixels which are nearby both in space domain and in feature domain. The proposed bilateral back-projection algorithm strives to integrate the bilateral filtering into the back-projection method. In our approach, the back-projection process can be guided by the edge information to avoid across-edge smoothing, thus the chessboard effect and ringing effect along image edges are removed. Promising results can be obtained by the proposed bilateral back-projection method efficiently.


Prediction of air temperature using Multi-layer perceptrons with Levenberg-Marquardt training algorithm

related to the prediction of the air temperature in the region of Meknes in Morocco. Depending on weather parameters, such as atmospheric pressure (Pr), humidity (H), visibility (Vis), wind speed (V), dew point (Tr) and the precipitation (P). The database used contains a history of meteorological parameters covering 3288 days, from 2004 to 2012. This multilayer perceptron type of neuron (MLP) was used to approximate the relationship between these parameters with minimum square error. For a better air temperature prediction, we developed a stochastic neural model. The mean square error (MSE) and the correlation coefficient (R) were used to evaluate the performance of the developed models. The study of these statistical indicators demonstrated that the prediction of the air temperature was powerful with the Levenberg-Marquard algorithm, with architecture [610-1], the Tansig function in the hidden layer and the Purelin function in the output layer.


FiberMesh: designing freeform surfaces with 3D curves

This paper presents a system for designing freeform surfaces with a collection of 3D curves. The user first creates a rough 3D model by using a sketching interface. Unlike previous sketching systems, the user-drawn strokes stay on the model surface and serve as handles for controlling the geometry. The user can add, remove, and deform these control curves easily, as if working with a 2D line drawing. The curves can have arbitrary topology; they need not be connected to each other. For a given set of curves, the system automatically constructs a smooth surface embedding by applying functional optimization. Our system provides real-time algorithms for both control curve deformation and the subsequent surface optimization. We show that one can create sophisticated models using this system, which have not yet been seen in previous sketching or functional optimization systems.


A framework and theory for cyber security assessments

Information system security risk, defined as the product of the monetary losses associated with security incidents and the probability that they occur, is a suitable decision criterion when considering different information system architectures. This paper describes how probabilistic relational models can be used to specify architecture metamodels so that security risk can be inferred from metamodel-instantiations. A probabilistic relational model contains classes, attributes, and class-relationships. It can be used to specify architectural metamodels similar to class diagrams in the Unified Modeling Language. In addition, a probabilistic relational model makes it possible to associate a probabilistic dependency model to the attributes of classes in the architectural metamodel. This paper proposes a set of abstract classes that can be used to create probabilistic relational models so that they enable inference of security risk from instantiated architecture models. If an architecture metamodel is created by specializing the abstract classes proposed in this paper, the instantiations of the metamodel will generate a probabilistic dependency model that can be used to calculate the security risk associated with these instantiations. The abstract classes make it possible to derive the dependency model and calculate security risk from an instance model that only specifies assets and their relationships to each other. Hence, the person instantiating the architecture metamodel is not required to assess complex security attributes to quantify security risk using the instance model. Paper A: A probabilistic relational model for security risk analysis


An interface for targeted collection of common sense knowledge using a mixture model

We present a game-based interface for acquiring common sense knowledge. In addition to being interactive and entertaining, our interface guides the knowledge acquisition process to learn about the most salient characteristics of a particular concept. We use statistical classification methods to discover the most informative characteristics in the Open Mind Common Sense knowledge base, and use these characteristics to play a game of 20 Questions with the user. Our interface also allows users to enter knowledge more quickly than a more traditional knowledge-acquisition interface. An evaluation showed that users enjoyed the game and that it increased the speed of knowledge acquisition.


DISCO: Distributed multi-domain SDN controllers

Software-Defined Networking (SDN) is now envisioned for Wide Area Networks (WAN) and constrained overlay networks. Such networks require a resilient, scalable and easily extensible SDN control plane. In this paper, we propose DISCO, an extensible DIstributed SDN COntrol plane able to cope with the distributed and heterogeneous nature of modern overlay networks. A DISCO controller manages its own network domain and communicates with other controllers to provide end-to-end network services. This east-west communication is based on a lightweight and highly manageable control channel. We implemented DISCO on top of the Floodlight OpenFlow controller and the AMQP protocol and we evaluated it through an inter-domain topology disruption use case.


Folksonomies-Cooperative Classification and Communication Through Shared Metadata

This paper examines user-generated metadata as implemented and applied in two web services designed to share and organize digital media to better understand grassroots classification. Metadata data about data allows systems to collocate related information, and helps users find relevant information. The creation of metadata has generally been approached in two ways: professional creation and author creation. In libraries and other organizations, creating metadata, primarily in the form of catalog records, has traditionally been the domain of dedicated professionals working with complex, detailed rule sets and vocabularies. The primary problem with this approach is scalability and its impracticality for the vast amounts of content being produced and used, especially on the World Wide Web. The apparatus and tools built around professional cataloging systems are generally too complicated for anyone without specialized training and knowledge. A second approach is for metadata to be created by authors. The movement towards creator described documents was heralded by SGML, the WWW, and the Dublin Core Metadata Initiative. There are problems with this approach as well often due to inadequate or inaccurate description, or outright deception. This paper examines a third approach: user-created metadata, where users of the documents and media create metadata for their own individual use that is also shared throughout a community. 1 The Creation of Metadata: Professionals, Content Creators, Users Metadata is often characterized as “data about data.” Metadata is information, often highly structured, about documents, books, articles, photographs, or other items that is designed to support specific functions. These functions are usually


Hemihyperplasia-multiple lipomatosis syndrome (HHML): a challenge in spinal care.

A 15-year-old girl developed a progressive paraparesis over a period of six months, secondary to spinal cord compression by a lipomatous mass and anomalies of the vertebral column. Clinically, a right hemihyperplasia affecting the trunk and lower limb was evident, as well as a right convex lumbar scoliosis. CT and MRI demonstrated severe spinal cord compression resulting from intraspinal lipomatosis, overgrowth of right facet joints (T8 to L5), and kyphoscoliosis. Surgical decompression was undertaken. A lumbar scoliosis of 48 degrees was partially corrected by means of dual-rod instrumentation. The neurological deficit improved significantly, and ambulation was progressively restored. The patient carried the diagnosis of Proteus syndrome for several years, but reevaluation of clinical features prompted the diagnosis of Hemihyperplasia Multiple Lipomatosis syndrome (HHML). This rare sporadic disorder is often confused with Proteus syndrome. As in Proteus syndrome, spinal cord compression in patients with HHML can result from lipomatous infiltration and/or significant spinal abnormalities including kyphoscoliosis and overgrowth. HHML and Proteus syndrome are discussed and compared with special emphasis on spinal and orthopaedic pathologies.


Reverse Word Order Models

In this work, we study the impact of the word order decoding direction for statistical machine translation (SMT). Both phrase-based and hierarchical phrasebased SMT systems are investigated by reversing the word order of the source and/or target language and comparing the translation results with the normal direction. Analysis are done on several components such as alignment model, language model and phrase table to see which of them accounts for the differences generated by various translation directions. Furthermore, we propose to use system combination, alignment combinations and phrase table combinations to take benefit from systems trained with different translation directions. Experimental results show improvements of up to 1.7 points in BLEU and 3.1 points in TER compared to the normal direction systems for the NTCIR9 Japanese-English and Chinese-English tasks.


Groove Radio: A Bayesian Hierarchical Model for Personalized Playlist Generation

This paper describes an algorithm designed for Microsoft's Groove music service, which serves millions of users world wide. We consider the problem of automatically generating personalized music playlists based on queries containing a ``seed'' artist and the listener's user ID. Playlist generation may be informed by a number of information sources including: user specific listening patterns, domain knowledge encoded in a taxonomy, acoustic features of audio tracks, and overall popularity of tracks and artists. The importance assigned to each of these information sources may vary depending on the specific combination of user and seed~artist.
 The paper presents a method based on a variational Bayes solution for learning the parameters of a model containing a four-level hierarchy of global preferences, genres, sub-genres and artists. The proposed model further incorporates a personalization component for user-specific preferences. Empirical evaluations on both proprietary and public datasets demonstrate the effectiveness of the algorithm and showcase the contribution of each of its components.


Communication-optimal parallel algorithm for strassen's matrix multiplication

Parallel matrix multiplication is one of the most studied fundamental problems in distributed and high performance computing. We obtain a new parallel algorithm that is based on Strassen's fast matrix multiplication and minimizes communication. The algorithm outperforms all known parallel matrix multiplication algorithms, classical and Strassen-based, both asymptotically and in practice. A critical bottleneck in parallelizing Strassen's algorithm is the communication between the processors. Ballard, Demmel, Holtz, and Schwartz (SPAA '11) prove lower bounds on these communication costs, using expansion properties of the underlying computation graph. Our algorithm matches these lower bounds, and so is communication-optimal. It exhibits perfect strong scaling within the maximum possible range.
 Benchmarking our implementation on a Cray XT4, we obtain speedups over classical and Strassen-based algorithms ranging from 24% to 184% for a fixed matrix dimension n=94080, where the number of processors ranges from 49 to 7203.
 Our parallelization approach generalizes to other fast matrix multiplication algorithms.


Road scene analysis for determination of road traffic density

Road traffic density has always been a concern in large cities around the world, and many approaches were developed to assist in solving congestions related to slow traffic flow. This work proposes a congestion rate estimation approach that relies on real-time video scenes of road traffic, and was implemented and evaluated on eight different hotspots covering 33 different urban roads. The approach relies on road scene morphology for estimation of vehicles average speed along with measuring the overall video scenes randomness acting as a frame texture analysis indicator. Experimental results shows the feasibility of the proposed approach in reliably estimating traffic density and in providing an early warning to drivers on road conditions, thereby mitigating the negative effect of slow traffic flow on their daily lives.


Recurrent Continuous Translation Models

We introduce a class of probabilistic continuous translation models called Recurrent Continuous Translation Models that are purely based on continuous representations for words, phrases and sentences and do not rely on alignments or phrasal translation units. The models have a generation and a conditioning aspect. The generation of the translation is modelled with a target Recurrent Language Model, whereas the conditioning on the source sentence is modelled with a Convolutional Sentence Model. Through various experiments, we show first that our models obtain a perplexity with respect to gold translations that is > 43% lower than that of stateof-the-art alignment-based translation models. Secondly, we show that they are remarkably sensitive to the word order, syntax, and meaning of the source sentence despite lacking alignments. Finally we show that they match a state-of-the-art system when rescoring n-best lists of translations.


The principles of software QRS detection

The QRS complex is the most striking waveform within the electrocardiogram (ECG). Since it reflects the electrical activity within the heart during the ventricular contraction, the time of its occurrence as well as its shape provide much information about the current state of the heart. Due to its characteristic shape it serves as the basis for the automated determination of the heart rate, as an entry point for classification schemes of the cardiac cycle, and often it is also used in ECG data compression algorithms. In that sense, QRS detection provides the fundamentals for almost all automated ECG analysis algorithms. Software QRS detection has been a research topic for more than 30 years. The evolution of these algorithms clearly reflects the great advances in computer technology. Within the last decade many new approaches to QRS detection have been proposed; for example, algorithms from the field of artificial neural networks genetic algorithms wavelet transforms, filter banks as well as heuristic methods mostly based on nonlinear transforms. The authors provide an overview of these recent developments as well as of formerly proposed algorithms.


http://www.jstor.org A Language and Program for Complex Bayesian Modelling Author(s): W. R. Gilks, A. Thomas and D. J. Spiegelhalter Source: Journal of the Royal Statistical Society. Series D (The Statistician), Vol. 43, No. 1, Special Issue: Conference on Practical Bayesian Statistics, 1992 (3) (1994), pp. 169-177 Published by: for the Wiley Royal Statistical Society Stable URL: http://www.jstor.org/stable/2348941 Accessed: 19-08-2014 17:40 UTC


Statistical Methods in Psychology Journals Guidelines and Explanations

n the light of continuing debate over the applications of significance testing in psychology journals and following the publication of Cohen's (1994) article, the Board of Scientific Affairs (BSA) of the American Psychological Association (APA) convened a committee called the Task Force on Statistical Inference (TFSI) whose charge was "to elucidate some of the controversial issues surrounding applications of statistics including significance testing and its alternatives; alternative underlying models and data transformation; and newer methods made possible by powerful computers" (BSA, personal communication, February 28, 1996). Robert Rosenthal, Robert Abelson, and Jacob Cohen (cochairs) met initially and agreed on the desirability of having several types of specialists on the task force: statisticians, teachers of statistics, journal editors, authors of statistics books, computer experts, and wise elders. Nine individuals were subsequently invited to join and all agreed. These were Leona Aiken, Mark Appelbaum, Gwyneth Boodoo, David A. Kenny, Helena Kraemer, Donald Rubin, Bruce Thompson, Howard Wainer, and Leland Wilkinson. In addition, Lee Cronbach, Paul Meehl, Frederick Mosteller and John Tukey served as Senior Advisors to the Task Force and commented on written materials. The TFSI met twice in two years and corresponded throughout that period. After the first meeting, the task force circulated a preliminary report indicating its intention to examine issues beyond null hypothesis significance testing. The task force invited comments and used this feedback in the deliberations during its second meeting. After the second meeting, the task force recommended several possibilities for further action, chief of which would be to revise the statistical sections of the American Psychological Association Publication Manual (APA, 1994). After extensive discussion, the BSA recommended that "before the TFSI undertook a revision of the APA Publication Manual, it might want to consider publishing an article in American Psychologist, as a way to initiate discussion in the field about changes in current practices of data analysis and reporting" (BSA, personal communication, November 17, 1997). This report follows that request. The sections in italics are proposed guidelines that the TFSI recommends could be used for revising the APA publication manual or for developing other BSA supporting materials. Following each guideline are comments, explanations, or elaborations assembled by Leland Wilkinson for the task force and under its review. This report is concerned with the use of statistical methods only and is not meant as an assessment of research methods in general. Psychology is a broad science. Methods appropriate in one area may be inappropriate in another. The title and format of this report are adapted from a similar article by Bailar and Mosteller (1988). That article should be consulted, because it overlaps somewhat with this one and discusses some issues relevant to research in psychology. Further detail can also be found in the publications on this topic by several committee members (Abelson, 1995, 1997; Rosenthal, 1994; Thompson, 1996; Wainer, in press; see also articles in Harlow, Mulaik, & Steiger, 1997).


A generative theory of similarity

We argue that similarity judgments are inferences about generative processes, and that two objects appear similar when they are likely to have been generated by the same process. We describe a formal model based on this idea and show how featural and spatial models emerge as special cases. We compare our approach to the transformational approach, and present an experiment where our model performs better than a transformational model. Every object is the outcome of a generative process. An animal grows from a fertilized egg into an adult, a city develops from a settlement into a metropolis, and an artifact is assembled from a pile of raw materials according to the plan of its designer. Observations like these motivate the generative approach, which proposes that an object may be understood by thinking about the process that generated it. The promise of the approach is that apparently complex objects may be produced by simple processes, an insight that has proved productive across disciplines including biology [18], physics [21], and architecture [1]. To give two celebrated examples from biology, the shape of a pinecone and the markings on a cheetah’s tail can be generated by remarkably simple processes of growth. These patterns can be characterized much more compactly by describing their causal history than by attempting to describe them directly. Leyton has argued that the generative approach provides a general framework for understanding cognition. Applications of the approach can be found in generative theories of perception [12], memory [12], language [3], categorization [2], and music [11]. This paper offers a generative theory of similarity, a notion often invoked by models of high-level cognition. We argue that two objects are similar to the extent that they seem to have been generated by the same underlying process. The literature on similarity covers settings that extend from the comparison of simple stimuli like tones and colored patches to the comparison of highly-structured objects like narratives. The generative approach is relevant to the entire spectrum of applications, but we are particularly interested in high-level similarity. In particular, we are interested in how similarity judgments draw on intuitive theories, or systems of rich conceptual knowledge [15]. Generative processes and theories are intimately linked. Murphy [14], for example, defines a theory as ‘a set of causal relations that collectively generate or explain the phenomena in a domain.’ We hope that our generative theory provides a framework in which to model how similarity judgments emerge from intuitive theories. We develop a formal theory of similarity and compare it to three existing theories. The featural account [20] suggests that the similarity of two objects is a function of their common and distinctive features, the spatial account suggests that similarity is inversely proportional to distance in a spatial representation, [19] and the transformation account suggests that similarity depends on the number of operations required to transform one object into the other [6]. We show that versions of each of these approaches emerge as special cases of our generative approach, and present an experiment that directly compares our approach with the transformation account. A fourth theory suggests that similarity relies on a process of analogical mapping [5]. We will not discuss this approach in detail, but finish by suggesting how a generative approach to analogy differs from the standard view. Generative processes and similarity Before describing our formal model, we give an informal motivation for a generative approach to similarity. Suppose we are shown a prototype object and asked to describe similar objects we might find in the world. There are two kinds of answers: small perturbations of the prototype, or objects produced by small perturbations of the process that generated the prototype. The second strategy is likely to be more successful than the first, since many perturbations of the prototype will not arise from any plausible generative process, and thus could never appear in practice. By construction, however, an object produced by a perturbation of an existing generative process will have a plausible causal history. To give a concrete example, suppose the prototype is a bug generated by a biological process of growth (Figure 1ii). The bug in i is a small perturbation of the prototype, but seems unlikely to arise since legs are generated in pairs. A perturbation of the generative process might produce a bug with more segments, such as the bug in iii. If we hope to find a bug that is similar but not identical to the prototype, iii is a better bet than i. A sceptic might argue that this one-shot learning problem can be solved by taking the intersection of the set of objects similar to the prototype and the set of obi) ii) Prototype iii) Figure 1: Three bugs. Which is more similar to the prototype — i or iii? jects that are likely to exist. The second set depends critically on generative processes, but the first set (and therefore the notion of similarity) need not. We think it more likely that the notion of similarity is ultimately grounded in the world, and that it evolved for the purpose of comparing real-world objects. If so, then knowledge about what kinds of objects are likely to exist may be deeply bound up with the notion of similarity. The one-shot learning problem is of practical importance, but is not the standard context in which similarity is discussed. More commonly, subjects are shown a pair of objects and asked to rate the similarity of the pair. Note that both objects are observed to exist and the previous argument does not apply. Yet generative processes are still important, since they help pick out the features critical for the similarity comparison. Suppose, for instance, that a forest-dweller discovers a nutritious mushroom. Which is more similar to the mushroom: a mushroom identical except for its size, or a mushroom identical except for its color? Knowing how mushrooms are formed suggests that size is not a key feature. Mushrooms grow from small to large, and the final size of a plant depends on factors like the amount of sunlight it received and the fertility of the soil that it grew in. Reflections like these suggest that the differently-sized mushroom should be judged more similar. A final reason why generative processes matter is that they are deeply related to essentialism. Medin and Ortony [13] note that ‘surface features are frequently constrained by, and sometimes generated by, the deeper, more central parts of objects.’ Even if we observe only the surface features of two objects, it may make sense to judge their similarity by comparing the deeper properties inferred to generate the surface features. Yet we can say more: just as surface features are generated by the essence of the object, the essence itself has a generative history. Surface features are often reliable guides to the essence of an object, but the object’s causal history is a still more reliable indicator, if not a defining criterion of its essence. Keil [9] discusses the case of an animal that is born a skunk, then undergoes surgery that leaves it looking exactly like a raccoon. Since the animal is generated in the same way as a skunk (born of skunk parents), we conclude that it remains a skunk, no matter how it appears on the surface. These examples suggest that the generative approach may help to explain a broad class of theory-dependent inferences. We now present a formal model that attempts to capture the intuitions behind all of these cases. A computational theory of similarity Given a domain D, we develop a theory that specifies the similarity between any two samples from D. A sample from D will usually contain a single object, but working with similarities between sets of objects is useful for some applications. We formalize a generative process as a probability distribution over D that depends on parameter vector θ. Suppose that s1 and s2 are samples from D. We consider two hypotheses: H1 holds that s1 and s2 are independent samples from a single generative process, and H2 holds that the samples are generated from two independently chosen processes. Similarity is defined as the probability that the objects are generated by the same process: that is, the relative posterior probability of H1 compared to H2: sim(s1, s2) = P (H1|s1, s2)


Explaining the Gibbs Sampler

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The Adaptive Nature of Human Categorization

A rational model of human categorization behavior is presented that assumes that categorization reflects the derivation of optimal estimates of the probability of unseen features of objects. A Bayesian analysis is performed of what optimal estimations would be if categories formed a disjoint partitioning of the object space and if features were independently displayed within a category. This Bayesian analysis is placed within an incremental categorization algorithm. The resulting rational model accounts for effects of central tendency of categories, effects of specific instances, learning of linearly nonseparable categories, effects of category labels, extraction of basic level categories, base-rate effects, probability matching in categorization, and trial-by-trial learning functions. Although the rational model considers just I level of categorization, it is shown how predictions can be enhanced by considering higher and lower levels. Considering prediction at the lower, individual level allows integration of this rational analysis of categorization with the earlier rational analysis of memory (Anderson & Milson, 1989).


Workflow mining: discovering process models from event logs

Contemporary workflow management systems are driven by explicit process models, i.e., a completely specified workflow design is required in order to enact a given workflow process. Creating a workflow design is a complicated time-consuming process and, typically, there are discrepancies between the actual workflow processes and the processes as perceived by the management. Therefore, we have developed techniques for discovering workflow models. The starting point for such techniques is a so-called "workflow log" containing information about the workflow process as it is actually being executed. We present a new algorithm to extract a process model from such a log and represent it in terms of a Petri net. However, we also demonstrate that it is not possible to discover arbitrary workflow processes. We explore a class of workflow processes that can be discovered. We show that the /spl alpha/-algorithm can successfully mine any workflow represented by a so-called SWF-net.


Yelp Restaurant Photo Classification

Everyday, millions of users upload numerous photos of different businesses to Yelp. Though most of them are tagged with simple information such as business names, the deep-level information typically are not revealed until looked by humans. Such deep-level information contains things like whether the restaurant is good for lunch or dinner, whether it has outdoor seating, etc. This information can be crucial in helping potential customers to determine whether its the ideal restaurant they want to dine in. Hand labeling these restaurants can be not only costly but also tedious since photos are uploaded almost every second to Yelp. In this project, we used machine learning techniques, specifically image classification methods, to identify the context and rich information of different photos on Yelp. We used three different models to train and test the given dataset. The dataset is provided by Yelp on Kaggle.com. In the training set, 234842 arbitrary-sized images submitted by Yelp users are correlated to one of 2000 Yelp businesses. The number of corresponding photos for each business ranges from 2 to 2974, with mean of 117. The image count distribution is shown by Fig1. Every business is tagged with multiple descriptive labels in 9 aspects. The labels includes: 0: good for lunch 1: good for dinner 2: takes reservations 3: outdoor seating 4: restaurant is expensive 5: has alcohol 6: has table service 7: ambience is classy 8: good for kids As an example, Fig2 illustrates the structure of the dataset. Our goal is to predict the labels of a business in these 9 aspects given its photos. A notable challenge is that the labels are not directly correlated to each image. Individual photos can possibly be indicative in one aspect, yet it is tagged with all the labels of the business. As a result, the dataset might exhibit high noise level. The fraction of each label across the training set is shown in Fig3. Though assumed independent, some label pairs exhibit high covariance, such as ”has alcohol” and ”has table service” (Fig4).


Be Conservative: Enhancing Failure Diagnosis with Proactive Logging

When systems fail in the field, logged error or warning messages are frequently the only evidence available for assessing and diagnosing the underlying cause. Consequently, the efficacy of such logging—how often and how well error causes can be determined via postmortem log messages—is a matter of significant practical importance. However, there is little empirical data about how well existing logging practices work and how they can yet be improved. We describe a comprehensive study characterizing the efficacy of logging practices across five large and widely used software systems. Across 250 randomly sampled reported failures, we first identify that more than half of the failures could not be diagnosed well using existing log data. Surprisingly, we find that majority of these unreported failures are manifested via a common set of generic error patterns (e.g., system call return errors) that, if logged, can significantly ease the diagnosis of these unreported failure cases. We further mechanize this knowledge in a tool called Errlog , that proactively adds appropriate logging statements into source code while adding only 1.4% performance overhead. A controlled user study suggests that Errlog can reduce diagnosis time by 60.7%.


Applying Semantic Web Technologies for Diagnosing Road Traffic Congestions

Diagnosis, or the method to connect causes to its effects, is an important reasoning task for obtaining insight on cities and reaching the concept of sustainable and smarter cities that is envisioned nowadays. This paper, focusing on transportation and its road traffic, presents how road traffic congestions can be detected and diagnosed in quasi real-time. We adapt pure Artificial Intelligence diagnosis techniques to fully exploit knowledge which is captured through relevant semantics-augmented stream and static data from various domains. Our prototype of semantic-aware diagnosis of road traffic congestions, experimented in Dublin Ireland, works efficiently with large, heterogeneous information sources and delivers value-added services to citizens and city managers in quasi real-time.


Analytic Performance Modeling and Optimization of Live VM Migration

Earlier virtual machine (VM) migration techniques consisted of stop-and-copy: the VM was stopped, its address space was copied to a different physical machine, and the VM was restarted at that machine. Recent VM hypervisors support live VM migration, which allows pages to be copied while the VM is running. If any copied page is dirtied (i.e., modified), it has to be copied again. The process stops when a fraction α of the pages need to be copied. Then, the VM is stopped and the remaining pages are copied. This paper derives a model to compute the downtime, total number of pages copied, and network utilization due to VM migration, as a function of α and other parameters under uniform and non-uniform dirtying rates. The paper also presents a non-linear optimization model to find the value of α that minimizes the downtime subject to network utilization constraints.


Trends, challenges and needs for lattice-based cryptography implementations: special session

Advances in computing steadily erode computer security at its foundation, calling for fundamental innovations to strengthen the weakening cryptographic primitives and security protocols. At the same time, the emergence of new computing paradigms, such as Cloud Computing and Internet of Everything, demand that innovations in security extend beyond their foundational aspects, to the actual design and deployment of these primitives and protocols while satisfying emerging design constraints such as latency, compactness, energy efficiency, and agility. While many alternatives have been proposed for symmetric key cryptography and related protocols (e.g., lightweight ciphers and authenticated encryption), the alternatives for public key cryptography are limited to post-quantum cryptography primitives and their protocols. In particular, lattice-based cryptography is a promising candidate, both in terms of foundational properties, as well as its application to traditional security problems such as key exchange, digital signature, and encryption/decryption. We summarize trends in lattice-based cryptographic schemes, some fundamental recent proposals for the use of lattices in computer security, challenges for their implementation in software and hardware, and emerging needs.


Interaction-Aware Topic Model for Microblog Conversations through Network Embedding and User Attention

Traditional topic models are insufficient for topic extraction in social media. The existing methods only consider text information or simultaneously model the posts and the static characteristics of social media. They ignore that one discusses diverse topics when dynamically interacting with different people. Moreover, people who talk about the same topic have different effects on the topic. In this paper, we propose an Interaction-Aware Topic Model (IATM) for microblog conversations by integrating network embedding and user attention. A conversation network linking users based on reposting and replying relationship is constructed to mine the dynamic user behaviours. We model dynamic interactions and user attention so as to learn interaction-aware edge embeddings with social context. Then they are incorporated into neural variational inference for generating the more consistent topics. The experiments on three real-world datasets show that our proposed model is effective.


Dyslexia in English as a second language.

This study focused on English as L2 in a group of Norwegian dyslexic 12 year olds, compared to an age and gender matched control group. Norwegian school children learn English from the first grades on. The subjects were assessed with a test battery of verbal and written tasks. First, they were given a comprehension task; second, a model sentence task; third, two pragmatic tasks, and fourth, three tasks of literacy. The verbal tasks were scored according to comprehension, morphology, syntax and semantics, while the literacy tasks were scored by spelling, translation and reading skills. It was hypothesized that the results of the control group and the dyslexia group would differ on all tasks, but that subgrouping the dyslexia group by comprehension skills would show heterogeneity within the dyslexia group. The data analyses confirmed these hypotheses. Significant differences were seen between the dyslexia group and the control group. However, the subgrouping revealed minor differences between the control group and the subgroup with good comprehension skills, and major differences between the control group and the subgroup with poor comprehension skills. Especially morphology and spelling were difficult for the dyslexia group. The results were tentatively discussed within the framework of biological and cognitive models of how to interpret L2 performance in dyslexia, underlining the importance of further research in L2 acquisition in dyslexia.


Impact of Social Media on Social Anxiety: A Systematic Review

Introduction: Online social networking sites are being used all around the world. However, only recently researchers have started to investigate their relationship with mental health. Evidence coming from literature suggests that they have both advantages and disadvantages for individuals. The aim of this study is to critically review the existent research conducted on the relationship between online social networking and social anxiety. Method: According to PRISMA guidelines, comprehensive systematic searches of electronic databases were conducted (PsychInfo, Cochrane, PubMed, Scopus, Web of Science). Terms related to online social networking were combined with social anxiety terms. After identifying relevant papers, the relationship between social media networking and social anxiety as well as limitations of each study were presented. All of the papers included were cross-sectional, relying on self-report assessments. Conclusion: There are several papers published on the relationship between social media and social anxiety; however, more research needs to be done in order to clarify the influence of one variable over the other. Rigorous experimental studies need to be conducted with different time-point assessments, and bidirectionality should be investigated. Important mediators and moderators have to be considered in order to explain the relationship between social media networking and social anxiety.


Self-organizing neural integration of pose-motion features for human action recognition

The visual recognition of complex, articulated human movements is fundamental for a wide range of artificial systems oriented toward human-robot communication, action classification, and action-driven perception. These challenging tasks may generally involve the processing of a huge amount of visual information and learning-based mechanisms for generalizing a set of training actions and classifying new samples. To operate in natural environments, a crucial property is the efficient and robust recognition of actions, also under noisy conditions caused by, for instance, systematic sensor errors and temporarily occluded persons. Studies of the mammalian visual system and its outperforming ability to process biological motion information suggest separate neural pathways for the distinct processing of pose and motion features at multiple levels and the subsequent integration of these visual cues for action perception. We present a neurobiologically-motivated approach to achieve noise-tolerant action recognition in real time. Our model consists of self-organizing Growing When Required (GWR) networks that obtain progressively generalized representations of sensory inputs and learn inherent spatio-temporal dependencies. During the training, the GWR networks dynamically change their topological structure to better match the input space. We first extract pose and motion features from video sequences and then cluster actions in terms of prototypical pose-motion trajectories. Multi-cue trajectories from matching action frames are subsequently combined to provide action dynamics in the joint feature space. Reported experiments show that our approach outperforms previous results on a dataset of full-body actions captured with a depth sensor, and ranks among the best results for a public benchmark of domestic daily actions.


Bag-of-Discriminative-Words (BoDW) Representation via Topic Modeling

Many of the words in a given document either deliver facts (<italic>objective</italic>) or express opinions (<italic> subjective</italic>), respectively, depending on the topics they are involved in. For example, given a bunch of documents, the word “bug” assigned to the topic “<italic>order Hemiptera</italic>” apparently remarks one object (i.e., one kind of insects), while the same word assigned to the topic “<italic>software </italic>” probably conveys a negative opinion. Motivated by the intuitive assumption that different words have varying degrees of <italic>discriminative</italic> power in delivering the objective sense or the subjective sense with respect to their assigned topics, a model named as <italic>d</italic>iscriminatively <italic>o</italic>bjective- <italic>s</italic>ubjective LDA (dosLDA) is proposed in this paper. The essential idea underlying the proposed dosLDA is that a pair of objective and subjective selection variables are explicitly employed to encode the interplay between topics and discriminative power for the words in documents in a supervised manner. As a result, each document is appropriately represented as “<italic>bag-of-discriminative-words</italic>” (BoDW). The experiments reported on documents and images demonstrate that dosLDA not only performs competitively over traditional approaches in terms of topic modeling and document classification, but also has the ability to discern the discriminative power of each word in terms of its objective or subjective sense with respect to its assigned topic.


Forensic mycology: current perspectives

(unported, v3.0) License. The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed. Permissions beyond the scope of the License are administered by Dove Medical Press Limited. Information on how to request permission may be found at: http://www.dovepress.com/permissions.php Research and Reports in Forensic Medical Science 2015:5 75–83 Research and Reports in Forensic Medical Science Dovepress


Guidelines for the process of cross-cultural adaptation of self-report measures.

With the increase in the number of multinational and multicultural research projects, the need to adapt health status measures for use in other than the source language has also grown rapidly. Most questionnaires were developed in English-speaking countries, but even within these countries, researchers must consider immigrant populations in studies of health, especially when their exclusion could lead to a systematic bias in studies of health care utilization or quality of life. The cross-cultural adaptation of a health status selfadministered questionnaire for use in a new country, culture, and/or language necessitates use of a unique method, to reach equivalence between the original source and target versions of the questionnaire. It is now recognized that if measures are to be used across cultures, the items must not only be translated well linguistically, but also must be adapted culturally to maintain the content validity of the instrument at a conceptual level across different cultures. Attention to this level of detail allows increased confidence that the impact of a disease or its treatment is described in a similar manner in multinational trials or outcome evaluations. The term “cross-cultural adaptation” is used to encompass a process that looks at both language (translation) and cultural adaptation issues in the process of preparing a questionnaire for use in another setting. Cross-cultural adaptations should be considered for several different scenarios. In some cases, this is more obvious than in others. Guillemin et al suggest five different examples of when attention should be paid to this adaptation by comparing the target (where it is going to be used) and source (where it was developed) language and culture. The first scenario is that it is to be used in the same language and culture in which it was developed. No adaptation is necessary. The last scenario is the opposite extreme, the application of a questionnaire in a different culture, language and country—moving the Short Form 36-item questionnaire from the United States (source) to Japan (target) which would necessitate translation and cultural adaptation. The other scenarios are summarized in Table 1 and reflect situations when some translation and/or adaptation is needed. The guidelines described in this document are based on a review of cross-cultural adaptation in the medical, sociological, and psychological literature. This review led to the description of a thorough adaptation process designed to maximize the attainment of semantic, idiomatic, experiential, and conceptual equivalence between the source and target questionnaires.. Further experience in cross-cultural adaptation of generic and diseasespecific instruments and alternative strategies driven by different research groups have led to some refinements in methodology since the 1993 publication.. These guidelines serve as a template for the translation and cultural adaptation process. The process involves the adaptation of individual items, the instructions for the questionnaire, and the response options. The text in the next section outlines the methodology suggested (Stages I–V). The subsequent section (Stage VI) presents a suggested appraisal process whereby an advisory committee or the developers review the process and determine whether this is an acceptable translation. Although such a committee or the developers may not be engaged in tracking translated versions of the instrument, this stage has been included in case there is a tracking system. Records of translated versions not only can save considerable time and effort (by using already available questionnaires) but also avoid erroneous comparisons of results across different translated versions. The process of cross-cultural adaptation tries to produce equivalency between source and target based on content. The assumption that is sometimes made is that this process will ensure retention of psychometric properties such as validity and reliability at an item and/or a scale level. However, this is not necessarily the case: For instance, if the new culture has a different way of approaching a task that makes it inherently more or less difficult compared with other items, it would change the validity, certainly in terms of item-level analyses (such as item response theory, similar to Rasch). Further tests should be conducted on the psychometric properties of the adapted questionnaire after the translation is complete. This will be discussed briefly at the end of the guidelines. In fact, the translation process outlined in this article is the first step in the three-step process adopted by the International Society for Quality of Life Assessment (IQOLA) project. The other two steps From the *Institute for Work and Health; †St. Michael’s Hospital; Departments of ‡Occupational Therapy and 4Medicine and the §Clinical Epidemiology and Health Care Research Program, University of Toronto ¶The University Health Network, Toronto General Hospital; and #Mt. Sinai Hospital, Toronto, Ontario, Canada; **Ecole de Sant, Publique, Facult, de M,decine,Vandoeuvre-les-Nancy, France; and the ††Division of Rheumatology, Department of Medicine, Escola Paulista de Medicina, Universidade Federal de Sõo Paulo, Sõo Paulo, Brazil. Supported in part by the American Academy of Orthopaedic Surgeons and the Institute for Work & Health who co-sponsored the development of these guidelines. DB was supported by a Medical Research Council of Canada PhD Fellowship during the preparation of this work.


Data processing and semantics for advanced internet of things (IoT) applications: modeling, annotation, integration, and perception

This tutorial presents tools and techniques for effectively utilizing the Internet of Things (IoT) for building advanced applications, including the Physical-Cyber-Social (PCS) systems. The issues and challenges related to IoT, semantic data modelling, annotation, knowledge representation (e.g. modelling for constrained environments, complexity issues and time/location dependency of data), integration, analysis, and reasoning will be discussed. The tutorial will describe recent developments on creating annotation models and semantic description frameworks for IoT data (e.g. such as W3C Semantic Sensor Network ontology). A review of enabling technologies and common scenarios for IoT applications from the data and knowledge engineering point of view will be discussed. Information processing, reasoning, and knowledge extraction, along with existing solutions related to these topics will be presented. The tutorial summarizes state-of-the-art research and developments on PCS systems, IoT related ontology development, linked data, domain knowledge integration and management, querying large-scale IoT data, and AI applications for automated knowledge extraction from real world data.


THE RELATIONSHIPS BETWEEN IT FLEXIBILITY, IT-BUSINESS STRATEGIC ALIGNMENT, AND IT CAPABILITY

What seems to still be the main concern for managers in the corporate world across the globe is ITbusiness strategic alignment. This study seeks to address the research problem about the lack of alignment between IT and business strategies. Upon reviewing various literature on this subject, it was found that IT flexibility is one of the most vital factors that help sustain strategic alignment. The researcher upon having a detailed discussion on the possible areas associated with the present body of knowledge has discovered gaps in the studies that have been undertaken on strategic alignment and IT flexibility. This is because IT capability in relation to IT flexibility and strategic alignment has been ignored in the previous studies. As a result, this research proposes a relationship between IT flexibility (i.e., modularity, connectivity and compatibility), IT capability, and strategic alignment.


Deep Blind Image Inpainting

Image inpainting is a challenging problem as it needs to fill the information of the corrupted regions. Most of the existing inpainting algorithms assume that the positions of the corrupted regions are known. Different from the existing methods that usually make some assumptions on the corrupted regions, we present an efficient blind image inpainting algorithm to directly restore a clear image from a corrupted input. Our algorithm is motivated by the residual learning algorithm which aims to learn the missing information in corrupted regions. However, directly using existing residual learning algorithms in image restoration does not well solve this problem as little information is available in the corrupted regions. To solve this problem, we introduce an encoder and decoder architecture to capture more useful information and develop a robust loss function to deal with outliers. Our algorithm can predict the missing information in the corrupted regions, thus facilitating the clear image restoration. Both qualitative and quantitative experimental demonstrate that our algorithm can deal with the corrupted regions of arbitrary shapes and performs favorably against state-of-the-art methods.


Localizing Ashkenazic Jews to Primeval Villages in the Ancient Iranian Lands of Ashkenaz

The Yiddish language is over 1,000 years old and incorporates German, Slavic, and Hebrew elements. The prevalent view claims Yiddish has a German origin, whereas the opposing view posits a Slavic origin with strong Iranian and weak Turkic substrata. One of the major difficulties in deciding between these hypotheses is the unknown geographical origin of Yiddish speaking Ashkenazic Jews (AJs). An analysis of 393 Ashkenazic, Iranian, and mountain Jews and over 600 non-Jewish genomes demonstrated that Greeks, Romans, Iranians, and Turks exhibit the highest genetic similarity with AJs. The Geographic Population Structure analysis localized most AJs along major primeval trade routes in northeastern Turkey adjacent to primeval villages with names that may be derived from "Ashkenaz." Iranian and mountain Jews were localized along trade routes on the Turkey's eastern border. Loss of maternal haplogroups was evident in non-Yiddish speaking AJs. Our results suggest that AJs originated from a Slavo-Iranian confederation, which the Jews call "Ashkenazic" (i.e., "Scythian"), though these Jews probably spoke Persian and/or Ossete. This is compatible with linguistic evidence suggesting that Yiddish is a Slavic language created by Irano-Turko-Slavic Jewish merchants along the Silk Roads as a cryptic trade language, spoken only by its originators to gain an advantage in trade. Later, in the 9th century, Yiddish underwent relexification by adopting a new vocabulary that consists of a minority of German and Hebrew and a majority of newly coined Germanoid and Hebroid elements that replaced most of the original Eastern Slavic and Sorbian vocabularies, while keeping the original grammars intact.


A 6.25Gbps Feed-forward Equalizer in 0.18μm CMOS Technology for SerDes

This paper presents a 6.25Gbps feed-forward equalizer (FFE) to reduce the inter-symbol-interference (ISI) in high-speed transmission backplane. The 3-tap fractionally spaced equalizer consists of a delay line, multiplier & summer cells and an output circuit. Active-inductive peaking circuit and capacitor-degenerated circuit are used in the delay line and the output stage respectively to meet the bandwidth demand. The proposed FFE has been implemented in TSMC 0.18μm CMOS technology with a whole area of 0.5mm×0.51mm including I/O pads. The power consumption of the core circuit is 31.7mW under 1.8V power supply. Post simulation results show that the distorted signal through a 24-inch backplane is well recovered by this equalizer.


Spatiotemporal Sequential Influence Modeling for Location Recommendations: A Gravity-based Approach

Recommending to users personalized locations is an important feature of Location-Based Social Networks (LBSNs), which benefits users who wish to explore new places and businesses to discover potential customers. In LBSNs, social and geographical influences have been intensively used in location recommendations. However, human movement also exhibits spatiotemporal sequential patterns, but only a few current studies consider the spatiotemporal sequential influence of locations on users’ check-in behaviors. In this article, we propose a new gravity model for location recommendations, called LORE, to exploit the spatiotemporal sequential influence on location recommendations. First, LORE extracts sequential patterns from historical check-in location sequences of all users as a Location-Location Transition Graph (L2TG), and utilizes the L2TG to predict the probability of a user visiting a new location through the developed additive Markov chain that considers the effect of all visited locations in the check-in history of the user on the new location. Furthermore, LORE applies our contrived gravity model to weigh the effect of each visited location on the new location derived from the personalized attractive force (i.e., the weight) between the visited location and the new location. The gravity model effectively integrates the spatiotemporal, social, and popularity influences by estimating a power-law distribution based on (i) the spatial distance and temporal difference between two consecutive check-in locations of the same user, (ii) the check-in frequency of social friends, and (iii) the popularity of locations from all users. Finally, we conduct a comprehensive performance evaluation for LORE using three large-scale real-world datasets collected from Foursquare, Gowalla, and Brightkite. Experimental results show that LORE achieves significantly superior location recommendations compared to other state-of-the-art location recommendation techniques.


Orthogonal Coaxial Cavity Filters With Distributed Cross-Coupling

In this letter, six-pole filters are presented, in which the coaxial cavity resonators are arranged with 90 degree rotated spatial orientation. Cross-coupling between nonadjacent resonators will appear. In the new proposed filter topologies the cross-coupling elements are not arranged in separated triplet or quadruplet blocks. They interlace to form distributed cross-coupling. Synthesis of the distributed coupling matrix based on the symmetric canonical prototype is discussed. Two different bandpass filter topologies are presented. The first one has an inline arrangement of six orthogonal coaxial cavity resonators. Its distributed cross-coupling consists of four interlaced triplets, which generate two transmission zeros above the passband. The second filter design has a folded arrangement of two inline triplets with strong cross-coupling. An additional interlaced quadruplet pushes the resulting transmission zeros to the complex frequency domain. Measurement results of two corresponding filter prototypes confirm the predicted performance.


A virtual reality intervention to improve the understanding and empathy for people with dementia in informal caregivers: results of a pilot study.

OBJECTIVE
Informal caregivers often experience psychological distress due to the changing functioning of the person with dementia they care for. Improved understanding of the person with dementia reduces psychological distress. To enhance understanding and empathy in caregivers, an innovative technology virtual reality intervention Through the D'mentia Lens (TDL) was developed to experience dementia, consisting of a virtual reality simulation movie and e-course. A pilot study of TDL was conducted.

METHODS
A pre-test-post-test design was used. Informal caregivers filled out questionnaires assessing person-centeredness, empathy, perceived pressure from informal care, perceived competence and quality of the relationship. At post-test, additional questions about TDL's feasibility were asked.

RESULTS
Thirty-five caregivers completed the pre-test and post-test. Most participants were satisfied with TDL and stated that TDL gave more insight in the perception of the person with dementia. The simulation movie was graded 8.03 out of 10 and the e-course 7.66. Participants significantly improved in empathy, confidence in caring for the person with dementia, and positive interactions with the person with dementia.

CONCLUSION
TDL is feasible for informal caregivers and seems to lead to understanding of and insight in the experience of people with dementia. Therefore, TDL could support informal caregivers in their caregiving role.


Substrate Integrated Waveguide ( SIW ) to Microstrip Transition at X-Band

Substrate integrated waveguide (SIW) is a new form of transmission line. It facilitates the realization of nonplanar (waveguide based) circuits into planar form for easy integration with other planar (microstrip) circuits and systems. This paper describes the design of an SIW to microstrip transition. The transition is broadband covering the frequency range of 8 – 12GHz. The measured in-band insertion loss is below 0.6dB while the return loss is less than 10dB. The circuit is simulated in HFSS and results are measured on vector network analyzer (VNA). Keywords— Microstrip; Substrate Integrated Waveguide; Transition


Why experts make errors

Expert latent f ingerprint examiners were presented with fingerprints taken from real criminal cases. Half of the prints had been previously judged as individualizations and the other half as exclusions. We re-presented the same prints to the same experts who had judged them previously, but provided biasing contextual information in both the individualizations and exclusions. A control set of individualizations and exclusions was also re-presented as part of the study. The control set had no biasing contextual information associated with it. Each expert examined a total of eight past decisions. Two-thirds of the experts made inconsistent decisions. The f indings are discussed in terms of psychological and cognitive vulnerabilities.


Completed Local Binary Count for Rotation Invariant Texture Classification

In this brief, a novel local descriptor, named local binary count (LBC), is proposed for rotation invariant texture classification. The proposed LBC can extract the local binary grayscale difference information, and totally abandon the local binary structural information. Although the LBC codes do not represent visual microstructure, the statistics of LBC features can represent the local texture effectively. In addition, a completed LBC (CLBC) is also proposed to enhance the performance of texture classification. Experimental results obtained from three databases demonstrate that the proposed CLBC can achieve comparable accurate classification rates with completed local binary pattern.


Description of interest regions with local binary patterns

This paper presents a novel method for interest region description. We adopted the idea that the appearance of an interest region can be well characterized by the distribution of its local features. The most well-known descriptor built on this idea is the SIFT descriptor that uses gradient as the local feature. Thus far, existing texture features are not widely utilized in the context of region description. In this paper, we introduce a new texture feature called center-symmetric local binary pattern (CS-LBP) that is a modified version of the well-known local binary pattern (LBP) feature. To combine the strengths of the SIFT and LBP, we use the CS-LBP as the local feature in the SIFT algorithm. The resulting descriptor is called the CS-LBP descriptor. In the matching and object category classification experiments, our descriptor performs favorably compared to the SIFT. Furthermore, the CS-LBP descriptor is computationally simpler than the SIFT.


Survey on LBP based texture descriptors for image classification

The aim of this work is to find the best way for describing a given texture using a local binary pattern (LBP) based approach. First several different approaches are compared, then the best fusion approach is tested on different datasets and compared with several approaches proposed in the literature (for fair comparisons, when possible we have used code shared by the original authors). Our experiments show that a fusion approach based on uniform local quinary pattern (LQP) and a rotation invariant local quinary pattern, where a bin selection based on variance is performed and Neighborhood Preserving Embedding (NPE) feature transform is applied, obtains a method that performs well on all tested datasets. As the classifier, we have tested a stand-alone support vector machine (SVM) and a random subspace ensemble of SVM. We compare several texture descriptors and show that our proposed approach coupled with random subspace ensemble outperforms other recent state-of-the-art approaches. This conclusion is based on extensive experiments conducted in several domains using six benchmark databases. 2011 Elsevier Ltd. All rights reserved.


The Language of Place: Semantic Value from Geospatial Context

There is a relationship between what we say and where we say it. Word embeddings are usually trained assuming that semantically-similar words occur within the same textual contexts. We investigate the extent to which semantically-similar words occur within the same geospatial contexts. We enrich a corpus of geolocated Twitter posts with physical data derived from Google Places and OpenStreetMap, and train word embeddings using the resulting geospatial contexts. Intrinsic evaluation of the resulting vectors shows that geographic context alone does provide useful information about semantic relatedness.


Spherical CNNs

Convolutional Neural Networks (CNNs) have become the method of choice for learning problems involving 2D planar images. However, a number of problems of recent interest have created a demand for models that can analyze spherical images. Examples include omnidirectional vision for drones, robots, and autonomous cars, molecular regression problems, and global weather and climate modelling. A naive application of convolutional networks to a planar projection of the spherical signal is destined to fail, because the space-varying distortions introduced by such a projection will make translational weight sharing ineffective. In this paper we introduce the building blocks for constructing spherical CNNs. We propose a definition for the spherical cross-correlation that is both expressive and rotation-equivariant. The spherical correlation satisfies a generalized Fourier theorem, which allows us to compute it efficiently using a generalized (non-commutative) Fast Fourier Transform (FFT) algorithm. We demonstrate the computational efficiency, numerical accuracy, and effectiveness of spherical CNNs applied to 3D model recognition and atomization energy regression.


A novel method for providing relational databases with rich semantics and natural language processing

Purpose – With the development of systems and applications, the number of users interacting with databases has increased considerably. The relational database model is still considered as the most used model for data storage and manipulation. However, it doesn’t offer any semantic support for the stored data which can facilitate data access for users. Indeed, a large number of users are intimidated when retrieving data because they are non-technical or have little technical knowledge. To overcome this problem, researchers are continuously developing new techniques for Natural Language Interfaces to Databases (NLIDB). Nowadays, the usage of existing NLIDB’s is not wide-spread due to their deficiencies in understanding natural language queries. In this sense, this paper proposes a novel method for intelligent understanding of natural language queries using semantically enriched database sources. Design/Methodology/Approach – First a reverse engineering process is applied to extract relational database hidden semantics. In the second step, the extracted semantics are enriched further using a domain ontology. After this, all semantics are stored in the same relational database. The phase of processing natural language queries uses the stored semantics to generate a semantic-tree. Findings – The evaluation part of our work show the advantages of using a semantically enriched database source to understand natural language queries. Additionally, enriching a relational database has given more flexibility to understand contextual and synonymous words that may be used in a natural language query. Originality/value – Existing NLIDB’s aren’t yet a standard option for interfacing a relational database due their lack for understanding natural language queries. Indeed, the techniques used in the literature have their limits. This paper handles those limits by identifying natural language elements by their semantic nature in order to generate a semantic-tree. This last is a key solution toward intelligent understanding of natural language queries to relational databases.


Teaching a lay theory before college narrows achievement gaps at scale.

Previous experiments have shown that college students benefit when they understand that challenges in the transition to college are common and improvable and, thus, that early struggles need not portend a permanent lack of belonging or potential. Could such an approach-called a lay theory intervention-be effective before college matriculation? Could this strategy reduce a portion of racial, ethnic, and socioeconomic achievement gaps for entire institutions? Three double-blind experiments tested this possibility. Ninety percent of first-year college students from three institutions were randomly assigned to complete single-session, online lay theory or control materials before matriculation (n > 9,500). The lay theory interventions raised first-year full-time college enrollment among students from socially and economically disadvantaged backgrounds exiting a high-performing charter high school network or entering a public flagship university (experiments 1 and 2) and, at a selective private university, raised disadvantaged students' cumulative first-year grade point average (experiment 3). These gains correspond to 31-40% reductions of the raw (unadjusted) institutional achievement gaps between students from disadvantaged and nondisadvantaged backgrounds at those institutions. Further, follow-up surveys suggest that the interventions improved disadvantaged students' overall college experiences, promoting use of student support services and the development of friendship networks and mentor relationships. This research therefore provides a basis for further tests of the generalizability of preparatory lay theories interventions and of their potential to reduce social inequality and improve other major life transitions.


Experts ’ Views on Digital Parenting Strategies

American teenagers are spending much of their time online. Online communities offer teens perspective, acceptance, connection, and education that were once more limited by geography. However, the online world also opens teens to risks. Adults must decide how to guide teen online behavior in a space that they may not understand. We interviewed 16 experts about teen online behavior, related benefits and risks, and risk mitigation methods. We found that experts agree on certain mitigation methods, especially communication and education about online behavior, but are divided over monitoring. We propose a set of possible solutions that promote online safety while preserving teens’ privacy.


Using Machine Learning Methods and Linguistic Features in Single-Document Extractive Summarization

Extractive summarization of text documents usually consists of ranking the document sentences and extracting the top-ranked sentences subject to the summary length constraints. In this paper, we explore the contribution of various supervised learning algorithms to the sentence ranking task. For this purpose, we introduce a novel sentence ranking methodology based on the similarity score between a candidate sentence and benchmark summaries. Our experiments are performed on three benchmark summarization corpora: DUC-2002, DUC2007 and MultiLing-2013. The popular linear regression model achieved the best results in all evaluated datasets. Additionally, the linear regression model, which included POS (Part-of-Speech)-based features, outperformed the one with statistical features only.


How effective is the IEEE 802.11 RTS/CTS handshake in ad hoc networks

IEEE 802.11 MAC mainly relies on two techniques to combat interference: physical carrier sensing and RTS/CTS handshake (also known as “virtual carrier sensing”). Ideally, the RTS/CTS handshake can eliminate most interference. However, the effectiveness of RTS/CTS handshake is based on the assumption that hidden nodes are within transmission range of receivers. In this paper, we prove using analytic models that in ad hoc networks, such an assumption cannot hold due to the fact that power needed for interrupting a packet reception is much lower than that of delivering a packet successfully. Thus, the “virtual carrier sensing” implemented by RTS/CTS handshake cannot prevent all interference as we expect in theory. Physical carrier sensing can complement this in some degree. However, since interference happens at receivers, while physical carrier sensing is detecting transmitters (the same problem causing the hidden terminal situation), physical carrier sensing cannot help much, unless a very large carrier sensing range is adopted, which is limited by the antenna sensitivity. In this paper, we investigate how effective is the RTS/CTS handshake in terms of reducing interference. We show that in some situations, the interference range is much larger than transmission range, where RTS/CTS cannot function well. Then, a simple MAC layer scheme is proposed to solve this problem. Simulation results verify that our scheme can help IEEE 802.11 resolve most interference caused by large interference range.


Learning recursion: Multiple nested and crossed dependencies

Language acquisition in both natural and artificial language learning settings crucially depends on extracting information from ordered sequences. A shared sequence learning mechanism is thus assumed to underlie both natural and artificial language learning. A growing body of empirical evidence is consistent with this hypothesis. By means of artificial language learning experiments, we may therefore gain more insight in this shared mechanism. In this paper, we review empirical evidence from artificial language learning and computational modeling studies, as well as natural language data, and suggest that there are two key factors that help determine processing complexity in sequence learning, and thus in natural language processing. We propose that the specific ordering of non-adjacent dependencies (i.e. nested or crossed), as well as the number of non-adjacent dependencies to be resolved simultaneously (i.e. two or three) are important factors in gaining more insight into the boundaries of human sequence learning; and thus, also in natural language processing. The implications for theories of linguistic competence are discussed.


Validity and Utility of Alternative Predictors of Job Performance

Meta-analysis of the cumulative research on various predictors of job performance shows that for entry-level jobs there is no predictor with validity equal to that of ability, which has a mean validity of .53. For selection on the basis of current job performance, the work sample test, with mean validity of .54, is slightly better. For federal entry-level jobs, substitution of an alternative predictor would cost from $3.12 billion (job tryout) to $15.89 billion per year (age). Hiring on ability has a utility of $15.61 billion per year, but affects minority groups adversely. Hiring on ability by quotas would decrease this utility by 5%. A third strategy—using a low cutoff score—would decrease utility by 83%. Using other predictors in conjunction with ability tests might improve validity and reduce adverse impact, but there is as yet no data base for studying this possibility.


Enhancing Cross-Device Interaction Scripting with Interactive Illustrations

Cross-device interactions involve input and output on multiple computing devices. Implementing and reasoning about interactions that cover multiple devices with a diversity of form factors and capabilities can be complex. To assist developers in programming cross-device interactions, we created DemoScript, a technique that automatically analyzes a cross-device interaction program while it is being written. DemoScript visually illustrates the step-by-step execution of a selected portion or the entire program with a novel, automatically generated cross-device storyboard visualization. In addition to helping developers understand the behavior of the program, DemoScript also allows developers to revise their program by interactively manipulating the cross-device storyboard. We evaluated DemoScript with 8 professional programmers and found that DemoScript significantly improved development efficiency by helping developers interpret and manage cross-device interaction; it also encourages testing to think through the script in a development process.


An alginate-antacid formulation (Gaviscon Double Action Liquid) can eliminate or displace the postprandial 'acid pocket' in symptomatic GERD patients.

BACKGROUND
Recently, an 'acid pocket' has been described in the proximal stomach, particularly evident postprandially in GERD patients, when heartburn is common. By creating a low density gel 'raft' that floats on top of gastric contents, alginate-antacid formulations may neutralise the 'acid pocket'.

AIM
To assess the ability of a commercial high-concentration alginate-antacid formulation to neutralize and/or displace the acid pocket in GERD patients.

METHODS
The 'acid pocket' was studied in ten symptomatic GERD patients. Measurements were made using concurrent stepwise pH pull-throughs, high resolution manometry and fluoroscopy in a semi-recumbent posture. Each subject was studied in three conditions: fasted, 20 min after consuming a high-fat meal and 20 min later after a 20 mL oral dose of an alginate-antacid formulation (Gaviscon Double Action Liquid, Reckitt Benckiser Healthcare, Hull, UK). The relative position of pH transition points (pH >4) to the EGJ high-pressure zone was analysed.

RESULTS
Most patients (8/10) exhibited an acidified segment extending from the proximal stomach into the EGJ when fasted that persisted postprandially. Gaviscon neutralised the acidified segment in six of the eight subjects shifting the pH transition point significantly away from the EGJ. The length and pressure of the EGJ high-pressure zone were minimally affected.

CONCLUSIONS
Gaviscon can eliminate or displace the 'acid pocket' in GERD patients. Considering that EGJ length was unchanged throughout, this effect was likely attributable to the alginate 'raft' displacing gastric contents away from the EGJ. These findings suggest the alginate-antacid formulation to be an appropriately targeted postprandial GERD therapy.


Model compression via distillation and quantization

Deep neural networks (DNNs) continue to make significant advances, solving tasks from image classification to translation or reinforcement learning. One aspect of the field receiving considerable attention is efficiently executing deep models in resource-constrained environments, such as mobile or embedded devices. This paper focuses on this problem, and proposes two new compression methods, which jointly leverage weight quantization and distillation of larger networks, called “teachers,” into compressed “student” networks. The first method we propose is called quantized distillation and leverages distillation during the training process, by incorporating distillation loss, expressed with respect to the teacher network, into the training of a smaller student network whose weights are quantized to a limited set of levels. The second method, differentiable quantization, optimizes the location of quantization points through stochastic gradient descent, to better fit the behavior of the teacher model. We validate both methods through experiments on convolutional and recurrent architectures. We show that quantized shallow students can reach similar accuracy levels to state-of-the-art full-precision teacher models, while providing up to order of magnitude compression, and inference speedup that is almost linear in the depth reduction. In sum, our results enable DNNs for resource-constrained environments to leverage architecture and accuracy advances developed on more powerful devices.


Expectancy-Value Theory of Achievement Motivation.

We discuss the expectancy-value theory of motivation, focusing on an expectancy-value model developed and researched by Eccles, Wigfield, and their colleagues. Definitions of crucial constructs in the model, including ability beliefs, expectancies for success, and the components of subjective task values, are provided. These definitions are compared to those of related constructs, including self-efficacy, intrinsic and extrinsic motivation, and interest. Research is reviewed dealing with two issues: (1) change in children's and adolescents' ability beliefs, expectancies for success, and subjective values, and (2) relations of children's and adolescents' ability-expectancy beliefs and subjective task values to their performance and choice of activities. Copyright 2000 Academic Press.


Smart street light system looking like usual street lights based on sensor networks

Currently, in the whole world, enormous electric energy is consumed by the street lights, which are automatically turn on when it becomes dark and automatically turn off when it becomes bright. This is the huge waste of energy in the whole world and should be changed. This paper discusses a smart street light system, whose concept is proposed by Fujii et al. The main aim of smart street light systems is that lights turn on when needed and light turn off when not needed. Moreover, the smart street light system in this paper behaves like usual street lights that turn on all night. The ideal behavior of the smart street light system is that no one finds turn-off of street lights at night. Whenever someone see street lights, they turn on and whenever no one see street lights, they turn off. The smart street light system consists of LED lights, brightness sensors, motion sensors and short-distance communication networks. The lights turn on before pedestrians and vehicles come and turn off or reduce brightness when there is no one. It will be difficult for pedestrians and drivers of vehicles to distinguish our smart street lights and the conventional street lights because our street lights all turn on before they come.


On the Consistency of Top-k Surrogate Losses

The top-k error is often employed to evaluate performance for challenging classification tasks in computer vision as it is designed to compensate for ambiguity in ground truth labels. This practical success motivates our theoretical analysis of consistent top-k classification. To this end, we define top-k calibration as a necessary and sufficient condition for consistency, for bounded below loss functions. Unlike prior work, our analysis of top-k calibration handles non-uniqueness of the predictor scores, and extends calibration to consistency – providing a theoretically sound basis for analysis of this topic. Based on the topk calibration analysis, we propose a rich class of top-k calibrated Bregman divergence surrogates. Our analysis continues by showing previously proposed hinge-like top-k surrogate losses are not top-k calibrated and thus inconsistent. On the other hand, we propose two new hinge-like losses, one which is similarly inconsistent, and one which is consistent. Our empirical results highlight theoretical claims, confirming our analysis of the consistency of these losses.


Genetic studies of human diversity in East Asia.

East Asia is one of the most important regions for studying evolution and genetic diversity of human populations. Recognizing the relevance of characterizing the genetic diversity and structure of East Asian populations for understanding their genetic history and designing and interpreting genetic studies of human diseases, in recent years researchers in China have made substantial efforts to collect samples and generate data especially for markers on Y chromosomes and mtDNA. The hallmark of these efforts is the discovery and confirmation of consistent distinction between northern and southern East Asian populations at genetic markers across the genome. With the confirmation of an African origin for East Asian populations and the observation of a dominating impact of the gene flow entering East Asia from the south in early human settlement, interpretation of the north-south division in this context poses the challenge to the field. Other areas of interest that have been studied include the gene flow between East Asia and its neighbouring regions (i.e. Central Asia, the Sub-continent, America and the Pacific Islands), the origin of Sino-Tibetan populations and expansion of the Chinese.


A Secured System for Information Hiding in Image Steganography using Genetic Algorithm and Cryptography

Encryption is used to securely communicate data in open networks. Each type of data has its own structures; therefore according to the data the different techniques should be used to defend confidential data. Among them digital images are also very popular to carry confidential information in untrusted networks. . For pleasing the defense of data hiding and communication over network, the proposed system uses cryptographic algorithm along with Steganography. In the proposed system, the file which user want to make secure is firstly compressed to shrink in size and then the compressed data is altered into cipher text by using AES cryptographic algorithm and then the encrypted data is concealed in the image. In order to hide the information over the image in complex manner the genetic algorithm based technique is implemented which is used to evaluate the valuable pixels where the data can be hide in a secure manner. In addition of that, for hiding the information in images, the LSB (least significant bits) based steganographic method is used after the selection of eligible pixels. The implementation of the anticipated technique is performed using JAVA technology and for performance evaluation the time and space complexity is computed. In addition of that a


Generating multi-fingered robotic grasps via deep learning

This paper presents a deep learning architecture for detecting the palm and fingertip positions of stable grasps directly from partial object views. The architecture is trained using RGBD image patches of fingertip and palm positions from grasps computed on complete object models using a grasping simulator. At runtime, the architecture is able to estimate grasp quality metrics without the need to explicitly calculate the given metric. This ability is useful as the exact calculation of these quality functions is impossible from an incomplete view of a novel object without any tactile feedback. This architecture for grasp quality prediction provides a framework for generalizing grasp experience from known to novel objects.