Taking perspective into account in a communicative task

Previous neuroimaging studies of spatial perspective taking have tended not to activate the brain's mentalising network. We predicted that a task that requires the use of perspective taking in a communicative context would lead to the activation of mentalising regions. In the current task, participants followed auditory instructions to move objects in a set of shelves. A 2x2 factorial design was employed. In the Director factor, two directors (one female and one male) either stood behind or next to the shelves, or were replaced by symbolic cues. In the Object factor, participants needed to use the cues (position of the directors or symbolic cues) to select one of three possible objects, or only one object could be selected. Mere presence of the Directors was associated with activity in the superior dorsal medial prefrontal cortex (MPFC) and the superior/middle temporal sulci, extending into the extrastriate body area and the posterior superior temporal sulcus (pSTS), regions previously found to be responsive to human bodies and faces respectively. The interaction between the Director and Object factors, which requires participants to take into account the perspective of the director, led to additional recruitment of the superior dorsal MPFC, a region activated when thinking about dissimilar others' mental states, and the middle temporal gyri, extending into the left temporal pole. Our results show that using perspective taking in a communicative context, which requires participants to think not only about what the other person sees but also about his/her intentions, leads to the recruitment of superior dorsal MPFC and parts of the social brain network.


3-Axis Magnetic Sensor Array System for Tracking Magnet's Position and Orientation

In medical diagnoses and treatments, e.g., the endoscopy, the dosage transition monitoring, it is often desirable to wirelessly track an object that moves through the human GI tract. In this paper, we present a magnetic localization and orientation system for such applications. This system uses a small magnet enclosed in the object to serve as excitation source. It does not require the connection wire and power supply for excitation signal. When the magnet moves, it establishes a static magnetic field around, whose intensity is related to the magnet's position and orientation. With the magnetic sensors, the magnetic intensities in some pre-determined spatial points can be detected, and the magnet's position and orientation parameters can be calculated based on an appropriate algorithm. Here, we propose a real-time tracking system built by Honeywell 3-axis magnetic sensors, HMC1053, as well as the computer sampling circuit. The results show that satisfactory tracking accuracy (average localization error is 3.3 mm) can be achieved using a sensor array with enough number of the 3-axis magnetic sensors


A Polymer-Based Capacitive Sensing Array for Normal and Shear Force Measurement

In this work, we present the development of a polymer-based capacitive sensing array. The proposed device is capable of measuring normal and shear forces, and can be easily realized by using micromachining techniques and flexible printed circuit board (FPCB) technologies. The sensing array consists of a polydimethlysiloxane (PDMS) structure and a FPCB. Each shear sensing element comprises four capacitive sensing cells arranged in a 2 × 2 array, and each capacitive sensing cell has two sensing electrodes and a common floating electrode. The sensing electrodes as well as the metal interconnect for signal scanning are implemented on the FPCB, while the floating electrodes are patterned on the PDMS structure. This design can effectively reduce the complexity of the capacitive structures, and thus makes the device highly manufacturable. The characteristics of the devices with different dimensions were measured and discussed. A scanning circuit was also designed and implemented. The measured maximum sensitivity is 1.67%/mN. The minimum resolvable force is 26 mN measured by the scanning circuit. The capacitance distributions induced by normal and shear forces were also successfully captured by the sensing array.


The Balanced Scorecard: Translating Strategy Into Action


Workflow Automation: Overview and Research Issues

Workflow management systems, a relatively recent technology, are designed to make work more efficient, integrate heterogeneous application systems, and support interorganizational processes in electronic commerce applications. In this paper, we introduce the field of workflow automation, the subject of this special issue of Information Systems Frontiers. In the first part of the paper, we provide basic definitions and frameworks to aid understanding of workflow management technologies. In the remainder of the paper, we discuss technical and management research opportunities in this field and discuss the other contributions to the special issue.


A Novel Anomaly Detection Scheme Based on Principal Component Classifier

This paper proposes a novel scheme that uses robust principal component classifier in intrusion detection problems where the training data may be unsupervised. Assuming that anomalies can be treated as outliers, an intrusion predictive model is constructed from the major and minor principal components of the normal instances. A measure of the difference of an anomaly from the normal instance is the distance in the principal component space. The distance based on the major components that account for 50% of the total variation and the minor components whose eigenvalues less than 0.20 is shown to work well. The experiments with KDD Cup 1999 data demonstrate that the proposed method achieves 98.94% in recall and 97.89% in precision with the false alarm rate 0.92% and outperforms the nearest neighbor method, density-based local outliers (LOF) approach, and the outlier detection algorithm based on Canberra metric.


CredFinder: A real-time tweets credibility assessing system

Lately, Twitter has grown to be one of the most favored ways of disseminating information to people around the globe. However, the main challenge faced by the users is how to assess the credibility of information posted through this social network in real time. In this paper, we present a real-time content credibility assessment system named CredFinder, which is capable of measuring the trustworthiness of information through user analysis and content analysis. The proposed system is capable of providing a credibility score for each user's tweets. Hence, it provides users with the opportunity to judge the credibility of information faster. CredFinder consists of two parts: a frontend in the form of an extension to the Chrome browser that collects tweets in real time from a Twitter search or a user-timeline page and a backend that analyzes the collected tweets and assesses their credibility.


Design of an Ultra-Wideband Pulse Generator Based on Avalanche Transistor

Based on the avalanche effect of avalanche transistor, a kind of ultra-wideband nanosecond pulse circuit has been designed, whose frequency, pulse width and amplitude are tunable. In this paper, the principle, structure and selection of components' parameters in the circuit are analyzed in detail. The circuit generates periodic negative pulse, whose pulse full width is 890 ps and pulse amplitude is -11.2 V in simulation mode. By setting up circuit for experiment and changing parameters properly, a kind of ultra-wideband pulse with pulse width of 2.131 ns and pulse amplitude of -9.23 V is achieved. With the features such as simple structure, stable and reliable performance and low cost, this pulse generator is applicable to ultra-wideband wireless communication system.


A 0.5ps 1.4mW 50MS/s Nyquist bandwidth time amplifier based two-step flash-ΔΣ time-to-digital converter

We propose a 50-MS/s two-step flash-ΔΣ time-to-digital converter (TDC) using stable time amplifiers (TAs). The TDC demonstrates low-levels of shaped quantization noise. The system is simulated in 40-nm CMOS and consumes 1.3 mA from a 1.1 V supply. The bandwidth is broadened to Nyquist rate. At frequencies below 25 MHz, the integrated TDC error is as low as 143 fsrms, which is equal to an equivalent TDC resolution of 0.5 ps.


Brain-computer interfaces: communication and restoration of movement in paralysis.

The review describes the status of brain-computer or brain-machine interface research. We focus on non-invasive brain-computer interfaces (BCIs) and their clinical utility for direct brain communication in paralysis and motor restoration in stroke. A large gap between the promises of invasive animal and human BCI preparations and the clinical reality characterizes the literature: while intact monkeys learn to execute more or less complex upper limb movements with spike patterns from motor brain regions alone without concomitant peripheral motor activity usually after extensive training, clinical applications in human diseases such as amyotrophic lateral sclerosis and paralysis from stroke or spinal cord lesions show only limited success, with the exception of verbal communication in paralysed and locked-in patients. BCIs based on electroencephalographic potentials or oscillations are ready to undergo large clinical studies and commercial production as an adjunct or a major assisted communication device for paralysed and locked-in patients. However, attempts to train completely locked-in patients with BCI communication after entering the complete locked-in state with no remaining eye movement failed. We propose that a lack of contingencies between goal directed thoughts and intentions may be at the heart of this problem. Experiments with chronically curarized rats support our hypothesis; operant conditioning and voluntary control of autonomic physiological functions turned out to be impossible in this preparation. In addition to assisted communication, BCIs consisting of operant learning of EEG slow cortical potentials and sensorimotor rhythm were demonstrated to be successful in drug resistant focal epilepsy and attention deficit disorder. First studies of non-invasive BCIs using sensorimotor rhythm of the EEG and MEG in restoration of paralysed hand movements in chronic stroke and single cases of high spinal cord lesions show some promise, but need extensive evaluation in well-controlled experiments. Invasive BMIs based on neuronal spike patterns, local field potentials or electrocorticogram may constitute the strategy of choice in severe cases of stroke and spinal cord paralysis. Future directions of BCI research should include the regulation of brain metabolism and blood flow and electrical and magnetic stimulation of the human brain (invasive and non-invasive). A series of studies using BOLD response regulation with functional magnetic resonance imaging (fMRI) and near infrared spectroscopy demonstrated a tight correlation between voluntary changes in brain metabolism and behaviour.


iCONCUR: informed consent for clinical data and bio-sample use for research

Background
Implementation of patient preferences for use of electronic health records for research has been traditionally limited to identifiable data. Tiered e-consent for use of de-identified data has traditionally been deemed unnecessary or impractical for implementation in clinical settings.

Methods
We developed a web-based tiered informed consent tool called informed consent for clinical data and bio-sample use for research (iCONCUR) that honors granular patient preferences for use of electronic health record data in research. We piloted this tool in 4 outpatient clinics of an academic medical center.

Results
Of patients offered access to iCONCUR, 394 agreed to participate in this study, among whom 126 patients accessed the website to modify their records according to data category and data recipient. The majority consented to share most of their data and specimens with researchers. Willingness to share was greater among participants from an Human Immunodeficiency Virus (HIV) clinic than those from internal medicine clinics. The number of items declined was higher for for-profit institution recipients. Overall, participants were most willing to share demographics and body measurements and least willing to share family history and financial data. Participants indicated that having granular choices for data sharing was appropriate, and that they liked being informed about who was using their data for what purposes, as well as about outcomes of the research.

Conclusion
This study suggests that a tiered electronic informed consent system is a workable solution that respects patient preferences, increases satisfaction, and does not significantly affect participation in research.


Automotive radome design - fishnet structure for 79 GHz

Metamaterials are considered as an option for quasi-optical matching of automotive radar radomes, lowering transmission loss and minimizing reflections. This paper shows a fishnet structure design for the 79 GHz band which is suitable for this type of matching and which exhibits a negative index of refraction. The measured transmission loss is 0.9 dB at 79 GHz. A tolerance study concerning copper plating, substrate permittivity, oblique incidence, and polarization is shown. Quasi-optical measurements were done in the range of 60 – 90 GHz, which agree with simulated results.


Avatar-independent scripting for real-time gesture animation

When animation of a humanoid figure is to be generated at run-time, instead of by replaying precomposed motion clips, some method is required of specifying the avatar’s movements in a form from which the required motion data can be automatically generated. This form must be of a more abstract nature than raw motion data: ideally, it should be independent of the particular avatar’s proportions, and both writable by hand and suitable for automatic generation from higher-level descriptions of the required actions. We describe here the development and implementation of such a scripting language for the particular area of sign languages of the deaf, called SiGML (Signing Gesture Markup Language), based on the existing HamNoSys notation for sign languages. We conclude by suggesting how this work may be extended to more general animation for interactive virtual reality applications.


Analyzing multiple configurations of a C program

Preprocessor conditionals are heavily used in C programs since they allow the source code to be configured for different platforms or capabilities. However, preprocessor conditionals, as well as other preprocessor directives, are not part of the C language. They need to be evaluated and removed, and so a single configuration selected, before parsing can take place. Most analysis and program understanding tools run on this preprocessed version of the code so their results are based on a single configuration. This paper describes the approach of CRefactory, a refactoring tool for C programs. A refactoring tool cannot consider only a single configuration: changing the code for one configuration may break the rest of the code. CRefactory analyses the program for all possible configurations simultaneously. CRefactory also preserves preprocessor directives and integrates them in the internal representations. The paper also presents metrics from two case studies to show that CRefactory's program representation is practical.


The contribution of electrophysiology to functional connectivity mapping

A powerful way to probe brain function is to assess the relationship between simultaneous changes in activity across different parts of the brain. In recent years, the temporal activity correlation between brain areas has frequently been taken as a measure of their functional connections. Evaluating 'functional connectivity' in this way is particularly popular in the fMRI community, but has also drawn interest among electrophysiologists. Like hemodynamic fluctuations observed with fMRI, electrophysiological signals display significant temporal fluctuations, even in the absence of a stimulus. These neural fluctuations exhibit a correlational structure over a wide range of spatial and temporal scales. Initial evidence suggests that certain aspects of this correlational structure bear a high correspondence to so-called functional networks defined using fMRI. The growing family of methods to study activity covariation, combined with the diverse neural mechanisms that contribute to the spontaneous fluctuations, has somewhat blurred the operational concept of functional connectivity. What is clear is that spontaneous activity is a conspicuous, energy-consuming feature of the brain. Given its prominence and its practical applications for the functional connectivity mapping of brain networks, it is of increasing importance that we understand its neural origins as well as its contribution to normal brain function.


Practical Backscatter Communication Systems for Battery-Free Internet of Things: A Tutorial and Survey of Recent Research

Backscatter presents an emerging ultralow-power wireless communication paradigm. The ability to offer submilliwatt power consumption makes it a competitive core technology for Internet of Things (IoT) applications. In this article, we provide a tutorial of backscatter communication from the signal processing perspective as well as a survey of the recent research activities in this domain, primarily focusing on bistatic backscatter systems. We also discuss the unique real-world applications empowered by backscatter communication and identify open questions in this domain. We believe this article will shed light on the low-power wireless connectivity design toward building and deploying IoT services in the wild.


OF A TUNABLE STIFFNESS COMPOSITE LEG FOR DYNAMIC LOCOMOTION

Passively compliant legs have been instrumental in the development of dynamically running legged robots. Having properly tuned leg springs is essential for stable, robust and energetically efficient running at high speeds. Recent simulation studies indicate that having variable stiffness legs, as animals do, can significantly improve the speed and stability of these robots in changing environmental conditions. However, to date, the mechanical complexities of designing usefully robust tunable passive compliance into legs has precluded their implementation on practical running robots. This paper describes a new design of a ”structurally controlled variable stiffness” leg for a hexapedal running robot. This new leg improves on previous designs’ performance and enables runtime modification of leg stiffness in a small, lightweight, and rugged package. Modeling and leg test experiments are presented that characterize the improvement in stiffness range, energy storage, and dynamic coupling properties of these legs. We conclude that this variable stiffness leg design is now ready for implementation and testing on a dynamical running robot.


An in-depth study of the promises and perils of mining GitHub

With over 10 million git repositories, GitHub is becoming one of the most important sources of software artifacts on the Internet. Researchers mine the information stored in GitHub’s event logs to understand how its users employ the site to collaborate on software, but so far there have been no studies describing the quality and properties of the available GitHub data. We document the results of an empirical study aimed at understanding the characteristics of the repositories and users in GitHub; we see how users take advantage of GitHub’s main features and how their activity is tracked on GitHub and related datasets to point out misalignment between the real and mined data. Our results indicate that while GitHub is a rich source of data on software development, mining GitHub for research purposes should take various potential perils into consideration. For example, we show that the majority of the projects are personal and inactive, and that almost 40 % of all pull requests do not appear as merged even though they were. Also, approximately half of GitHub’s registered users do not have public activity, while the activity of GitHub users in repositories is not always easy to pinpoint. We use our identified perils to see if they can pose validity threats; we review selected papers from the MSR 2014 Mining Challenge and see if there are potential impacts to consider. We provide a set of recommendations for software engineering researchers on how to approach the data in GitHub.


Unsupervised Segmentation of 3D Medical Images Based on Clustering and Deep Representation Learning

This paper presents a novel unsupervised segmentation method for 3D medical images. Convolutional neural networks (CNNs) have brought significant advances in image segmentation. However, most of the recent methods rely on supervised learning, which requires large amounts of manually annotated data. Thus, it is challenging for these methods to cope with the growing amount of medical images. This paper proposes a unified approach to unsupervised deep representation learning and clustering for segmentation. Our proposed method consists of two phases. In the first phase, we learn deep feature representations of training patches from a target image using joint unsupervised learning (JULE) that alternately clusters representations generated by a CNN and updates the CNN parameters using cluster labels as supervisory signals. We extend JULE to 3D medical images by utilizing 3D convolutions throughout the CNN architecture. In the second phase, we apply k-means to the deep representations from the trained CNN and then project cluster labels to the target image in order to obtain the fully segmented image. We evaluated our methods on three images of lung cancer specimens scanned with micro-computed tomography (micro-CT). The automatic segmentation of pathological regions in micro-CT could further contribute to the pathological examination process. Hence, we aim to automatically divide each image into the regions of invasive carcinoma, noninvasive carcinoma, and normal tissue. Our experiments show the potential abilities of unsupervised deep representation learning for medical image segmentation.


Optimal Multiserver Configuration for Profit Maximization in Cloud Computing

As cloud computing becomes more and more popular, understanding the economics of cloud computing becomes critically important. To maximize the profit, a service provider should understand both service charges and business costs, and how they are determined by the characteristics of the applications and the configuration of a multiserver system. The problem of optimal multiserver configuration for profit maximization in a cloud computing environment is studied. Our pricing model takes such factors into considerations as the amount of a service, the workload of an application environment, the configuration of a multiserver system, the service-level agreement, the satisfaction of a consumer, the quality of a service, the penalty of a low-quality service, the cost of renting, the cost of energy consumption, and a service provider's margin and profit. Our approach is to treat a multiserver system as an M/M/m queuing model, such that our optimization problem can be formulated and solved analytically. Two server speed and power consumption models are considered, namely, the idle-speed model and the constant-speed model. The probability density function of the waiting time of a newly arrived service request is derived. The expected service charge to a service request is calculated. The expected net business gain in one unit of time is obtained. Numerical calculations of the optimal server size and the optimal server speed are demonstrated.


Overview on Additive Manufacturing Technologies

This paper provides an overview on the main additive manufacturing/3D printing technologies suitable for many satellite applications and, in particular, radio-frequency components. In fact, nowadays they have become capable of producing complex net-shaped or nearly net-shaped parts in materials that can be directly used as functional parts, including polymers, metals, ceramics, and composites. These technologies represent the solution for low-volume, high-value, and highly complex parts and products.


Churn Prediction : Does Technology Matter ?

The aim of this paper is to identify the most suitable model for churn prediction based on three different techniques. The paper identifies the variables that affect churn in reverence of customer complaints data and provides a comparative analysis of neural networks, regression trees and regression in their capabilities of predicting customer churn. Keywords—Churn, Decision Trees, Neural Networks, Regression.


Learning from imbalanced data sets with boosting and data generation: the DataBoost-IM approach

Learning from imbalanced data sets, where the number of examples of one (majority) class is much higher than the others, presents an important challenge to the machine learning community. Traditional machine learning algorithms may be biased towards the majority class, thus producing poor predictive accuracy over the minority class. In this paper, we describe a new approach that combines boosting, an ensemble-based learning algorithm, with data generation to improve the predictive power of classifiers against imbalanced data sets consisting of two classes. In the DataBoost-IM method, hard examples from both the majority and minority classes are identified during execution of the boosting algorithm. Subsequently, the hard examples are used to separately generate synthetic examples for the majority and minority classes. The synthetic data are then added to the original training set, and the class distribution and the total weights of the different classes in the new training set are rebalanced. The DataBoost-IM method was evaluated, in terms of the F-measures, G-mean and overall accuracy, against seventeen highly and moderately imbalanced data sets using decision trees as base classifiers. Our results are promising and show that the DataBoost-IM method compares well in comparison with a base classifier, a standard benchmarking boosting algorithm and three advanced boosting-based algorithms for imbalanced data set. Results indicate that our approach does not sacrifice one class in favor of the other, but produces high predictions against both minority and majority classes.


Two Modifications of CNN


Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms

With the goal of reducing computational costs without sacrificing accuracy, we describe two algorithms to find sets of prototypes for nearest neighbor classification. Here, the term “prototypes” refers to the reference instances used in a nearest neighbor computation — the instances with respect to which similarity is assessed in order to assign a class to a new data item. Both algorithms rely on stochastic techniques to search the space of sets of prototypes and are simple to implement. The first is a Monte Carlo sampling algorithm; the second applies random mutation hill climbing. On four datasets we show that only three or four prototypes sufficed to give predictive accuracy equal or superior to a basic nearest neighbor algorithm whose run-time storage costs were approximately 10 to 200 times greater. We briefly investigate how random mutation hill climbing may be applied to select features and prototypes simultaneously. Finally, we explain the performance of the sampling algorithm on these datasets in terms of a statistical measure of the extent of clustering displayed by the target classes.


Bayesian network classifiers for identifying the slope of the customer lifecycle of long-life customers

Undoubtedly, Customer Relationship Management (CRM) has gained its importance through the statement that acquiring a new customer is several times more costly than retaining and selling additional products to existing customers. Consequently, marketing practitioners are currently often focusing on retaining customers for as long as possible. However, recent findings in relationship marketing literature have shown that large differences exist within the group of long-life customers in terms of spending and spending evolution. Therefore, this paper focuses on introducing a measure of a customer’s future spending evolution that might improve relationship marketing decision making. In this study, from a marketing point of view, we focus on predicting whether a newly acquired customer will increase or decrease his/her future spending from initial purchase information. This is essentially a classification task. The main contribution of this study lies in comparing and evaluating several Bayesian network classifiers with statistical and other artificial intelligence techniques for the purpose of classifying customers in the binary classification problem at hand. Certain Bayesian network classifiers have been recently proposed in the artificial


Big Data : New Tricks for Econometrics

5 Nowadays computers are in the middle of most economic transactions. 6 These “computer-mediated transactions” generate huge amounts of 7 data, and new tools can be used to manipulate and analyze this data. 8 This essay offers a brief introduction to some of these tools and meth9 ods. 10 Computers are now involved in many economic transactions and can cap11 ture data associated with these transactions, which can then be manipulated 12 and analyzed. Conventional statistical and econometric techniques such as 13 regression often work well but there are issues unique to big data sets that 14 may require different tools. 15 First, the sheer size of the data involved may require more powerful data 16 manipulation tools. Second, we may have more potential predictors than 17 appropriate for estimation, so we need to do some kind of variable selection. 18 Third, large data sets may allow for more flexible relationships than simple 19 ∗Hal Varian is Chief Economist, Google Inc., Mountain View, California, and Emeritus Professor of Economics, University of California, Berkeley, California. Thanks to Jeffrey Oldham, Tom Zhang, Rob On, Pierre Grinspan, Jerry Friedman, Art Owen, Steve Scott, Bo Cowgill, Brock Noland, Daniel Stonehill, Robert Snedegar, Gary King, the editors of this journal for comments on earlier versions of this paper. 1 linear models. Machine learning techniques such as decision trees, support 20 vector machines, neural nets, deep learning and so on may allow for more 21 effective ways to model complex relationships. 22 In this essay I will describe a few of these tools for manipulating and an23 alyzing big data. I believe that these methods have a lot to offer and should 24 be more widely known and used by economists. In fact, my standard advice 25 to graduate students these days is “go to the computer science department 26 and take a class in machine learning.” There have been very fruitful collabo27 rations between computer scientists and statisticians in the last decade or so, 28 and I expect collaborations between computer scientists and econometricians 29 will also be productive in the future. 30 1 Tools to manipulate big data 31 Economists have historically dealt with data that fits in a spreadsheet, but 32 that is changing as new more detailed data becomes available; see Einav 33 and Levin [2013] for several examples and discussion. If you have more 34 than a million or so rows in a spreadsheet, you probably want to store it in a 35 relational database, such as MySQL. Relational databases offer a flexible way 36 to store, manipulate and retrieve data using a Structured Query Language 37 (SQL) which is easy to learn and very useful for dealing with medium-sized 38 data sets. 39 However, if you have several gigabytes of data or several million observa40 tions, standard relational databases become unwieldy. Databases to manage 41 data of this size are generically known as “NoSQL” databases. The term is 42 used rather loosely, but is sometimes interpreted as meaning “not only SQL.” 43 NoSQL databases are more primitive than SQL databases in terms of data 44 manipulation capabilities but can handle larger amounts of data. 45 Due to the rise of computer mediated transactions, many companies have 46 found it necessary to develop systems to process billions of transactions per 47


Cooperative Intersection Management: A Survey

Intersection management is one of the most challenging problems within the transport system. Traffic light-based methods have been efficient but are not able to deal with the growing mobility and social challenges. On the other hand, the advancements of automation and communications have enabled cooperative intersection management, where road users, infrastructure, and traffic control centers are able to communicate and coordinate the traffic safely and efficiently. Major techniques and solutions for cooperative intersections are surveyed in this paper for both signalized and nonsignalized intersections, whereas focuses are put on the latter. Cooperative methods, including time slots and space reservation, trajectory planning, and virtual traffic lights, are discussed in detail. Vehicle collision warning and avoidance methods are discussed to deal with uncertainties. Concerning vulnerable road users, pedestrian collision avoidance methods are discussed. In addition, an introduction to major projects related to cooperative intersection management is presented. A further discussion of the presented works is given with highlights of future research topics. This paper serves as a comprehensive survey of the field, aiming at stimulating new methods and accelerating the advancement of automated and cooperative intersections.


Sequential Mining: Patterns and Algorithms Analysis

This paper presents and analysis the common existing sequential pattern mining algorithms. It presents a classifying study of sequential pattern-mining algorithms into five extensive classes. First, on the basis of Apriori-based algorithm, second on Breadth First Search-based strategy, third on Depth First Search strategy, fourth on sequential closed-pattern algorithm and five on the basis of incremental pattern mining algorithms. At the end, a comparative analysis is done on the basis of important key features supported by various algorithms. This study gives an enhancement in the understanding of the approaches of sequential


Microstrip-fed circular substrate integrated waveguide (SIW) cavity resonator and antenna

Substrate integrated waveguide (SIW) cavity resonators are among the emerging group of SIW-based circuit components that are gaining popularity and increasingly employed in integrated microwave and mm-wave circuits. The SIW cavities offer a significantly enhanced performance in comparison to the previously available planar microwave resonators. The high quality factor of the waveguide-based cavity resonators enables designing microwave oscillators with very low phase noise as well as compact high-gain antennas [1–4]. The SIW-cavity-based antennas also show much promise for implementation of low-cost lightweight fully-integrated high-gain antennas that find application in ultra-light communication satellites, low payload spacecrafts, high-frequency radar, and sensors. In this paper, a circular SIW cavity resonator, which is fed by a microstrip and via probe, is presented. The microstrip feed is optimized to achieve a return loss of better than 20dB from both simulation and measurements. A resonance frequency of 16.79GHz and quality factor of 76.3 are determined from vector network analyzer (VNA) measurements. Due to the folded slot on the upper conductor layer, the resonator is an efficient cavity backed antenna. A maximum gain of over 7dB is measured in an anechoic chamber at the resonance frequency of 16.79 GHz.


Quality driven web services composition

The process-driven composition of Web services is emerging as a promising approach to integrate business applications within and across organizational boundaries. In this approach, individual Web services are federated into composite Web services whose business logic is expressed as a process model. The tasks of this process model are essentially invocations to functionalities offered by the underlying component services. Usually, several component services are able to execute a given task, although with different levels of pricing and quality. In this paper, we advocate that the selection of component services should be carried out during the execution of a composite service, rather than at design-time. In addition, this selection should consider multiple criteria (e.g., price, duration, reliability), and it should take into account global constraints and preferences set by the user (e.g., budget constraints). Accordingly, the paper proposes a global planning approach to optimally select component services during the execution of a composite service. Service selection is formulated as an optimization problem which can be solved using efficient linear programming methods. Experimental results show that this global planning approach outperforms approaches in which the component services are selected individually for each task in a composite service.


Navigating the massive world of reddit: using backbone networks to map user interests in social media

In the massive online worlds of social media, users frequently rely on organizing themselves around specific topics of interest to find and engage with like-minded people. However, navigating these massive worlds and finding topics of specific interest often proves difficult because the worlds are mostly organized haphazardly, leaving users to find relevant interests by word of mouth or using a basic search feature. Here, we report on a method using the backbone of a network to create a map of the primary topics of interest in any social network. To demonstrate the method, we build an interest map for the social news web site reddit and show how such a map could be used to navigate a social media world. Moreover, we analyze the network properties of the reddit social network and find that it has a scale-free, small-world, and modular community structure, much like other online social networks such as Facebook and Twitter. We suggest that the integration of interest maps into popular social media platforms will assist users in organizing themselves into more specific interest groups, which will help alleviate the overcrowding effect often observed in large online communities. Subjects Network Science and Online Social Networks, Visual Analytics


Examining the Technology Acceptance Model Using Physician Acceptance of Telemedicine Technology

The rapid growth of investment in information technology (IT) by organizations worldwide has made user acceptance an increasingly critical technology implementation a d management issue. While such acceptance has received fairly extensive attention from previous research, additional efforts are needed to examine or validate existing research results, particularly those involving different technologies, user populations, and/or organizational contexts. In response, this paper reports a research work that examined the applicability of the Technology Acceptance Model (ΤΑΜ) in explaining physicians' decisions to accept telemedicine technology in the health-care context. The technology, the user group, and the organizational context are all new to IT acceptance/adoption research. The study also addressed a pragmatic technology management need resulting from millions of dollars invested by healthcare organizations in developing and implementing telemedicine programs in recent years. The model's overall fit, explanatory power, and the individual causal links that it postulates were evaluated by examining the acceptance of telemedicine technology among physicians practicing at public tertiary hospitals in Hong Kong. Our results suggested that ΤΑΜ was able to provide a reasonable depiction of physicians' intention to use telemedicine technology. Perceived usefulness was found to be a significant determinant ofattitude and intention but perceived ease of use was not. The relatively low R-square of the model suggests both the limitations of the parsimonious model and the need for incorporating additional factors or integrating with other IT acceptance models in order to improve its specificity and explanatory utility in a health-care context. Based on the study findings, implications for user technology acceptance research and telemedicine management are discussed.


DevOps for Developers

ed descriptions of machines by using a DSL while enjoying the full power of scripting languages (in both Puppet and Chef, you can describe behavior in the Ruby language (a dynamic, general-purpose object-oriented programming language), see http://www.ruby-lang. org/en/). Declarative descriptions of target behavior (i.e., what the system must be). Thus, running the scripts will always lead to the same end result. Management of code in version control. By using a version control system as the leading medium, you do not need to adjust the machines manually (which is not reproducible). Synchronization of environments by using a version control system and automatic provisioning of environments. Continuous integration servers, such as Jenkins, simply have to listen to the path in the version control system to detect changes. Then the configuration management tool (e.g., Puppet) ensures that the corresponding machines apply the behavior that is described in version control. Using tools such as Jenkins (see Chapter 8) and Puppet and Vagrant (see Chapter 9), complete setups, including virtualizations, can be managed automatically. Sharing of scripts (e.g., Puppet manifests). A cross-functional team that includes development and operations can develop this function. Sharing the scripts in the version control system enables all parties, particularly development and operations, to use those scripts to set up their respective environments: test environments (used by development) and production environments (managed by operations). Automation is an essential backbone of DevOps (see Chapter 3 and Chapter 8 for more information on automation). Automation is the use of solutions to reduce the need for human work. Automation can ensure that the software is built the same way each time, that the team sees every change made to the software, and that the software is tested and reviewed in the same way every day so that no defects slip through or are introduced through human error. In software development projects, a high level of automation is a prerequisite for quickly delivering the best quality and for obtaining feedback from stakeholders early and often. Automating aspects of DevOps helps to make parts of the process transparent for the whole team and also helps deploy software to different target environments in the same way. You can best improve what you measure; and to measure something usefully, you need a process that delivers results in a reproducible way. DevOps addresses aspects similar to those tackled by Agile development, but the former focuses on breaking down the walls between developers and operations workers. The challenge is to communicate the benefits of DevOps to both development and operations teams. Both groups may be reluctant to start implementing the shift toward DevOps because their day is already full of activities. So why should they be concerned with the work of others? Why should DEVOPS FOR DEVELOPERS 31 operations want to use unfamiliar tools and adjust their daily routines when their self-made, isolated solutions have worked just fine for years? Because of this resistance, the incentives and commitment provided by upper management are important. Incentives alone are not enough: unified processes and tool chains are also important. Upper management will also resist by questioning the wisdom of implementing DevOps if the concrete benefits are not visible. Better cash flow and improved time to market are hard to measure. Management asks questions that address the core problems of software engineering while ignoring the symptoms: how can the company achieve maximal earnings in a short period of time? How can requirements be made stable and delivered to customers quickly? These results and visions should be measured with metrics that are shared by development and operations. Existing metrics can be further used or replaced by metrics that accurately express business value. One example of an end-to-end metric is the cycle time, which we will discuss in detail in Chapter 3.


Naive Bayes Word Sense Induction

We introduce an extended naive Bayes model for word sense induction (WSI) and apply it to a WSI task. The extended model incorporates the idea the words closer to the target word are more relevant in predicting its sense. The proposed model is very simple yet effective when evaluated on SemEval-2010 WSI data.


Dispatching optimization and routing guidance for emergency vehicles in disaster

Based on the problem that disasters occur frequently all over the world recently. This paper aims to develop dispatching optimization and dynamical routing guidance techniques for emergency vehicles under disaster conditions, so as to reduce emergency response time and avoid further possible deterioration of disaster situation. As to dispatching for emergency vehicles, firstly, classify the casualties into several regions based on the pickup locations, quantity and severity of casualties by an adaptive spectral clustering method, and then work out dispatching strategies for emergency vehicles by k-means clustering method based on the distance among casualties regions, emergency supply stations and hospitals. As to routing guidance for emergency vehicles, centrally dynamic route guidance system based on parallel computing technology is presented to offer safe, reliable and fast routes for emergency vehicles, which are subject to the network's impedance function based on real-time forecasted travel time. Finally, the algorithms presented in this paper are validated based on the platform of ArcGIS by generating casualties randomly in random areas and damaging the simulation network of Changchun city randomly.


Breaking CAPTCHAs on the Dark Web 11 February , 2018

On the Dark Web, several websites inhibit automated scraping attempts by employing CAPTCHAs. Scraping important content from a website is possible if these CAPTCHAs are solved by a web scraper. For this purpose, a Machine Learning tool is used, TensorFlow and an Optical Character Recognition tool, Tesseract to solve simple CAPTCHAs. Two sets of CATPCHAs, which are also used on some Dark Web websites, were generated for testing purposes. Tesseract achieved a success rate of 27.6% and 13.7% for set 1 and 2, respectively. A total of three models were created for TensorFlow. One model per set of CAPTCHAs and one model with the two sets mixed together. TensorFlow achieved a success rate of 94.6%, 99.7%, and 70.1% for the first, second, and mixed set, respectively. The initial investment to train TensorFlow can take up to two days to train for a single type of CAPTCHA, depending on implementation efficiency and hardware. The CAPTCHA images, including the answers, are also a requirement for training TensorFlow. Whereas Tesseract can be used on-demand without need for prior training.


Business Intelligence

Business intelligence systems combine operational data with analytical tools to present complex and competitive information to planners and decision makers. The objective is to improve the timeliness and quality of inputs to the decision process. Business Intelligence is used to understand the capabilities available in the firm; the state of the art, trends, and future directions in the markets, the technologies, and the regulatory environment in which the firm competes; and the actions of competitors and the implications of these actions. The emergence of the data warehouse as a repository, advances in data cleansing, increased capabilities of hardware and software, and the emergence of the web architecture all combine to create a richer business intelligence environment than was available previously. Although business intelligence systems are widely used in industry, research about them is limited. This paper, in addition to being a tutorial, proposes a BI framework and potential research topics. The framework highlights the importance of unstructured data and discusses the need to develop BI tools for its acquisition, integration, cleanup, search, analysis, and delivery. In addition, this paper explores a matrix for BI data types (structured vs. unstructured) and data sources (internal and external) to guide research.


Automated API Property Inference Techniques

Frameworks and libraries offer reusable and customizable functionality through Application Programming Interfaces (APIs). Correctly using large and sophisticated APIs can represent a challenge due to hidden assumptions and requirements. Numerous approaches have been developed to infer properties of APIs, intended to guide their use by developers. With each approach come new definitions of API properties, new techniques for inferring these properties, and new ways to assess their correctness and usefulness. This paper provides a comprehensive survey of over a decade of research on automated property inference for APIs. Our survey provides a synthesis of this complex technical field along different dimensions of analysis: properties inferred, mining techniques, and empirical results. In particular, we derive a classification and organization of over 60 techniques into five different categories based on the type of API property inferred: unordered usage patterns, sequential usage patterns, behavioral specifications, migration mappings, and general information.


Trajectory pattern mining: Exploring semantic and time information

With the development of GPS and the popularity of smart phones and wearable devices, users can easily log their daily trajectories. Prior works have elaborated on mining trajectory patterns from raw trajectories. Trajectory patterns consist of hot regions and the sequential relationships among them, where hot regions refer the spatial regions with a higher density of data points. Note that some hot regions do not have any meaning for users. Moreover, trajectory patterns do not have explicit time information or semantic information. To enrich trajectory patterns, we propose semantic trajectory patterns which are referred to as the moving patterns with spatial, temporal, and semantic attributes. Given a user trajectory, we aim at mining frequent semantic trajectory patterns. Explicitly, we extract the three attributes from a raw trajectory, and convert it into a semantic mobility sequence. Given such a semantic mobility sequence, we propose two algorithms to discover frequent semantic trajectory patterns. The first algorithm, MB (standing for matching-based algorithm), is a naive method to find frequent semantic trajectory patterns. It generates all possible patterns and extracts the occurrence of the patterns from the semantic mobility sequence. The second algorithm, PS (standing for PrefixSpan-based algorithm), is developed to efficiently mine semantic trajectory patterns. Due to the good efficiency of PrefixSpan, algorithm PS will fully utilize the advantage of PrefixSpan. Since the semantic mobility sequence contains three attributes, we need to further transform it into a raw sequence before using algorithm PrefixSpan. Therefore, we propose the SS algorithm (standing for sequence symbolization algorithm) to achieve this purpose. To evaluate our proposed algorithms, we conducted experiments on the real datasets of Google Location History, and the experimental results show the effectiveness and efficiency of our proposed algorithms.


Performance Analysis of Modified LLC Resonant Converter

In this paper a modified form of the most efficient resonant LLC series parallel converter configuration is proposed. The proposed system comprises of an additional LC circuit synchronized with the existing resonant tank of LLC configuration (LLC-LC configuration). With the development of power electronics devices, resonant converters have been proved to be more efficient than conventional converters as they employ soft switching technique. Among the three basic configurations of resonant converter, Series Resonant Converter (SRC), Parallel Resonant Converter (PRC) and Series Parallel Resonant Converter (SPRC), the LLC configuration under SPRC is proved to be most efficient providing narrow switching frequency range for wide range of load variation, improved efficiency and providing ZVS capability even under no load condition. The modified LLC configuration i.e., LLC-LC configuration offers better efficiency as well as better output voltage and gain. The efficiency tends to increase with increase in input voltage and hence these are suitable for high input voltage operation. The simulation and analysis has been done for full bridge configuration of the switching circuit and the results are presented


Deep Recurrent Neural Networks for seizure detection and early seizure detection systems

Epilepsy is common neurological diseases, affecting about 0.6-0.8 % of world population. Epileptic patients suffer from chronic unprovoked seizures, which can result in broad spectrum of debilitating medical and social consequences. Since seizures, in general, occur infrequently and are unpredictable, automated seizure detection systems are recommended to screen for seizures during long-term electroencephalogram (EEG) recordings. In addition, systems for early seizure detection can lead to the development of new types of intervention systems that are designed to control or shorten the duration of seizure events. In this article, we investigate the utility of recurrent neural networks (RNNs) in designing seizure detection and early seizure detection systems. We propose a deep learning framework via the use of Gated Recurrent Unit (GRU) RNNs for seizure detection. We use publicly available data in order to evaluate our method and demonstrate very promising evaluation results with overall accuracy close to 100 %. We also systematically investigate the application of our method for early seizure warning systems. Our method can detect about 98% of seizure events within the first 5 seconds of the overall epileptic seizure duration.


Document Image Binarization Using Recurrent Neural Networks

In the context of document image analysis, image binarization is an important preprocessing step for other document analysis algorithms, but also relevant on its own by improving the readability of images of historical documents. While historical document image binarization is challenging due to common image degradations, such as bleedthrough, faded ink or stains, achieving good binarization performance in a timely manner is a worthwhile goal to facilitate efficient information extraction from historical documents. In this paper, we propose a recurrent neural network based algorithm using Grid Long Short-Term Memory cells for image binarization, as well as a pseudo F-Measure based weighted loss function. We evaluate the binarization and execution performance of our algorithm for different choices of footprint size, scale factor and loss function. Our experiments show a significant trade-off between binarization time and quality for different footprint sizes. However, we see no statistically significant difference when using different scale factors and only limited differences for different loss functions. Lastly, we compare the binarization performance of our approach with the best performing algorithm in the 2016 handwritten document image binarization contest and show that both algorithms perform equally well.


A collaborative filtering framework based on fuzzy association rules and multiple-level similarity

The rapid development of Internet technologies in recent decades has imposed a heavy information burden on users. This has led to the popularity of recommender systems, which provide advice to users about items they may like to examine. Collaborative Filtering (CF) is the most promising technique in recommender systems, providing personalized recommendations to users based on their previously expressed preferences and those of other similar users. This paper introduces a CF framework based on Fuzzy Association Rules and Multiple-level Similarity (FARAMS). FARAMS extended existing techniques by using fuzzy association rule mining, and takes advantage of product similarities in taxonomies to address data sparseness and nontransitive associations. Experimental results show that FARAMS improves prediction quality, as compared to similar approaches.


Greed is good: Approximating independent sets in sparse and bounded-degree graphs

Theminimum-degree greedy algorithm, or Greedy for short, is a simple and well-studied method for finding independent sets in graphs. We show that it achieves a performance ratio of (Δ+2)/3 for approximating independent sets in graphs with degree bounded by Δ. The analysis yields a precise characterization of the size of the independent sets found by the algorithm as a function of the independence number, as well as a generalization of Turán’s bound. We also analyze the algorithm when run in combination with a known preprocessing technique, and obtain an improved $$(2\bar d + 3)/5$$ performance ratio on graphs with average degree $$\bar d$$ , improving on the previous best $$(\bar d + 1)/2$$ of Hochbaum. Finally, we present an efficient parallel and distributed algorithm attaining the performance guarantees of Greedy.


Mixed Methods Research : A Research Paradigm Whose Time Has Come

14 The purposes of this article are to position mixed methods research (mixed research is a synonym) as the natural complement to traditional qualitative and quantitative research, to present pragmatism as offering an attractive philosophical partner for mixed methods research, and to provide a framework for designing and conducting mixed methods research. In doing this, we briefly review the paradigm “wars” and incompatibility thesis, we show some commonalities between quantitative and qualitative research, we explain the tenets of pragmatism, we explain the fundamental principle of mixed research and how to apply it, we provide specific sets of designs for the two major types of mixed methods research (mixed-model designs and mixed-method designs), and, finally, we explain mixed methods research as following (recursively) an eight-step process. A key feature of mixed methods research is its methodological pluralism or eclecticism, which frequently results in superior research (compared to monomethod research). Mixed methods research will be successful as more investigators study and help advance its concepts and as they regularly practice it.


Development Emails Content Analyzer: Intention Mining in Developer Discussions (T)

Written development communication (e.g. mailing lists, issue trackers) constitutes a precious source of information to build recommenders for software engineers, for example aimed at suggesting experts, or at redocumenting existing source code. In this paper we propose a novel, semi-supervised approach named DECA (Development Emails Content Analyzer) that uses Natural Language Parsing to classify the content of development emails according to their purpose (e.g. feature request, opinion asking, problem discovery, solution proposal, information giving etc), identifying email elements that can be used for specific tasks. A study based on data from Qt and Ubuntu, highlights a high precision (90%) and recall (70%) of DECA in classifying email content, outperforming traditional machine learning strategies. Moreover, we successfully used DECA for re-documenting source code of Eclipse and Lucene, improving the recall, while keeping high precision, of a previous approach based on ad-hoc heuristics.


Shrink Globally , Act Locally : Sparse Bayesian Regularization and Prediction

We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based on scale mixtures of normals. Two theorems are presented which characterize certain desirable properties of shrinkage priors for sparse problems. Next, we review some recent results showing how Lévy processes can be used to generate infinite-dimensional versions of standard normal scale-mixture priors, along with new priors that have yet to be seriously studied in the literature. This approach provides an intuitive framework both for generating new regularization penalties and shrinkage rules, and for performing asymptotic analysis on existing models.


A randomized trial of a low-carbohydrate diet for obesity.

BACKGROUND
Despite the popularity of the low-carbohydrate, high-protein, high-fat (Atkins) diet, no randomized, controlled trials have evaluated its efficacy.

METHODS
We conducted a one-year, multicenter, controlled trial involving 63 obese men and women who were randomly assigned to either a low-carbohydrate, high-protein, high-fat diet or a low-calorie, high-carbohydrate, low-fat (conventional) diet. Professional contact was minimal to replicate the approach used by most dieters.

RESULTS
Subjects on the low-carbohydrate diet had lost more weight than subjects on the conventional diet at 3 months (mean [+/-SD], -6.8+/-5.0 vs. -2.7+/-3.7 percent of body weight; P=0.001) and 6 months (-7.0+/-6.5 vs. -3.2+/-5.6 percent of body weight, P=0.02), but the difference at 12 months was not significant (-4.4+/-6.7 vs. -2.5+/-6.3 percent of body weight, P=0.26). After three months, no significant differences were found between the groups in total or low-density lipoprotein cholesterol concentrations. The increase in high-density lipoprotein cholesterol concentrations and the decrease in triglyceride concentrations were greater among subjects on the low-carbohydrate diet than among those on the conventional diet throughout most of the study. Both diets significantly decreased diastolic blood pressure and the insulin response to an oral glucose load.

CONCLUSIONS
The low-carbohydrate diet produced a greater weight loss (absolute difference, approximately 4 percent) than did the conventional diet for the first six months, but the differences were not significant at one year. The low-carbohydrate diet was associated with a greater improvement in some risk factors for coronary heart disease. Adherence was poor and attrition was high in both groups. Longer and larger studies are required to determine the long-term safety and efficacy of low-carbohydrate, high-protein, high-fat diets.


Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.

The assessment of the discrimination ability of a survival analysis model is a problem of considerable theoretical interest and important practical applications. This issue is, however, more complex than evaluating the performance of a linear or logistic regression. Several different measures have been proposed in the biostatistical literature. In this paper we investigate the properties of the overall C index introduced by Harrell as a natural extension of the ROC curve area to survival analysis. We develop the overall C index as a parameter describing the performance of a given model applied to the population under consideration and discuss the statistic used as its sample estimate. We discover a relationship between the overall C and the modified Kendall's tau and construct a confidence interval for our measure based on the asymptotic normality of its estimate. Then we investigate via simulations the length and coverage probability of this interval. Finally, we present a real life example evaluating the performance of a Framingham Heart Study model.


The apnea-ECG database

Sleep apnea is a sleep disorder with a high prevalence in the adult male population. Sleep apnea is regarded as an independent risk factor,for cardiovascular sequelae such as ischemic heart attacks and stroke. The diagnosis of sleep apnea requires polysomnographic studies in sleep laboratories with expensive equipment and attending personnel. Sleep apnea can be treated effectively using nasal ventilation therapy (nCPAP). Early recognition and selection of patients with sleep related breathing disorders is an important task. Although it has been suggested that this can be done on the basis of the ECG, careful quantitative studies of the accuracy of such techniques are needed. An annotated database with 70 nighttime ECG recordings has been created to support such studies. The annotations were based on visual scoring of disordered breathing during sleep.


Empirical comparison of algorithms for network community detection

Detecting clusters or communities in large real-world graphs such as large social or information networks is a problem of considerable interest. In practice, one typically chooses an objective function that captures the intuition of a network cluster as set of nodes with better internal connectivity than external connectivity, and then one applies approximation algorithms or heuristics to extract sets of nodes that are related to the objective function and that "look like" good communities for the application of interest.
 In this paper, we explore a range of network community detection methods in order to compare them and to understand their relative performance and the systematic biases in the clusters they identify. We evaluate several common objective functions that are used to formalize the notion of a network community, and we examine several different classes of approximation algorithms that aim to optimize such objective functions. In addition, rather than simply fixing an objective and asking for an approximation to the best cluster of any size, we consider a size-resolved version of the optimization problem. Considering community quality as a function of its size provides a much finer lens with which to examine community detection algorithms, since objective functions and approximation algorithms often have non-obvious size-dependent behavior.


Community detection algorithms: a comparative analysis.

Uncovering the community structure exhibited by real networks is a crucial step toward an understanding of complex systems that goes beyond the local organization of their constituents. Many algorithms have been proposed so far, but none of them has been subjected to strict tests to evaluate their performance. Most of the sporadic tests performed so far involved small networks with known community structure and/or artificial graphs with a simplified structure, which is very uncommon in real systems. Here we test several methods against a recently introduced class of benchmark graphs, with heterogeneous distributions of degree and community size. The methods are also tested against the benchmark by Girvan and Newman [Proc. Natl. Acad. Sci. U.S.A. 99, 7821 (2002)] and on random graphs. As a result of our analysis, three recent algorithms introduced by Rosvall and Bergstrom [Proc. Natl. Acad. Sci. U.S.A. 104, 7327 (2007); Proc. Natl. Acad. Sci. U.S.A. 105, 1118 (2008)], Blondel [J. Stat. Mech.: Theory Exp. (2008), P10008], and Ronhovde and Nussinov [Phys. Rev. E 80, 016109 (2009)] have an excellent performance, with the additional advantage of low computational complexity, which enables one to analyze large systems.


Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities.

Many complex networks display a mesoscopic structure with groups of nodes sharing many links with the other nodes in their group and comparatively few with nodes of different groups. This feature is known as community structure and encodes precious information about the organization and the function of the nodes. Many algorithms have been proposed but it is not yet clear how they should be tested. Recently we have proposed a general class of undirected and unweighted benchmark graphs, with heterogeneous distributions of node degree and community size. An increasing attention has been recently devoted to develop algorithms able to consider the direction and the weight of the links, which require suitable benchmark graphs for testing. In this paper we extend the basic ideas behind our previous benchmark to generate directed and weighted networks with built-in community structure. We also consider the possibility that nodes belong to more communities, a feature occurring in real systems, such as social networks. As a practical application, we show how modularity optimization performs on our benchmark.


Link communities reveal multiscale complexity in networks

Networks have become a key approach to understanding systems of interacting objects, unifying the study of diverse phenomena including biological organisms and human society. One crucial step when studying the structure and dynamics of networks is to identify communities: groups of related nodes that correspond to functional subunits such as protein complexes or social spheres. Communities in networks often overlap such that nodes simultaneously belong to several groups. Meanwhile, many networks are known to possess hierarchical organization, where communities are recursively grouped into a hierarchical structure. However, the fact that many real networks have communities with pervasive overlap, where each and every node belongs to more than one group, has the consequence that a global hierarchy of nodes cannot capture the relationships between overlapping groups. Here we reinvent communities as groups of links rather than nodes and show that this unorthodox approach successfully reconciles the antagonistic organizing principles of overlapping communities and hierarchy. In contrast to the existing literature, which has entirely focused on grouping nodes, link communities naturally incorporate overlap while revealing hierarchical organization. We find relevant link communities in many networks, including major biological networks such as protein–protein interaction and metabolic networks, and show that a large social network contains hierarchically organized community structures spanning inner-city to regional scales while maintaining pervasive overlap. Our results imply that link communities are fundamental building blocks that reveal overlap and hierarchical organization in networks to be two aspects of the same phenomenon.


Topic-link LDA: joint models of topic and author community

Given a large-scale linked document collection, such as a collection of blog posts or a research literature archive, there are two fundamental problems that have generated a lot of interest in the research community. One is to identify a set of high-level topics covered by the documents in the collection; the other is to uncover and analyze the social network of the authors of the documents. So far these problems have been viewed as separate problems and considered independently from each other. In this paper we argue that these two problems are in fact inter-dependent and should be addressed together. We develop a Bayesian hierarchical approach that performs topic modeling and author community discovery in one unified framework. The effectiveness of our model is demonstrated on two blog data sets in different domains and one research paper citation data from CiteSeer.


The role of brand logos in fi rm performance

a r t i c l e i n f o Keywords: Brand logos Brand management Aesthetics Commitment Brand extensions Firm performance This research demonstrates that the positive effects of brand logos on customer brand commitment and firm performance derive not from enabling brand identification, as is currently understood, but primarily from facilitating customer self-identity/expressiveness, representing a brand's functional benefits, and offering aesthetic appeal. This study examines whether brand names or visual symbols as logos are more effective at creating these benefits and whether or not the impact of the three aforementioned brand logo benefits on customer brand commitment and firm performance is contingent on the extent to which a firm leverages its brand (i.e., employs brand extensions to different product categories).


A greedy approach to sparse canonical correlation analysis

We consider the problem of sparse canonical correlation analysis (CCA), i.e., the search for two linear combi nations, one for each multivariate, that yield maximum correlation using a specified number of variables. We propose an efficient numeri cal approximation based on a direct greedy approach which bound s the correlation at each stage. The method is specifically des igned to cope with large data sets and its computational complexit y depends only on the sparsity levels. We analyze the algorith m’s performance through the tradeoff between correlation and parsimony. The results of numerical simulation suggest that a significant portion of the correlation may be captured using a relatively small number of variables. In addition, we exami ne the use of sparse CCA as a regularization method when the number of available samples is small compared to the dimensions of t he multivariates. I. I NTRODUCTION Canonical correlation analysis (CCA), introduced by Harol d Hotelling [1], is a standard technique in multivariate data n lysis for extracting common features from a pair of data sourc es [2], [3]. Each of these data sources generates a random vecto r that we call a multivariate. Unlike classical dimensionali ty reduction methods which address one multivariate, CCA take s into account the statistical relations between samples fro m two spaces of possibly different dimensions and structure. In particular, it searches for two linear combinations, one fo r each multivariate, in order to maximize their correlation. It is used in different disciplines as a stand-alone tool or as a preprocessing step for other statistical methods. Further more, CCA is a generalized framework which includes numerous classical methods in statistics, e.g., Principal Componen t Analysis (PCA), Partial Least Squares (PLS) and Multiple Linear Regression (MLR) [4]. CCA has recently regained attention with the advent of kernel CCA and its application to independent component analysis [5], [6]. The last decade has witnessed a growing interest in the search for sparse representations of signals and sparse numerical methods. Thus, we consider the problem of sparse CCA, i.e., the search for linear combinations with maximal correlation using a small number of variables. The quest for sparsity can be motivated through various reasonings. First is the ability to interpret and visualize the results. A small number of variables allows us to get the “big picture”, while sacrificing some of the small details. Moreover, spars e representations enable the use of computationally efficien t The first two authors contributed equally to this manuscript . This work was supported in part by an AFOSR MURI under Grant FA9550-06-1-0 324. numerical methods, compression techniques, as well as nois e reduction algorithms. The second motivation for sparsity i s regularization and stability. One of the main vulnerabilit ies of CCA is its sensitivity to a small number of observations. Thu s, regularized methods such as ridge CCA [7] must be used. In this context, sparse CCA is a subset selection scheme which allows us to reduce the dimensions of the vectors and obtain a stable solution. To the best of our knowledge the first reference to sparse CCA appeared in [2] where backward and stepwise subset selection were proposed. This discussion was of qualitativ e nature and no specific numerical algorithm was proposed. Recently, increasing demands for multidimensional data pr ocessing and decreasing computational cost has caused the topic to rise to prominence once again [8]–[13]. The main disadvantages with these current solutions is that there is no direct control over the sparsity and it is difficult (and nonintuitive) to select their optimal hyperparameters. In add ition, the computational complexity of most of these methods is too high for practical applications with high dimensional data sets. Sparse CCA has also been implicitly addressed in [9], [14] an d is intimately related to the recent results on sparse PCA [9] , [15]–[17]. Indeed, our proposed solution is an extension of the results in [17] to CCA. The main contribution of this work is twofold. First, we derive CCA algorithms with direct control over the sparsity in each of the multivariates and examine their performance. Our computationally efficient methods are specifically aime d at understanding the relations between two data sets of larg e dimensions. We adopt a forward (or backward) greedy approach which is based on sequentially picking (or dropping) variables. At each stage, we bound the optimal CCA solution and bypass the need to resolve the full problem. Moreover, the computational complexity of the forward greedy method does not depend on the dimensions of the data but only on the sparsity parameters. Numerical simulation results show th at a significant portion of the correlation can be efficiently cap tured using a relatively low number of non-zero coefficients. Our second contribution is investigation of sparse CCA as a regularization method. Using empirical simulations we examin e the use of the different algorithms when the dimensions of the multivariates are larger than (or of the same order of) the number of samples and demonstrate the advantage of sparse CCA. In this context, one of the advantages of the greedy approach is that it generates the full sparsity path i n a single run and allows for efficient parameter tuning using


Matching Web Tables with Knowledge Base Entities: From Entity Lookups to Entity Embeddings

Web tables constitute valuable sources of information for various applications, ranging from Web search to Knowledge Base (KB) augmentation. An underlying common requirement is to annotate the rows of Web tables with semantically rich descriptions of entities published in Web KBs. In this paper, we evaluate three unsupervised annotation methods: (a) a lookup-based method which relies on the minimal entity context provided in Web tables to discover correspondences to the KB, (b) a semantic embeddings method that exploits a vectorial representation of the rich entity context in a KB to identify the most relevant subset of entities in the Web table, and (c) an ontology matching method, which exploits schematic and instance information of entities available both in a KB and a Web table. Our experimental evaluation is conducted using two existing benchmark data sets in addition to a new large-scale benchmark created using Wikipedia tables. Our results show that: 1) our novel lookup-based method outperforms state-of-theart lookup-based methods, 2) the semantic embeddings method outperforms lookup-based methods in one benchmark data set, and 3) the lack of a rich schema in Web tables can limit the ability of ontology matching tools in performing high-quality table annotation. As a result, we propose a hybrid method that significantly outperforms individual methods on all the benchmarks.


Blood Code: The History and Future of Video Game Censorship

INTRODUCTION ................................................................................... 571 I. FIRST AMENDMENT BACKGROUND ....................................... 573 II. THE ANALOGOUS HISTORIES OF FILMS AND VIDEO GAMES ....................................................................................... 576 A. Film Controversy and the Formation of the MPAA ................ 576 B. Early Video Game Controversy and the Formation of the ESRB ................................................................................... 580 C. Doom and Columbine ........................................................... 584 D. Jack Thompson and Grand Theft Auto ................................... 586 III. WHY VIDEO GAMES SHOULD NOT BE TREATED DIFFERENTLY THAN FILMS .................................................... 593 A. Violent and Sexual Content in Video Games is Distinguishable from Pornography and Obscenity. .................. 594 B. Violent Game Content is Similar to Violent Film Content. ..... 596 C. Positive Social Aspects of Violent Gaming............................... 597 D. Desensitization Will Lead to a Decrease in Political Outrage. ............................................................................... 604 IV. EXISTING VIDEO GAME JURISPRUDENCE .............................. 605 V. RATINGS AND LABELS AS UNCONSTITUTIONAL CENSORSHIP.............................................................................. 607 CONCLUSION ....................................................................................... 609


Characterizing the life cycle of online news stories using social media reactions

This paper presents a study of the life cycle of news articles posted online. We describe the interplay between website visitation patterns and social media reactions to news content. We show that we can use this hybrid observation method to characterize distinct classes of articles. We also find that social media reactions can help predict future visitation patterns early and accurately. We validate our methods using qualitative analysis as well as quantitative analysis on data from a large international news network, for a set of articles generating more than 3,000,000 visits and 200,000 social media reactions. We show that it is possible to model accurately the overall traffic articles will ultimately receive by observing the first ten to twenty minutes of social media reactions. Achieving the same prediction accuracy with visits alone would require to wait for three hours of data. We also describe significant improvements on the accuracy of the early prediction of shelf-life for news stories.


Improving www proxies performance with greedy-dual-size-frequency caching policy

Web, HTTP, WWW proxies, caching policies, replacement algorithms, performance Web proxy caches are used to improve performance of the WWW. Since the majority of Web documents are static documents, caching them at WWW proxies reduces both network traffic and response time. One of the keys to better proxy cache performance is an efficient caching policy which keeps in the cache popular documents and replaces rarely used ones. This paper introduces the Greedy-Dual-Size-Frequency caching policy to maximize hit and byte hit rates for WWW proxies. Proposed caching strategy incorporates in a simple way the most important characteristics of the file and its accesses such as file size, file access frequency and recentness of the last access. Greedy-Dual-Size-Frequency is an improvement of Greedy-Dual-Size algorithm – the current champion among the replacement strategies proposed for Web proxy caches.


Modeling Review Argumentation for Robust Sentiment Analysis

Most text classification approaches model text at the lexical and syntactic level only, lacking domain robustness and explainability. In tasks like sentiment analysis, such approaches can result in limited effectiveness if the texts to be classified consist of a series of arguments. In this paper, we claim that even a shallow model of the argumentation of a text allows for an effective and more robust classification, while providing intuitive explanations of the classification results. Here, we apply this idea to the supervised prediction of sentiment scores for reviews. We combine existing approaches from sentiment analysis with novel features that compare the overall argumentation structure of the given review text to a learned set of common sentiment flow patterns. Our evaluation in two domains demonstrates the benefit of modeling argumentation for text classification in terms of effectiveness and robustness.


Accounting for the effects of accountability.

This article reviews the now extensive research literature addressing the impact of accountability on a wide range of social judgments and choices. It focuses on 4 issues: (a) What impact do various accountability ground rules have on thoughts, feelings, and action? (b) Under what conditions will accountability attenuate, have no effect on, or amplify cognitive biases? (c) Does accountability alter how people think or merely what people say they think? and (d) What goals do accountable decision makers seek to achieve? In addition, this review explores the broader implications of accountability research. It highlights the utility of treating thought as a process of internalized dialogue; the importance of documenting social and institutional boundary conditions on putative cognitive biases; and the potential to craft empirical answers to such applied problems as how to structure accountability relationships in organizations.


An Effective Gated and Attention-Based Neural Network Model for Fine-Grained Financial Target-Dependent Sentiment Analysis


Security Issues in the Internet of Things ( IoT ) : A Comprehensive Study

Wireless communication networks are highly prone to security threats. The major applications of wireless communication networks are in military, business, healthcare, retail, and transportations. These systems use wired, cellular, or adhoc networks. Wireless sensor networks, actuator networks, and vehicular networks have received a great attention in society and industry. In recent years, the Internet of Things (IoT) has received considerable research attention. The IoT is considered as future of the internet. In future, IoT will play a vital role and will change our living styles, standards, as well as business models. The usage of IoT in different applications is expected to rise rapidly in the coming years. The IoT allows billions of devices, peoples, and services to connect with others and exchange information. Due to the increased usage of IoT devices, the IoT networks are prone to various security attacks. The deployment of efficient security and privacy protocols in IoT networks is extremely needed to ensure confidentiality, authentication, access control, and integrity, among others. In this paper, an extensive comprehensive study on security and privacy issues in IoT networks is provided. Keywords—Internet of Things (IoT); security issues in IoT; security; privacy


Networks of the Brain

Models of Network Growth All networks, whether they are social, technological, or biological, are the result of a growth process. Many of these networks continue to grow for prolonged periods of time, continually modifying their connectivity structure throughout their entire existence. For example, the World Wide Web has grown from a small number of cross-linked documents in the early 1 990s to an estimated 30 billion indexed web pages in 2009.3 The extraordinary growth of the Web continues unabated and has occurred without any top-down design, yet the topology of its hyperlink structure exhibits characteristic statistical patterns (Pastor-Satorras and Vespig nani, 2004). Other technological networks such as the power grid, global transportation networks, or mobile communication networks continue to grow and evolve, each displaying characteristic patterns of expansion and elaboration. Growth and change in social and organizational


Comparative Analysis of ADS-B Verification Techniques

ADS-B is one of many Federal Aviation Administration (FAA) regulated technologies used to monitor air traffic with high precision, while reducing dependencies on dated and costly radar equipment [1]. The FAA hopes to decrease the separation between aircraft, reduce risk of collision as air traffic density increases, save fuel costs, and increase situational awareness of both commercial and general aviation aircraft within United States airspace. Several aviation technology experts have expressed concern over the security of the ADS-B protocol [2] [3]. ADS-B has an open and well known data format, which is broadcast on known frequencies. This means that the protocol is highly susceptible to radio frequency (RF) attacks such as eavesdropping, jamming, and spoofing. Eavesdropping and jamming will be reviewed in Section 3.4. While eavesdropping and jamming attacks are well studied, due to their applicability in many radio technologies, spoofing attacks against ADS-B are particular to this system. As such, the latter is the focus of our research. This paper evaluates so-called Kalman Filtering and Group Validation techniques (described below) in order to assess which would be a better position verification method of ADS-B signals. The parameters for the comparative analysis include both technical feasibility and practical implementation of each position verification technique. The goal is to offer a practical position verification process which could be implemented with limited government funding within the next 10 years.


Deep Learning and Its Applications to Signal and Information Processing [Exploratory DSP]

The purpose of this article is to introduce the readers to the emerging technologies enabled by deep learning and to review the research work conducted in this area that is of direct relevance to signal processing. We also point out, in our view, the future research directions that may attract interests of and require efforts from more signal processing researchers and practitioners in this emerging area for advancing signal and information processing technology and applications.


The Anti-vaccination Movement: A Regression in Modern Medicine

There have been recent trends of parents in Western countries refusing to vaccinate their children due to numerous reasons and perceived fears. While opposition to vaccines is as old as the vaccines themselves, there has been a recent surge in the opposition to vaccines in general, specifically against the MMR (measles, mumps, and rubella) vaccine, most notably since the rise in prominence of the notorious British ex-physician, Andrew Wakefield, and his works. This has caused multiple measles outbreaks in Western countries where the measles virus was previously considered eliminated. This paper evaluates and reviews the origins of the anti-vaccination movement, the reasons behind the recent strengthening of the movement, role of the internet in the spread of anti-vaccination ideas, and the repercussions in terms of public health and safety.


Emotion control in collaborative learning situations: do students regulate emotions evoked by social challenges?

BACKGROUND
During recent decades, self-regulated learning (SRL) has become a major research field. SRL successfully integrates the cognitive and motivational components of learning. Self-regulation is usually seen as an individual process, with the social aspects of regulation conceptualized as one aspect of the context. However, recent research has begun to investigate whether self-regulation processes are complemented by socially shared regulation processes.

AIMS
The presented study investigated what kind of socio-emotional challenges students experience during collaborative learning and whether the students regulate the emotions evoked during these situations. The interplay of the emotion regulation processes between the individual and the group was also studied.

SAMPLE
The sample for this study was 63 teacher education students who studied in groups of three to five during three collaborative learning tasks.

METHOD
Students' interpretations of experienced social challenges and their attempts to regulate emotions evoked by these challenges were collected following each task using the Adaptive Instrument for the Regulation of Emotions.

RESULTS
The results indicated that students experienced a variety of social challenges. Students also reported the use of shared regulation in addition to self-regulation. Finally, the results suggested that intrinsic group dynamics are derived from both individual and social elements of collaborative situations.

CONCLUSION
The findings of the study support the assumption that students can regulate emotions collaboratively as well as individually. The study contributes to our understanding of the social aspects of emotional regulation in collaborative learning contexts.


PAPILLON: expressive eyes for interactive characters

PAPILLON is a technology for designing highly expressive animated eyes for interactive characters, robots and toys. Expressive eyes are essential in any form of face-to-face communication [2] and designing them has been a critical challenge in robotics, as well as in interactive character and toy development.


Heart-Brain Neurodynamics : The Making of Emotions

As pervasive and vital as they are in human experience, emotions have long remained an enigma to science. This monograph explores recent scientific advances that clarify central controversies in the study of emotion, including the relationship between intellect and emotion, and the historical debate on the source of emotional experience. Particular attention is given to the intriguing body of research illuminating the critical role of ascending input from the body to the brain in the generation and perception of emotions. This discussion culminates in the presentation of a systems-oriented model of emotion in which the brain functions as a complex pattern-matching system, continually processing input from both the external and internal environments. From this perspective it is shown that the heart is a key component of the emotional system, thus providing a physiological basis for the long-acknowledged link between the heart and our emotional life.


The James-Lange theory of emotions: a critical examination and an alternative theory. By Walter B. Cannon, 1927.


DeepHand: Robust Hand Pose Estimation by Completing a Matrix Imputed with Deep Features

We propose DeepHand to estimate the 3D pose of a hand using depth data from commercial 3D sensors. We discriminatively train convolutional neural networks to output a low dimensional activation feature given a depth map. This activation feature vector is representative of the global or local joint angle parameters of a hand pose. We efficiently identify 'spatial' nearest neighbors to the activation feature, from a database of features corresponding to synthetic depth maps, and store some 'temporal' neighbors from previous frames. Our matrix completion algorithm uses these 'spatio-temporal' activation features and the corresponding known pose parameter values to estimate the unknown pose parameters of the input feature vector. Our database of activation features supplements large viewpoint coverage and our hierarchical estimation of pose parameters is robust to occlusions. We show that our approach compares favorably to state-of-the-art methods while achieving real time performance (≈ 32 FPS) on a standard computer.


Correctness By Construction: Better Can Also Be Cheaper

I n December 1999 CrossTalk [3], David Cook provided a well-reasoned historical analysis of programming language development and considered the role languages play in the software development process. The article was valuable because it showed that programming language developments are not sufficient to ensure success; however, it would be dangerous to conclude from this that they are not necessary for success. Cook rightly identifies other issues such as requirements capture, specifications, and verification and validation (V&V) that need to be addressed. Perhaps we need to look at programming languages not just in terms of their ability to code some particular design but in the influence the language has on some of these other vital aspects of the development process. The key notion is that of the benefit of a precise language or language subset. If the term subset has set anyone thinking " oh no, not another coding standard, " then read on, the topic is much more interesting and useful than that! Language Issues Programming languages have evolved in three main ways. First came improvements in structure; then attempts at improving compile-time error detection through such things as strong typing; and, most significantly , facilities to improve our ability to express abstractions. All of these have shaped the way we think about problem solving. However, programming languages have not evolved in their precision of expression. In fact, they may have actually gotten worse since the meaning of a sample of machine code is exact and unequivocal , whereas the meaning of the constructs of typical modern high-order languages are substantially less certain. The evolution of C into C++ certainly improved its ability to express design abstractions but, if anything, the predictability of the compiled code decreased. These ambiguities arise either from deficiencies in the original language definition or from implementation freedoms given to the compiler writer for ease of implementation or efficiency reasons. None of this may look like a very serious problem. We can still do code walk-throughs and reviews and, after all, we still have to do dynamic testing that should flush out any remaining ambiguities. In fact the evidence is quite strong that it does matter because it creates an environment where we are encouraged to make little attempt to reason about the software we are producing at each stage of its development. Since we typically do not have formal mathematical specifications and we use imprecise …


A lifelong learning perspective for mobile robot control

Abstmct-Designing robots that learn by themselves to perform complex real-world tasks is a still-open challenge for the field of Robotics and Artificial Intelligence. In this paper we present the robot learning problem as a lifelong problem, in which a robot faces a collection of tasks over its entire lifetime. Such a scenario provides the opportunity to gather general-purpose knowledge that transfers across tasks. We illustrate a particular learning mechanism, explanation-based neural network learning, that transfers knowledge between related tasks via neural network action models. The learning approach is illustrated using a mobile robot, equipped with visual, ultrasonic and laser sensors. In less than 10 minutes operation time, the robot is able to learn to navigate to a marked target object in a natural office environment.