Scene Understanding by Reasoning Stability and Safety

This paper presents a new perspective for 3D scene understanding by reasoning object stability and safety using intuitive mechanics. Our approach utilizes a simple observation that, by human design, objects in static scenes should be stable in the gravity field and be safe with respect to various physical disturbances such as human activities. This assumption is applicable to all scene categories and poses useful constraints for the plausible interpretations (parses) in scene understanding. Given a 3D point cloud captured for a static scene by depth cameras, our method consists of three steps: (i) recovering solid 3D volumetric primitives from voxels; (ii) reasoning stability by grouping the unstable primitives to physically stable objects by optimizing the stability and the scene prior; and (iii) reasoning safety by evaluating the physical risks for objects under physical disturbances, such as human activity, wind or earthquakes. We adopt a novel intuitive physics model and represent the energy landscape of each primitive and object in the scene by a disconnectivity graph (DG). We construct a contact graph with nodes being 3D volumetric primitives and edges representing the supporting relations. Then we adopt a Swendson–Wang Cuts algorithm to partition the contact graph into groups, each of which is a stable object. In order to detect unsafe objects in a static scene, our method further infers hidden and situated causes (disturbances) in the scene, and then introduces intuitive physical mechanics to predict possible effects (e.g., falls) as consequences of the disturbances. In experiments, we demonstrate that the algorithm achieves a substantially better performance for (i) object segmentation, (ii) 3D volumetric recovery, and (iii) scene understanding with respect to other state-of-the-art methods. We also compare the safety prediction from the intuitive mechanics model with human judgement.


Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography.

Understanding the long association pathways that convey cortical connections is a critical step in exploring the anatomic substrates of cognition in health and disease. Diffusion tensor imaging (DTI) is able to demonstrate fibre tracts non-invasively, but present approaches have been hampered by the inability to visualize fibres that have intersecting trajectories (crossing fibres), and by the lack of a detailed map of the origins, course and terminations of the white matter pathways. We therefore used diffusion spectrum imaging (DSI) that has the ability to resolve crossing fibres at the scale of single MRI voxels, and identified the long association tracts in the monkey brain. We then compared the results with available expositions of white matter pathways in the monkey using autoradiographic histological tract tracing. We identified 10 long association fibre bundles with DSI that match the observations in the isotope material: emanating from the parietal lobe, the superior longitudinal fasciculus subcomponents I, II and III; from the occipital-parietal region, the fronto-occipital fasciculus; from the temporal lobe, the middle longitudinal fasciculus and from rostral to caudal, the uncinate fasciculus, extreme capsule and arcuate fasciculus; from the occipital-temporal region, the inferior longitudinal fasciculus; and from the cingulate gyrus, the cingulum bundle. We suggest new interpretations of the putative functions of these fibre bundles based on the cortical areas that they link. These findings using DSI and validated with reference to autoradiographic tract tracing in the monkey represent a considerable advance in the understanding of the fibre pathways in the cerebral white matter. By replicating the major features of these tracts identified by histological techniques in monkey, we show that DSI has the potential to cast new light on the organization of the human brain in the normal state and in clinical disorders.


The role of the self in mindblindness in autism

Since its inception the 'mindblindness' theory of autism has greatly furthered our understanding of the core social-communication impairments in autism spectrum conditions (ASC). However, one of the more subtle issues within the theory that needs to be elaborated is the role of the 'self'. In this article, we expand on mindblindness in ASC by addressing topics related to the self and its central role in the social world and then review recent research in ASC that has yielded important insights by contrasting processes relating to both self and other. We suggest that new discoveries lie ahead in understanding how self and other are interrelated and/or distinct, and how understanding atypical self-referential and social-cognitive mechanisms may lead to novel ideas as to how to facilitate social-communicative abilities in ASC.


A review of functional Near-Infrared Spectroscopy measurements in naturalistic environments

The development of novel miniaturized wireless and wearable functional Near-Infrared Spectroscopy (fNIRS) devices have paved the way to new functional brain imaging that can revolutionize the cognitive research fields. Over the past few decades, several studies have been conducted with conventional fNIRS systems that have demonstrated the suitability of this technology for a wide variety of populations and applications, to investigate both the healthy brain and the diseased brain. However, what makes wearable fNIRS even more appealing is its capability to allow more ecologically-valid measurements in everyday life scenarios that are not possible with other gold-standard neuroimaging modalities, such as functional Magnetic Resonance Imaging. This can have


Programming with Abstract Data Types

The motivation behind the work in very-high-level languages is to ease the programming task by providing the programmer with a language containing primitives or abstractions suitable to his problem area. The programmer is then able to spend his effort in the right place; he concentrates on solving his problem, and the resulting program will be more reliable as a result. Clearly, this is a worthwhile goal.
 Unfortunately, it is very difficult for a designer to select in advance all the abstractions which the users of his language might need. If a language is to be used at all, it is likely to be used to solve problems which its designer did not envision, and for which the abstractions embedded in the language are not sufficient.
 This paper presents an approach which allows the set of built-in abstractions to be augmented when the need for a new data abstraction is discovered. This approach to the handling of abstraction is an outgrowth of work on designing a language for structured programming. Relevant aspects of this language are described, and examples of the use and definitions of abstractions are given.


The influence of user-generated content on traveler behavior: An empirical investigation on the effects of e-word-of-mouth to hotel online bookings

The increasing use of web 2.0 applications has generated numerous online user reviews. Prior studies have revealed the influence of user-generated reviews on the sales of products such as CDs, books, and movies. However, the influence of online user-generated reviews in the tourism industry is still largely unknown both to tourism researchers and practitioners. To bridge this knowledge gap in tourism management, we conducted an empirical study to identify the impact of online user-generated reviews on business performance using data extracted from a major online travel agency in China. The empirical findings show that traveler reviews have a significant impact on online sales, with a 10 percent increase in traveler review ratings boosting online bookings by more than five percent. Our results highlight the importance of online user-generated reviews to business performance in tourism. 2010 Elsevier Ltd. All rights reserved.


Designing for Exploratory Search on Touch Devices

Exploratory search confront users with challenges in expressing search intents as the current search interfaces require investigating result listings to identify search directions, iterative typing, and reformulating queries. We present the design of Exploration Wall, a touch-based search user interface that allows incremental exploration and sense-making of large information spaces by combining entity search, flexible use of result entities as query parameters, and spatial configuration of search streams that are visualized for interaction. Entities can be flexibly reused to modify and create new search streams, and manipulated to inspect their relationships with other entities. Data comprising of task-based experiments comparing Exploration Wall with conventional search user interface indicate that Exploration Wall achieves significantly improved recall for exploratory search tasks while preserving precision. Subjective feedback supports our design choices and indicates improved user satisfaction and engagement. Our findings can help to design user interfaces that can effectively support exploratory search on touch devices.


The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations

A useful starting point for designing advanced graphical user interfaces is the Visual InformationSeeking Mantra: Overview first, zoom and filter, then details-on-demand. But this is only a starting point in trying to understand the rich and varied set of information visualizations that have been proposed in recent years. This paper offers a task by data type taxonomy with seven data types (1-, 2-, 3-dimensional data, temporal and multi-dimensional data, and tree and network data) and seven tasks (overview, zoom, filter, details-on-demand, relate, history, and extract).


TopicPanorama: A Full Picture of Relevant Topics

This paper presents a visual analytics approach to analyzing a full picture of relevant topics discussed in multiple sources, such as news, blogs, or micro-blogs. The full picture consists of a number of common topics covered by multiple sources, as well as distinctive topics from each source. Our approach models each textual corpus as a topic graph. These graphs are then matched using a consistent graph matching method. Next, we develop a level-of-detail (LOD) visualization that balances both readability and stability. Accordingly, the resulting visualization enhances the ability of users to understand and analyze the matched graph from multiple perspectives. By incorporating metric learning and feature selection into the graph matching algorithm, we allow users to interactively modify the graph matching result based on their information needs. We have applied our approach to various types of data, including news articles, tweets, and blog data. Quantitative evaluation and real-world case studies demonstrate the promise of our approach, especially in support of examining a topic-graph-based full picture at different levels of detail.


A user-centric evaluation framework for recommender systems

This research was motivated by our interest in understanding the criteria for measuring the success of a recommender system from users' point view. Even though existing work has suggested a wide range of criteria, the consistency and validity of the combined criteria have not been tested. In this paper, we describe a unifying evaluation framework, called ResQue (Recommender systems' Quality of user experience), which aimed at measuring the qualities of the recommended items, the system's usability, usefulness, interface and interaction qualities, users' satisfaction with the systems, and the influence of these qualities on users' behavioral intentions, including their intention to purchase the products recommended to them and return to the system. We also show the results of applying psychometric methods to validate the combined criteria using data collected from a large user survey. The outcomes of the validation are able to 1) support the consistency, validity and reliability of the selected criteria; and 2) explain the quality of user experience and the key determinants motivating users to adopt the recommender technology. The final model consists of thirty two questions and fifteen constructs, defining the essential qualities of an effective and satisfying recommender system, as well as providing practitioners and scholars with a cost-effective way to evaluate the success of a recommender system and identify important areas in which to invest development resources.


Interaction Design for Recommender Systems

1 kirstens@sims.berkeley.edu, sinha@sims.berkeley.edu ABSTRACT Recommender systems act as personalized decision guides for users, aiding them in decision making about matters related to personal taste. Research has focused mostly on the algorithms that drive the system, with little understanding of design issues from the user’s perspective. The goal of our research is to study users’ interactions with recommender systems in order to develop general design guidelines. We have studied users’ interactions with 11 online recommender systems. Our studies have highlighted the role of transparency (understanding of system logic), familiar recommendations, and information about recommended items in the user’s interaction with the system. Our results also indicate that there are multiple models for successful recommender systems.


Driving cycle-based design optimization of interior permanent magnet synchronous motor drives for electric vehicle application

The paper discusses the influence of driving cycles on the design optimization of permanent magnet synchronous machines. A bi-objective design optimization is presented for synchronous machines with V-shaped buried magnets. The machine length and the loss energy over a given driving cycle are defined as objectives. A total of 14 parameters defining geometry and winding layout are chosen as design parameters. Additionally some constraints like speed-torque-requirements and minimal stray field bridge widths are set. The optimization problem is solved using 2D finite element analysis and a high-performance differential evolution algorithm. The analyses are performed for the ARTEMIS driving cycle due to the more realistic driving behavior in comparison to the most commonly used New European Driving Cycle. Furthermore, a reference design optimization against the rated point loss energy is presented. The results show a much better performance of the driving cycle optimized machines in comparison to the rated point optimized machines in terms of the cycle-based loss energy. Loss savings depend strongly on the machine length and are approximately in between 15% and 45%.


Learning From Positive and Unlabeled Data: A Survey

Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data. The assumption is that the unlabeled data can contain both positive and negative examples. This setting has attracted increasing interest within the machine learning literature as this type of data naturally arises in applications such as medical diagnosis and knowledge base completion. This article provides a survey of the current state of the art in PU learning. It proposes seven key research questions that commonly arise in this field and provides a broad overview of how the field has tried to address them.


Machine Learning Techniques in ADAS: A Review

What machine learning (ML) technique is used for system intelligence implementation in ADAS (advanced driving assistance system)? This paper tries to answer this question. This paper analyzes ADAS and ML independently and then relate which ML technique is applicable to what ADAS component and why. The paper gives a good grasp of the current state-of-the-art. Sample works in supervised, unsupervised, deep and reinforcement learnings are presented; their strengths and rooms for improvements are also discussed. This forms part of the basics in understanding autonomous vehicle. This work is a contribution to the ongoing research in ML aimed at reducing road traffic accidents and fatalities, and the invocation of safe driving.


Radian: Visual Exploration of Traceroutes

Several projects deploy probes in the Internet. Probes are systems that continuously perform traceroutes and other networking measurements (e.g., ping) towards selected targets. Measurements can be stored and analyzed to gain knowledge on several aspects of the Internet, but making sense of such data requires suitable methods and tools for exploration and visualization. We present Radian, a tool that allows to visualize traceroute paths at different levels of detail and to animate their evolution during a selected time interval. We also describe extensive tests of the tool using traceroutes performed by RIPE Atlas Internet probes.


DifNet: Semantic Segmentation by Diffusion Networks

Deep Neural Networks (DNNs) have recently shown state of the art performance on semantic segmentation tasks, however, they still suffer from problems of poor boundary localization and spatial fragmented predictions. The difficulties lie in the requirement of making dense predictions from a long path model all at once, since details are hard to keep when data goes through deeper layers. Instead, in this work, we decompose this difficult task into two relative simple sub-tasks: seed detection which is required to predict initial predictions without the need of wholeness and preciseness, and similarity estimation which measures the possibility of any two nodes belong to the same class without the need of knowing which class they are. We use one branch network for one sub-task each, and apply a cascade of random walks base on hierarchical semantics to approximate a complex diffusion process which propagates seed information to the whole image according to the estimated similarities. The proposed DifNet consistently produces improvements over the baseline models with the same depth and with the equivalent number of parameters, and also achieves promising performance on Pascal VOC and Pascal Context dataset. Our DifNet is trained end-to-end without complex loss functions.


Any domain parsing: automatic domain adaptation for natural language parsing

of “Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing” by David McClosky , Ph.D., Brown University, May, 2010. Current efforts in syntactic parsing are largely data-driv en. These methods require labeled examples of syntactic structures to learn statistical patterns governing these structures. Labeled data typically requires expert annotators which makes it both time consuming and costly to p roduce. Furthermore, once training data has been created for one textual domain, portability to similar domains is limited. This domain-dependence has inspired a large body of work since syntactic parsing aims to capture syntactic patterns across an entire language rather than just a specific domain. The simplest approach to this task is to assume that the targe t domain is essentially the same as the source domain. No additional knowledge about the target domain is i cluded. A more realistic approach assumes that only raw text from the target domain is available. This a s umption lends itself well to semi-supervised learning methods since these utilize both labeled and unlab eled examples. This dissertation focuses on a family of semi-supervised me thods called self-training. Self-training creates semi-supervised learners from existing supervised learne rs with minimal effort. We first show results on self-training for constituency parsing within a single dom ain. While self-training has failed here in the past, we present a simple modification which allows it to succeed, p roducing state-of-the-art results for English constituency parsing. Next, we show how self-training is be neficial when parsing across domains and helps further when raw text is available from the target domain. On e of the remaining issues is that one must choose a training corpus appropriate for the target domain or perfo rmance may be severely impaired. Humans can do this in some situations, but this strategy becomes less prac tical as we approach larger data sets. We present a technique, Any Domain Parsing, which automatically detect s useful source domains and mixes them together to produce a customized parsing model. The resulting models perform almost as well as the best seen parsing models (oracle) for each target domain. As a result, we have a fully automatic syntactic constituency parser which can produce high-quality parses for all types of text, regardless of domain. Abstract of “Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing” by David McClosky , Ph.D., Brown University, May, 2010.of “Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing” by David McClosky , Ph.D., Brown University, May, 2010. Current efforts in syntactic parsing are largely data-driv en. These methods require labeled examples of syntactic structures to learn statistical patterns governing these structures. Labeled data typically requires expert annotators which makes it both time consuming and costly to p roduce. Furthermore, once training data has been created for one textual domain, portability to similar domains is limited. This domain-dependence has inspired a large body of work since syntactic parsing aims to capture syntactic patterns across an entire language rather than just a specific domain. The simplest approach to this task is to assume that the targe t domain is essentially the same as the source domain. No additional knowledge about the target domain is i cluded. A more realistic approach assumes that only raw text from the target domain is available. This a s umption lends itself well to semi-supervised learning methods since these utilize both labeled and unlab eled examples. This dissertation focuses on a family of semi-supervised me thods called self-training. Self-training creates semi-supervised learners from existing supervised learne rs with minimal effort. We first show results on self-training for constituency parsing within a single dom ain. While self-training has failed here in the past, we present a simple modification which allows it to succeed, p roducing state-of-the-art results for English constituency parsing. Next, we show how self-training is be neficial when parsing across domains and helps further when raw text is available from the target domain. On e of the remaining issues is that one must choose a training corpus appropriate for the target domain or perfo rmance may be severely impaired. Humans can do this in some situations, but this strategy becomes less prac tical as we approach larger data sets. We present a technique, Any Domain Parsing, which automatically detect s useful source domains and mixes them together to produce a customized parsing model. The resulting models perform almost as well as the best seen parsing models (oracle) for each target domain. As a result, we have a fully automatic syntactic constituency parser which can produce high-quality parses for all types of text, regardless of domain. Any Domain Parsing: Automatic Domain Adaptation for Natural Language Parsing by David McClosky B. S., University of Rochester, 2004 Sc. M., Brown University, 2006 A dissertation submitted in partial fulfillment of the requirements for the Degree of Doctor of Philosophy in the Department of Computer Science at Brown University. Providence, Rhode Island May, 2010 c © Copyright 2010 by David McClosky This dissertation by David McClosky is accepted in its prese nt form by the Department of Computer Science as satisfying the disser tation requirement for the degree of Doctor of Philosophy. Date Eugene Charniak, Director Recommended to the Graduate Council Date Mark Johnson, Reader Cognitive and Linguistic Sciences Date Dan Klein, Reader University of California at Berkeley Approved by the Graduate Council Date Sheila Bond Dean of the Graduate School


Walking > Walking-in-Place > Flying, in Virtual Environments

A study by Slater, et al., [1995] indicated that naive subjects in an immersive virtual environment experience a higher subjective sense of presence when they locomote by walking-in-place (virtual walking) than when they push-button-fly (along the floor plane). We replicated their study, adding real walking as a third condition. Our study confirmed their findings. We also found that real walking is significantly better than both virtual walking and flying in ease (simplicity, straightforwardness, naturalness) as a mode of locomotion. The greatest difference in subjective presence was between flyers and both kinds of walkers. In addition, subjective presence was higher for real walkers than virtual walkers, but the difference was statistically significant only in some models. Follow-on studies show virtual walking can be substantially improved by detecting footfalls with a head accelerometer. As in the Slater study, subjective presence significantly correlated with subjects’ degree of association with their virtual bodies (avatars). This, our strongest statistical result, suggests that substantial potential presence gains can be had from tracking all limbs and customizing avatar appearance. An unexpected by-product was that real walking through our enhanced version of Slater’s visual-cliff virtual environment (Figure 1) yielded a strikingly compelling virtual experience—the strongest we and most of our visitors have yet experienced. The most needed system improvement is the substitution of wireless technology for all links to the user. CR


NL2KB: Resolving Vocabulary Gap between Natural Language and Knowledge Base in Knowledge Base Construction and Retrieval

Words to express relations in natural language (NL) statements may be different from those to represen t properties in knowledge bases (KB). The vocabulary g p becomes barriers for knowledge base construction and retrieval. With the demo system called NL2KB in this paper, users can browse which properties in KB side may be mapped to for a given relati onal pattern in NL side. Besides, they can retrieve the sets of relational patterns in NL side for a given property in KB side. We describe how the mapping is established in detail. Although the mined patterns are used for Chinese knowledge base applications, t he methodology can be extended to other languages.


Building a Web-Scale Dependency-Parsed Corpus from CommonCrawl

We present DEPCC, the largest-to-date linguistically analyzed corpus in English including 365 million documents, composed of 252 billion tokens and 7.5 billion of named entity occurrences in 14.3 billion sentences from a web-scale crawl of the COMMON CRAWL project. The sentences are processed with a dependency parser and with a named entity tagger and contain provenance information, enabling various applications ranging from training syntax-based word embeddings to open information extraction and question answering. We built an index of all sentences and their linguistic meta-data enabling quick search across the corpus. We demonstrate the utility of this corpus on the verb similarity task by showing that a distributional model trained on our corpus yields better results than models trained on smaller corpora, like Wikipedia. This distributional model outperforms the state of art models of verb similarity trained on smaller corpora on the SimVerb3500 dataset.


Universal Blind Quantum Computation

We present a protocol which allows a client to have a server carry out a quantum computation for her such that the client's inputs, outputs and computation remain perfectly private, and where she does not require any quantum computational power or memory. The client only needs to be able to prepare single qubits randomly chosen from a finite set and send them to the server, who has the balance of the required quantum computational resources. Our protocol is interactive: after the initial preparation of quantum states, the client and server use two-way classical communication which enables the client to drive the computation, giving single-qubit measurement instructions to the server, depending on previous measurement outcomes. Our protocol works for inputs and outputs that are either classical or quantum. We give an authentication protocol that allows the client to detect an interfering server; our scheme can also be made fault-tolerant. We also generalize our result to the setting of a purely classical client who communicates classically with two non-communicating entangled servers, in order to perform a blind quantum computation. By incorporating the authentication protocol, we show that any problem in BQP has an entangled two-prover interactive proof with a purely classical verifier. Our protocol is the first universal scheme which detects a cheating server, as well as the first protocol which does not require any quantum computation whatsoever on the client's side. The novelty of our approach is in using the unique features of measurement-based quantum computing which allows us to clearly distinguish between the quantum and classical aspects of a quantum computation.


The evolution to 4G cellular systems: LTE-Advanced

This paper provides an in-depth view on the technologies being considered for Long Term Evolution-Advanced (LTE-Advanced). First, the evolution from third generation (3G) to fourth generation (4G) is described in terms of performance requirements and main characteristics. The new network architecture developed by the Third Generation Partnership Project (3GPP), which supports the integration of current and future radio access technologies, is highlighted. Then, the main technologies for LTE-Advanced are explained, together with possible improvements, their associated challenges, and some approaches that have been considered to tackle those challenges.


A closer look at GPUs

As the line between GPUs and CPUs begins to blur, it's important to understand what makes GPUs tick.


MRR: an unsupervised algorithm to rank reviews by relevance

The automatic detection of relevant reviews plays a major role in tasks such as opinion summarization, opinion-based recommendation, and opinion retrieval. Supervised approaches for ranking reviews by relevance rely on the existence of a significant, domain-dependent training data set. In this work, we propose MRR (Most Relevant Reviews), a new unsupervised algorithm that identifies relevant revisions based on the concept of graph centrality. The intuition behind MRR is that central reviews highlight aspects of a product that many other reviews frequently mention, with similar opinions, as expressed in terms of ratings. MRR constructs a graph where nodes represent reviews, which are connected by edges when a minimum similarity between a pair of reviews is observed, and then employs PageRank to compute the centrality. The minimum similarity is graph-specific, and takes into account how reviews are written in specific domains. The similarity function does not require extensive pre-processing, thus reducing the computational cost. Using reviews from books and electronics products, our approach has outperformed the two unsupervised baselines and shown a comparable performance with two supervised regression models in a specific setting. MRR has also achieved a significantly superior run-time performance in a comparison with the unsupervised baselines.


A generalized uncertainty principle and sparse representation in pairs of bases

An elementary proof of a basic uncertainty principle concerning pairs of representations of RN vectors in different orthonormal bases is provided. The result, slightly stronger than stated before, has a direct impact on the uniqueness property of the sparse representation of such vectors using pairs of orthonormal bases as overcomplete dictionaries. The main contribution in this paper is the improvement of an important result due to Donoho and Huo concerning the replacement of the l0 optimization problem by a linear programming minimization when searching for the unique sparse representation.


Happiness unpacked: positive emotions increase life satisfaction by building resilience.

Happiness-a composite of life satisfaction, coping resources, and positive emotions-predicts desirable life outcomes in many domains. The broaden-and-build theory suggests that this is because positive emotions help people build lasting resources. To test this hypothesis, the authors measured emotions daily for 1 month in a sample of students (N = 86) and assessed life satisfaction and trait resilience at the beginning and end of the month. Positive emotions predicted increases in both resilience and life satisfaction. Negative emotions had weak or null effects and did not interfere with the benefits of positive emotions. Positive emotions also mediated the relation between baseline and final resilience, but life satisfaction did not. This suggests that it is in-the-moment positive emotions, and not more general positive evaluations of one's life, that form the link between happiness and desirable life outcomes. Change in resilience mediated the relation between positive emotions and increased life satisfaction, suggesting that happy people become more satisfied not simply because they feel better but because they develop resources for living well.


Interpretable Learning for Self-Driving Cars by Visualizing Causal Attention

Deep neural perception and control networks are likely to be a key component of self-driving vehicles. These models need to be explainable - they should provide easy-tointerpret rationales for their behavior - so that passengers, insurance companies, law enforcement, developers etc., can understand what triggered a particular behavior. Here we explore the use of visual explanations. These explanations take the form of real-time highlighted regions of an image that causally influence the network’s output (steering control). Our approach is two-stage. In the first stage, we use a visual attention model to train a convolution network endto- end from images to steering angle. The attention model highlights image regions that potentially influence the network’s output. Some of these are true influences, but some are spurious. We then apply a causal filtering step to determine which input regions actually influence the output. This produces more succinct visual explanations and more accurately exposes the network’s behavior. We demonstrate the effectiveness of our model on three datasets totaling 16 hours of driving. We first show that training with attention does not degrade the performance of the end-to-end network. Then we show that the network causally cues on a variety of features that are used by humans while driving.


An exploratory study of backtracking strategies used by developers

Developers frequently backtrack while coding. They go back to an earlier state by removing inserted code or by restoring removed code for various reasons. However, little is known about when and how the developers backtrack, and modern IDEs do not provide much assistance for backtracking. As a first step towards gathering baseline knowledge about backtracking and designing more robust backtracking assistance tools in modern IDEs, we conducted an exploratory study with 12 professional developers and a follow-up online survey. Our study revealed several barriers they faced while backtracking. Subjects often manually commented and uncommented code, and often had difficulty finding relevant parts to backtrack. Backtracking was reported to be needed by 3/4 of the developers at least "sometimes".


Scientific and Pragmatic Challenges for Bridging Education and Neuroscience

Neuroscience has experienced rapid growth in recent years, spurred in part by the U.S. government’s designation of the 1990s as “The Decade of the Brain” (Jones & Mendell, 1999). The rapid development of functional neuroimaging techniques has given researchers unprecedented access to the behaving brains of healthy children and adults. The result has been a wave of new insights into thinking, emotion, motivation, learning, and development. As these insights suffuse the social sciences, they sometimes inspire reconsideration of existing explanations. This is most true in psychology, as marked by the births of cognitive neuroscience (Gazzaniga, Ivry, & Mangun, 2002), developmental neuroscience (Johnson, Munakata, & Gilmore, 2001), and social neuroscience (Cacioppo, Visser, & Pickett, 2005). It is increasingly true in economics, where the rapid rise of neuroeconomics (Camerer, Loewenstein, & Prelec, 2005) has caught the attention of the popular press (Cassidy, 2006). Other social sciences, including communication (Anderson et al., 2006), political science (McDermott, 2004), and sociology (Wexler, 2006), are just beginning to confront the question of whether their research can be informed by neuroscience. Education is somewhere between the two poles of early adopters and tentative newcomers. A decade ago, in this journal, Bruer (1997) forcefully considered the relevance of neuroscience to education. His conclusion—that neuroscience is “a bridge too far”—was noteworthy because Bruer was then director of the McDonnell Foundation, which was actively funding research in both disciplines. Although it was in his best interests to find connections between the disciplines, he found instead poorly drawn extrapolations that inflated neuroscience findings into educational neuromyths. Since Bruer’s cautionary evaluation, a number of commentators have considered the prospects for educational neuroscience. Many sound a more optimistic note (Ansari & Coch, 2006; Byrnes & Fox, 1998; Geake & Cooper, 2003; Goswami, 2006; Petitto & Dunbar, in press), and a textbook has even appeared (Blakemore & Frith, 2005). In this article, we negotiate the middle ground between the pessimism of Bruer and the optimism of those who followed. Table 1 summarizes eight concerns about connecting education and neuroscience. Some are drawn from Bruer (1997) and the ensuing commentaries. Others come from conversations with colleagues in both disciplines, and still others from our own experiences. These concerns do not seem to represent a blanket dismissal but rather a genuine curiosity (tempered by a healthy skepticism) about the implications of neuroscience for education. We begin by articulating the concerns along with some facts about neuroscience that make the concerns more concrete. We voice them in the strong tone in which we have heard them espoused. We then revisit the concerns, reinterpreting them as potential opportunities (also in Table 1). This approach permits us to review a selection of neuroscience studies relevant to content learning. We focus on recent functional magnetic resonance imaging (fMRI), or neuroimaging, studies for reasons of space and because these are the findings that have captured the most attention, both in the academy and in the popular press. Ideally, our review illustrates some elements of neuroscience so that education researchers can think more specifically about the prospects of educational neuroscience. We conclude with two reflections on moving from armchair arguments of a philosophical nature to scientific action on the ground. First, we argue that education and neuroscience can be bridged if (and only if) researchers collaborate across disciplinary lines on tractable problems of common interest. It is the success


Animal-assisted therapy and loneliness in nursing homes: use of robotic versus living dogs.

Loneliness is a common problem in long-term care facilities (LTCF) and previous work has shown that animal-assisted therapy (AAT) can to some degree reverse loneliness. Here, we compared the ability of a living dog (Dog) and a robotic dog (AIBO) to treat loneliness in elderly patients living in LTCF. In comparison with a control group not receiving AAT, both the Dog and AIBO groups had statistically significant improvements in their levels of loneliness. As measured by a modified Lexington Attachment to Pets Scale (MLAPS), residents showed high levels of attachment to both the dog and AIBO. Subscale analysis showed that the AIBO group scored lower than the living dog on "animal rights/animal welfare" but not on "general attachment" or "people substituting." However, MLAPS measures did not correlate with changes in loneliness, showing that attachment was not the mechanism by which AAT decreases loneliness. We conclude that interactive robotic dogs can reduce loneliness in residents of LTCF and that residents become attached to these robots. However, level of attachment does not explain the decrease in loneliness associated with AAT conducted with either a living or robotic dog.


Modelling and Simulation of Four Quadrant Operation of Three Phase Brushless DC Motor With Hysteresis Current

Brushless DC (BLDC) motor drives are becoming more popular in industrial, traction applications. This makes the control of BLDC motor in all the four quadrants very vitalThe motor is operated in four steady state operating modes of torque-speed plane. To control a BLDC machine it is generally required to measure the speed and position of rotor by using the sensor because the inverter phases, acting at any time, must be commutated depending on the rotor position. Simulation of the proposed model was done using MATLAB/ SIMULINK.


3D Soft Tissue Analysis – Part 1: Sagittal Parameters

The aim of this study was to develop a reliable threedimensional (3D) analysis of facial soft tissues. We determined the mean sagittal 3D values and relationships between sagittal skeletal parameters, and digitally recorded 3D soft tissue parameters. A total of 100 adult patients (nª = 53, nº = 47) of Caucasian ethnic origin were included in this study. Patients with syndromes, cleft lip and palate, noticeable asymmetry or anomalies in the number of teeth were excluded. Arithmetic means for seven sagittal 3D soft tissue parameters were calculated. The parameters were analyzed biometrically in terms of their reliability and gender-specific differences. The 3D soft tissue values were further analyzed for any correlations with sagittal cephalometric values. Reproducible 3D mean values were defined for 3D soft tissue parameters. We detected gender-specific differences among the parameters. Correlations between the sagittal 3D soft tissue and cephalometric measurements were statistically significant. 3D soft tissue analysis provides additional information on the sagittal position of the jaw bases and on intermaxillary sagittal relations. Further studies are needed to integrate 3D soft tissue analysis in future treatment planning and assessment as a supportive diagnostic tool. Ziel dieser Untersuchung war die Entwicklung einer reliablen dreidimensionalen (3D) Analyse der Gesichtsweichteile. Es sollten sagittale 3D-Durchschnittswerte bestimmt werden und Beziehungen zwischen sagittalen skelettalen Parametern und digital erfassten 3D-Weichteilparametern dargestellt werden. In die Studie eingeschlossen wurden 100 erwachsene Patienten (nª = 53, nº = 47) kaukasischer Herkunft. Ausgeschlossen wurden Patienten mit Syndromen, LKGSSpalten, auffälligen Asymmetrien oder Anomalien der Zahnzahl. Es wurden arithmetische Mittelwerte für sieben sagittale 3DWeichteilparameter ermittelt. Die Parameter wurden biometrisch hinsichtlich ihrer Reliabilität und geschlechtsspezifischer Unterschiede analysiert. Des Weiteren wurden die 3D-Weichteilwerte bezüglich bestehender Korrelationen zu sagittalen kephalometrischen Werten untersucht. Für die 3D-Weichteilparameter konnten reproduzierbare 3D-Durchschnittswerte definiert werden. Innerhalb der Parameter ließen sich geschlechtsspezifische Unterschiede feststellen. Die Korrelationen zwischen den sagittalen 3D-Weichteilmessungen und den kephalometrischen Messungen waren statistisch signifikant. Die 3D-Weichteilanalyse lässt Aussagen sowohl über den sagittalen Einbau der Kieferbasen als auch über die sagittalen intermaxillären Beziehungen zu. Weitere Untersuchungen sind wünschenswert, um die 3D-Weichteildiagnostik zukünftig als unterstützendes diagnostisches Instrument in die Behandlungsplanung und -bewertung integrieren zu können.


Binary partition tree as an efficient representation for image processing, segmentation, and information retrieval

This paper discusses the interest of binary partition trees as a region-oriented image representation. Binary partition trees concentrate in a compact and structured representation a set of meaningful regions that can be extracted from an image. They offer a multiscale representation of the image and define a translation invariant 2-connectivity rule among regions. As shown in this paper, this representation can be used for a large number of processing goals such as filtering, segmentation, information retrieval and visual browsing. Furthermore, the processing of the tree representation leads to very efficient algorithms. Finally, for some applications, it may be interesting to compute the binary partition tree once and to store it for subsequent use for various applications. In this context, the paper shows that the amount of bits necessary to encode a binary partition tree remains moderate.


Making sense of social media streams through semantics: A survey

Using semantic technologies for mining and intelligent information access to social media is a challenging, emerging research area. Traditional search methods are no longer able to address the more complex information seeking behaviour in media streams, which has evolved towards sense making, learning, investigation, and social search. Unlike carefully authored news text and longer web context, social media streams pose a number of new challenges, due to their large-scale, short, noisy, contextdependent, and dynamic nature. This paper defines five key research questions in this new application area, examined through a survey of state-of-the-art approaches for mining semantics from social media streams; user, network, and behaviour modelling; and intelligent, semanticbased information access. The survey includes key methods not just from the Semantic Web research field, but also from the related areas of natural language processing and user modelling. In conclusion, key outstanding challenges are discussed and new directions for research are proposed.


A Document Skew Detection Method Using the Hough Transform

Document image processing has become an increasingly important technology in the automation of office documentation tasks. Automatic document scanners such as text readers and OCR (Optical Character Recognition) systems are an essential component of systems capable of those tasks. One of the problems in this field is that the document to be read is not always placed correctly on a flatbed scanner. This means that the document may be skewed on the scanner bed, resulting in a skewed image. This skew has a detrimental effect on document on document analysis, document understanding, and character segmentation and recognition. Consequently, detecting the skew of a document image and correcting it are important issues in realising a practical document reader. In this paper we describe a new algorithm for skew detection. We then compare the performance and results of this skew detection algorithm to other publidhed methods form O'Gorman, Hinds, Le, Baird, Posel and Akuyama. Finally, we discuss the theory of skew detection and the different apporaches taken to solve the problem of skew in documents. The skew correction algorithm we propose has been shown to be extremenly fast, with run times averaging under 0.25 CPU seconds to calculate the angle on the DEC 5000/20 workstation.


Performance Evaluation of Bridgeless PFC Boost Rectifiers

In this paper, a systematic review of bridgeless power factor correction (PFC) boost rectifiers, also called dual boost PFC rectifiers, is presented. Performance comparison between the conventional PFC boost rectifier and a representative member of the bridgeless PFC boost rectifier family is performed. Loss analysis and experimental efficiency evaluation for both CCM and DCM/CCM boundary operations are provided.


State-of-the-art, single-phase, active power-factor-correction techniques for high-power applications - an overview

A review of high-performance, state-of-the-art, active power-factor-correction (PFC) techniques for high-power, single-phase applications is presented. The merits and limitations of several PFC techniques that are used in today's network-server and telecom power supplies to maximize their conversion efficiencies are discussed. These techniques include various zero-voltage-switching and zero-current-switching, active-snubber approaches employed to reduce reverse-recovery-related switching losses, as well as techniques for the minimization of the conduction losses. Finally, the effect of recent advancements in semiconductor technology, primarily silicon-carbide technology, on the performance and design considerations of PFC converters is discussed.


Bridgeless PFC implementation using one cycle control technique

Conventional boost PFC suffers from the high conduction loss in the input rectifier-bridge. Higher efficiency can be achieved by using the bridgeless boost topology. This new circuit has issues such as voltage sensing, current sensing and EMI noise. In this paper, one cycle control technique is used to solve the issues of the voltage sensing and current sensing. Experimental results show efficiency improvement and EMI performance


Common mode noise modeling and analysis of dual boost PFC circuit

To achieve high efficiency PFC front stage in switching mode power supply (SMPS), dual boost PFC (DBPFC) topology shows superior characteristics compared with traditional boost PFC, but it by nature brings higher EMI noise, especially common mode (CM) noise. This paper deals with the modeling and analysis of DBPFC CM noise based on and compared with boost PFC, noise propagation equivalent circuits of both topologies are deduced, and theoretical analysis illustrates the difference. Experiments are performed to validate the EMI model and analysis.


Common mode EMI noise suppression in bridgeless boost PFC converter

Bridgeless boost PFC converter has high efficiency by eliminating the input diode bridge. However, Common Mode (CM) conducted EMI becomes a great issue. The goal of this paper is to study the possibility to minimize the CM noise in this converter. First the noise model is studied. Then a balance concept is introduced and applied to cancel the CM noise. Two approaches to minimize CM noise are introduced and compared. Experiments verify the effectiveness of both approaches.


A design methodology of chip-to-chip wireless power transmission system

A design methodology to transmit power using a chip-to-chip wireless interface is proposed. The proposed power transmission system is based on magnetic coupling, and the power transmission of 5mW/mm2 was verified. The transmission efficiency trade-off with the transmitted power is also discussed.


Options for Control of Reactive Power by Distributed Photovoltaic Generators

High-penetration levels of distributed photovoltaic (PV) generation on an electrical distribution circuit present several challenges and opportunities for distribution utilities. Rapidly varying irradiance conditions may cause voltage sags and swells that cannot be compensated by slowly responding utility equipment resulting in a degradation of power quality. Although not permitted under current standards for interconnection of distributed generation, fast-reacting, VAR-capable PV inverters may provide the necessary reactive power injection or consumption to maintain voltage regulation under difficult transient conditions. As side benefit, the control of reactive power injection at each PV inverter provides an opportunity and a new tool for distribution utilities to optimize the performance of distribution circuits, e.g., by minimizing thermal losses. We discuss and compare via simulation various design options for control systems to manage the reactive power generated by these inverters. An important design decision that weighs on the speed and quality of communication required is whether the control should be centralized or distributed (i.e., local). In general, we find that local control schemes are able to maintain voltage within acceptable bounds. We consider the benefits of choosing different local variables on which to control and how the control system can be continuously tuned between robust voltage control, suitable for daytime operation when circuit conditions can change rapidly, and loss minimization better suited for nighttime operation.


Deep Convolutional Neural Network Design Patterns

Recent research in the deep learning field has produced a plethora of new architectures. At the same time, a growing number of groups are applying deep learning to new applications. Some of these groups are likely to be composed of inexperienced deep learning practitioners who are baffled by the dizzying array of architecture choices and therefore opt to use an older architecture (i.e., Alexnet). Here we attempt to bridge this gap by mining the collective knowledge contained in recent deep learning research to discover underlying principles for designing neural network architectures. In addition, we describe several architectural innovations, including Fractal of FractalNet network, Stagewise Boosting Networks, and Taylor Series Networks (our Caffe code and prototxt files is available at https://github.com/iPhysicist/CNNDesignPatterns). We hope others are inspired to build on our preliminary work.


A linear regression analysis of the effects of age related pupil dilation change in iris biometrics

Medical studies have shown that average pupil size decreases linearly throughout adult life. Therefore, on average, the longer the time between acquisition of two images of the same iris, the larger the difference in dilation between the two images. Several studies have shown that increased difference in dilation causes an increase in the false nonmatch rate for iris recognition. Thus, increased difference in pupil dilation is one natural mechanism contributing to an iris template aging effect. We present an experimental analysis of the change in genuine match scores in the context of dilation differences due to aging.


Controlling Robot Morphology From Incomplete Measurements

Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions. Since teleoperation of the complex morphology causes high cognitive load of the operator, the morphology is controlled autonomously. The autonomous control measures the robot state and surrounding terrain which is usually only partially observable, and thus the data are often incomplete. We marginalize the control over the missing measurements and evaluate an explicit safety condition. If the safety condition is violated, tactile terrain exploration by the body-mounted robotic arm gathers the missing data.


Computational imaging with multi-camera time-of-flight systems

Depth cameras are a ubiquitous technology used in a wide range of applications, including robotic and machine vision, human-computer interaction, autonomous vehicles as well as augmented and virtual reality. In this paper, we explore the design and applications of phased multi-camera time-of-flight (ToF) systems. We develop a reproducible hardware system that allows for the exposure times and waveforms of up to three cameras to be synchronized. Using this system, we analyze waveform interference between multiple light sources in ToF applications and propose simple solutions to this problem. Building on the concept of orthogonal frequency design, we demonstrate state-of-the-art results for instantaneous radial velocity capture via Doppler time-of-flight imaging and we explore new directions for optically probing global illumination, for example by de-scattering dynamic scenes and by non-line-of-sight motion detection via frequency gating.


Merging What's Cracked, Cracking What's Merged: Adaptive Indexing in Main-Memory Column-Stores

Adaptive indexing is characterized by the partial creation and refinement of the index as side effects of query execution. Dynamic or shifting workloads may benefit from preliminary index structures focused on the columns and specific key ranges actually queried — without incurring the cost of full index construction. The costs and benefits of adaptive indexing techniques should therefore be compared in terms of initialization costs, the overhead imposed upon queries, and the rate at which the index converges to a state that is fully-refined for a particular workload component. Based on an examination of database cracking and adaptive merging, which are two techniques for adaptive indexing, we seek a hybrid technique that has a low initialization cost and also converges rapidly. We find the strengths and weaknesses of database cracking and adaptive merging complementary. One has a relatively high initialization cost but converges rapidly. The other has a low initialization cost but converges relatively slowly. We analyze the sources of their respective strengths and explore the space of hybrid techniques. We have designed and implemented a family of hybrid algorithms in the context of a column-store database system. Our experiments compare their behavior against database cracking and adaptive merging, as well as against both traditional full index lookup and scan of unordered data. We show that the new hybrids significantly improve over past methods while at least two of the hybrids come very close to the “ideal performance” in terms of both overhead per query and convergence to a final state.


Positioning for the Internet of Things: A 3GPP Perspective

Many use cases in the Internet of Things (IoT) will require or benefit from location information, making positioning a vital dimension of the IoT. The 3GPP has dedicated a significant effort during its Release 14 to enhance positioning support for its IoT technologies to further improve the 3GPPbased IoT eco-system. In this article, we identify the design challenges of positioning support in LTE-M and NB-IoT, and overview the 3GPP's work in enhancing the positioning support for LTE-M and NB-IoT. We focus on OTDOA, which is a downlink based positioning method. We provide an overview of the OTDOA architecture and protocols, summarize the designs of OTDOA positioning reference signals, and present simulation results to illustrate the positioning performance.


Autonomous Takeoff and Flight of a Tethered Aircraft for Airborne Wind Energy

A control design approach to achieve fully autonomous takeoff and flight maneuvers with a tethered aircraft is presented and demonstrated in real-world flight tests with a small-scale prototype. A ground station equipped with a controlled winch and a linear motion system accelerates the aircraft to takeoff speed and controls the tether reeling in order to limit the pulling force. This setup corresponds to airborne wind energy (AWE) systems with ground-based energy generation and rigid aircrafts. A simple model of the aircraft’s dynamics is introduced and its parameters are identified from experimental data. A model-based, hierarchical feedback controller is then designed, whose aim is to manipulate the elevator, aileron, and propeller inputs in order to stabilize the aircraft during the takeoff and to achieve figure-of-eight flight patterns parallel to the ground. The controller operates in a fully decoupled mode with respect to the ground station. Parameter tuning and stability/robustness aspect are discussed, too. The experimental results indicate that the controller is able to achieve satisfactory performance and robustness, notwithstanding its simplicity, and confirm that the considered takeoff approach is technically viable and solves the issue of launching this kind of AWE systems in a compact space and at low additional cost.


STOCK MARKET PREDICTION USING BIO-INSPIRED COMPUTING : A SURVEY

Bio-inspired evolutionary algorithms are probabilistic search methods that mimic natural biological evolution. They show the behavior of the biological entities interacting locally with one another or with their environment to solve complex problems. This paper aims to analyze the most predominantly used bio-inspired optimization techniques that have been used for stock market prediction and hence present a comparative study between them.


Security slicing for auditing XML, XPath, and SQL injection vulnerabilities

XML, XPath, and SQL injection vulnerabilities are among the most common and serious security issues for Web applications and Web services. Thus, it is important for security auditors to ensure that the implemented code is, to the extent possible, free from these vulnerabilities before deployment. Although existing taint analysis approaches could automatically detect potential vulnerabilities in source code, they tend to generate many false warnings. Furthermore, the produced traces, i.e. dataflow paths from input sources to security-sensitive operations, tend to be incomplete or to contain a great deal of irrelevant information. Therefore, it is difficult to identify real vulnerabilities and determine their causes. One suitable approach to support security auditing is to compute a program slice for each security-sensitive operation, since it would contain all the information required for performing security audits (Soundness). A limitation, however, is that such slices may also contain information that is irrelevant to security (Precision), thus raising scalability issues for security audits. In this paper, we propose an approach to assist security auditors by defining and experimenting with pruning techniques to reduce original program slices to what we refer to as security slices, which contain sound and precise information. To evaluate the proposed pruning mechanism by using a number of open source benchmarks, we compared our security slices with the slices generated by a state-of-the-art program slicing tool. On average, our security slices are 80% smaller than the original slices, thus suggesting significant reduction in auditing costs.


A fully integrated multi-CPU, GPU and memory controller 32nm processor

This paper describes the 32nm Sandy Bridge processor that integrates up to 4 high performance Intel Architecture (IA) cores, a power/performance optimized graphic processing unit (GPU) and memory and PCIe controllers in the same die. The Sandy Bridge architecture block diagram is shown in Fig. 15.1.1 and the floorplan of a four IA-core version is shown in Fig. 15.1.2. The Sandy Bridge IA core implements an improved branch prediction algorithm, a micro-operation (Uop) cache, a floating point Advanced Vector Extension (AVX), a second load port in the L1 cache and bigger register files in the out-of-order part of the machine; all these architecture improvements boost the IA core performance without increasing the thermal power dissipation envelope or the average power consumption (to preserve battery life in mobile systems). The CPUs and GPU share the same 8MB level-3 cache memory. The data flow is optimized by a high performance on die interconnect fabric (called “ring”) that connects between the CPUs, the GPU, the L3 cache and the system agent (SA) unit that houses a 1600MT/s, dual channel DDR3 memory controller, a 20-lane PCIe gen2 controller, a two parallel pipe display engine, the power management control unit and the testability logic. An on die EPROM is used for configurability and yield optimization.


Position-based impedance control of an industrial hydraulic manipulator

This paper addresses the problem of impedance control in hydraulic robots. Whereas most impedance and hybrid force/position control formulations have focussed on electrically-driven robots with controllable actuator torques, torque control of hydraulic actuators is a difficult task. Motivated by the previous works [2,9, lo], a position-based impedance controller is proposed and demonstrated on an existing industrial hydraulic robot (a Unimate MKII-2000). A nonlinear proportional-integral (NPI) controller is first developed to meet the accurate positioning requirements of this impedance control formulation. The NPI controller is shown to make the manipulator match a range of second-order target impedances. Various experiments in free space and in environmental contact, including a simple impedance modulation experiment, demonstrate the feasibility and the promise of the technique.


Clustered Collaborative Filtering Approach for Distributed Data Mining on Electronic Health Records

Distributed Data Mining (DDM) has become one of the promising areas of Data Mining. DDM techniques include classifier approach and agent-approach. Classifier approach plays a vital role in mining distributed data, having homogeneous and heterogeneous approaches depend on data sites. Homogeneous classifier approach involves ensemble learning, distributed association rule mining, meta-learning and knowledge probing. Heterogeneous classifier approach involves collective principle component analysis, distributed clustering, collective decision tree and collective bayesian learning model. In this paper, classifier approach for DDM is summarized and an architectural model based on clustered-collaborative filtering for Electronic Health Records (EHR) is proposed.


Regulating the new information intermediaries as gatekeepers of information diversity

Natali Helberger is Professor at the Institute for Information Law, University of Amsterdam, Amsterdam, The Netherlands. Katharina Kleinen-von Königslöw is Assistant Professor for Political Communication at the University of Zurich, Zurich, Switzerland. Rob van der Noll is Senior Researcher at SEO Economic Research, Amsterdam, The Netherlands. Abstract Purpose – The purposes of this paper are to deal with the questions: because search engines, social networks and app-stores are often referred to as gatekeepers to diverse information access, what is the evidence to substantiate these gatekeeper concerns, and to what extent are existing regulatory solutions to control gatekeeper control suitable at all to address new diversity concerns? It will also map the different gatekeeper concerns about media diversity as evidenced in existing research before the background of network gatekeeping theory critically analyses some of the currently discussed regulatory approaches and develops the contours of a more user-centric approach towards approaching gatekeeper control and media diversity.


A hierarchical lstm model with multiple features for sentiment analysis of sina weibo texts

Sentiment analysis has long been a hot topic in natural language processing. With the development of social network, sentiment analysis on social media such as Facebook, Twitter and Weibo becomes a new trend in recent years. Many different methods have been proposed for sentiment analysis, including traditional methods (SVM and NB) and deep learning methods (RNN and CNN). In addition, the latter always outperform the former. However, most of existing methods only focus on local text information and ignore the user personality and content characteristics. In this paper, we propose an improved LSTM model with considering the user-based features and content-based features. We first analysis the training dataset to extract artificial features which consists of user-based and content-based. Then we construct a hierarchical LSTM model, named LSTM-MF (a hierarchical LSTM model with multiple features), and introduce the features into the model to generate sentence and document representations. The experimental results show that our model achieves significant and consistent improvements compared to all state-of-the-art methods.


A Distribution-Based Clustering Algorithm for Mining in Large Spatial Databases

The problem of detecting clusters of points belonging to a spatial point process arises in many applications. In this paper , we introduce the new clustering algorithm DBCLASD (Distribution Based Clustering of LArge Spatial Databases) to discover clusters of this type. The results of experiments demonstrate that DBCLASD, contrary to partitioning algorithms such as CLARANS, discovers clusters of arbitrary shape. Furthermore, DBCLASD does not require any input parameters, in contrast to the clustering algorithm DBSCAN requiring two input parameters which may be difficult to provide for large databases. In terms of efficiency, DBCLASD is between CLARANS and DBSCAN, close to DBSCAN. Thus, the efficiency of DBCLASD on large spatial databases is very attractive when considering its nonparametric nature and its good quality for clusters of arbitrary shape.


Impact of Depression and Antidepressant Treatment on Heart Rate Variability: A Review and Meta-Analysis

BACKGROUND
Depression is associated with an increase in the likelihood of cardiac events; however, studies investigating the relationship between depression and heart rate variability (HRV) have generally focused on patients with cardiovascular disease (CVD). The objective of the current report is to examine with meta-analysis the impact of depression and antidepressant treatment on HRV in depressed patients without CVD.

METHODS
Studies comparing 1) HRV in patients with major depressive disorder and healthy control subjects and 2) the HRV of patients with major depressive disorder before and after treatment were considered for meta-analysis.

RESULTS
Meta-analyses were based on 18 articles that met inclusion criteria, comprising a total of 673 depressed participants and 407 healthy comparison participants. Participants with depression had lower HRV (time frequency: Hedges' g = -.301, p < .001; high frequency: Hedges' g = -.293, p < .001; nonlinear: Hedges' g = -1.955, p = .05; Valsalva ratio: Hedges' g = -.712, p < .001) than healthy control subjects, and depression severity was negatively correlated with HRV (r = -.354, p < .001). Tricyclic medication decreased HRV, although serotonin reuptake inhibitors, mirtazapine, and nefazodone had no significant impact on HRV despite patient response to treatment.

CONCLUSIONS
Depression without CVD is associated with reduced HRV, which decreases with increasing depression severity, most apparent with nonlinear measures of HRV. Critically, a variety of antidepressant treatments do not resolve these decreases despite resolution of symptoms, highlighting that antidepressant medications might not have HRV-mediated cardioprotective effects and the need to identify individuals at risk among patients in remission.


Temporal Sequence Learning, Prediction, and Control: A Review of Different Models and Their Relation to Biological Mechanisms

In this review, we compare methods for temporal sequence learning (TSL) across the disciplines machine-control, classical conditioning, neuronal models for TSL as well as spike-timing-dependent plasticity (STDP). This review introduces the most influential models and focuses on two questions: To what degree are reward-based (e.g., TD learning) and correlation-based (Hebbian) learning related? and How do the different models correspond to possibly underlying biological mechanisms of synaptic plasticity? We first compare the different models in an open-loop condition, where behavioral feedback does not alter the learning. Here we observe that reward-based and correlation-based learning are indeed very similar. Machine control is then used to introduce the problem of closed-loop control (e.g., actor-critic architectures). Here the problem of evaluative (rewards) versus nonevaluative (correlations) feedback from the environment will be discussed, showing that both learning approaches are fundamentally different in the closed-loop condition. In trying to answer the second question, we compare neuronal versions of the different learning architectures to the anatomy of the involved brain structures (basal-ganglia, thalamus, and cortex) and the molecular biophysics of glutamatergic and dopaminergic synapses. Finally, we discuss the different algorithms used to model STDP and compare them to reward-based learning rules. Certain similarities are found in spite of the strongly different timescales. Here we focus on the biophysics of the different calcium-release mechanisms known to be involved in STDP.


A Survey of Outlier Detection Methods in Network Anomaly Identification

The detection of outliers has gained considerable interest in data mining with the realization that outliers can be the key discovery to be made from very large databases. Outliers arise due to various reasons such as mechanical faults, changes in system behavior, fraudulent behavior, human error and instrument error. Indeed, for many applications the discovery of outliers leads to more interesting and useful results than the discovery of inliers. Detection of outliers can lead to identification of system faults so that administrators can take preventive measures before they escalate. It is possible that anomaly detection may enable detection of new attacks. Outlier detection is an important anomaly detection approach. In this paper, we present a comprehensive survey of well known distance-based, density-based and other techniques for outlier detection and compare them. We provide definitions of outliers and discuss their detection based on supervised and unsupervised learning in the context of network anomaly detection.


Managing Risk in Software Process Improvement: An Action Research Approach

Many software organizations engage in software process improvement (SPI) initiatives to increase their capability to develop quality solutions at a competitive level. Such efforts, however, are complex and very demanding. A variety of risks makes it difficult to develop and implement new processes. We studied SPI in its organizational context through collaborative practice research (CPR), a particular form of action research. The CPR program involved close collaboration between practitioners and researchers over a three-year period to understand and improve SPI initiatives in four Danish software organizations. The problem of understanding and managing risks in SPI teams emerged in one of the participating organizations and led to this research. We draw upon insights from the literature on SPI and software risk management as well as practical lessons learned from managing SPI risks in the participating software organizations. Our research offers two contributions. First, we contribute to knowledge on SPI by proposing an approach to understand and manage risks in SPI teams. This risk management approach consists of a framework for understanding risk areas and risk resolution strategies within SPI and a related Iversen et al./Managing Risk in SPI 396 MIS Quarterly Vol. 28 No. 3/September 2004 process for managing SPI risks. Second, we contribute to knowledge on risk management within the information systems and software engineering disciplines. We propose an approach to tailor risk management to specific contexts. This approach consists of a framework for understanding and choosing between different forms of risk management and a process to tailor risk management to specific contexts.


CONTEXT-CONDITIONAL GENERATIVE ADVERSARIAL NETWORKS

We introduce a simple semi-supervised learning approach for images based on in-painting using an adversarial loss. Images with random patches removed are presented to a generator whose task is to fill in the hole, based on the surrounding pixels. The in-painted images are then presented to a discriminator network that judges if they are real (unaltered training images) or not. This task acts as a regularizer for standard supervised training of the discriminator. Using our approach we are able to directly train large VGG-style networks in a semi-supervised fashion. We evaluate on STL-10 and PASCAL datasets, where our approach obtains performance comparable or superior to existing methods.


Ensemble methods for spoken emotion recognition in call-centres

Machine-based emotional intelligence is a requirement for more natural interaction between humans and computer interfaces and a basic level of accurate emotion perception is needed for computer systems to respond adequately to human emotion. Humans convey emotional information both intentionally and unintentionally via speech patterns. These vocal patterns are perceived and understood by listeners during conversation. This research aims to improve the automatic perception of vocal emotion in two ways. First, we compare two emotional speech data sources: natural, spontaneous emotional speech and acted or portrayed emotional speech. This comparison demonstrates the advantages and disadvantages of both acquisition methods and how these methods affect the end application of vocal emotion recognition. Second, we look at two classification methods which have not been applied in this field: stacked generalisation and unweighted vote. We show how these techniques can yield an improvement over traditional classification methods. 2006 Elsevier B.V. All rights reserved.


Laser ranging : a critical review of usual techniques for distance measurement

Marc Rioux National Research Council, Canada Institute for Information Technology Visual Information Technology Ottawa, Canada, K1A 0R6 Abstract. We review some usual laser range finding techniques for industrial applications. After outlining the basic principles of triangulation and time of flight [pulsed, phase-shift and frequency modulated continuous wave (FMCW)], we discuss their respective fundamental limitations. Selected examples of traditional and new applications are also briefly presented. © 2001 Society of Photo-Optical Instrumentation Engineers. [DOI: 10.1117/1.1330700]


Liquid cooled permanent-magnet traction motor design considering temporary overloading

This paper focuses on traction motor design in a variable speed drive of an Electric Mini Bus, aiming to deliver high mean mechanical power over the whole speed range, using single gear transmission. Specific design considerations aim to minimize oversizing, by utilizing temporary overloading capability using liquid cooled motor housing. Constant toque - constant power control strategy is adopted by appropriately programming the over-torque and field weakening operation areas of the motor. A good quantitative analysis between over-torque, rated and field weakening operation areas, is given, focusing on efficiency and paying particular attention to iron losses both on stator and rotor. The motor has been designed in order to meet traction application specifications and allow operation in higher specific electric loading to increase power density.


Nitroglycerin: a review of its use in the treatment of vascular occlusion after soft tissue augmentation.

BACKGROUND
Skin necrosis after soft tissue augmentation with dermal fillers is a rare but potentially severe complication. Nitroglycerin paste may be an important treatment option for dermal and epidermal ischemia in cosmetic surgery.

OBJECTIVES
To summarize the knowledge about nitroglycerin paste in cosmetic surgery and to understand its current use in the treatment of vascular compromise after soft tissue augmentation. To review the mechanism of action of nitroglycerin, examine its utility in the dermal vasculature in the setting of dermal filler-induced ischemia, and describe the facial anatomy danger zones in order to avoid vascular injury.

METHODS
A literature review was conducted to examine the mechanism of action of nitroglycerin, and a treatment algorithm was proposed from clinical observations to define strategies for impending facial necrosis after filler injection.

RESULTS AND CONCLUSIONS
Our experience with nitroglycerin paste and our review of the medical literature supports the use of nitroglycerin paste on the skin to help improve flow in the dermal vasculature because of its vasodilatory effect on small-caliber arterioles.


Factors contributing to the facial aging of identical twins.

BACKGROUND
The purpose of this study was to identify the environmental factors that contribute to facial aging in identical twins.

METHODS
During the Twins Day Festival in Twinsburg, Ohio, 186 pairs of identical twins completed a comprehensive questionnaire, and digital images were obtained. A panel reviewed the images independently and recorded the differences in the perceived twins' ages and their facial features. The perceived age differences were then correlated with multiple factors.

RESULTS
Four-point higher body mass index was associated with an older appearance in twins younger than age 40 but resulted in a younger appearance after age 40 (p = 0.0001). Eight-point higher body mass index was associated with an older appearance in twins younger than age 55 but was associated with a younger appearance after age 55 (p = 0.0001). The longer the twins smoked, the older they appeared (p < 0.0001). Increased sun exposure was associated with an older appearance and accelerated with age (p = 0.015), as was a history of outdoor activities and lack of sunscreen use. Twins who used hormone replacement had a younger appearance (p = 0.002). Facial rhytids were more evident in twins with a history of skin cancer (p = 0.05) and in those who smoked (p = 0.005). Dark and patchy skin discoloration was less prevalent in twins with a higher body mass index (p = 0.01) and more common in twins with a history of smoking (p = 0.005) and those with sun exposure (p = 0.005). Hair quantity was better with a higher body mass index (p = 0.01) although worse with a history of skin cancer (p = 0.005) and better with the use of hormones (p = 0.05).

CONCLUSION
This study offers strong statistical evidence to support the role of some of the known factors that govern facial aging.


Foreign body granulomas after all injectable dermal fillers: part 2. Treatment options.

SUMMARY
Foreign body granulomas occur at certain rates with all injectable dermal fillers. They have to be distinguished from early implant nodules, which usually appear 2 to 4 weeks after injection. In general, foreign body granulomas appear after a latent period of several months at all injected sites at the same time. If diagnosed early and treated correctly, they can be diminished within a few weeks. The treatment of choice of this hyperactive granulation tissue is the intralesional injection of corticosteroid crystals (triamcinolone, betamethasone, or prednisolone), which may be repeated in 4-week cycles until the right dose is found. To lower the risk of skin atrophy, corticosteroids can be combined with antimitotic drugs such as 5-fluorouracil and pulsed lasers. Because foreign body granulomas grow fingerlike into the surrounding tissue, surgical excision should be the last option. Surgery or drainage is indicated to treat normal lumps and cystic foreign body granulomas with little tissue ingrowth. In most patients, a foreign body granuloma is a single event during a lifetime, often triggered by a systemic bacterial infection.


Aging changes of the midfacial fat compartments: a computed tomographic study.

BACKGROUND
The restoration of a natural volume distribution is a major goal in facial rejuvenation. The aims of this study were to establish a radiographic method enabling effective measurements of the midfacial fat compartments and to compare the anatomy between human cadavers of younger versus older age.

METHODS
Data from computed tomographic scans of 12 nonfixed cadaver heads, divided into two age groups (group 1, 54 to 75 years, n = 6; and group 2, 75 to 104 years, n = 6), were analyzed. For evaluation of the volume distribution within a specific compartment, the sagittal diameter of the upper, middle, and lower thirds of each compartment was determined. For evaluation of a "sagging" of the compartments, the distance between the cephalad border and the infraorbital rim was determined.

RESULTS
Computed tomography enables a reproducible depiction of the facial fat compartments and reveals aging changes. The distance between the fat compartments and the infraorbital rim was higher in group 2 compared with group 1. The sagittal diameter of the lower third of the compartments was higher, and the sagittal diameter of the upper third was smaller in group 2 compared with group 1. The buccal extension of the buccal fat pad was shown to be an independent, separate compartment.

CONCLUSIONS
This study demonstrates an inferior migration of the midfacial fat compartments and an inferior volume shift within the compartments during aging. Additional distinct compartment-specific changes (e.g., volume loss of the deep medial cheek fat and buccal extension of the buccal fat pad) contribute to the appearance of the aged face.


Validated assessment scales for the lower face.

BACKGROUND
Aging in the lower face leads to lines, wrinkles, depression of the corners of the mouth, and changes in lip volume and lip shape, with increased sagging of the skin of the jawline. Refined, easy-to-use, validated, objective standards assessing the severity of these changes are required in clinical research and practice.

OBJECTIVE
To establish the reliability of eight lower face scales assessing nasolabial folds, marionette lines, upper and lower lip fullness, lip wrinkles (at rest and dynamic), the oral commissure and jawline, aesthetic areas, and the lower face unit.

METHODS AND MATERIALS
Four 5-point rating scales were developed to objectively assess upper and lower lip wrinkles, oral commissures, and the jawline. Twelve experts rated identical lower face photographs of 50 subjects in two separate rating cycles using eight 5-point scales. Inter- and intrarater reliability of responses was assessed.

RESULTS
Interrater reliability was substantial or almost perfect for all lower face scales, aesthetic areas, and the lower face unit. Intrarater reliability was high for all scales, areas and the lower face unit.

CONCLUSION
Our rating scales are reliable tools for valid and reproducible assessment of the aging process in lower face areas.


Millimetre-wave T-shaped antenna with defected ground structures for 5G wireless networks

This paper presents a T-shaped antenna at millimetre-wave (MMW) frequency ranges to offer a number of advantages including simple structure, high operating bandwidth, and high gain. Defected ground structures (DGS) have been symmetrically added in ground in order to produce multiple resonating bands, accompanied by partial ground plane to achieve continuous operating bandwidth. The antenna consists of T-shaped radiating patch with a coplanar waveguide (CPW) feed. The bottom part has a partial ground plane loaded with five symmetrical split-ring slots. Measured results of antenna prototype show a wide bandwidth of 25.1-37.5 GHz. Moreover, simulation evaluation of peak gain of the antenna is 9.86 dBi at 36.8 GHz, and efficiency is higher than 80% in complete range of operation. The proposed antenna is considered as a potential candidate for the 5G wireless networks and applications.


Design and implementation of full bridge bidirectional isolated DC-DC converter for high power applications

This paper proposes the design and implementation of a high power full bridge bidirectional isolated DC-DC converter (BIDC) which comprises of two symmetrical voltage source converters and a high frequency transformer. In the proposed BIDC, a well-known PI controller based single phase-shift (SPS) modulation technique is used in order to achieve high power transfer. Besides, different phase shift methods such as extended phase-shift (EPS) and double phase-shift (DPS) are compared with SPS. Both simulation and experimental results are caried out to verify PI controller based simple phase-shift controlled BIDC prototype that is designed for 300-V 2.4-kW and operating at 20 kHz.


Ubii: Physical World Interaction Through Augmented Reality

We describe a new set of interaction techniques that allow users to interact with physical objects through augmented reality (AR). Previously, to operate a smart device, physical touch is generally needed and a graphical interface is normally involved. These become limitations and prevent the user from operating a device out of reach or operating multiple devices at once. Ubii (Ubiquitous interface and interaction) is an integrated interface system that connects a network of smart devices together, and allows users to interact with the physical objects using hand gestures. The user wears a smart glass which displays the user interface in an augmented reality view. Hand gestures are captured by the smart glass, and upon recognizing the right gesture input, Ubii will communicate with the connected smart devices to complete the designated operations. Ubii supports common inter-device operations such as file transfer, printing, projecting, as well as device pairing. To improve the overall performance of the system, we implement computation offloading to perform the image processing computation. Our user test shows that Ubii is easy to use and more intuitive than traditional user interfaces. Ubii shortens the operation time on various tasks involving operating physical devices. The novel interaction paradigm attains a seamless interaction between the physical and digital worlds.


Early androgen exposure and human gender development

During early development, testosterone plays an important role in sexual differentiation of the mammalian brain and has enduring influences on behavior. Testosterone exerts these influences at times when the testes are active, as evidenced by higher concentrations of testosterone in developing male than in developing female animals. This article critically reviews the available evidence regarding influences of testosterone on human gender-related development. In humans, testosterone is elevated in males from about weeks 8 to 24 of gestation and then again during early postnatal development. Individuals exposed to atypical concentrations of testosterone or other androgenic hormones prenatally, for example, because of genetic conditions or because their mothers were prescribed hormones during pregnancy, have been consistently found to show increased male-typical juvenile play behavior, alterations in sexual orientation and gender identity (the sense of self as male or female), and increased tendencies to engage in physically aggressive behavior. Studies of other behavioral outcomes following dramatic androgen abnormality prenatally are either too small in their numbers or too inconsistent in their results, to provide similarly conclusive evidence. Studies relating normal variability in testosterone prenatally to subsequent gender-related behavior have produced largely inconsistent results or have yet to be independently replicated. For studies of prenatal exposures in typically developing individuals, testosterone has been measured in single samples of maternal blood or amniotic fluid. These techniques may not be sufficiently powerful to consistently detect influences of testosterone on behavior, particularly in the relatively small samples that have generally been studied. The postnatal surge in testosterone in male infants, sometimes called mini-puberty, may provide a more accessible opportunity for measuring early androgen exposure during typical development. This approach has recently begun to be used, with some promising results relating testosterone during the first few months of postnatal life to later gender-typical play behavior. In replicating and extending these findings, it may be important to assess testosterone when it is maximal (months 1 to 2 postnatal) and to take advantage of the increased reliability afforded by repeated sampling.


Effect of Manipulated Amplitude and Frequency of Human Voice on Dominance and Persuasiveness in Audio Conferences

We propose to artificially manipulate participants' vocal cues, amplitude and frequency, in real time to adjust their dominance and persuasiveness during audio conferences. We implemented a prototype system and conducted two experiments. The first experiment investigated the effect of vocal cue manipulation on the perception of dominance. The results showed that participants perceived higher dominance while listening to a voice with a high amplitude and low frequency. The second experiment investigated the effect of vocal cue manipulation on persuasiveness. The results indicated that a person with a low amplitude and low frequency voice had greater persuasiveness in conversations with biased dominance, while there was no statistically significant difference in persuasiveness in conversations with unbiased dominance.


A Theoretical Framework for Serious Game Design: Exploring Pedagogy, Play and Fidelity and their Implications for the Design Process

It is widely acknowledged that digital games can provide an engaging, motivating and “fun” experience for students. However an entertaining game does not necessarily constitute a meaningful, valuable learning experience. For this reason, experts espouse the importance of underpinning serious games with a sound theoretical framework which integrates and balances theories from two fields of practice: pedagogy and game design (Kiili, 2005; Seeney & Routledge, 2009). Additionally, with the advent of sophisticated, immersive technologies, and increasing interest in the opportunities for constructivist learning offered by these technologies, concepts of fidelity and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore a triadic theoretical framework for serious game design comprising play, pedagogy and fidelity. It will outline underpinning theories, review key literatures and identify challenges and issues involved in balancing these elements in the process of serious game design. DOI: 10.4018/ijgbl.2012100103 42 International Journal of Game-Based Learning, 2(4), 41-60, October-December 2012 Copyright © 2012, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. game design (Kiili, 2005; Seeney & Routledge, 2009). While a sound pedagogical framework is considered essential to their effectiveness as learning tools, equally important is the integration of game play elements which harness and sustain player engagement. Additionally, with the advent of sophisticated and immersive technologies, as exemplified in the virtual worlds of contemporary games, and increasing interest in the opportunities for constructivist learning offered by these worlds, concepts of fidelity, and its impact on student learning and engagement, have emerged (Aldrich, 2005; Harteveld et al., 2007, 2010). This paper will explore this triadic theoretical framework for serious game design, outlining underpinning theories, reviewing key literatures and identifying associated challenges and issues (Figure 1). The paper begins by reflecting on pedagogical theories commonly utilised to conceptualise game-based learning, focusing on three constructivist theories. Following this, attention switches to theories used to conceptualise players’ engagement with digital games, and thus inform effective, engaging and “fun” game design. As a key component of engaging and pedagogically effective game design, the concept of fidelity, and how it relates to game design and game-based learning, is discussed. The paper will conclude by reflecting on issues and challenges involved in balancing these components when designing


Design and evaluation of a wide-area event notification service

The components of a loosely coupled system are typically designed to operate by generating and responding to asynchronous events. An event notification service is an application-independent infrastructure that supports the construction of event-based systems, whereby generators of events publish event notifications to the infrastructure and consumers of events subscribe with the infrastructure to receive relevant notifications. The two primary services that should be provided to components by the infrastructure are notification selection (i. e., determining which notifications match which subscriptions) and notification delivery (i.e., routing matching notifications from publishers to subscribers). Numerous event notification services have been developed for local-area networks, generally based on a centralized server to select and deliver event notifications. Therefore, they suffer from an inherent inability to scale to wide-area networks, such as the Internet, where the number and physical distribution of the service's clients can quickly overwhelm a centralized solution. The critical challenge in the setting of a wide-area network is to maximize the expressiveness in the selection mechanism without sacrificing scalability in the delivery mechanism. This paper presents SIENA, an event notification service that we have designed and implemented to exhibit both expressiveness and scalability. We describe the service's interface to applications, the algorithms used by networks of servers to select and deliver event notifications, and the strategies used to optimize performance. We also present results of simulation studies that examine the scalability and performance of the service.


Comparing Children's Crosshair and Finger Interactions in Handheld Augmented Reality: Relationships Between Usability and Child Development

Augmented reality technology is a unique medium that can be helpful for young children's entertainment and education, but in order to achieve the benefits of this technology, augmented reality experiences need to be appropriately designed for young children's developing physical and cognitive skills. In the present study we investigated how 5-10 year-old children react to typical handheld augmented reality interaction techniques such as crosshair selection and finger selection, in AR environments that require them to change perspective or not. Our analysis shows significant impacts of age upon AR performance, with young children having slower selection times, more tracking losses, and taking longer to recover tracking. Significant differences were also found between AR interaction technique conditions, with finger selection being faster than crosshair selection, and interactions which required changes in perspective taking longer, generating more tracking losses, and more errors in selection. Furthermore, by analyzing children's performance in relation to metrics of physical and cognitive development, we identified correlations between AR interaction techniques performance and developmental tests of spatial relations, block construction and visuomotor precision. Gender differences were analyzed but no significant effects were detected.