Query: Affordances and Limitations of Immersive Participatory Augmented Reality Simulations for Teaching and Learning

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Reconsidering Research on Learning from Media <=SEP=> Recent meta-analyses and other studies of media's influence on learning are reviewed, Consistent evidence is found for the generalization that there are no learning benefits to be gained from employing any specific medium to deliver instruction. Research showing performance or timesaving gains from one or another medium are shown to be vulnerable to compelling rival hypotheses concerning the uncontrolled effects of instructional method and novelty. Problems with current media attribute and symbol system theories are described and suggestions made for more promising research directions. Studies of the influence of media on learning have ken a fixed feature of educational research since Thorndike (1912) recommended pictures as a laborsaving device in instruction, Most of this research is buttressed by the hope that learning will be enhanced with the proper mix of medium, student, subject matter content, and learning task. A typical study compares the relative achievement of groups who have received similar subject matter from different media. This research has led to so-called 'media selection" schemes or models 1982). These models generally promise to incorporate existing research and practice into procedures for selecting the best medium or mix of media to deliver instruction. Most of these models base many of their prescriptions on presumed learning benefits from media (Jamison, Suppes, & Welles, 1974). However, this article will argue that most current summaries and meta-analyses of media comparison studies clearly suggest that media do not influence learning under any conditions. Even in the few cases where dramatic changes in achievement or ability have followed the introduction of a medium, as was the case with television in El Salvador (Schramm, 1977), it was not the medium that caused the change but rather a curricular reform that accompanied the change. The best current evidence is that media are mere vehicles that deliver instruction but do not influence student achievement any more than the truck that delivers our groceries causes changes in our nutrition. Basically, the choice of vehicle might influence the cost or extent of distributing instruction, but only the content of the vehicle can influence achievement. While research often shows a slight learning advantage for newer media over more conventional instructional vehicles, this advantage will be __________________ The author wishes to acknowledge the substantive advice of Gavriel Salomon, William Winn, and anonymous reviewers without making them responsible for errors. ___________ p 445 shown to be vulnerable to compelling rival hypotheses. Among these rival explanations is evidence of artifact and confounding in existing studies and biased editorial decisions which may favor research showing larger effect sizes for newer media. After summarizing evidence from current meta-analyses of media research, I will discuss advantages and problems with current "media attribute" and 'symbol system" theories and will conclude by suggesting tentative solutions to past problems and future directions for research involving media. Media Comparison Studies In the 1960s, Lumsdaine (1963) and others (e.g,, Mielke, 1968) argued that gross media comparison and selection studies might not pay off. They implied that media, when viewed as collections of mechanical instruments, such as television and computers, were sample delivery devices. Nevertheless, earlier reviewers also held the door open to learning effects from media by attributing much of the lack of significance in prior research to poor design and lack of adequate models or theory. Lumsdaine (1963) dealt primarily with adequate studies that had used defensible methodology, and had found significant differences between treatments. With the benefit of hindsight it is not surprising that most of the studies he selected for review employed media as simple vehicles for instructional methods, such as text organization, size of step in programming, cueing, repeated exposures, and prompting. These studies compared the effects of. for example, different step size in programmed instruction via television. It was step size (and other methods), not television (or other media), which were the focus of thew studies. This is an example of what Salomon and Clark (1977) called research with media. In these studies media are mere conveyances for the treatments being examined and are not the focus of the study, though the results are often mistakenly interpreted as suggesting benefits for various media. An example of instructional research with media would be a study which contrasted a logically organized audiotutorial lesson on photosynthesis with a randomly sequenced presentation of the same frames (cf. Clark & Snow, 1975; Salomon & Clark, 1977, for a review of similar studies). Perhaps as a result of this confusion, Lumsdaine (1963) reached few conclusions beyond the suggestion that media might reduce the cost of instruction when many students are served because “the cost of perfecting it can be prorated in terms of a denominator representing thousands of students" (p. 670). A decade later, Glaser and Cooley (1973) and Levie and Dickie (1973) were cautious about media comparison studies, which apparently were still being conducted in large numbers. Glaser and Cooley (1973) recommended using acceptable medium as "a vehicle for making available to schools what psychologists have learned about learning" (p. 855). Levie and Dickie (1973) noted that most media comparison studies to that date had been fruitless and suggested that objectives can be attained through "instruction presented by any of a variety of different media" (p. 859). At that time televised education was still a lively topic and studies of computerized instruction were just beginning to appear. During the past decade, television research seems to have diminished considerably, but computer learning studies are now popular. This current h belongs to the familiar but generally fruitless media comparison approach or is concerned _____________ p 446 with different contents or methods being presented via different media (e.g., science teaching via computers). Generally, each new medium seems to attract its own set of advocates who make claims for improved learning and stimulate research questions which are similar to those asked about the previously popular medium. Most of the radio research approaches suggested in the 1950s (e.g., Hovland, Lumsdaine, & Sheffield, 1949) were very similar to those employed by the television movement of the 1960s (e.g., Schramm, 1977) and to the more recent reports of the computer-assisted instruction studies of the 1970s and 1980s (e.g., Dixon & Judd, 1977). It seems that similar research questions have resulted in similar and ambiguous data. Media comparison studies, regardless of the media employed, tend to result i n "no significant difference" conclusions (Mielke, 1968). These findings were incorrectly offered-as-evidence that different media were “equally effective” as conventional means in promoting learning. No significant difference results simply suggest that changes in the outcome scores (e.g., learning) did not result from any systematic differences in the treatments compared. Occasionally a study would find evidence for one or another medium. When this happens, Mielke (1968) has suggested that the active ingredient might be some uncontrolled aspect of the content or instructional strategy rather than the medium. When we investigate these positive studies, we find that the treatments are confounded. The evidence for this confounding may be found in the current meta-analyses of media comparison studies. The next section argues that it is the uncontrolled effects of novelty and instructional method which account for the existing evidence for the effects of various media on learning gains. Reviews and Meta-analyses of Media Research One of the most interesting trends in the past decade has been a significant increase in the number of excellent reviews and meta-analyses of research comparing the learning advantage of various media. The results of these overviews of past comparison studies seem to be reasonably unambiguous and unanimous. Taken together, they provide strong evidence that media comparison studies that find causal connections between media and achievement are confounded. Size of Effect of Media Treatments A recent series of meta-analyses of media comparison studies have been conducted by James Kulik and his colleagues at the University of Michigan (Cohen, Ebling & Kulik, 1981; C. Kulik, Kulik, & Cohen, 1980; J. Kulik, Bangert, & Williams, 1983; J. Kulik, Kulik, & Cohen, 1979). These reviews employ the relatively new technology of meta-analysis (Glass, 1976), which provides more precise estimates of the effect size of various media treatments than were possible a few years ago. Previous reviews dealing primarily with "box score" sums of significant findings for media versus conventional instructional delivery were sometimes misleading. Effect size estimates often were expressed in portions of standard score advantages for one or another type of treatment. This discussion will express effects in one of two ways: (a) the number of standard deviations separating experimental and control groups, and (b) as improvements in percent) scores on a final examination. ____________ p 447 Box Scores versus Effect Size An illustration of the advantage of meta-analytical effect size descriptions of past research over "box scores" is available in a recent review of Postlethwait's audiotutorial instruction studies (J. Kulik, Kulik, & Cohen,, 1979). The authors found 42 adequate studies, of which 29 favored audiotutorial instruction and only 13 favored conventional instruction. Of those 42, only 15 reported significant differences, but 11 of the 15 favored audiotutorial and only 4 favored conventional instruction. This type of box

Environmental Detectives--The Development of an Augmented Reality Platform for Environmental Simulations <=SEP=> This research was supported with a grant from Microsoft MIT iCampus as a part of the Games-to-Teach Project. The authors would like to thank the PIs of the Games-to-Teach Project, Randy Hinrichs at Microsoft Research and Henry Jenkins, Director MIT Comparative Media Studies for their support of this project, as well as Kodjo Hesse, Gunnar Harboe, and Walter Holland for their hard work in the development of Environmental Detectives.

Social constructivist perspectives on teaching and learning. <=SEP=> Social constructivist perspectives focus on the interdependence of social and individual processes in the co-construction of knowledge. After the impetus for understanding the influence of social and cultural factors on cognition is reviewed, mechanisms hypothesized to account for learning from this perspective are identified, drawing from Piagetian and Vygotskian accounts. The empirical research reviewed illustrates (a) the application of institutional analyses to investigate schooling as a cultural process, (b) the application of interpersonal analyses to examine how interactions promote cognition and learning, and (c) discursive analyses examining and manipulating the patterns and opportunities in instructional conversation. The review concludes with a discussion of the application of this perspective to selected contemporary issues, including: acquiring expertise across domains, assessment, educational equity, and educational reform.

What video games have to teach us about learning and literacy <=SEP=> Good computer and video games like System Shock 2, Deus Ex, Pikmin, Rise of Nations, Neverwinter Nights, and Xenosaga: Episode 1 are learning machines. They get themselves learned and learned well, so that they get played long and hard by a great many people. This is how they and their designers survive and perpetuate themselves. If a game cannot be learned and even mastered at a certain level, it won't get played by enough people, and the company that makes it will go broke. Good learning in games is a capitalist-driven Darwinian process of selection of the fittest. Of course, game designers could have solved their learning problems by making games shorter and easier, by dumbing them down, so to speak. But most gamers don't want short and easy games. Thus, designers face and largely solve an intriguing educational dilemma, one also faced by schools and workplaces: how to get people, often young people, to learn and master something that is long and challenging--and enjoy it, to boot.

Participatory Simulations: Building Collaborative Understanding Through Immersive Dynamic Modeling <=SEP=> The Participatory Simulations Project explores how a new kind of collaborative learning environment, which is supported by small, wearable computers, can facilitate collaborative theory-building and lead to a richer understanding of scientific experimentation. In a Participatory Simulation, participants become "agents" in a full-scale simulation. Unlike previous work, Participatory Simulations combines the notion of a microworld with the affordances of real world experience. By involving a large number of people (typically between 15 and 30) in a "life-sized" experience, the project brings a microworld off of the computer screen and into the physical world. In particular, this study explored the use of a series of Participatory Simulations in a high school Biology class. The students became "agents" in a simulation of a disease infecting their community. The results of this project show that Participatory Simulations fully engage participants in the simulation, facilitate collaborative problem solving, provide a substrate for collaboratively designing and running experiments, and support the definition of new vocabulary to discuss the underlying rules of the simulation. Thesis Advisor: Mitchel Resnick Associate Professor of Media Arts and Sciences Fukutake Career Development Professor of Research in Education This research was sponsored by the National Science Foundation (grant # CDA-9616444), the Lego Group, and the Things That Think and Digital Life consortia. Participatory Simulations: Building Collaborative Understanding through Immersive Dynamic Modeling

The Adoption Process of Cryptocurrencies Identifying Factors That Influence the Adoption of Cryptocurrencies from a Multiple Stakeholder Perspective. Identifying Factors That Influence the Adoption of Cryptocurrencies from a Multiple Stakeholder Perspective <=SEP=> II The adoption process of cryptocurrencies Management Summary Cryptocurrencies 1 are rapidly gaining more and more interest as a technology that is potentially groundbreaking and disruptive for the whole payments industry on a global scale. However the future of cryptocurrencies is very unclear as there are many different usage scenarios and different stakeholders have different needs. In order to be able to give a better future perspective and to determine possibilities for improvement of cryptocurrencies factors that influence adoption will have to be determined. To achieve this the following main research question was formulated: What are factors influencing the adoption of cryptocurrencies in different usage scenarios for different stakeholders? The way current payment systems work throughout the world varies widely. The added value of cryptocurrencies therefore also hugely differs per geographical area. In order to be able to give clear and concise conclusions this research is scoped towards looking at the European market and the Dutch market in particular. Based on the Diffusion of Innovations Theory, which defines characteristics necessary for an innovation in order to be successfully adopted, a conceptual model to describe the adoption of cryptocurrencies was formulated. This model was based on academic literature resulting from a literature review. The model was then validated by means of qualitative semi‐structured interviews with subject experts. To make sure that a balanced view was obtained stakeholders from a wide range of industries were interviewed: employees from the four biggest Dutch banks, the Dutch Central Bank, the three largest Dutch cryptocurrency exchanges, senior payments consultants, Payment Service Providers, a cryptography expert and the largest Dutch company that accepts cryptocurrency, Thuisbezorgd.nl. During the interviews barriers were identified that have to be overcome in order for cryptocurrencies to be adopted on a large scale. The three main pillars which are important for future mass adoption are: 1. Ease of use: at the moment there is a lack of user‐friendliness when using bitcoins. Sending and receiving bitcoin is still cumbersome and holding bitcoins is prone to many risks. Users need to be able to have more confidence in the safety of their funds. 2. Price stability: the current price volatility driven by speculation and a lack of liquidity makes that it is very risky for a user to keep his funds in cryptocurrency as the value varies wildly. This undermines the function of cryptocurrency as a store of value. 3. Governance: the current bitcoin …

Predicting the Success of Online Petitions Leveraging Multidimensional Time-Series <=SEP=> Applying classical time-series analysis techniques to online content is challenging, as web data tends to have data quality issues and is often incomplete, noisy, or poorly aligned. In this paper, we tackle the problem of predicting the evolution of a time series of user activity on the web in a manner that is both accurate and interpretable, using related time series to produce a more accurate prediction. We test our methods in the context of predicting signatures for online petitions using data from thousands of petitions posted on The Petition Site—one of the largest platforms of its kind. We observe that the success of these petitions is driven by a number of factors, including promotion through social media channels and on the front page of the petitions platform. We propose an interpretable model that incorporates seasonality, aging effects, self-excitation, and external effects. The interpretability of the model is important for understanding the elements that drives the activity of an online content. We show through an extensive empirical evaluation that our model is significantly better at predicting the outcome of a petition than state-of-the-art techniques.

A spatio-temporal reference model of the aging brain <=SEP=> Both normal aging and neurodegenerative disorders such as Alzheimer's disease (AD) cause morphological changes of the brain. It is generally difficult to distinguish these two causes of morphological change by visual inspection of magnetic resonance (MR) images. To facilitate making this distinction and thus aid the diagnosis of neurodegenerative disorders, we propose a method for developing a spatio-temporal model of morphological differences in the brain due to normal aging. The method utilizes groupwise image registration to characterize morphological variation across brain scans of people with different ages. To extract the deformations that are due to normal aging we use partial least squares regression, which yields modes of deformations highly correlated with age, and corresponding scores for each input subject. Subsequently, we determine a distribution of morphologies as a function of age by fitting smooth percentile curves to these scores. This distribution is used as a reference to which a person's morphology score can be compared. We validate our method on two different datasets, using images from both cognitively normal subjects and patients with Alzheimer disease (AD). Results show that the proposed framework extracts the expected atrophy patterns. Moreover, the morphology scores of cognitively normal subjects are on average lower than the scores of AD subjects, indicating that morphology differences between AD subjects and healthy subjects can be partly explained by accelerated aging. With our methods we are able to assess accelerated brain aging on both population and individual level. A spatio-temporal aging brain model derived from 988 T1-weighted MR brain scans from a large population imaging study (age range 45.9-91.7y, mean age 68.3y) is made publicly available at www.agingbrain.nl.

Autonomous website categorization with pre-defined dictionary <=SEP=> In this technology emerging era, the number of websites is increasing dramatically. The content and category of information are overflowing the Internet World. Finding the right information from almost a billion of websites is considerably hard, but finding the accurate and quality one is even harder. Hence, the need of website categorization's demand is increasing tremendously. Unfortunately, the website categorization techniques in previous works are still immature and not good enough to satisfy the need. Additionally, a training dataset is a limitation of supervised learning algorithm and unsupervised learning algorithm also have a complex algorithm. Regularly, they can categorize into only 1 category but the content usually contains various types. Therefore, in this paper, we propose the simple yet powerful algorithm for website categorization which can give a multi-category results with confidence level, distributed systems supported, and does not even need to be trained because the algorithm uses word frequency in the content of each website to match with the categories in a pre-defined dictionary. The result shows that the accuracy of our proposed algorithm is over 95% when tested with Reuters dataset. The comparison of our algorithm and another Text Analysis API shows that our algorithm has more accuracy with less computation time. The accuracy can also be increased by improving the pre-defined dictionary and filtering noise words.

FPGA Vendor Agnostic True Random Number Generator <=SEP=> This paper describes a solution for the generation of true random numbers in a purely digital fashion; making it suitable for any FPGA type, because no FPGA vendor specific features (e.g., like phase-locked loop) or external analog components are required. Our solution is based on a framework for a provable secure true random number generator recently proposed by Sunar, Martin and Stinson. It uses a large amount of ring oscillators with identical ring lengths as a fast noise source - but with some deterministic bits - and eliminates the non-random samples by appropriate post-processing based on resilient functions. This results in a slower bit stream with high entropy. Our FPGA implementation achieves a random bit throughput of more than 2 Mbps, remains fairly compact (needing minimally 110 ring oscillators of 3 inverters) and is highly portable

Algorithmic Bayesian persuasion <=SEP=> Persuasion, defined as the act of exploiting an informational advantage in order to effect the decisions of others, is ubiquitous. Indeed, persuasive communication has been estimated to account for almost a third of all economic activity in the US. This paper examines persuasion through a computational lens, focusing on what is perhaps the most basic and fundamental model in this space: the celebrated Bayesian persuasion model of Kamenica and Gentzkow. Here there are two players, a sender and a receiver. The receiver must take one of a number of actions with a-priori unknown payoff, and the sender has access to additional information regarding the payoffs of the various actions for both players. The sender can commit to revealing a noisy signal regarding the realization of the payoffs of various actions, and would like to do so as to maximize her own payoff in expectation assuming that the receiver rationally acts to maximize his own payoff. When the payoffs of various actions follow a joint distribution (the common prior), the sender's problem is nontrivial, and its computational complexity depends on the representation of this prior. We examine the sender's optimization task in three of the most natural input models for this problem, and essentially pin down its computational complexity in each. When the payoff distributions of the different actions are i.i.d. and given explicitly, we exhibit a polynomial-time (exact) algorithmic solution, and a ``simple'' (1-1/e)-approximation algorithm. Our optimal scheme for the i.i.d. setting involves an analogy to auction theory, and makes use of Border's characterization of the space of reduced-forms for single-item auctions. When action payoffs are independent but non-identical with marginal distributions given explicitly, we show that it is #P-hard to compute the optimal expected sender utility. In doing so, we rule out a generalized Border's theorem, as defined by Gopalan et al, for this setting. Finally, we consider a general (possibly correlated) joint distribution of action payoffs presented by a black box sampling oracle, and exhibit a fully polynomial-time approximation scheme (FPTAS) with a bi-criteria guarantee. Our FPTAS is based on Monte-Carlo sampling, and its analysis relies on the principle of deferred decisions. Moreover, we show that this result is the best possible in the black-box model for information-theoretic reasons.

Functional genomic analysis of the AUXIN RESPONSE FACTOR gene family members in Arabidopsis thaliana: unique and overlapping functions of ARF7 and ARF19. <=SEP=> The AUXIN RESPONSE FACTOR (ARF) gene family products, together with the AUXIN/INDOLE-3-ACETIC ACID proteins, regulate auxin-mediated transcriptional activation/repression. The biological function(s) of most ARFs is poorly understood. Here, we report the identification and characterization of T-DNA insertion lines for 18 of the 23 ARF gene family members in Arabidopsis thaliana. Most of the lines fail to show an obvious growth phenotype except of the previously identified arf2/hss, arf3/ett, arf5/mp, and arf7/nph4 mutants, suggesting that there are functional redundancies among the ARF proteins. Subsequently, we generated double mutants. arf7 arf19 has a strong auxin-related phenotype not observed in the arf7 and arf19 single mutants, including severely impaired lateral root formation and abnormal gravitropism in both hypocotyl and root. Global gene expression analysis revealed that auxin-induced gene expression is severely impaired in the arf7 single and arf7 arf19 double mutants. For example, the expression of several genes, such as those encoding members of LATERAL ORGAN BOUNDARIES domain proteins and AUXIN-REGULATED GENE INVOLVED IN ORGAN SIZE, are disrupted in the double mutant. The data suggest that the ARF7 and ARF19 proteins play essential roles in auxin-mediated plant development by regulating both unique and partially overlapping sets of target genes. These observations provide molecular insight into the unique and overlapping functions of ARF gene family members in Arabidopsis.

Design of 4-Bit Universal Shift Register Using Reversible Gates <=SEP=> Power dissipation is one of the important factor in digital circuit design. Landauer's principle states that logic computations which are not reversible necessarily generate KT*ln2 Joules of heat energy for every bit of information that is lost, where k is Boltzmann's constant and T the absolute temperature at which computation is performed. Reversibility is the property of digital circuits in which there is one-to-one mapping between the inputs and the output vectors that is for each input vector there is a unique output vector and vice-versa. Thus one of the primary motivations for adopting reversible logic lies in the fact that it can provide a logic design methodologies for designing low power dissipating circuits. Fredkin gate, Feynman Gate is popularly used reversible logic gate which are used to implement various sequential circuits which are basic for design of digital circuits.

In Defense of the Triplet Loss for Person Re-Identification <=SEP=> In the past few years, the field of computer vision has gone through a revolution fueled mainly by the advent of large datasets and the adoption of deep convolutional neural networks for end-to-end learning. The person reidentification subfield is no exception to this, thanks to the notable publication of the Market-1501 and MARS datasets and several strong deep learning approaches. Unfortunately, a prevailing belief in the community seems to be that the triplet loss is inferior to using surrogate losses (classification, verification) followed by a separate metric learning step. We show that, for models trained from scratch as well as pretrained ones, using a variant of the triplet loss to perform end-to-end deep metric learning outperforms any other published method by a large margin.

3D Simulation for Robot Arm Control with Deep Q-Learning <=SEP=> Intelligent control of robotic arms has huge potential over the coming years, but as of now will often fail to adapt when presented with new and unfamiliar environments. Recent trends to solve this problem have seen a shift to endto-end solutions using deep reinforcement learning to learn policies from visual input, rather than relying on a handcrafted, modular pipeline. Building upon the recent success of deep Q-networks, we present an approach which uses threedimensional simulations to train a 7-DOF robotic arm in a robot arm control task without any prior knowledge. Policies accept images of the environment as input and output motor actions. However, the high-dimensionality of the policies as well as the large state space makes policy search difficult. This is overcome by ensuring interesting states are explored via intermediate rewards that guide the policy towards higher reward states. Our results demonstrate that deep Q-networks can be used to learn policies for a task that involves locating a cube, grasping, and then finally lifting. The agent is able to learn to deal with a range of starting joint configurations and starting cube positions when tested in simulation. Moreover, we show that policies trained via simulation have the potential to be directly applied to real-world equivalents without any further training. We believe that robot simulations can decrease the dependency on physical robots and ultimately improve productivity of training robot control tasks.

SARL: A General-Purpose Agent-Oriented Programming Language <=SEP=> Complex software systems development require appropriate high-level features to better and easily tackle the new requirements in terms of interactions, concurrency and distribution. This requires a paradigm change in software engineering and corresponding programming languages. We are convinced that agent-oriented programming may be the support for this change by focusing on a small corpus of commonly accepted concepts and the corresponding programming language in line with the current developers' programming practices. This papers introduces SARL, a new general-purpose agent-oriented programming language undertaking this challenge. SARL comes with its full support in the Eclipse IDE for compilation and debugging, and a new version 2.0 of the Janus platform for execution purposes. The main perspective that guided the creation of SARL is the establishment of an open and easily extensible language. Our expectation is to provide the community with a common forum in terms of a first working test bed to study and compare various programming alternatives and associated metamodels.

On the Impossibility of Basing Identity Based Encryption on Trapdoor Permutations <=SEP=> We ask whether an identity based encryption (IBE) system can be built from simpler public-key primitives. We show that there is no black-box construction of IBE from trapdoor permutations (TDP) or even from chosen ciphertext secure public key encryption (CCA-PKE). These black-box separation results are based on an essential property of IBE, namely that an IBE system is able to compress exponentially many public-keys into a short public parameters string.

Discovery of convoys in trajectory databases <=SEP=> As mobile devices with positioning capabilities continue to proliferate, data management for so-called trajectory databases that capture the historical movements of populations of moving objects becomes important. This paper considers the querying of such databases for convoys, a convoy being a group of objects that have traveled together for some time. More specifically, this paper formalizes the concept of a convoy query using density-based notions, in order to capture groups of arbitrary extents and shapes. Convoy discovery is relevant for reallife applications in throughput planning of trucks and carpooling of vehicles. Although there has been extensive research on trajectories in the literature, none of this can be applied to retrieve correctly exact convoy result sets. Motivated by this, we develop three efficient algorithms for convoy discovery that adopt the wellknown filter-refinement framework. In the filter step, we apply linesimplification techniques on the trajectories and establish distance bounds between the simplified trajectories. This permits efficient convoy discovery over the simplified trajectories without missing any actual convoys. In the refinement step, the candidate convoys are further processed to obtain the actual convoys. Our comprehensive empirical study offers insight into the properties of the paper’s proposals and demonstrates that the proposals are effective and efficient on real-world trajectory data.

RGB-D Mapping: Using Depth Cameras for Dense 3D Modeling of Indoor Environments <=SEP=> RGB-D cameras are novel sensing systems that capture RGB images along with per-pixel depth information. RGB-D cameras rely on either structured light patterns combined with stereo sensing [6,10] or time-of-flight laser sensing [1] to generate depth estimates that can be associated with RGB pixels. Very soon, small, high-quality RGB-D cameras developed for computer gaming and home entertainment applications will become available at cost below $100. In this paper we investigate how such cameras can be used in the context of robotics, specifically for building dense 3D maps of indoor environments. Such maps have applications in robot navigation, manipulation, semantic mapping, and telepresence. The robotics and computer vision communities have developed a variety of techniques for 3D mapping based on laser range scans [8, 11], stereo cameras [7], monocular cameras [3], and unsorted collections of photos [4]. While RGB-D cameras provide the opportunity to build 3D maps of unprecedented richness, they have drawbacks that make their application to 3D mapping difficult: They provide depth only up to a limited distance (typically less than 5m), depth values are much noisier than those provided by laser scanners, and their field of view (∼ 60◦) is far more constrained than that of specialized cameras or laser scanners typically used for 3D mapping (∼ 180◦). In our work, we use a camera developed by PrimeSense [10]. The key insights of this investigation are: first, that existing frame matching techniques are not sufficient to provide robust visual odometry with these cameras; second, that a tight integration of depth and color information can yield robust frame matching and loop closure detection; third, that building on best practice techniques in SLAM and computer graphics makes it possible to build and visualize accurate and extremely rich 3D maps with such cameras; and, fourth, that it will be feasible to build complete robot navigation and interaction systems solely based on cheap depth cameras.

Time scales in motor learning and development. <=SEP=> A theoretical framework based on the concepts and tools of nonlinear dynamical systems is advanced to account for both the persistent and transitory changes traditionally shown for the learning and development of motor skills. The multiple time scales of change in task outcome over time are interpreted as originating from the system's trajectory on an evolving attractor landscape. Different bifurcations between attractor organizations and transient phenomena can lead to exponential, power law, or S-shaped learning curves. This unified dynamical account of the functions and time scales in motor learning and development offers several new hypotheses for future research on the nature of change in learning theory.

Adaptive Image Registration via Hierarchical Voronoi Subdivision <=SEP=> Advances in image acquisition systems have made it possible to capture high-resolution images of a scene, recording considerable scene details. With increased resolution comes increased image size and geometric difference between multiview images, complicating image registration. Through Voronoi subdivision, we subdivide large images into small corresponding regions, and by registering small regions, we register the images in a piecewise manner. Image subdivision reduces the geometric difference between regions that are registered and simplifies the correspondence process. The proposed method is a hierarchical one. While previous methods use the same block size and shape at a hierarchy, the proposed method adapts the block size and shape to the local image details and geometric difference between the images. This adaptation makes it possible to keep geometric difference between corresponding regions small and simplifies the correspondence process. Implementational details of the proposed image registration method are provided, and experimental results on various types of images are presented and analyzed.

Structured Time Series Analysis for Human Action Segmentation and Recognition <=SEP=> We address the problem of structure learning of human motion in order to recognize actions from a continuous monocular motion sequence of an arbitrary person from an arbitrary viewpoint. Human motion sequences are represented by multivariate time series in the joint-trajectories space. Under this structured time series framework, we first propose Kernelized Temporal Cut (KTC), an extension of previous works on change-point detection by incorporating Hilbert space embedding of distributions, to handle the nonparametric and high dimensionality issues of human motions. Experimental results demonstrate the effectiveness of our approach, which yields realtime segmentation, and produces high action segmentation accuracy. Second, a spatio-temporal manifold framework is proposed to model the latent structure of time series data. Then an efficient spatio-temporal alignment algorithm Dynamic Manifold Warping (DMW) is proposed for multivariate time series to calculate motion similarity between action sequences (segments). Furthermore, by combining the temporal segmentation algorithm and the alignment algorithm, online human action recognition can be performed by associating a few labeled examples from motion capture data. The results on human motion capture data and 3D depth sensor data demonstrate the effectiveness of the proposed approach in automatically segmenting and recognizing motion sequences, and its ability to handle noisy and partially occluded data, in the transfer learning module.

Analyzing Hypersensitive AI: Instability in Corporate-Scale Machine Learning <=SEP=> Predictive geometric models deliver excellent results for many Machine Learning use cases. Despite their undoubted performance, neural predictive algorithms can show unexpected degrees of instability and variance, particularly when applied to large datasets. We present an approach to measure changes in geometric models with respect to both output consistency and topological stability. Considering the example of a recommender system using word2vec, we analyze the influence of single data points, approximation methods and parameter settings. Our findings can help to stabilize models where needed and to detect differences in informational value of data points on a large scale.

Comprehensive Evaluation of Deep Learning Architectures for Prediction of DNA/RNA Sequence Binding Specificities <=SEP=> Motivation: Deep learning architectures have recently demonstrated their power in predicting DNAand RNA-binding specificities. Existing methods fall into three classes: Some are based on Convolutional Neural Networks (CNNs), others use Recurrent Neural Networks (RNNs), and others rely on hybrid architectures combining CNNs and RNNs. However, based on existing studies it is still unclear which deep learning architecture is achieving the best performance. Thus an in-depth analysis and evaluation of the different methods is needed to fully evaluate their relative. Results: In this study, We present a systematic exploration of various deep learning architectures for predicting DNAand RNA-binding specificities. For this purpose, we present deepRAM, an end-to-end deep learning tool that provides an implementation of novel and previously proposed architectures; its fully automatic model selection procedure allows us to perform a fair and unbiased comparison of deep learning architectures. We find that an architecture that uses k-mer embedding to represent the sequence, a convolutional layer and a recurrent layer, outperforms all other methods in terms of model accuracy. Our work provides guidelines that will assist the practitioner in choosing the best architecture for the task at hand, and provides some insights on the differences between the models learned by convolutional and recurrent networks. In particular, we find that although recurrent networks improve model accuracy, this comes at the expense of a loss in the interpretability of the features learned by the model. Availability and implementation: The source code for deepRAM is available at https://github.com/MedChaabane/deepRAM Contact: asa@cs.colostate.edu Supplementary information: Supplementary data are available at Bioinformatics online.

Educating the Disagreeable Extravert : Narcissism , the Big Five Personality Traits , and Achievement Goal Orientation <=SEP=> Despite the fact that longitudinal data have been compiled over the past 30 years among undergraduate students in higher education settings regarding narcissism, the literature is devoid of empirical investigations that explore the relationships between narcissism and learning. Because the data suggest that narcissism scores are increasing each year among this population, an exploration of the relationship between narcissism and learning is timely and warranted. Sampling from university undergraduate students, this study uses the Narcissistic Personality Inventory, the Big Five Inventory, and the Achievement Goal Questionnaire to verify the known relationships between narcissism and the Big Five personality traits of extraversion and agreeableness; to verify the known relationships between the Big Five personality traits of extraversion and agreeableness and goal orientation; and to explore a previously undocumented empirical relationship between narcissism and performance goal orientation. Results of this exploratory study indicate that while narcissism does contribute to a performance goal orientation, it is not a substantial variable in determining achievement goal orientation in general. The study addresses the implications and limitations of this research in addition to areas for additional investigation.

Altitude optimization of Airborne Wind Energy systems: A Bayesian Optimization approach <=SEP=> This study presents a data-driven approach for optimizing the operating altitude of Airborne Wind Energy (AWE) systems to maximize net energy production. Determining the optimal operating altitude of an AWE system is challenging, as the wind speed constantly varies with both time and altitude. Furthermore, without expensive auxiliary equipment, the wind speed is only measurable at the AWE system's operating altitude. The work presented in this paper shows how tools from machine learning can be blended with real-time control to optimize the AWE system's operating altitude efficiently, without the use of auxiliary wind profiling equipment. Specifically, Bayesian Optimization, which is a data-driven technique for finding the optimum of an unknown and expensive-to-evaluate objective function, is applied to the real-time control of an AWE system. The underlying objective function is modeled by a Gaussian Process (GP); then, Bayesian Optimization utilizes the predictive uncertainty information from the GP to decide the best subsequent operating altitude. In the AWE application, conventional Bayesian Optimization is extended to handle the time-varying nature of the wind shear profile (wind speed vs. time). Using real wind data, our method is validated against three baseline approaches. Our simulation results show that the Bayesian Optimization method is successful in dramatically increasing power production over these baselines.

Collecting Large, Richly Annotated Facial-Expression Databases from Movies <=SEP=> Two large facial-expression databases depicting challenging real-world conditions were constructed using a semi-automatic approach via a recommender system based on subtitles.

Dynamic mode decomposition of numerical and experimental data <=SEP=> HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers. L’archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d’enseignement et de recherche français ou étrangers, des laboratoires publics ou privés. Dynamic mode decomposition of numerical and experimental data Peter Schmid

Traffic monitoring and accident detection at intersections <=SEP=> Among the most important research in Intelligent Transportation Systems (ITS) is the development of systems that automatically monitor traffic flow at intersections. Rather than being based on global flow analysis as is currently done, these automatic monitoring systems should be based on local analysis of the behavior of each vehicle at the intersection. The systems should be able to identify each vehicle and track its behavior, and to recognize situations or events that are likely to result from a chain of such behavior. The most difficult problem associated with vehicle tracking is the occlusion effect among vehicles. In order to solve this problem we have developed an algorithm, referred to as spatio-temporal Markov random field (MRF), for traffic images at intersections. This algorithm models a tracking problem by determining the state of each pixel in an image and its transit, and how such states transit along both the – image axes as well as the time axes. Vehicles, of course, are of various shapes and they move in random fashion, thereby leading to full or partial occlusion at intersections. Despite these complications, our algorithm is sufficiently robust to segment and track occluded vehicles at a high success rate of 93%–96%. This success has led to the development of an extendable robust event recognition system based on the hidden Markov model (HMM). The system learns various event behavior patterns of each vehicle in the HMM chains and then, using the output from the tracking system, identifies current event chains. The current system can recognize bumping, passing, and jamming. However, by including other event patterns in the training set, the system can be extended to recognize those other events, e.g., illegal U-turns or reckless driving. We have implemented this system, evaluated it using the tracking results, and demonstrated its effectiveness.

Higher-Order Pooling of CNN Features via Kernel Linearization for Action Recognition <=SEP=> Most successful deep learning algorithms for action recognition extend models designed for image-based tasks such as object recognition to video. Such extensions are typically trained for actions on single video frames or very short clips, and then their predictions from sliding-windows over the video sequence are pooled for recognizing the action at the sequence level. Usually this pooling step uses the first-order statistics of frame-level action predictions. In this paper, we explore the advantages of using higherorder correlations, specifically, we introduce Higher-order Kernel (HOK) descriptors generated from the late fusion of CNN classifier scores from all the frames in a sequence. To generate these descriptors, we use the idea of kernel linearization. Specifically, a similarity kernel matrix, which captures the temporal evolution of deep classifier scores, is first linearized into kernel feature maps. The HOK descriptors are then generated from the higher-order cooccurrences of these feature maps, and are then used as input to a video-level classifier. We provide experiments on two fine-grained action recognition datasets, and show that our scheme leads to state-of-the-art results.