Query: Mapping underwater ship hulls using a model-assisted bundle adjustment framework

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Stereo Processing by Semiglobal Matching and Mutual Information <=SEP=> This paper describes the semiglobal matching (SGM) stereo method. It uses a pixelwise, mutual information (Ml)-based matching cost for compensating radiometric differences of input images. Pixelwise matching is supported by a smoothness constraint that is usually expressed as a global cost function. SGM performs a fast approximation by pathwise optimizations from all directions. The discussion also addresses occlusion detection, subpixel refinement, and multibaseline matching. Additionally, postprocessing steps for removing outliers, recovering from specific problems of structured environments, and the interpolation of gaps are presented. Finally, strategies for processing almost arbitrarily large images and fusion of disparity images using orthographic projection are proposed. A comparison on standard stereo images shows that SGM is among the currently top-ranked algorithms and is best, if subpixel accuracy is considered. The complexity is linear to the number of pixels and disparity range, which results in a runtime of just 1-2 seconds on typical test images. An in depth evaluation of the Ml-based matching cost demonstrates a tolerance against a wide range of radiometric transformations. Finally, examples of reconstructions from huge aerial frame and pushbroom images demonstrate that the presented ideas are working well on practical problems.

Least-Squares Fitting of Two 3-D Point Sets <=SEP=> Two point sets {pi} and {p'i}; i = 1, 2,..., N are related by p'i = Rpi + T + Ni, where R is a rotation matrix, T a translation vector, and Ni a noise vector. Given {pi} and {p'i}, we present an algorithm for finding the least-squares solution of R and T, which is based on the singular value decomposition (SVD) of a 3 × 3 matrix. This new algorithm is compared to two earlier algorithms with respect to computer time requirements.

Visual odometry for ground vehicle applications <=SEP=> We present a system that estimates the motion of a stereo head or a single moving camera based on video input. The system operates in real-time with low delay and the motion estimates are used for navigational purposes. The front end of the system is a feature tracker. Point features are matched between pairs of frames and linked into image trajectories at video rate. Robust estimates of the camera motion are then produced from the feature tracks using a geometric hypothesize-and-test architecture. This generates motion estimates from visual input alone. No prior knowledge of the scene nor the motion is necessary. The visual estimates can also be used in conjunction with information from other sources such as GPS, inertia sensors, wheel encoders, etc. The pose estimation method has been applied successfully to video from aerial, automotive and handheld platforms. We focus on results obtained with a stereo-head mounted on an autonomous ground vehicle. We give examples of camera trajectories estimated in real-time purely from images over previously unseen distances (600 meters) and periods of time .

A Morphable Model for the Synthesis of 3D Faces <=SEP=> In this paper, a new technique for modeling textured 3D faces is introduced. 3D faces can either be generated automatically from one or more photographs, or modeled directly through an intuitive user interface. Users are assisted in two key problems of computer aided face modeling. First, new face images or new 3D face models can be registered automatically by computing dense one-to-one correspondence to an internal face model. Second, the approach regulates the naturalness of modeled faces avoiding faces with an “unlikely” appearance. Starting from an example set of 3D face models, we derive a morphable face model by transforming the shape and texture of the examples into a vector space representation. New faces and expressions can be modeled by forming linear combinations of the prototypes. Shape and texture constraints derived from the statistics of our example faces are used to guide manual modeling or automated matching algorithms. We show 3D face reconstructions from single images and their applications for photo-realistic image manipulations. We also demonstrate face manipulations according to complex parameters such as gender, fullness of a face or its distinctiveness.

A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms <=SEP=> Stereo matching is one of the most active research areas in computer vision. While a large number of algorithms for stereo correspondence have been developed, relatively little work has been done on characterizing their performance. In this paper, we present a taxonomy of dense, two-frame stereo methods. Our taxonomy is designed to assess the different components and design decisions made in individual stereo algorithms. Using this taxonomy, we compare existing stereo methods and present experiments evaluating the performance of many different variants. In order to establish a common software platform and a collection of data sets for easy evaluation, we have designed a stand-alone, flexible C++ implementation that enables the evaluation of individual components and that can easily be extended to include new algorithms. We have also produced several new multi-frame stereo data sets with ground truth and are making both the code and data sets available on the Web. Finally, we include a comparative evaluation of a large set of today's best-performing stereo algorithms.

Area-Preservation Mapping using Optimal Mass Transport <=SEP=> We present a novel area-preservation mapping/flattening method using the optimal mass transport technique, based on the Monge-Brenier theory. Our optimal transport map approach is rigorous and solid in theory, efficient and parallel in computation, yet general for various applications. By comparison with the conventional Monge-Kantorovich approach, our method reduces the number of variables from O(n2) to O(n), and converts the optimal mass transport problem to a convex optimization problem, which can now be efficiently carried out by Newton's method. Furthermore, our framework includes the area weighting strategy that enables users to completely control and adjust the size of areas everywhere in an accurate and quantitative way. Our method significantly reduces the complexity of the problem, and improves the efficiency, flexibility and scalability during visualization. Our framework, by combining conformal mapping and optimal mass transport mapping, serves as a powerful tool for a broad range of applications in visualization and graphics, especially for medical imaging. We provide a variety of experimental results to demonstrate the efficiency, robustness and efficacy of our novel framework.

Hierarchical Reinforcement Learning <=SEP=> A hierarchical representation of the input-output transition function in a learning system is suggested. The choice of either representing the knowledge in a learning system as a discrete set of input-output pairs or as a continuous input-output transition function is discussed. The conclusion that both representations could be e cient, but at di erent levels is made. The di erence between strategies and actions is de ned. An algorithm for using adaptive critic methods in a two-level reinforcement learning system is presented. Two problems that are faced, the hierarchical credit assignment problem and the equalized state problem are described. Simulations of a one dimensional hierarchical reinforcement learning system is presented.

Noise-contrastive estimation: A new estimation principle for unnormalized statistical models <=SEP=> We present a new estimation principle for parameterized statistical models. The idea is to perform nonlinear logistic regression to discriminate between the observed data and some artificially generated noise, using the model log-density function in the regression nonlinearity. We show that this leads to a consistent (convergent) estimator of the parameters, and analyze the asymptotic variance. In particular, the method is shown to directly work for unnormalized models, i.e. models where the density function does not integrate to one. The normalization constant can be estimated just like any other parameter. For a tractable ICA model, we compare the method with other estimation methods that can be used to learn unnormalized models, including score matching, contrastive divergence, and maximum-likelihood where the normalization constant is estimated with importance sampling. Simulations show that noise-contrastive estimation offers the best trade-off between computational and statistical efficiency. The method is then applied to the modeling of natural images: We show that the method can successfully estimate a large-scale two-layer model and a Markov random field.

Social Dominance An Intergroup Theory of Social Hierarchy and Oppression <=SEP=> Social dominance : an intergroup theory of social hierarchy and oppression / Jim Sidanius, Felicia Pratto. p. cm. Includes bibliographical references and index.

D-Sempre: Learning Deep Semantic-Preserving Embeddings for User interests-Social Contents Modeling <=SEP=> Exponential growth of social media consumption demands eective user interests-social contents modeling for more personalized recommendation and social media summarization. However, due to the heterogeneous nature of social contents, traditional approaches lack the ability of capturing the hidden semantic correlations across these multi-modal data, which leads to semantic gaps between social content understanding and user interests. To eectively bridge the semantic gaps, we propose a novel deep learning framework for user interests-social contents modeling. We rst mine and parse data, i.e. textual content, visual content, social context and social relation, from heterogeneous social media feeds. en, we design a two-branch network to map the social contents and users into a same latent space. Particularly, the network is trained by a largemargin objective that combines a cross-instance distance constraint with a within-instance semantic-preserving constraint in an endto-end manner. At last, a Deep Semantic-Preserving Embedding (D-Sempre) is learned, and the ranking results can be given by calculating distances between social contents and users. To demonstrate the eectiveness of D-Sempre in user interests-social contents modeling, we construct a Twier dataset and conduct extensive experiments on it. As a result, D-Sempre eectively integrates the multimodal data from heterogeneous social media feeds and captures the hidden semantic correlations between users’ interests and social contents.

A fuzzy classification system for prediction of the results of the basketball games <=SEP=> Prediction of the sports game results is an interesting topic that has gained attention lately. Mostly there are used stochastical methods of uncertainty description. In this work it is presented a preliminary approach to build a fuzzy model to basketball game results prediction. Ten fuzzy rule learning algorithms are selected, conducted and compared against standard linear regression with use of the KEEL system. Feature selection algorithms are applied and a majority voting is used in order to select the most representative features.

Inpainting by Flexible Haar-Wavelet Shrinkage <=SEP=> We present novel wavelet-based inpainting algorithms. Applying ideas from anisotropic regularization and diffusion, our models can better handle degraded pixels at edges. We interpret our algorithms within the framework of forward-backward splitting methods in convex analysis and prove that the conditions for ensuring their convergence are fulfilled. Numerical examples illustrate the good performance of our algorithms.

Tutorial on Machine Learning for Spectrum Sharing in Wireless Networks <=SEP=> As spectrum utilization efficiency is the major bottleneck in current wireless networking, many stakeholders discuss that spectrum should be shared rather than being exclusively allocated. Shared spectrum access raises many challenges which, if not properly addressed, degrades the performance level of the co-existing networks. Coexistence scenarios may involve two or more networks: same or different types; operated by the same or different operators. The complex interactions among the coexisting networks can be addressed by the machine learning tools which by their nature embrace uncertainty and can model the complex interactions. In this tutorial, we start with the basics of coexistence of wireless networks in the unlicensed bands. Then, we focus on WiFi and LTE-U coexistence. After providing a brief overview of machine learning topics such as supervised learning, unsupervised learning, reinforcement learning, we overview five particular examples which exploit learning schemes to enable efficient spectrum sharing entailing a generic cognitive radio setting as well as LTE and WiFi coexistence scenarios. We conclude with a list of challenges and research directions. I. SPECTRUM SHARING IN WIRELESS NETWORKS Spectrum sharing is the situation where at least two users or technologies are authorized to use the same portion of the radio spectrum on a non-exclusive manner1. We overview the current state of spectrum sharing and provide a taxonomy of spectrum sharing scenarios. We can list the main challenges in providing peaceful coexistence as follows: (i) scarcity of the resources, (ii) heterogeneity of the coexisting networks, (iii) power asymmetry, and (iv) lack of coordination, communication, and cooperation among the coexisting networks. II. COEXISTENCE IN THE UNLICENSED BANDS: THE CASE OF WIFI AND LTE-U The success of IEEE 802.11 networks in the unlicensed bands, i.e., 2.4 GHz and 5 GHz, has proved the efficiency and feasibility of using spectrum in a license-exempt manner. Currently, even the cellular providers consider expanding their network’s capacity with unlicensed spectrum to cope with the increasing wireless traffic demand. More particularly, Qualcomm’s 2013 proposal [1] of aggregating 5GHz bands with the licensed carriers of an LTE network has paved the way for LTE unlicensed networks. However, operation in the unlicensed bands has to address the coexistence challenges. For example, WiFi networks at 2.4 GHz bands, e.g., 802.11b/g/n, have to find the best channel 1S. Forge, R. Horvitz, and C. Blackman. Perspectives on the value of shared spectrum access. Final Report for the European Commission, 2012. among three non-overlapping channels for operation in a very-dense WLAN deployment. Additionally, 2.4 GHz band accommodates also non-WiFi technologies such as Bluetooth, ZigBee, or microwave ovens which all create interference on WLANs. As for 5GHz which has many more non-overlapping channels compared to 2.4 GHz, the more severe challenge is to coexist with technologies other than 802.11n/ac/ax networks, namely unlicensed LTE networks and radars. The main coexistence mechanism of WiFi is listen-beforetalk (LBT) which is also known as carrier sense multiple access with collision avoidance (CSMA/CA). A station with a traffic to transmit has to first check whether the medium is free or not. To decide on the state of the medium, two approaches exist: carrier sense and energy detection. In carrier sensing, a WiFi node decodes the preamble of a WiFi frame that is received above some energy detection level. The node extracts the information from the PLCP header which carries information about the occupancy duration of the medium by that ongoing flow. This mechanism is also referred to as channel reservation. With this information, a WiFi node knows when to re-start sensing the medium for a transmission opportunity. Energy detection (ED) is a simpler approach in which a candidate transmitter decides that the air is free if the signal level is below a predefined ED threshold. This approach is used for detecting inter-technology signals, where the received signal is not decodable, i.e., it belongs to other technologies (or corrupted WiFi signals). Despite its simplicity, ED requires more effort on a potential transmitter as it must constantly sense the energy level in the air to detect a transmission opportunity. As LTE follows a scheduled medium access on the licensedspectrum, there is no notion or necessity of politeness or LBT in more technical terms. However, it is vital for LTE unlicensed to implement such mechanisms for coexistence with WiFi and other unlicensed LTE networks at 5 GHz bands. Currently, frequency-domain sharing is a first step only. In other words, an LTE small cell first checks the channel activities and selects a clear channel, if any. For time sharing, there are two approaches taken by two variants of LTE unlicensed: duty cycling by LTE-U and LBT by License-Assisted-Access (LAA). LTE-U which is an industry-led effort lets small cells apply duty cycling where during the OFF periods WiFi can access the medium. As this approach does not mandate LBT before turning small cell transmissions on, it may degrade WiFi performance drastically. LAA requires LBT similar to WiFi’s CSMA/CA. LAA speciCROWNCOM2017 TUTORIAL 2 fication is led by 3GPP and aims to develop a global solution in contrast to LTE-U which is only compliant to countries like US, Korea, China where LBT is not mandatory. We overview basics of these two variants and list the major issues in their peaceful coexistence with WiFi networks. III. BACKGROUND ON MACHINE LEARNING We provide a sparse overview of learning approaches: supervised, unsupervised, and reinforcement learning. IV. THE ROLE OF MACHINE LEARNING IN SPECTRUM SHARING AND COEXISTENCE In this part, we examine the literature using ML approaches to solve the coexistence issues as our case studies. Is the channel idle or busy?This question is at the heart of coexistence of networks in a multi-channel environment, as the first step of coexistence is to choose a channel that is clear. For cognitive radio networks, it is mandatory to detect the channel state to avoid violating the rules of secondary spectrum access. Casting this question into a binary classification problem, authors [2] introduce several (un)supervised learning algorithms to correctly identify the state of a channel. While supervised approaches require the real channel state information from the Primary Users, unsupervised learning such as K-means does not require any input from the PUs which is a desirable property of classification scheme in a practical setting. Which unlicensed channel to select for each LAA SBS for inter-operator coexistence?As we expect multiple LAA operators deploy their small cells independently, there is surely the question of how to select an unlicensed channel to aggregate, particularly in case there are more cells than the number of available channels. One way of channel selection is to let every LAA BS learn from its own observations via trial-and-error, Q-learning [3]. Which unlicensed carrier to aggregate and how long to use this carrier?Q-learning framework can also be applied to an LAA setting where an LAA BS needs to select an unlicensed carrier and the transmission duration on the selected carrier [4]. Can WiFi exploit ML for defending itself against LTEU interference?Different than the literature which develops coexistence solutions to be deployed at the LTE base stations for WiFi/LTE setting, [5] proposes to also equip the WiFi APs with a tool that estimates the ON-duration of an existing LTE-U network in the neighborhood. Moreover, the developed solution can estimate the remaining airtime for the WiFi AP based on the LTE’s predicted ON duration. Key idea of WiPLUS is to detect the times where LTE-U has an ongoing transmission using the data passively collected from the MAC FSM of the NIC. However, although LTE-U signal may not be detected above the ED level, it may still have a severe impact on WiFi. Thus, PHY-layer analysis solely on signal level is short of detecting the moderate interference regime. WiPLUS overcomes this challenge by combining data from MAC FSM states and ARQ missing acknowledgments. Sampled data from a testbed has a lot of noise due to imperfections of the measuring devices and the complex interactions among the coexisting systems as well as PHY and MAC layers. WiPLUS applies K-means clustering to detect outliers on the estimated LTE-U on-durations. After filtering the data points based on the signal’s frequency harmonics, WiPLUS calculates the LTEU on-time as the average of the data points, each of which corresponds to an estimate of LTE-U on-time. Can we estimate WiFi link performance by learning from real-world link capacity measurements?In a multiAP setting, an AP can select the operation channel based on the expected capacity of the existing links. The traditional way is to take the SNIR-based capacity estimate into account, i.e., Shannon’s capacity formula. However, this capacity model may sometimes fail to represent the complex interactions between PHY and MAC layers, e.g., partially-overlapping channels in case of channel bonding in new 802.11ac/ax standards. The idea of [6] is to use supervised learning as a tool to model the complex interactions among many factors such as power and PHY rate of a neighboring WiFi link implicitly rather than modelling it explicitly. V. OPEN RESEARCH DIRECTIONS Machine learning-based solutions can embrace the complexity and uncertainty prevalent in the complex scenarios, especially hybrid horizontal spectrum sharing, by learning from the observations. However, a wireless network poses peculiar challenges such as the energy limitations, real-time operation, and sometimes fast changes in the operation environment that render learning less effective. We overview such challenges and conclude with some open questions in this

Obfuscation procedure based in dead code insertion into crypter <=SEP=> The threat that attacks cyberspace is known as malware. In order to infect the technologic devices that are attacked, malware needs to evade the different antivirus systems. To avoid detection, an obfuscation technique must be applied so malware is updated and ready to be performed. No obstant, the technique implementation presents difficulties in terms of its required ability, evasion tests and infection functionality that turn outs to be a problem to keep malware updated. Therefore, a procedure is proposed that allows applying AVFUCKER or DSPLIT techniques. The purpose is to optimize the required technical means, reduce the antivirus analysis and malware functionality check times.

GRAPHITE : Polyhedral Analyses and Optimizations for GCC <=SEP=> We present a plan to add loop nest optimizations in GCC based on polyhedral representations of loop nests. We advocate a static analysis approach based on a hierarchy of interchangeable abstractions with solvers that range from the exact solvers such as OMEGA, to faster but less precise solvers based on more coarse abstractions. The intermediate representation GRAPHITE1 (GIMPLE Represented as Polyhedra with Interchangeable Envelopes), built on GIMPLE and the natural loops, hosts the high level loop transformations. We base this presentation on the WRaP-IT project developed in the Alchemy group at INRIA Futurs and Paris-Sud University, on the PIPS compiler developed at École des mines de Paris, and on a joint work with several members of the static analysis and polyhedral compilation community in France. The main goal of this project is to bring more high level loop optimizations to GCC: loop fusion, tiling, strip mining, etc. Thanks to the 1This work was partially supported by ACI/APRON. WRaP-IT experience, we know that the polyhedral analyzes and transformations are affordable in a production compiler. A second goal of this project is to experiment with compile time reduction versus attainable precision when replacing operations on polyhedra with faster operations on more abstract domains. However, the use of a too coarse representation for computing might also result in an over approximated solution that cannot be used in subsequent computations. There exists a trade off between speed of the computation and the attainable precision that has not yet been analyzed for real world programs.

Dragonfly wing nodus: A one-way hinge contributing to the asymmetric wing deformation. <=SEP=> Dragonfly wings are highly specialized locomotor systems, which are formed by a combination of several structural components. The wing components, also known as structural elements, are responsible for the various aspects of the wing functionality. Considering the complex interactions between the wing components, modelling of the wings as a whole is only possible with inevitable huge oversimplifications. In order to overcome this difficulty, we have recently proposed a new approach to model individual components of complex wings comparatively. Here, we use this approach to study nodus, a structural element of dragonfly wings which has been less studied to date. Using a combination of several imaging techniques including scanning electron microscopy (SEM), wide-field fluorescence microscopy (WFM), confocal laser scanning microscopy (CLSM) and micro-computed tomography (micro-CT) scanning, we aim to characterize the spatial morphology and material composition of fore- and hindwing nodi of the dragonfly Brachythemis contaminata. The microscopy results show the presence of resilin in the nodi, which is expected to help the deformability of the wings. The computational results based on three-dimensional (3D) structural data suggest that the specific geometry of the nodus restrains its displacements when subjected to pressure on the ventral side. This effect, resulting from an interlocking mechanism, is expected to contribute to the dorso-ventral asymmetry of wing deformation and to provide a higher resistance to aerodynamic forces during the downstroke. Our results provide an important step towards better understanding of the structure-property-function relationship in dragonfly wings.


STATEMENT OF SIGNIFICANCE
In this study, we investigate the wing nodus, a specialized wing component in dragonflies. Using a combination of modern imaging techniques, we demonstrate the presence of resilin in the nodus, which is expected to facilitate the wing deformability in flight. The specific geometry of the nodus, however, seems to restrain its displacements when subjected to pressure on the ventral side. This effect, resulting from an interlocking mechanism, is suggested to contribute to dorso-ventral asymmetry of wing deformations and to provide a higher resistance to aerodynamic forces during the downstroke. Our results provide an important step towards better understanding of the structure-property-function relationship in dragonfly wings and might help to design more efficient wings for biomimetic micro-air vehicles.

From piecemeal to configurational representation of faces. <=SEP=> Unlike older children and adults, children of less than about 10 years of age remember photographs of faces presented upside down almost as well as those shown upright and are easily fooled by simple disguises. The development at age 10 of the ability to encode orientation-specific configurational aspects of a face may reflect completion of certain maturational changes in the right cerebral hemisphere.

Face spoofing detection from single images using texture and local shape analysis <=SEP=> Current face biometric systems are vulnerable to spoofing attacks. A spoofing attack occurs when a person tries to masquerade as someone else by falsifying data and thereby gaining illegitimate access. Inspired by image quality assessment, characterisation of printing artefacts and differences in light reflection, the authors propose to approach the problem of spoofing detection from texture analysis point of view. Indeed, face prints usually contain printing quality defects that can be well detected using texture and local shape features. Hence, the authors present a novel approach based on analysing facial image for detecting whether there is a live person in front of the camera or a face print. The proposed approach analyses the texture and gradient structures of the facial images using a set of low-level feature descriptors, fast linear classification scheme and score level fusion. Compared to many previous works, the authors proposed approach is robust and does not require user-cooperation. In addition, the texture features that are used for spoofing detection can also be used for face recognition. This provides a unique feature space for coupling spoofing detection and face recognition. Extensive experimental analysis on three publicly available databases showed excellent results compared to existing works.

Learning and Recognition of Clothing Genres From Full-Body Images <=SEP=> According to the theory of clothing design, the genres of clothes can be recognized based on a set of visually differentiable style elements, which exhibit salient features of visual appearance and reflect high-level fashion styles for better describing clothing genres. Instead of using less-discriminative low-level features or ambiguous keywords to identify clothing genres, we proposed a novel approach for automatically classifying clothing genres based on the visually differentiable style elements. A set of style elements, that are crucial for recognizing specific visual styles of clothing genres, were identified based on the clothing design theory. In addition, the corresponding salient visual features of each style element were identified and formulated with variables that can be computationally derived with various computer vision algorithms. To evaluate the performance of our algorithm, a dataset containing 3250 full-body shots crawled from popular online stores was built. Recognition results show that our proposed algorithms achieved promising overall precision, recall, and  ${F}$ -score of 88.76%, 88.53%, and 88.64% for recognizing upperwear genres, and 88.21%, 88.17%, and 88.19% for recognizing lowerwear genres, respectively. The effectiveness of each style element and its visual features on recognizing clothing genres was demonstrated through a set of experiments involving different sets of style elements or features. In summary, our experimental results demonstrate the effectiveness of the proposed method in clothing genre recognition.

Teaching Introductory Artificial Intelligence with Pac-Man <=SEP=> The projects that we have developed for UC Berkeley’s introductory artificial intelligence (AI) course teach foundational concepts using the classic video game Pac-Man. There are four project topics: state-space search, multi-agent search, probabilistic inference, and reinforcement learning. Each project requires students to implement general-purpose AI algorithms and then to inject domain knowledge about the PacMan environment using search heuristics, evaluation functions, and feature functions. We have found that the Pac-Man theme adds consistency to the course, as well as tapping in to students’ excitement about video games.

Considering Social and Emotional Artificial Intelligence <=SEP=> This paper presents the concepts of social and emotional intelligence as elements of human intelligence that are complementary to the intelligence assessed by the Turing Test. We argue that these elements are gaining importance as human users are increasingly conceptualising machines as social entities. We describe an implementation of Sensitive Artificial Listeners which provides a hands-on example of technology with some emotional and social skills, and discuss first elements of test methodologies for such technology.

Low numeracy and dyscalculia : identification and intervention <=SEP=> One important factor in the failure to learn arithmetic in the normal way is an endogenous core deficit in the sense of number. This has been associated with low numeracy in general (e.g. Halberda et al. in Nature 455:665–668, 2008) and with dyscalculia more specifically (e.g. Landerl et al. in Cognition 93:99–125, 2004). Here, we describe straightforward ways of identifying this deficit, and offer some new ways of strengthening the sense of number using learning technologies.

Visualizing LSTM decisions <=SEP=> Long Short-Term Memory (LSTM) recurrent neural networks are renowned for being uninterpretable "black boxes". In the medical domain where LSTMs have shown promise, this is specifically concerning because it is imperative to understand the decisions made by machine learning models in such acute situations. This study employs techniques used in the Convolutional Neural Network domain to elucidate the operations that LSTMs perform on time series. The visualization techniques include input saliency by means of occlusion and derivatives, class mode visualization, and temporal outputs. Moreover, we demonstrate that LSTMs appear to extract features similar to those extracted by wavelets. It was found that deriving the inputs for saliency is a poor approximation and occlusion is a better approach. Moreover, analyzing LSTMs on different sets of data provide novel interpretations.

A survey of performance measures to evaluate ego-lane estimation and a novel sensor-independent measure along with its applications <=SEP=> Lane estimation plays a central role for Driver Assistance Systems, therefore many approaches have been proposed to measure its performance. However, no commonly agreed metric exists. In this work, we first present a detailed survey of the current measures. Most of them apply pixel-level benchmarks on camera images and require a time-consuming and fault-prone labeling process. Moreover, these metrics cannot be used to assess other sources such as the detected guardrails, curbs or other vehicles. Therefore, we introduce an efficient and sensor-independent metric, which provides an objective and intuitive self-assessment for the entire road estimation process at multiple levels: individual detectors, lane estimation itself, and the target applications (e.g., lane keeping system). Our metric does not require a high labeling effort and can be used both online and offline. By selecting the evaluated points in specific distances, it can be applied to any road model representation. By comparing in 2D vehicle coordinate system, two possibilities exist to generate the ground-truth: the human-driven path or the expensive alternative with DGPS and detailed maps. This paper applies both methods and reveals that the human-driven path also qualifies for this task and it is applicable to scenarios without GPS signal, e.g., tunnel. Although the lateral offset between reference and detection is widely used in the majority of works, this paper shows that another criterion, the angle deviation, is more appropriate. Finally, we compare our metric with other state-of-the-art metrics using real data recordings from different scenarios.

Why and How Investors Use ESG Information : Evidence from a Global Survey <=SEP=> Using survey data from a sample of senior investmen professionals from mainstream (i.e. not SRI funds ) investment organizations we provide insights into w hy and how investors use reported environmental, social and governance (ESG) information. The primar y reason survey respondents consider ESG information in investment decisions is because they consider it financially material to investment performance. ESG information is perceived to provid e information primarily about risk rather than a company’s competitive positioning. There is no one siz fits all, with the financial materiality of di fferent ESG issues varying across sectors. Lack of comparab ility due to the lack of reporting standards is the primary impediment to the use of ESG information. M ost frequently, the information is used to screen companies with the most often used method being neg ative screening. However, negative screening is perceived as the least investment beneficial while ful integration into stock valuation and positive screening considered more beneficial. Respondents expect nega tiv screening to be used less in the future, while positive screening and active ownership to be used more.

The trouble with overconfidence. <=SEP=> The authors present a reconciliation of 3 distinct ways in which the research literature has defined overconfidence: (a) overestimation of one's actual performance, (b) overplacement of one's performance relative to others, and (c) excessive precision in one's beliefs. Experimental evidence shows that reversals of the first 2 (apparent underconfidence), when they occur, tend to be on different types of tasks. On difficult tasks, people overestimate their actual performances but also mistakenly believe that they are worse than others; on easy tasks, people underestimate their actual performances but mistakenly believe they are better than others. The authors offer a straightforward theory that can explain these inconsistencies. Overprecision appears to be more persistent than either of the other 2 types of overconfidence, but its presence reduces the magnitude of both overestimation and overplacement.

Dynamic XML documents with distribution and replication <=SEP=> The advent of XML as a universal exchange format, and of Web services as a basis for distributed computing, has fostered the apparition of a new class of documents: dynamic XML documents. These are XML documents where some data is given explicitly while other parts are given only intensionally by means of embedded calls to web services that can be called to generate the required information. By the sole presence of Web services, dynamic documents already include inherently some form of distributed computation. A higher level of distribution that also allows (fragments of) dynamic documents to be distributed and/or replicated over several sites is highly desirable in today's Web architecture, and in fact is also relevant for regular (non dynamic) documents.The goal of this paper is to study new issues raised by the distribution and replication of dynamic XML data. Our study has originated in the context of the Active XML system [1, 3, 22] but the results are applicable to many other systems supporting dynamic XML data. Starting from a data model and a query language, we describe a complete framework for distributed and replicated dynamic XML documents. We provide a comprehensive cost model for query evaluation and show how it applies to user queries and service calls. Finally, we describe an algorithm that, for a given peer, chooses data and services that the peer should replicate to improve the efficiency of maintaining and querying its dynamic data.

A measurement study of google play <=SEP=> Although millions of users download and use third-party Android applications from the Google Play store, little information is known on an aggregated level about these applications. We have built PlayDrone, the first scalable Google Play store crawler, and used it to index and analyze over 1,100,000 applications in the Google Play store on a daily basis, the largest such index of Android applications. PlayDrone leverages various hacking techniques to circumvent Google's roadblocks for indexing Google Play store content, and makes proprietary application sources available, including source code for over 880,000 free applications. We demonstrate the usefulness of PlayDrone in decompiling and analyzing application content by exploring four previously unaddressed issues: the characterization of Google Play application content at large scale and its evolution over time, library usage in applications and its impact on application portability, duplicative application content in Google Play, and the ineffectiveness of OAuth and related service authentication mechanisms resulting in malicious users being able to easily gain unauthorized access to user data and resources on Amazon Web Services and Facebook.

Asymmetrical Duty Cycle-Controlled LLC Resonant Converter With Equivalent Switching Frequency Doubler <=SEP=> In the conventional full-bridge LLC converter, the duty cycle is kept as 0.5 and the phase-shift angle of the half-bridge modules is 0° to be a symmetrical operation, which makes the resonant tank operating frequency only equal to the switching frequency of the power devices. By regulating the duty cycles of the upper and lower switches in each half-bridge module to be 0.75 and 0.25 and the phase-shift angle of the half-bridge modules to be 180°, the asymmetrical duty cycle controlled full-bridge LLC resonant converter is derived. The proposed asymmetrical duty cycle controlled scheme halves the switching frequency of the primary switches. As a result, the driving losses are effectively reduced. Compared with the conventional full-bridge LLC converter, the soft-switching condition of the derived asymmetrical controlled LLC converter becomes easier to reduce the resonant current. Consequently, the conduction losses are decreased and the conversion efficiency is improved. The asymmetrical control scheme can be also extended to the stacked structure for high input voltage applications. Finally, two LLC converter prototypes both with 200-kHz resonant frequency for asymmetrical and symmetrical control schemes are built and compared to validate the effectiveness of the proposed control strategy.

Behavioural Analytics using Process Mining in On-line Advertising <=SEP=> Online behavioural targeting is one of the most popular business strategies on the display advertising today. It is based primarily on analysing web user behavioural data with the usage of machine learning techniques with the aim to optimise web advertising. Being able to identify “unknown” and “first time seen” customers is of high importance in online advertising since a successful guess could identify “possible prospects” who would be more likely to purchase an advertisement’s product. By identifying prospective customers, online advertisers may be able to optimise campaign performance, maximise their revenue as well as deliver advertisements tailored to a variety of user interests. This work presents a hybrid approach benchmarking machine-learning algorithms and attribute preprocessing techniques in the context of behavioural targeting in process oriented environments. The performance of our suggested methodology is evaluated using the key performance metric in online advertising which is the predicted conversion rate. Our experimental results indicate that the presented process mining framework can significantly identify prospect customers in most cases. Our results seem promising, indicating that there is a need for further workflow research in online display