May 7, 2019

2851 words 14 mins read

Paper Group ANR 96

Automatic Genre and Show Identification of Broadcast Media. Modeling Human Ad Hoc Coordination. Informal Physical Reasoning Processes. Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range. Normalizing Flows on Riemannian Manifolds. Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Orde …

Automatic Genre and Show Identification of Broadcast Media


Title	Automatic Genre and Show Identification of Broadcast Media
Authors	Mortaza Doulaty, Oscar Saz, Raymond W. M. Ng, Thomas Hain
Abstract	Huge amounts of digital videos are being produced and broadcast every day, leading to giant media archives. Effective techniques are needed to make such data accessible further. Automatic meta-data labelling of broadcast media is an essential task for multimedia indexing, where it is standard to use multi-modal input for such purposes. This paper describes a novel method for automatic detection of media genre and show identities using acoustic features, textual features or a combination thereof. Furthermore the inclusion of available meta-data, such as time of broadcast, is shown to lead to very high performance. Latent Dirichlet Allocation is used to model both acoustics and text, yielding fixed dimensional representations of media recordings that can then be used in Support Vector Machines based classification. Experiments are conducted on more than 1200 hours of TV broadcasts from the British Broadcasting Corporation (BBC), where the task is to categorise the broadcasts into 8 genres or 133 show identities. On a 200-hour test set, accuracies of 98.6% and 85.7% were achieved for genre and show identification respectively, using a combination of acoustic and textual features with meta-data.
Tasks
Published	2016-06-10
URL	http://arxiv.org/abs/1606.03333v1
PDF	http://arxiv.org/pdf/1606.03333v1.pdf
PWC	https://paperswithcode.com/paper/automatic-genre-and-show-identification-of
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Modeling Human Ad Hoc Coordination


Title	Modeling Human Ad Hoc Coordination
Authors	Peter M. Krafft, Chris L. Baker, Alex Pentland, Joshua B. Tenenbaum
Abstract	Whether in groups of humans or groups of computer agents, collaboration is most effective between individuals who have the ability to coordinate on a joint strategy for collective action. However, in general a rational actor will only intend to coordinate if that actor believes the other group members have the same intention. This circular dependence makes rational coordination difficult in uncertain environments if communication between actors is unreliable and no prior agreements have been made. An important normative question with regard to coordination in these ad hoc settings is therefore how one can come to believe that other actors will coordinate, and with regard to systems involving humans, an important empirical question is how humans arrive at these expectations. We introduce an exact algorithm for computing the infinitely recursive hierarchy of graded beliefs required for rational coordination in uncertain environments, and we introduce a novel mechanism for multiagent coordination that uses it. Our algorithm is valid in any environment with a finite state space, and extensions to certain countably infinite state spaces are likely possible. We test our mechanism for multiagent coordination as a model for human decisions in a simple coordination game using existing experimental data. We then explore via simulations whether modeling humans in this way may improve human-agent collaboration.
Tasks
Published	2016-02-11
URL	http://arxiv.org/abs/1602.03924v1
PDF	http://arxiv.org/pdf/1602.03924v1.pdf
PWC	https://paperswithcode.com/paper/modeling-human-ad-hoc-coordination
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Informal Physical Reasoning Processes


Title	Informal Physical Reasoning Processes
Authors	Kurt Ammon
Abstract	A fundamental question is whether Turing machines can model all reasoning processes. We introduce an existence principle stating that the perception of the physical existence of any Turing program can serve as a physical causation for the application of any Turing-computable function to this Turing program. The existence principle overcomes the limitation of the outputs of Turing machines to lists, that is, recursively enumerable sets. The principle is illustrated by productive partial functions for productive sets such as the set of the Goedel numbers of the Turing-computable total functions. The existence principle and productive functions imply the existence of physical systems whose reasoning processes cannot be modeled by Turing machines. These systems are called creative. Creative systems can prove the undecidable formula in Goedel’s theorem in another formal system which is constructed at a later point in time. A hypothesis about creative systems, which is based on computer experiments, is introduced.
Tasks
Published	2016-08-15
URL	http://arxiv.org/abs/1608.04672v1
PDF	http://arxiv.org/pdf/1608.04672v1.pdf
PWC	https://paperswithcode.com/paper/informal-physical-reasoning-processes
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Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range


Title	Hybrid Light Field Imaging for Improved Spatial Resolution and Depth Range
Authors	M. Zeshan Alam, Bahadir K. Gunturk
Abstract	Light field imaging involves capturing both angular and spatial distribution of light; it enables new capabilities, such as post-capture digital refocusing, camera aperture adjustment, perspective shift, and depth estimation. Micro-lens array (MLA) based light field cameras provide a cost-effective approach to light field imaging. There are two main limitations of MLA-based light field cameras: low spatial resolution and narrow baseline. While low spatial resolution limits the general purpose use and applicability of light field cameras, narrow baseline limits the depth estimation range and accuracy. In this paper, we present a hybrid stereo imaging system that includes a light field camera and a regular camera. The hybrid system addresses both spatial resolution and narrow baseline issues of the MLA-based light field cameras while preserving light field imaging capabilities.
Tasks	Depth Estimation
Published	2016-11-15
URL	http://arxiv.org/abs/1611.05008v2
PDF	http://arxiv.org/pdf/1611.05008v2.pdf
PWC	https://paperswithcode.com/paper/hybrid-light-field-imaging-for-improved
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Normalizing Flows on Riemannian Manifolds


Title	Normalizing Flows on Riemannian Manifolds
Authors	Mevlana C. Gemici, Danilo Rezende, Shakir Mohamed
Abstract	We consider the problem of density estimation on Riemannian manifolds. Density estimation on manifolds has many applications in fluid-mechanics, optics and plasma physics and it appears often when dealing with angular variables (such as used in protein folding, robot limbs, gene-expression) and in general directional statistics. In spite of the multitude of algorithms available for density estimation in the Euclidean spaces $\mathbf{R}^n$ that scale to large n (e.g. normalizing flows, kernel methods and variational approximations), most of these methods are not immediately suitable for density estimation in more general Riemannian manifolds. We revisit techniques related to homeomorphisms from differential geometry for projecting densities to sub-manifolds and use it to generalize the idea of normalizing flows to more general Riemannian manifolds. The resulting algorithm is scalable, simple to implement and suitable for use with automatic differentiation. We demonstrate concrete examples of this method on the n-sphere $\mathbf{S}^n$.
Tasks	Density Estimation
Published	2016-11-07
URL	http://arxiv.org/abs/1611.02304v2
PDF	http://arxiv.org/pdf/1611.02304v2.pdf
PWC	https://paperswithcode.com/paper/normalizing-flows-on-riemannian-manifolds
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Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Order Chaotic Financial Systems


Title	Multi-objective Active Control Policy Design for Commensurate and Incommensurate Fractional Order Chaotic Financial Systems
Authors	Indranil Pan, Saptarshi Das, Shantanu Das
Abstract	In this paper, an active control policy design for a fractional order (FO) financial system is attempted, considering multiple conflicting objectives. An active control template as a nonlinear state feedback mechanism is developed and the controller gains are chosen within a multi-objective optimization (MOO) framework to satisfy the conditions of asymptotic stability, derived analytically. The MOO gives a set of solutions on the Pareto optimal front for the multiple conflicting objectives that are considered. It is shown that there is a trade-off between the multiple design objectives and a better performance in one objective can only be obtained at the cost of performance deterioration in the other objectives. The multi-objective controller design has been compared using three different MOO techniques viz. Non Dominated Sorting Genetic Algorithm-II (NSGA-II), epsilon variable Multi-Objective Genetic Algorithm (ev-MOGA), and Multi Objective Evolutionary Algorithm with Decomposition (MOEA/D). The robustness of the same control policy designed with the nominal system settings have been investigated also for gradual decrease in the commensurate and incommensurate fractional orders of the financial system.
Tasks
Published	2016-11-29
URL	http://arxiv.org/abs/1611.09835v1
PDF	http://arxiv.org/pdf/1611.09835v1.pdf
PWC	https://paperswithcode.com/paper/multi-objective-active-control-policy-design
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A Kernel Test for Three-Variable Interactions with Random Processes


Title	A Kernel Test for Three-Variable Interactions with Random Processes
Authors	Paul K. Rubenstein, Kacper P. Chwialkowski, Arthur Gretton
Abstract	We apply a wild bootstrap method to the Lancaster three-variable interaction measure in order to detect factorisation of the joint distribution on three variables forming a stationary random process, for which the existing permutation bootstrap method fails. As in the i.i.d. case, the Lancaster test is found to outperform existing tests in cases for which two independent variables individually have a weak influence on a third, but that when considered jointly the influence is strong. The main contributions of this paper are twofold: first, we prove that the Lancaster statistic satisfies the conditions required to estimate the quantiles of the null distribution using the wild bootstrap; second, the manner in which this is proved is novel, simpler than existing methods, and can further be applied to other statistics.
Tasks
Published	2016-03-02
URL	http://arxiv.org/abs/1603.00929v2
PDF	http://arxiv.org/pdf/1603.00929v2.pdf
PWC	https://paperswithcode.com/paper/a-kernel-test-for-three-variable-interactions
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Singular ridge regression with homoscedastic residuals: generalization error with estimated parameters


Title	Singular ridge regression with homoscedastic residuals: generalization error with estimated parameters
Authors	Lyudmila Grigoryeva, Juan-Pablo Ortega
Abstract	This paper characterizes the conditional distribution properties of the finite sample ridge regression estimator and uses that result to evaluate total regression and generalization errors that incorporate the inaccuracies committed at the time of parameter estimation. The paper provides explicit formulas for those errors. Unlike other classical references in this setup, our results take place in a fully singular setup that does not assume the existence of a solution for the non-regularized regression problem. In exchange, we invoke a conditional homoscedasticity hypothesis on the regularized regression residuals that is crucial in our developments.
Tasks
Published	2016-05-29
URL	http://arxiv.org/abs/1605.09026v1
PDF	http://arxiv.org/pdf/1605.09026v1.pdf
PWC	https://paperswithcode.com/paper/singular-ridge-regression-with-homoscedastic
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Probabilistic Model Checking for Complex Cognitive Tasks – A case study in human-robot interaction


Title	Probabilistic Model Checking for Complex Cognitive Tasks – A case study in human-robot interaction
Authors	Sebastian Junges, Nils Jansen, Joost-Pieter Katoen, Ufuk Topcu
Abstract	This paper proposes to use probabilistic model checking to synthesize optimal robot policies in multi-tasking autonomous systems that are subject to human-robot interaction. Given the convincing empirical evidence that human behavior can be related to reinforcement models, we take as input a well-studied Q-table model of the human behavior for flexible scenarios. We first describe an automated procedure to distill a Markov decision process (MDP) for the human in an arbitrary but fixed scenario. The distinctive issue is that – in contrast to existing models – under-specification of the human behavior is included. Probabilistic model checking is used to predict the human’s behavior. Finally, the MDP model is extended with a robot model. Optimal robot policies are synthesized by analyzing the resulting two-player stochastic game. Experimental results with a prototypical implementation using PRISM show promising results.
Tasks
Published	2016-10-28
URL	http://arxiv.org/abs/1610.09409v1
PDF	http://arxiv.org/pdf/1610.09409v1.pdf
PWC	https://paperswithcode.com/paper/probabilistic-model-checking-for-complex
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Binary Distance Transform to Improve Feature Extraction


Title	Binary Distance Transform to Improve Feature Extraction
Authors	Mariane Barros Neiva, Antoine Manzanera, Odemir Martinez Bruno
Abstract	To recognize textures many methods have been developed along the years. However, texture datasets may be hard to be classified due to artefacts such as a variety of scale, illumination and noise. This paper proposes the application of binary distance transform on the original dataset to add information to texture representation and consequently improve recognition. Texture images, usually in grayscale, suffers a binarization prior to distance transform and one of the resulted images are combined with original texture to improve the amount of information. Four datasets are used to evaluate our approach. For Outex dataset, for instance, the proposal outperforms all rates, improvements of an up to 10%, compared to traditional approach where descriptors are applied on the original dataset, showing the importance of this approach.
Tasks
Published	2016-12-19
URL	http://arxiv.org/abs/1612.06443v1
PDF	http://arxiv.org/pdf/1612.06443v1.pdf
PWC	https://paperswithcode.com/paper/binary-distance-transform-to-improve-feature
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hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification


Title	hi-RF: Incremental Learning Random Forest for large-scale multi-class Data Classification
Authors	Tingting Xie, Yuxing Peng, Changjian Wang
Abstract	In recent years, dynamically growing data and incrementally growing number of classes pose new challenges to large-scale data classification research. Most traditional methods struggle to balance the precision and computational burden when data and its number of classes increased. However, some methods are with weak precision, and the others are time-consuming. In this paper, we propose an incremental learning method, namely, heterogeneous incremental Nearest Class Mean Random Forest (hi-RF), to handle this issue. It is a heterogeneous method that either replaces trees or updates trees leaves in the random forest adaptively, to reduce the computational time in comparable performance, when data of new classes arrive. Specifically, to keep the accuracy, one proportion of trees are replaced by new NCM decision trees; to reduce the computational load, the rest trees are updated their leaves probabilities only. Most of all, out-of-bag estimation and out-of-bag boosting are proposed to balance the accuracy and the computational efficiency. Fair experiments were conducted and demonstrated its comparable precision with much less computational time.
Tasks
Published	2016-08-31
URL	http://arxiv.org/abs/1608.08761v2
PDF	http://arxiv.org/pdf/1608.08761v2.pdf
PWC	https://paperswithcode.com/paper/hi-rf-incremental-learning-random-forest-for
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A Semiparametric Model for Bayesian Reader Identification


Title	A Semiparametric Model for Bayesian Reader Identification
Authors	Ahmed Abdelwahab, Reinhold Kliegl, Niels Landwehr
Abstract	We study the problem of identifying individuals based on their characteristic gaze patterns during reading of arbitrary text. The motivation for this problem is an unobtrusive biometric setting in which a user is observed during access to a document, but no specific challenge protocol requiring the user’s time and attention is carried out. Existing models of individual differences in gaze control during reading are either based on simple aggregate features of eye movements, or rely on parametric density models to describe, for instance, saccade amplitudes or word fixation durations. We develop flexible semiparametric models of eye movements during reading in which densities are inferred under a Gaussian process prior centered at a parametric distribution family that is expected to approximate the true distribution well. An empirical study on reading data from 251 individuals shows significant improvements over the state of the art.
Tasks
Published	2016-07-18
URL	http://arxiv.org/abs/1607.05271v1
PDF	http://arxiv.org/pdf/1607.05271v1.pdf
PWC	https://paperswithcode.com/paper/a-semiparametric-model-for-bayesian-reader
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Delta divergence: A novel decision cognizant measure of classifier incongruence


Title	Delta divergence: A novel decision cognizant measure of classifier incongruence
Authors	Josef Kittler, Cemre Zor
Abstract	Disagreement between two classifiers regarding the class membership of an observation in pattern recognition can be indicative of an anomaly and its nuance. As in general classifiers base their decision on class aposteriori probabilities, the most natural approach to detecting classifier incongruence is to use divergence. However, existing divergences are not particularly suitable to gauge classifier incongruence. In this paper, we postulate the properties that a divergence measure should satisfy and propose a novel divergence measure, referred to as Delta divergence. In contrast to existing measures, it is decision cognizant. The focus in Delta divergence on the dominant hypotheses has a clutter reducing property, the significance of which grows with increasing number of classes. The proposed measure satisfies other important properties such as symmetry, and independence of classifier confidence. The relationship of the proposed divergence to some baseline measures is demonstrated experimentally, showing its superiority.
Tasks
Published	2016-04-15
URL	http://arxiv.org/abs/1604.04451v2
PDF	http://arxiv.org/pdf/1604.04451v2.pdf
PWC	https://paperswithcode.com/paper/delta-divergence-a-novel-decision-cognizant
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Identification of repeats in DNA sequences using nucleotide distribution uniformity


Title	Identification of repeats in DNA sequences using nucleotide distribution uniformity
Authors	Changchuan Yin
Abstract	Repetitive elements are important in genomic structures, functions and regulations, yet effective methods in precisely identifying repetitive elements in DNA sequences are not fully accessible, and the relationship between repetitive elements and periodicities of genomes is not clearly understood. We present an $\textit{ab initio}$ method to quantitatively detect repetitive elements and infer the consensus repeat pattern in repetitive elements. The method uses the measure of the distribution uniformity of nucleotides at periodic positions in DNA sequences or genomes. It can identify periodicities, consensus repeat patterns, copy numbers and perfect levels of repetitive elements. The results of using the method on different DNA sequences and genomes demonstrate efficacy and accuracy in identifying repeat patterns and periodicities. The complexity of the method is linear with respect to the lengths of the analyzed sequences.
Tasks
Published	2016-07-31
URL	http://arxiv.org/abs/1608.00567v1
PDF	http://arxiv.org/pdf/1608.00567v1.pdf
PWC	https://paperswithcode.com/paper/identification-of-repeats-in-dna-sequences
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Mining Local Process Models


Title	Mining Local Process Models
Authors	Niek Tax, Natalia Sidorova, Reinder Haakma, Wil M. P. van der Aalst
Abstract	In this paper we describe a method to discover frequent behavioral patterns in event logs. We express these patterns as \emph{local process models}. Local process model mining can be positioned in-between process discovery and episode / sequential pattern mining. The technique presented in this paper is able to learn behavioral patterns involving sequential composition, concurrency, choice and loop, like in process mining. However, we do not look at start-to-end models, which distinguishes our approach from process discovery and creates a link to episode / sequential pattern mining. We propose an incremental procedure for building local process models capturing frequent patterns based on so-called process trees. We propose five quality dimensions and corresponding metrics for local process models, given an event log. We show monotonicity properties for some quality dimensions, enabling a speedup of local process model discovery through pruning. We demonstrate through a real life case study that mining local patterns allows us to get insights in processes where regular start-to-end process discovery techniques are only able to learn unstructured, flower-like, models.
Tasks	Sequential Pattern Mining
Published	2016-06-20
URL	http://arxiv.org/abs/1606.06066v2
PDF	http://arxiv.org/pdf/1606.06066v2.pdf
PWC	https://paperswithcode.com/paper/mining-local-process-models
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