July 29, 2019

3387 words 16 mins read

Paper Group ANR 115

Sampling Variations of Lead Sheets. Compact Hash Code Learning with Binary Deep Neural Network. The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient. Human activity recognition from mobile inertial sensors using recurrence plots. Automatic White-Box Testing of First-Order Logic Ontologies. Conv …

Sampling Variations of Lead Sheets


Title	Sampling Variations of Lead Sheets
Authors	Pierre Roy, Alexandre Papadopoulos, François Pachet
Abstract	Machine-learning techniques have been recently used with spectacular results to generate artefacts such as music or text. However, these techniques are still unable to capture and generate artefacts that are convincingly structured. In this paper we present an approach to generate structured musical sequences. We introduce a mechanism for sampling efficiently variations of musical sequences. Given a input sequence and a statistical model, this mechanism samples a set of sequences whose distance to the input sequence is approximately within specified bounds. This mechanism is implemented as an extension of belief propagation, and uses local fields to bias the generation. We show experimentally that sampled sequences are indeed closely correlated to the standard musical similarity measure defined by Mongeau and Sankoff. We then show how this mechanism can used to implement composition strategies that enforce arbitrary structure on a musical lead sheet generation problem.
Tasks
Published	2017-03-02
URL	http://arxiv.org/abs/1703.00760v1
PDF	http://arxiv.org/pdf/1703.00760v1.pdf
PWC	https://paperswithcode.com/paper/sampling-variations-of-lead-sheets
Repo
Framework

Compact Hash Code Learning with Binary Deep Neural Network


Title	Compact Hash Code Learning with Binary Deep Neural Network
Authors	Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan, Anh-Dzung Doan, Ngai-Man Cheung
Abstract	Learning compact binary codes for image retrieval problem using deep neural networks has recently attracted increasing attention. However, training deep hashing networks is challenging due to the binary constraints on the hash codes. In this paper, we propose deep network models and learning algorithms for learning binary hash codes given image representations under both unsupervised and supervised manners. The novelty of our network design is that we constrain one hidden layer to directly output the binary codes. This design has overcome a challenging problem in some previous works: optimizing non-smooth objective functions because of binarization. In addition, we propose to incorporate independence and balance properties in the direct and strict forms into the learning schemes. We also include a similarity preserving property in our objective functions. The resulting optimizations involving these binary, independence, and balance constraints are difficult to solve. To tackle this difficulty, we propose to learn the networks with alternating optimization and careful relaxation. Furthermore, by leveraging the powerful capacity of convolutional neural networks, we propose an end-to-end architecture that jointly learns to extract visual features and produce binary hash codes. Experimental results for the benchmark datasets show that the proposed methods compare favorably or outperform the state of the art.
Tasks	Image Retrieval
Published	2017-12-08
URL	https://arxiv.org/abs/1712.02956v3
PDF	https://arxiv.org/pdf/1712.02956v3.pdf
PWC	https://paperswithcode.com/paper/compact-hash-code-learning-with-binary-deep
Repo
Framework

The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient


Title	The Relationship Between Agnostic Selective Classification Active Learning and the Disagreement Coefficient
Authors	Roei Gelbhart, Ran El-Yaniv
Abstract	A selective classifier (f,g) comprises a classification function f and a binary selection function g, which determines if the classifier abstains from prediction, or uses f to predict. The classifier is called pointwise-competitive if it classifies each point identically to the best classifier in hindsight (from the same class), whenever it does not abstain. The quality of such a classifier is quantified by its rejection mass, defined to be the probability mass of the points it rejects. A “fast” rejection rate is achieved if the rejection mass is bounded from above by O(1/m) where m is the number of labeled examples used to train the classifier (and O hides logarithmic factors). Pointwise-competitive selective (PCS) classifiers are intimately related to disagreement-based active learning and it is known that in the realizable case, a fast rejection rate of a known PCS algorithm (called Consistent Selective Strategy) is equivalent to an exponential speedup of the well-known CAL active algorithm. We focus on the agnostic setting, for which there is a known algorithm called LESS that learns a PCS classifier and achieves a fast rejection rate (depending on Hanneke’s disagreement coefficient) under strong assumptions. We present an improved PCS learning algorithm called ILESS for which we show a fast rate (depending on Hanneke’s disagreement coefficient) without any assumptions. Our rejection bound smoothly interpolates the realizable and agnostic settings. The main result of this paper is an equivalence between the following three entities: (i) the existence of a fast rejection rate for any PCS learning algorithm (such as ILESS); (ii) a poly-logarithmic bound for Hanneke’s disagreement coefficient; and (iii) an exponential speedup for a new disagreement-based active learner called ActiveiLESS.
Tasks	Active Learning
Published	2017-03-19
URL	http://arxiv.org/abs/1703.06536v2
PDF	http://arxiv.org/pdf/1703.06536v2.pdf
PWC	https://paperswithcode.com/paper/the-relationship-between-agnostic-selective
Repo
Framework

Human activity recognition from mobile inertial sensors using recurrence plots


Title	Human activity recognition from mobile inertial sensors using recurrence plots
Authors	Otávio A. B. Penatti, Milton F. S. Santos
Abstract	Inertial sensors are present in most mobile devices nowadays and such devices are used by people during most of their daily activities. In this paper, we present an approach for human activity recognition based on inertial sensors by employing recurrence plots (RP) and visual descriptors. The pipeline of the proposed approach is the following: compute RPs from sensor data, compute visual features from RPs and use them in a machine learning protocol. As RPs generate texture visual patterns, we transform the problem of sensor data classification to a problem of texture classification. Experiments for classifying human activities based on accelerometer data showed that the proposed approach obtains the highest accuracies, outperforming time- and frequency-domain features directly extracted from sensor data. The best results are obtained when using RGB RPs, in which each RGB channel corresponds to the RP of an independent accelerometer axis.
Tasks	Activity Recognition, Human Activity Recognition, Texture Classification
Published	2017-12-05
URL	http://arxiv.org/abs/1712.01429v1
PDF	http://arxiv.org/pdf/1712.01429v1.pdf
PWC	https://paperswithcode.com/paper/human-activity-recognition-from-mobile
Repo
Framework

Automatic White-Box Testing of First-Order Logic Ontologies


Title	Automatic White-Box Testing of First-Order Logic Ontologies
Authors	Javier Álvez, Montserrat Hermo, Paqui Lucio, German Rigau
Abstract	Formal ontologies are axiomatizations in a logic-based formalism. The development of formal ontologies, and their important role in the Semantic Web area, is generating considerable research on the use of automated reasoning techniques and tools that help in ontology engineering. One of the main aims is to refine and to improve axiomatizations for enabling automated reasoning tools to efficiently infer reliable information. Defects in the axiomatization can not only cause wrong inferences, but can also hinder the inference of expected information, either by increasing the computational cost of, or even preventing, the inference. In this paper, we introduce a novel, fully automatic white-box testing framework for first-order logic ontologies. Our methodology is based on the detection of inference-based redundancies in the given axiomatization. The application of the proposed testing method is fully automatic since a) the automated generation of tests is guided only by the syntax of axioms and b) the evaluation of tests is performed by automated theorem provers. Our proposal enables the detection of defects and serves to certify the grade of suitability –for reasoning purposes– of every axiom. We formally define the set of tests that are generated from any axiom and prove that every test is logically related to redundancies in the axiom from which the test has been generated. We have implemented our method and used this implementation to automatically detect several non-trivial defects that were hidden in various first-order logic ontologies. Throughout the paper we provide illustrative examples of these defects, explain how they were found, and how each proof –given by an automated theorem-prover– provides useful hints on the nature of each defect. Additionally, by correcting all the detected defects, we have obtained an improved version of one of the tested ontologies: Adimen-SUMO.
Tasks
Published	2017-05-29
URL	http://arxiv.org/abs/1705.10219v3
PDF	http://arxiv.org/pdf/1705.10219v3.pdf
PWC	https://paperswithcode.com/paper/automatic-white-box-testing-of-first-order
Repo
Framework

Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay


Title	Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Exponential Decay
Authors	Hiba Chougrad, Hamid Zouaki, Omar Alheyane
Abstract	In this paper, we propose a Computer Assisted Diagnosis (CAD) system based on a deep Convolutional Neural Network (CNN) model, to build an end-to-end learning process that classifies breast mass lesions. We investigate the impact that has transfer learning when large data is scarce, and explore the proper way to fine-tune the layers to learn features that are more specific to the new data. The proposed approach showed better performance compared to other proposals that classified the same dataset.
Tasks	Transfer Learning
Published	2017-11-29
URL	http://arxiv.org/abs/1711.10752v1
PDF	http://arxiv.org/pdf/1711.10752v1.pdf
PWC	https://paperswithcode.com/paper/convolutional-neural-networks-for-breast
Repo
Framework

PFAx: Predictable Feature Analysis to Perform Control


Title	PFAx: Predictable Feature Analysis to Perform Control
Authors	Stefan Richthofer, Laurenz Wiskott
Abstract	Predictable Feature Analysis (PFA) (Richthofer, Wiskott, ICMLA 2015) is an algorithm that performs dimensionality reduction on high dimensional input signal. It extracts those subsignals that are most predictable according to a certain prediction model. We refer to these extracted signals as predictable features. In this work we extend the notion of PFA to take supplementary information into account for improving its predictions. Such information can be a multidimensional signal like the main input to PFA, but is regarded external. That means it won’t participate in the feature extraction - no features get extracted or composed of it. Features will be exclusively extracted from the main input such that they are most predictable based on themselves and the supplementary information. We refer to this enhanced PFA as PFAx (PFA extended). Even more important than improving prediction quality is to observe the effect of supplementary information on feature selection. PFAx transparently provides insight how the supplementary information adds to prediction quality and whether it is valuable at all. Finally we show how to invert that relation and can generate the supplementary information such that it would yield a certain desired outcome of the main signal. We apply this to a setting inspired by reinforcement learning and let the algorithm learn how to control an agent in an environment. With this method it is feasible to locally optimize the agent’s state, i.e. reach a certain goal that is near enough. We are preparing a follow-up paper that extends this method such that also global optimization is feasible.
Tasks	Dimensionality Reduction, Feature Selection
Published	2017-12-02
URL	http://arxiv.org/abs/1712.00634v1
PDF	http://arxiv.org/pdf/1712.00634v1.pdf
PWC	https://paperswithcode.com/paper/pfax-predictable-feature-analysis-to-perform
Repo
Framework

Network Coding Based Evolutionary Network Formation for Dynamic Wireless Networks


Title	Network Coding Based Evolutionary Network Formation for Dynamic Wireless Networks
Authors	Minhae Kwon, Hyunggon Park
Abstract	In this paper, we aim to find a robust network formation strategy that can adaptively evolve the network topology against network dynamics in a distributed manner. We consider a network coding deployed wireless ad hoc network where source nodes are connected to terminal nodes with the help of intermediate nodes. We show that mixing operations in network coding can induce packet anonymity that allows the inter-connections in a network to be decoupled. This enables each intermediate node to consider complex network inter-connections as a node-environment interaction such that the Markov decision process (MDP) can be employed at each intermediate node. The optimal policy that can be obtained by solving the MDP provides each node with optimal amount of changes in transmission range given network dynamics (e.g., the number of nodes in the range and channel condition). Hence, the network can be adaptively and optimally evolved by responding to the network dynamics. The proposed strategy is used to maximize long-term utility, which is achieved by considering both current network conditions and future network dynamics. We define the utility of an action to include network throughput gain and the cost of transmission power. We show that the resulting network of the proposed strategy eventually converges to stationary networks, which maintain the states of the nodes. Moreover, we propose to determine initial transmission ranges and initial network topology that can expedite the convergence of the proposed algorithm. Our simulation results confirm that the proposed strategy builds a network which adaptively changes its topology in the presence of network dynamics. Moreover, the proposed strategy outperforms existing strategies in terms of system goodput and successful connectivity ratio.
Tasks
Published	2017-12-02
URL	http://arxiv.org/abs/1712.00635v2
PDF	http://arxiv.org/pdf/1712.00635v2.pdf
PWC	https://paperswithcode.com/paper/network-coding-based-evolutionary-network
Repo
Framework

Improvised Comedy as a Turing Test


Title	Improvised Comedy as a Turing Test
Authors	Kory Wallace Mathewson, Piotr Mirowski
Abstract	The best improvisational theatre actors can make any scene partner, of any skill level or ability, appear talented and proficient in the art form, and thus “make them shine”. To challenge this improvisational paradigm, we built an artificial intelligence (AI) trained to perform live shows alongside human actors for human audiences. Over the course of 30 performances to a combined audience of almost 3000 people, we have refined theatrical games which involve combinations of human and (at times, adversarial) AI actors. We have developed specific scene structures to include audience participants in interesting ways. Finally, we developed a complete show structure that submitted the audience to a Turing test and observed their suspension of disbelief, which we believe is key for human/non-human theatre co-creation.
Tasks
Published	2017-11-23
URL	http://arxiv.org/abs/1711.08819v2
PDF	http://arxiv.org/pdf/1711.08819v2.pdf
PWC	https://paperswithcode.com/paper/improvised-comedy-as-a-turing-test
Repo
Framework

Latent hypernet: Exploring all Layers from Convolutional Neural Networks


Title	Latent hypernet: Exploring all Layers from Convolutional Neural Networks
Authors	Artur Jordao, Ricardo Kloss, William Robson Schwartz
Abstract	Since Convolutional Neural Networks (ConvNets) are able to simultaneously learn features and classifiers to discriminate different categories of activities, recent works have employed ConvNets approaches to perform human activity recognition (HAR) based on wearable sensors, allowing the removal of expensive human work and expert knowledge. However, these approaches have their power of discrimination limited mainly by the large number of parameters that compose the network and the reduced number of samples available for training. Inspired by this, we propose an accurate and robust approach, referred to as Latent HyperNet (LHN). The LHN uses feature maps from early layers (hyper) and projects them, individually, onto a low dimensionality space (latent). Then, these latent features are concatenated and presented to a classifier. To demonstrate the robustness and accuracy of the LHN, we evaluate it using four different networks architectures in five publicly available HAR datasets based on wearable sensors, which vary in the sampling rate and number of activities. Our experiments demonstrate that the proposed LHN is able to produce rich information, improving the results regarding the original ConvNets. Furthermore, the method outperforms existing state-of-the-art methods.
Tasks	Activity Recognition, Human Activity Recognition
Published	2017-11-07
URL	http://arxiv.org/abs/1711.02652v2
PDF	http://arxiv.org/pdf/1711.02652v2.pdf
PWC	https://paperswithcode.com/paper/latent-hypernet-exploring-all-layers-from
Repo
Framework

A unified treatment of multiple testing with prior knowledge using the p-filter


Title	A unified treatment of multiple testing with prior knowledge using the p-filter
Authors	Aaditya Ramdas, Rina Foygel Barber, Martin J. Wainwright, Michael I. Jordan
Abstract	There is a significant literature on methods for incorporating knowledge into multiple testing procedures so as to improve their power and precision. Some common forms of prior knowledge include (a) beliefs about which hypotheses are null, modeled by non-uniform prior weights; (b) differing importances of hypotheses, modeled by differing penalties for false discoveries; (c) multiple arbitrary partitions of the hypotheses into (possibly overlapping) groups; and (d) knowledge of independence, positive or arbitrary dependence between hypotheses or groups, suggesting the use of more aggressive or conservative procedures. We present a unified algorithmic framework called p-filter for global null testing and false discovery rate (FDR) control that allows the scientist to incorporate all four types of prior knowledge (a)-(d) simultaneously, recovering a variety of known algorithms as special cases.
Tasks
Published	2017-03-18
URL	https://arxiv.org/abs/1703.06222v5
PDF	https://arxiv.org/pdf/1703.06222v5.pdf
PWC	https://paperswithcode.com/paper/a-unified-treatment-of-multiple-testing-with
Repo
Framework

Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU


Title	Solving the Uncapacitated Single Allocation p-Hub Median Problem on GPU
Authors	Abdelhamid Benaini, Achraf Berrajaa, Jaouad Boukachour, Mustapha Oudani
Abstract	A parallel genetic algorithm (GA) implemented on GPU clusters is proposed to solve the Uncapacitated Single Allocation p-Hub Median problem. The GA uses binary and integer encoding and genetic operators adapted to this problem. Our GA is improved by generated initial solution with hubs located at middle nodes. The obtained experimental results are compared with the best known solutions on all benchmarks on instances up to 1000 nodes. Furthermore, we solve our own randomly generated instances up to 6000 nodes. Our approach outperforms most well-known heuristics in terms of solution quality and time execution and it allows hitherto unsolved problems to be solved.
Tasks
Published	2017-04-14
URL	http://arxiv.org/abs/1704.06258v1
PDF	http://arxiv.org/pdf/1704.06258v1.pdf
PWC	https://paperswithcode.com/paper/solving-the-uncapacitated-single-allocation-p
Repo
Framework

Simultaneous Joint and Object Trajectory Templates for Human Activity Recognition from 3-D Data


Title	Simultaneous Joint and Object Trajectory Templates for Human Activity Recognition from 3-D Data
Authors	Saeed Ghodsi, Hoda Mohammadzade, Erfan Korki
Abstract	The availability of low-cost range sensors and the development of relatively robust algorithms for the extraction of skeleton joint locations have inspired many researchers to develop human activity recognition methods using the 3-D data. In this paper, an effective method for the recognition of human activities from the normalized joint trajectories is proposed. We represent the actions as multidimensional signals and introduce a novel method for generating action templates by averaging the samples in a “dynamic time” sense. Then in order to deal with the variations in the speed and style of performing actions, we warp the samples to the action templates by an efficient algorithm and employ wavelet filters to extract meaningful spatiotemporal features. The proposed method is also capable of modeling the human-object interactions, by performing the template generation and temporal warping procedure via the joint and object trajectories simultaneously. The experimental evaluation on several challenging datasets demonstrates the effectiveness of our method compared to the state-of-the-arts.
Tasks	Activity Recognition, Human Activity Recognition, Human-Object Interaction Detection
Published	2017-11-05
URL	http://arxiv.org/abs/1711.01589v1
PDF	http://arxiv.org/pdf/1711.01589v1.pdf
PWC	https://paperswithcode.com/paper/simultaneous-joint-and-object-trajectory
Repo
Framework

The Stochastic Firefighter Problem


Title	The Stochastic Firefighter Problem
Authors	Guy Tennenholtz, Constantine Caramanis, Shie Mannor
Abstract	The dynamics of infectious diseases spread is crucial in determining their risk and offering ways to contain them. We study sequential vaccination of individuals in networks. In the original (deterministic) version of the Firefighter problem, a fire breaks out at some node of a given graph. At each time step, b nodes can be protected by a firefighter and then the fire spreads to all unprotected neighbors of the nodes on fire. The process ends when the fire can no longer spread. We extend the Firefighter problem to a probabilistic setting, where the infection is stochastic. We devise a simple policy that only vaccinates neighbors of infected nodes and is optimal on regular trees and on general graphs for a sufficiently large budget. We derive methods for calculating upper and lower bounds of the expected number of infected individuals, as well as provide estimates on the budget needed for containment in expectation. We calculate these explicitly on trees, d-dimensional grids, and Erd\H{o}s R'{e}nyi graphs. Finally, we construct a state-dependent budget allocation strategy and demonstrate its superiority over constant budget allocation on real networks following a first order acquaintance vaccination policy.
Tasks
Published	2017-11-22
URL	http://arxiv.org/abs/1711.08237v1
PDF	http://arxiv.org/pdf/1711.08237v1.pdf
PWC	https://paperswithcode.com/paper/the-stochastic-firefighter-problem
Repo
Framework

Learning an attention model in an artificial visual system


Title	Learning an attention model in an artificial visual system
Authors	Alon Hazan, Yuval Harel, Ron Meir
Abstract	The Human visual perception of the world is of a large fixed image that is highly detailed and sharp. However, receptor density in the retina is not uniform: a small central region called the fovea is very dense and exhibits high resolution, whereas a peripheral region around it has much lower spatial resolution. Thus, contrary to our perception, we are only able to observe a very small region around the line of sight with high resolution. The perception of a complete and stable view is aided by an attention mechanism that directs the eyes to the numerous points of interest within the scene. The eyes move between these targets in quick, unconscious movements, known as “saccades”. Once a target is centered at the fovea, the eyes fixate for a fraction of a second while the visual system extracts the necessary information. An artificial visual system was built based on a fully recurrent neural network set within a reinforcement learning protocol, and learned to attend to regions of interest while solving a classification task. The model is consistent with several experimentally observed phenomena, and suggests novel predictions.
Tasks
Published	2017-01-24
URL	http://arxiv.org/abs/1701.07398v1
PDF	http://arxiv.org/pdf/1701.07398v1.pdf
PWC	https://paperswithcode.com/paper/learning-an-attention-model-in-an-artificial
Repo
Framework