July 28, 2019

3208 words 16 mins read

Paper Group ANR 162

Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators. Human Action Forecasting by Learning Task Grammars. Analyzing Approximate Value Iteration Algorithms. Towards an Arabic-English Machine-Translation Based on Semantic Web. Exposing Computer Generated Images by Using Deep Convolutional Neural Networks. P …

Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators


Title	Structured Light Phase Measuring Profilometry Pattern Design for Binary Spatial Light Modulators
Authors	Daniel L. Lau, Yu Zhang, Kai Liu
Abstract	Structured light illumination is an active 3-D scanning technique based on projecting/capturing a set of striped patterns and measuring the warping of the patterns as they reflect off a target object’s surface. In the case of phase measuring profilometry (PMP), the projected patterns are composed of a rolling sinusoidal wave, but as a set of time-multiplexed patterns, PMP requires the target surface to remain motionless or for scanning to be performed at such high rates that any movement is small. But high speed scanning places a significant burden on the projector electronics to produce contone patterns inside of short exposure intervals. Binary patterns are, therefore, of great value, but converting contone patterns into binary comes with significant risk. As such, this paper introduces a contone-to-binary conversion algorithm for deriving binary patterns that best mimic their contone counterparts. Experimental results will show a greater than 3 times reduction in pattern noise over traditional halftoning procedures.
Tasks
Published	2017-06-08
URL	http://arxiv.org/abs/1706.02698v1
PDF	http://arxiv.org/pdf/1706.02698v1.pdf
PWC	https://paperswithcode.com/paper/structured-light-phase-measuring-profilometry
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Human Action Forecasting by Learning Task Grammars


Title	Human Action Forecasting by Learning Task Grammars
Authors	Tengda Han, Jue Wang, Anoop Cherian, Stephen Gould
Abstract	For effective human-robot interaction, it is important that a robotic assistant can forecast the next action a human will consider in a given task. Unfortunately, real-world tasks are often very long, complex, and repetitive; as a result forecasting is not trivial. In this paper, we propose a novel deep recurrent architecture that takes as input features from a two-stream Residual action recognition framework, and learns to estimate the progress of human activities from video sequences – this surrogate progress estimation task implicitly learns a temporal task grammar with respect to which activities can be localized and forecasted. To learn the task grammar, we propose a stacked LSTM based multi-granularity progress estimation framework that uses a novel cumulative Euclidean loss as objective. To demonstrate the effectiveness of our proposed architecture, we showcase experiments on two challenging robotic assistive tasks, namely (i) assembling an Ikea table from its constituents, and (ii) changing the tires of a car. Our results demonstrate that learning task grammars offers highly discriminative cues improving the forecasting accuracy by more than 9% over the baseline two-stream forecasting model, while also outperforming other competitive schemes.
Tasks	Temporal Action Localization
Published	2017-09-19
URL	http://arxiv.org/abs/1709.06391v1
PDF	http://arxiv.org/pdf/1709.06391v1.pdf
PWC	https://paperswithcode.com/paper/human-action-forecasting-by-learning-task
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Analyzing Approximate Value Iteration Algorithms


Title	Analyzing Approximate Value Iteration Algorithms
Authors	Arunselvan Ramaswamy, Shalabh Bhatnagar
Abstract	In this paper, we consider the stochastic iterative counterpart of the value iteration scheme wherein only noisy and possibly biased approximations of the Bellman operator are available. We call the aforementioned counterpart as the approximate value iteration (AVI) algorithm. The structure of AVI accounts for implementations with biased function approximations of the Bellman operator and sampling errors. This is pertinent since value iteration is combined with neural networks, which are used to approximate the Bellman operator, to solve complex problems that are susceptible to Bellman’s curse of dimensionality. Further, instead of taking an expectation to calculate the Bellman operator, one generally uses samples. We present verifiable sufficient conditions under which AVI is stable (almost surely bounded) and converges to a fixed point of the approximate Bellman operator. We show that AVI can also be used in more general circumstances, i.e., for finding fixed points of contractive set-valued maps. To ensure the stability of AVI, we present three different yet related set of sufficient conditions that are based on the existence of an appropriate Lyapunov function. These Lyapunov function based conditions are easily verifiable and new to the literature. The verifiability is enhanced by the fact that a recipe for the construction of the necessary Lyapunov function is also provided. Finally, we show that the stability analysis of AVI can be readily extended to the general case of set-valued stochastic approximations.
Tasks
Published	2017-09-14
URL	https://arxiv.org/abs/1709.04673v4
PDF	https://arxiv.org/pdf/1709.04673v4.pdf
PWC	https://paperswithcode.com/paper/analysis-of-set-valued-stochastic
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Towards an Arabic-English Machine-Translation Based on Semantic Web


Title	Towards an Arabic-English Machine-Translation Based on Semantic Web
Authors	Neama Abdulaziz Dahan, Fadl Mutaher Ba-Alwi, Ibrahim Ahmed Al-Baltah, Ghaleb H. Al-gapheri
Abstract	Communication tools make the world like a small village and as a consequence people can contact with others who are from different societies or who speak different languages. This communication cannot happen effectively without Machine Translation because they can be found anytime and everywhere. There are a number of studies that have developed Machine Translation for the English language with so many other languages except the Arabic it has not been considered yet. Therefore we aim to highlight a roadmap for our proposed translation machine to provide an enhanced Arabic English translation based on Semantic.
Tasks	Machine Translation
Published	2017-09-14
URL	http://arxiv.org/abs/1709.04682v1
PDF	http://arxiv.org/pdf/1709.04682v1.pdf
PWC	https://paperswithcode.com/paper/towards-an-arabic-english-machine-translation
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Exposing Computer Generated Images by Using Deep Convolutional Neural Networks


Title	Exposing Computer Generated Images by Using Deep Convolutional Neural Networks
Authors	Edmar R. S. de Rezende, Guilherme C. S. Ruppert, Antonio Theophilo, Tiago Carvalho
Abstract	The recent computer graphics developments have upraised the quality of the generated digital content, astonishing the most skeptical viewer. Games and movies have taken advantage of this fact but, at the same time, these advances have brought serious negative impacts like the ones yielded by fakeimages produced with malicious intents. Digital artists can compose artificial images capable of deceiving the great majority of people, turning this into a very dangerous weapon in a timespan currently know as Fake News/Post-Truth” Era. In this work, we propose a new approach for dealing with the problem of detecting computer generated images, through the application of deep convolutional networks and transfer learning techniques. We start from Residual Networks and develop different models adapted to the binary problem of identifying if an image was or not computer generated. Differently from the current state-of-the-art approaches, we don’t rely on hand-crafted features, but provide to the model the raw pixel information, achieving the same 0.97 of state-of-the-art methods with two main advantages: our methods show more stable results (depicted by lower variance) and eliminate the laborious and manual step of specialized features extraction and selection.
Tasks	Transfer Learning
Published	2017-11-28
URL	http://arxiv.org/abs/1711.10394v1
PDF	http://arxiv.org/pdf/1711.10394v1.pdf
PWC	https://paperswithcode.com/paper/exposing-computer-generated-images-by-using
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Proceedings of the Fifth Workshop on Proof eXchange for Theorem Proving


Title	Proceedings of the Fifth Workshop on Proof eXchange for Theorem Proving
Authors	Catherine Dubois, Bruno Woltzenlogel Paleo
Abstract	This volume of EPTCS contains the proceedings of the Fifth Workshop on Proof Exchange for Theorem Proving (PxTP 2017), held on September 23-24, 2017 as part of the Tableaux, FroCoS and ITP conferences in Brasilia, Brazil. The PxTP workshop series brings together researchers working on various aspects of communication, integration, and cooperation between reasoning systems and formalisms, with a special focus on proofs. The progress in computer-aided reasoning, both automated and interactive, during the past decades, made it possible to build deduction tools that are increasingly more applicable to a wider range of problems and are able to tackle larger problems progressively faster. In recent years, cooperation between such tools in larger systems has demonstrated the potential to reduce the amount of manual intervention. Cooperation between reasoning systems relies on availability of theoretical formalisms and practical tools to exchange problems, proofs, and models. The PxTP workshop series strives to encourage such cooperation by inviting contributions on all aspects of cooperation between reasoning tools, whether automatic or interactive.
Tasks	Automated Theorem Proving
Published	2017-12-04
URL	http://arxiv.org/abs/1712.00898v1
PDF	http://arxiv.org/pdf/1712.00898v1.pdf
PWC	https://paperswithcode.com/paper/proceedings-of-the-fifth-workshop-on-proof
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Solving Multi-Objective MDP with Lexicographic Preference: An application to stochastic planning with multiple quantile objective


Title	Solving Multi-Objective MDP with Lexicographic Preference: An application to stochastic planning with multiple quantile objective
Authors	Yan Li, Zhaohan Sun
Abstract	In most common settings of Markov Decision Process (MDP), an agent evaluate a policy based on expectation of (discounted) sum of rewards. However in many applications this criterion might not be suitable from two perspective: first, in risk aversion situation expectation of accumulated rewards is not robust enough, this is the case when distribution of accumulated reward is heavily skewed; another issue is that many applications naturally take several objective into consideration when evaluating a policy, for instance in autonomous driving an agent needs to balance speed and safety when choosing appropriate decision. In this paper, we consider evaluating a policy based on a sequence of quantiles it induces on a set of target states, our idea is to reformulate the original problem into a multi-objective MDP problem with lexicographic preference naturally defined. For computation of finding an optimal policy, we proposed an algorithm \textbf{FLMDP} that could solve general multi-objective MDP with lexicographic reward preference.
Tasks	Autonomous Driving
Published	2017-05-10
URL	http://arxiv.org/abs/1705.03597v1
PDF	http://arxiv.org/pdf/1705.03597v1.pdf
PWC	https://paperswithcode.com/paper/solving-multi-objective-mdp-with
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Constrained Deep Weak Supervision for Histopathology Image Segmentation


Title	Constrained Deep Weak Supervision for Histopathology Image Segmentation
Authors	Zhipeng Jia, Xingyi Huang, Eric I-Chao Chang, Yan Xu
Abstract	In this paper, we develop a new weakly-supervised learning algorithm to learn to segment cancerous regions in histopathology images. Our work is under a multiple instance learning framework (MIL) with a new formulation, deep weak supervision (DWS); we also propose an effective way to introduce constraints to our neural networks to assist the learning process. The contributions of our algorithm are threefold: (1) We build an end-to-end learning system that segments cancerous regions with fully convolutional networks (FCN) in which image-to-image weakly-supervised learning is performed. (2) We develop a deep week supervision formulation to exploit multi-scale learning under weak supervision within fully convolutional networks. (3) Constraints about positive instances are introduced in our approach to effectively explore additional weakly-supervised information that is easy to obtain and enjoys a significant boost to the learning process. The proposed algorithm, abbreviated as DWS-MIL, is easy to implement and can be trained efficiently. Our system demonstrates state-of-the-art results on large-scale histopathology image datasets and can be applied to various applications in medical imaging beyond histopathology images such as MRI, CT, and ultrasound images.
Tasks	Multiple Instance Learning, Semantic Segmentation
Published	2017-01-03
URL	http://arxiv.org/abs/1701.00794v1
PDF	http://arxiv.org/pdf/1701.00794v1.pdf
PWC	https://paperswithcode.com/paper/constrained-deep-weak-supervision-for
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Active One-shot Learning


Title	Active One-shot Learning
Authors	Mark Woodward, Chelsea Finn
Abstract	Recent advances in one-shot learning have produced models that can learn from a handful of labeled examples, for passive classification and regression tasks. This paper combines reinforcement learning with one-shot learning, allowing the model to decide, during classification, which examples are worth labeling. We introduce a classification task in which a stream of images are presented and, on each time step, a decision must be made to either predict a label or pay to receive the correct label. We present a recurrent neural network based action-value function, and demonstrate its ability to learn how and when to request labels. Through the choice of reward function, the model can achieve a higher prediction accuracy than a similar model on a purely supervised task, or trade prediction accuracy for fewer label requests.
Tasks	One-Shot Learning
Published	2017-02-21
URL	http://arxiv.org/abs/1702.06559v1
PDF	http://arxiv.org/pdf/1702.06559v1.pdf
PWC	https://paperswithcode.com/paper/active-one-shot-learning
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Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models


Title	Feature discovery and visualization of robot mission data using convolutional autoencoders and Bayesian nonparametric topic models
Authors	Genevieve Flaspohler, Nicholas Roy, Yogesh Girdhar
Abstract	The gap between our ability to collect interesting data and our ability to analyze these data is growing at an unprecedented rate. Recent algorithmic attempts to fill this gap have employed unsupervised tools to discover structure in data. Some of the most successful approaches have used probabilistic models to uncover latent thematic structure in discrete data. Despite the success of these models on textual data, they have not generalized as well to image data, in part because of the spatial and temporal structure that may exist in an image stream. We introduce a novel unsupervised machine learning framework that incorporates the ability of convolutional autoencoders to discover features from images that directly encode spatial information, within a Bayesian nonparametric topic model that discovers meaningful latent patterns within discrete data. By using this hybrid framework, we overcome the fundamental dependency of traditional topic models on rigidly hand-coded data representations, while simultaneously encoding spatial dependency in our topics without adding model complexity. We apply this model to the motivating application of high-level scene understanding and mission summarization for exploratory marine robots. Our experiments on a seafloor dataset collected by a marine robot show that the proposed hybrid framework outperforms current state-of-the-art approaches on the task of unsupervised seafloor terrain characterization.
Tasks	Scene Understanding, Topic Models
Published	2017-11-30
URL	http://arxiv.org/abs/1712.00028v1
PDF	http://arxiv.org/pdf/1712.00028v1.pdf
PWC	https://paperswithcode.com/paper/feature-discovery-and-visualization-of-robot
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Awareness improves problem-solving performance


Title	Awareness improves problem-solving performance
Authors	José F. Fontanari
Abstract	The brain’s self-monitoring of activities, including internal activities – a functionality that we refer to as awareness – has been suggested as a key element of consciousness. Here we investigate whether the presence of an inner-eye-like process (monitor) that supervises the activities of a number of subsystems (operative agents) engaged in the solution of a problem can improve the problem-solving efficiency of the system. The problem is to find the global maximum of a NK fitness landscape and the performance is measured by the time required to find that maximum. The operative agents explore blindly the fitness landscape and the monitor provides them with feedback on the quality (fitness) of the proposed solutions. This feedback is then used by the operative agents to bias their searches towards the fittest regions of the landscape. We find that a weak feedback between the monitor and the operative agents improves the performance of the system, regardless of the difficulty of the problem, which is gauged by the number of local maxima in the landscape. For easy problems (i.e., landscapes without local maxima), the performance improves monotonically as the feedback strength increases, but for difficult problems, there is an optimal value of the feedback strength beyond which the system performance degrades very rapidly.
Tasks
Published	2017-05-13
URL	http://arxiv.org/abs/1705.04885v1
PDF	http://arxiv.org/pdf/1705.04885v1.pdf
PWC	https://paperswithcode.com/paper/awareness-improves-problem-solving
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ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition


Title	ChineseFoodNet: A large-scale Image Dataset for Chinese Food Recognition
Authors	Xin Chen, Yu Zhu, Hua Zhou, Liang Diao, Dongyan Wang
Abstract	In this paper, we introduce a new and challenging large-scale food image dataset called “ChineseFoodNet”, which aims to automatically recognizing pictured Chinese dishes. Most of the existing food image datasets collected food images either from recipe pictures or selfie. In our dataset, images of each food category of our dataset consists of not only web recipe and menu pictures but photos taken from real dishes, recipe and menu as well. ChineseFoodNet contains over 180,000 food photos of 208 categories, with each category covering a large variations in presentations of same Chinese food. We present our efforts to build this large-scale image dataset, including food category selection, data collection, and data clean and label, in particular how to use machine learning methods to reduce manual labeling work that is an expensive process. We share a detailed benchmark of several state-of-the-art deep convolutional neural networks (CNNs) on ChineseFoodNet. We further propose a novel two-step data fusion approach referred as “TastyNet”, which combines prediction results from different CNNs with voting method. Our proposed approach achieves top-1 accuracies of 81.43% on the validation set and 81.55% on the test set, respectively. The latest dataset is public available for research and can be achieved at https://sites.google.com/view/chinesefoodnet.
Tasks	Food Recognition
Published	2017-05-08
URL	http://arxiv.org/abs/1705.02743v3
PDF	http://arxiv.org/pdf/1705.02743v3.pdf
PWC	https://paperswithcode.com/paper/chinesefoodnet-a-large-scale-image-dataset
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Native Language Identification using Stacked Generalization


Title	Native Language Identification using Stacked Generalization
Authors	Shervin Malmasi, Mark Dras
Abstract	Ensemble methods using multiple classifiers have proven to be the most successful approach for the task of Native Language Identification (NLI), achieving the current state of the art. However, a systematic examination of ensemble methods for NLI has yet to be conducted. Additionally, deeper ensemble architectures such as classifier stacking have not been closely evaluated. We present a set of experiments using three ensemble-based models, testing each with multiple configurations and algorithms. This includes a rigorous application of meta-classification models for NLI, achieving state-of-the-art results on three datasets from different languages. We also present the first use of statistical significance testing for comparing NLI systems, showing that our results are significantly better than the previous state of the art. We make available a collection of test set predictions to facilitate future statistical tests.
Tasks	Language Identification, Native Language Identification
Published	2017-03-19
URL	http://arxiv.org/abs/1703.06541v1
PDF	http://arxiv.org/pdf/1703.06541v1.pdf
PWC	https://paperswithcode.com/paper/native-language-identification-using-stacked
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Toward predictive machine learning for active vision


Title	Toward predictive machine learning for active vision
Authors	Emmanuel Daucé
Abstract	We develop a comprehensive description of the active inference framework, as proposed by Friston (2010), under a machine-learning compliant perspective. Stemming from a biological inspiration and the auto-encoding principles, the sketch of a cognitive architecture is proposed that should provide ways to implement estimation-oriented control policies. Computer simulations illustrate the effectiveness of the approach through a foveated inspection of the input data. The pros and cons of the control policy are analyzed in detail, showing interesting promises in terms of processing compression. Though optimizing future posterior entropy over the actions set is shown enough to attain locally optimal action selection, offline calculation using class-specific saliency maps is shown better for it saves processing costs through saccades pathways pre-processing, with a negligible effect on the recognition/compression rates.
Tasks
Published	2017-10-28
URL	http://arxiv.org/abs/1710.10460v3
PDF	http://arxiv.org/pdf/1710.10460v3.pdf
PWC	https://paperswithcode.com/paper/toward-predictive-machine-learning-for-active
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Loyalty in Online Communities


Title	Loyalty in Online Communities
Authors	William L. Hamilton, Justine Zhang, Cristian Danescu-Niculescu-Mizil, Dan Jurafsky, Jure Leskovec
Abstract	Loyalty is an essential component of multi-community engagement. When users have the choice to engage with a variety of different communities, they often become loyal to just one, focusing on that community at the expense of others. However, it is unclear how loyalty is manifested in user behavior, or whether loyalty is encouraged by certain community characteristics. In this paper we operationalize loyalty as a user-community relation: users loyal to a community consistently prefer it over all others; loyal communities retain their loyal users over time. By exploring this relation using a large dataset of discussion communities from Reddit, we reveal that loyalty is manifested in remarkably consistent behaviors across a wide spectrum of communities. Loyal users employ language that signals collective identity and engage with more esoteric, less popular content, indicating they may play a curational role in surfacing new material. Loyal communities have denser user-user interaction networks and lower rates of triadic closure, suggesting that community-level loyalty is associated with more cohesive interactions and less fragmentation into subgroups. We exploit these general patterns to predict future rates of loyalty. Our results show that a user’s propensity to become loyal is apparent from their first interactions with a community, suggesting that some users are intrinsically loyal from the very beginning.
Tasks
Published	2017-03-09
URL	http://arxiv.org/abs/1703.03386v3
PDF	http://arxiv.org/pdf/1703.03386v3.pdf
PWC	https://paperswithcode.com/paper/loyalty-in-online-communities
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