Paper Group ANR 714
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture. An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images. Vision-based deep execution monitoring. Theoretical and Experimental Analysis of the Canadian Traveler Problem. Alternatin …
Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture
| Title | Deep Anticipation: Light Weight Intelligent Mobile Sensing in IoT by Recurrent Architecture |
| Authors | Guang Chen, Shu Liu, Kejia Ren, Zhongnan Qu, Changhong Fu, Gereon Hinz, Alois Knoll |
| Abstract | The rapid growth of IoT era is shaping the future of mobile services. Advanced communication technology enables a heterogeneous connectivity where mobile devices broadcast information to everything. Mobile applications such as robotics and vehicles connecting to cloud and surroundings transfer the short-range on-board sensor perception system to long-range mobile-sensing perception system. However, the mobile sensing perception brings new challenges for how to efficiently analyze and intelligently interpret the deluge of IoT data in mission- critical services. In this article, we model the challenges as latency, packet loss and measurement noise which severely deteriorate the reliability and quality of IoT data. We integrate the artificial intelligence into IoT to tackle these challenges. We propose a novel architecture that leverages recurrent neural networks (RNN) and Kalman filtering to anticipate motions and interac- tions between objects. The basic idea is to learn environment dynamics by recurrent networks. To improve the robustness of IoT communication, we use the idea of Kalman filtering and deploy a prediction and correction step. In this way, the architecture learns to develop a biased belief between prediction and measurement in the different situation. We demonstrate our approach with synthetic and real-world datasets with noise that mimics the challenges of IoT communications. Our method brings a new level of IoT intelligence. It is also lightweight compared to other state-of-the-art convolutional recurrent architecture and is ideally suitable for the resource-limited mobile applications. |
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| Published | 2017-12-06 |
| URL | http://arxiv.org/abs/1801.01444v2 |
| http://arxiv.org/pdf/1801.01444v2.pdf | |
| PWC | https://paperswithcode.com/paper/deep-anticipation-light-weight-intelligent |
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An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images
| Title | An unsupervised bayesian approach for the joint reconstruction and classification of cutaneous reflectance confocal microscopy images |
| Authors | Abdelghafour Halimi, Hadj Batatia, Jimmy Le Digabel, Gwendal Josse, Jean-Yves Tourneret |
| Abstract | This paper studies a new Bayesian algorithm for the joint reconstruction and classification of reflectance confocal microscopy (RCM) images, with application to the identification of human skin lentigo. The proposed Bayesian approach takes advantage of the distribution of the multiplicative speckle noise affecting the true reflectivity of these images and of appropriate priors for the unknown model parameters. A Markov chain Monte Carlo (MCMC) algorithm is proposed to jointly estimate the model parameters and the image of true reflectivity while classifying images according to the distribution of their reflectivity. Precisely, a Metropolis-whitin-Gibbs sampler is investigated to sample the posterior distribution of the Bayesian model associated with RCM images and to build estimators of its parameters, including labels indicating the class of each RCM image. The resulting algorithm is applied to synthetic data and to real images from a clinical study containing healthy and lentigo patients. |
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| Published | 2017-03-04 |
| URL | http://arxiv.org/abs/1703.01444v1 |
| http://arxiv.org/pdf/1703.01444v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-unsupervised-bayesian-approach-for-the |
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Vision-based deep execution monitoring
| Title | Vision-based deep execution monitoring |
| Authors | Francesco Puja, Simone Grazioso, Antonio Tammaro, Valsmis Ntouskos, Marta Sanzari, Fiora Pirri |
| Abstract | Execution monitor of high-level robot actions can be effectively improved by visual monitoring the state of the world in terms of preconditions and postconditions that hold before and after the execution of an action. Furthermore a policy for searching where to look at, either for verifying the relations that specify the pre and postconditions or to refocus in case of a failure, can tremendously improve the robot execution in an uncharted environment. It is now possible to strongly rely on visual perception in order to make the assumption that the environment is observable, by the amazing results of deep learning. In this work we present visual execution monitoring for a robot executing tasks in an uncharted Lab environment. The execution monitor interacts with the environment via a visual stream that uses two DCNN for recognizing the objects the robot has to deal with and manipulate, and a non-parametric Bayes estimation to discover the relations out of the DCNN features. To recover from lack of focus and failures due to missed objects we resort to visual search policies via deep reinforcement learning. |
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| Published | 2017-09-29 |
| URL | http://arxiv.org/abs/1709.10507v1 |
| http://arxiv.org/pdf/1709.10507v1.pdf | |
| PWC | https://paperswithcode.com/paper/vision-based-deep-execution-monitoring |
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Theoretical and Experimental Analysis of the Canadian Traveler Problem
| Title | Theoretical and Experimental Analysis of the Canadian Traveler Problem |
| Authors | Doron Zarchy |
| Abstract | Devising an optimal strategy for navigation in a partially observable environment is one of the key objectives in AI. One of the problem in this context is the Canadian Traveler Problem (CTP). CTP is a navigation problem where an agent is tasked to travel from source to target in a partially observable weighted graph, whose edge might be blocked with a certain probability and observing such blockage occurs only when reaching upon one of the edges end points. The goal is to find a strategy that minimizes the expected travel cost. The problem is known to be P$#$ hard. In this work we study the CTP theoretically and empirically. First, we study the Dep-CTP, a CTP variant we introduce which assumes dependencies between the edges status. We show that Dep-CTP is intractable, and further we analyze two of its subclasses on disjoint paths graph. Second, we develop a general algorithm Gen-PAO that optimally solve the CTP. Gen-PAO is capable of solving two other types of CTP called Sensing-CTP and Expensive-Edges CTP. Since the CTP is intractable, Gen-PAO use some pruning methods to reduce the space search for the optimal solution. We also define some variants of Gen-PAO, compare their performance and show some benefits of Gen-PAO over existing work. |
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| Published | 2017-02-22 |
| URL | http://arxiv.org/abs/1702.07001v1 |
| http://arxiv.org/pdf/1702.07001v1.pdf | |
| PWC | https://paperswithcode.com/paper/theoretical-and-experimental-analysis-of-the |
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Alternating minimization and alternating descent over nonconvex sets
| Title | Alternating minimization and alternating descent over nonconvex sets |
| Authors | Wooseok Ha, Rina Foygel Barber |
| Abstract | We analyze the performance of alternating minimization for loss functions optimized over two variables, where each variable may be restricted to lie in some potentially nonconvex constraint set. This type of setting arises naturally in high-dimensional statistics and signal processing, where the variables often reflect different structures or components within the signals being considered. Our analysis relies on the notion of local concavity coefficients, which has been proposed in Barber and Ha to measure and quantify the concavity of a general nonconvex set. Our results further reveal important distinctions between alternating and non-alternating methods. Since computing the alternating minimization steps may not be tractable for some problems, we also consider an inexact version of the algorithm and provide a set of sufficient conditions to ensure fast convergence of the inexact algorithms. We demonstrate our framework on several examples, including low rank + sparse decomposition and multitask regression, and provide numerical experiments to validate our theoretical results. |
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| Published | 2017-09-13 |
| URL | http://arxiv.org/abs/1709.04451v3 |
| http://arxiv.org/pdf/1709.04451v3.pdf | |
| PWC | https://paperswithcode.com/paper/alternating-minimization-and-alternating |
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Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization
| Title | Behavior-based Navigation of Mobile Robot in Unknown Environments Using Fuzzy Logic and Multi-Objective Optimization |
| Authors | Thi Thanh Van Nguyen, Manh Duong Phung, Quang Vinh Tran |
| Abstract | This study proposes behavior-based navigation architecture, named BBFM, to deal with the problem of navigating the mobile robot in unknown environments in the presence of obstacles and local minimum regions. In the architecture, the complex navigation task is split into principal sub-tasks or behaviors. Each behavior is implemented by a fuzzy controller and executed independently to deal with a specific problem of navigation. The fuzzy controller is modified to contain only the fuzzification and inference procedures so that its output is a membership function representing the behavior’s objective. The membership functions of all controllers are then used as the objective functions for a multi-objective optimization process to coordinate all behaviors. The result of this process is an overall control signal, which is Pareto-optimal, used to control the robot. A number of simulations, comparisons, and experiments were conducted. The results show that the proposed architecture outperforms some popular behavior-based architectures in term of accuracy, smoothness, traveled distance, and time response. |
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| Published | 2017-03-09 |
| URL | http://arxiv.org/abs/1703.03161v1 |
| http://arxiv.org/pdf/1703.03161v1.pdf | |
| PWC | https://paperswithcode.com/paper/behavior-based-navigation-of-mobile-robot-in |
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Regularization via Mass Transportation
| Title | Regularization via Mass Transportation |
| Authors | Soroosh Shafieezadeh-Abadeh, Daniel Kuhn, Peyman Mohajerin Esfahani |
| Abstract | The goal of regression and classification methods in supervised learning is to minimize the empirical risk, that is, the expectation of some loss function quantifying the prediction error under the empirical distribution. When facing scarce training data, overfitting is typically mitigated by adding regularization terms to the objective that penalize hypothesis complexity. In this paper we introduce new regularization techniques using ideas from distributionally robust optimization, and we give new probabilistic interpretations to existing techniques. Specifically, we propose to minimize the worst-case expected loss, where the worst case is taken over the ball of all (continuous or discrete) distributions that have a bounded transportation distance from the (discrete) empirical distribution. By choosing the radius of this ball judiciously, we can guarantee that the worst-case expected loss provides an upper confidence bound on the loss on test data, thus offering new generalization bounds. We prove that the resulting regularized learning problems are tractable and can be tractably kernelized for many popular loss functions. We validate our theoretical out-of-sample guarantees through simulated and empirical experiments. |
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| Published | 2017-10-27 |
| URL | https://arxiv.org/abs/1710.10016v3 |
| https://arxiv.org/pdf/1710.10016v3.pdf | |
| PWC | https://paperswithcode.com/paper/regularization-via-mass-transportation |
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Efficient Feature Matching by Progressive Candidate Search
| Title | Efficient Feature Matching by Progressive Candidate Search |
| Authors | Sehyung Lee, Jongwoo Lim, Il Hong Suh |
| Abstract | We present a novel feature matching algorithm that systematically utilizes the geometric properties of features such as position, scale, and orientation, in addition to the conventional descriptor vectors. In challenging scenes with the presence of repetitive patterns or with a large viewpoint change, it is hard to find the correct correspondences using feature descriptors only, since the descriptor distances of the correct matches may not be the least among the candidates due to appearance changes. Assuming that the layout of the nearby features does not changed much, we propose the bidirectional transfer measure to gauge the geometric consistency of a pair of feature correspondences. The feature matching problem is formulated as a Markov random field (MRF) which uses descriptor distances and relative geometric similarities together. The unmatched features are explicitly modeled in the MRF to minimize its negative impact. For speed and stability, instead of solving the MRF on the entire features at once, we start with a small set of confident feature matches, and then progressively search the candidates in nearby features and expand the MRF with them. Experimental comparisons show that the proposed algorithm finds better feature correspondences, i.e. more matches with higher inlier ratio, in many challenging scenes with much lower computational cost than the state-of-the-art algorithms. |
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| Published | 2017-01-20 |
| URL | http://arxiv.org/abs/1701.05676v1 |
| http://arxiv.org/pdf/1701.05676v1.pdf | |
| PWC | https://paperswithcode.com/paper/efficient-feature-matching-by-progressive |
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Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks
| Title | Doppler-Radar Based Hand Gesture Recognition System Using Convolutional Neural Networks |
| Authors | Jiajun Zhang, Jinkun Tao, Jiangtao Huangfu, Zhiguo Shi |
| Abstract | Hand gesture recognition has long been a hot topic in human computer interaction. Traditional camera-based hand gesture recognition systems cannot work properly under dark circumstances. In this paper, a Doppler Radar based hand gesture recognition system using convolutional neural networks is proposed. A cost-effective Doppler radar sensor with dual receiving channels at 5.8GHz is used to acquire a big database of four standard gestures. The received hand gesture signals are then processed with time-frequency analysis. Convolutional neural networks are used to classify different gestures. Experimental results verify the effectiveness of the system with an accuracy of 98%. Besides, related factors such as recognition distance and gesture scale are investigated. |
| Tasks | Gesture Recognition, Hand Gesture Recognition, Hand-Gesture Recognition |
| Published | 2017-11-07 |
| URL | http://arxiv.org/abs/1711.02254v3 |
| http://arxiv.org/pdf/1711.02254v3.pdf | |
| PWC | https://paperswithcode.com/paper/doppler-radar-based-hand-gesture-recognition |
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A Novel Neural Network Model Specified for Representing Logical Relations
| Title | A Novel Neural Network Model Specified for Representing Logical Relations |
| Authors | Gang Wang |
| Abstract | With computers to handle more and more complicated things in variable environments, it becomes an urgent requirement that the artificial intelligence has the ability of automatic judging and deciding according to numerous specific conditions so as to deal with the complicated and variable cases. ANNs inspired by brain is a good candidate. However, most of current numeric ANNs are not good at representing logical relations because these models still try to represent logical relations in the form of ratio based on functional approximation. On the other hand, researchers have been trying to design novel neural network models to make neural network model represent logical relations. In this work, a novel neural network model specified for representing logical relations is proposed and applied. New neurons and multiple kinds of links are defined. Inhibitory links are introduced besides exciting links. Different from current numeric ANNs, one end of an inhibitory link connects an exciting link rather than a neuron. Inhibitory links inhibit the connected exciting links conditionally to make this neural network model represent logical relations correctly. This model can simulate the operations of Boolean logic gates, and construct complex logical relations with the advantages of simpler neural network structures than recent works in this area. This work provides some ideas to make neural networks represent logical relations more directly and efficiently, and the model could be used as the complement to current numeric ANN to deal with logical issues and expand the application areas of ANN. |
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| Published | 2017-08-02 |
| URL | http://arxiv.org/abs/1708.00580v1 |
| http://arxiv.org/pdf/1708.00580v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-novel-neural-network-model-specified-for |
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New Methods for Metadata Extraction from Scientific Literature
| Title | New Methods for Metadata Extraction from Scientific Literature |
| Authors | Dominika Tkaczyk |
| Abstract | Within the past few decades we have witnessed digital revolution, which moved scholarly communication to electronic media and also resulted in a substantial increase in its volume. Nowadays keeping track with the latest scientific achievements poses a major challenge for the researchers. Scientific information overload is a severe problem that slows down scholarly communication and knowledge propagation across the academia. Modern research infrastructures facilitate studying scientific literature by providing intelligent search tools, proposing similar and related documents, visualizing citation and author networks, assessing the quality and impact of the articles, and so on. In order to provide such high quality services the system requires the access not only to the text content of stored documents, but also to their machine-readable metadata. Since in practice good quality metadata is not always available, there is a strong demand for a reliable automatic method of extracting machine-readable metadata directly from source documents. This research addresses these problems by proposing an automatic, accurate and flexible algorithm for extracting wide range of metadata directly from scientific articles in born-digital form. Extracted information includes basic document metadata, structured full text and bibliography section. Designed as a universal solution, proposed algorithm is able to handle a vast variety of publication layouts with high precision and thus is well-suited for analyzing heterogeneous document collections. This was achieved by employing supervised and unsupervised machine-learning algorithms trained on large, diverse datasets. The evaluation we conducted showed good performance of proposed metadata extraction algorithm. The comparison with other similar solutions also proved our algorithm performs better than competition for most metadata types. |
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| Published | 2017-10-27 |
| URL | https://arxiv.org/abs/1710.10201v1 |
| https://arxiv.org/pdf/1710.10201v1.pdf | |
| PWC | https://paperswithcode.com/paper/new-methods-for-metadata-extraction-from |
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NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets
| Title | NPC: Neighbors Progressive Competition Algorithm for Classification of Imbalanced Data Sets |
| Authors | Soroush Saryazdi, Bahareh Nikpour, Hossein Nezamabadi-pour |
| Abstract | Learning from many real-world datasets is limited by a problem called the class imbalance problem. A dataset is imbalanced when one class (the majority class) has significantly more samples than the other class (the minority class). Such datasets cause typical machine learning algorithms to perform poorly on the classification task. To overcome this issue, this paper proposes a new approach Neighbors Progressive Competition (NPC) for classification of imbalanced datasets. Whilst the proposed algorithm is inspired by weighted k-Nearest Neighbor (k-NN) algorithms, it has major differences from them. Unlike k- NN, NPC does not limit its decision criteria to a preset number of nearest neighbors. In contrast, NPC considers progressively more neighbors of the query sample in its decision making until the sum of grades for one class is much higher than the other classes. Furthermore, NPC uses a novel method for grading the training samples to compensate for the imbalance issue. The grades are calculated using both local and global information. In brief, the contribution of this paper is an entirely new classifier for handling the imbalance issue effectively without any manually-set parameters or any need for expert knowledge. Experimental results compare the proposed approach with five representative algorithms applied to fifteen imbalanced datasets and illustrate this algorithms effectiveness. |
| Tasks | Decision Making |
| Published | 2017-11-29 |
| URL | http://arxiv.org/abs/1711.10934v1 |
| http://arxiv.org/pdf/1711.10934v1.pdf | |
| PWC | https://paperswithcode.com/paper/npc-neighbors-progressive-competition |
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Learning Compressible 360° Video Isomers
| Title | Learning Compressible 360° Video Isomers |
| Authors | Yu-Chuan Su, Kristen Grauman |
| Abstract | Standard video encoders developed for conventional narrow field-of-view video are widely applied to 360{\deg} video as well, with reasonable results. However, while this approach commits arbitrarily to a projection of the spherical frames, we observe that some orientations of a 360{\deg} video, once projected, are more compressible than others. We introduce an approach to predict the sphere rotation that will yield the maximal compression rate. Given video clips in their original encoding, a convolutional neural network learns the association between a clip’s visual content and its compressibility at different rotations of a cubemap projection. Given a novel video, our learning-based approach efficiently infers the most compressible direction in one shot, without repeated rendering and compression of the source video. We validate our idea on thousands of video clips and multiple popular video codecs. The results show that this untapped dimension of 360{\deg} compression has substantial potential–“good” rotations are typically 8-10% more compressible than bad ones, and our learning approach can predict them reliably 82% of the time. |
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| Published | 2017-12-12 |
| URL | http://arxiv.org/abs/1712.04083v1 |
| http://arxiv.org/pdf/1712.04083v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-compressible-360-video-isomers |
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Convolutional Phase Retrieval via Gradient Descent
| Title | Convolutional Phase Retrieval via Gradient Descent |
| Authors | Qing Qu, Yuqian Zhang, Yonina C. Eldar, John Wright |
| Abstract | We study the convolutional phase retrieval problem, of recovering an unknown signal $\mathbf x \in \mathbb C^n $ from $m$ measurements consisting of the magnitude of its cyclic convolution with a given kernel $\mathbf a \in \mathbb C^m $. This model is motivated by applications such as channel estimation, optics, and underwater acoustic communication, where the signal of interest is acted on by a given channel/filter, and phase information is difficult or impossible to acquire. We show that when $\mathbf a$ is random and the number of observations $m$ is sufficiently large, with high probability $\mathbf x$ can be efficiently recovered up to a global phase shift using a combination of spectral initialization and generalized gradient descent. The main challenge is coping with dependencies in the measurement operator. We overcome this challenge by using ideas from decoupling theory, suprema of chaos processes and the restricted isometry property of random circulant matrices, and recent analysis of alternating minimization methods. |
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| Published | 2017-12-03 |
| URL | https://arxiv.org/abs/1712.00716v3 |
| https://arxiv.org/pdf/1712.00716v3.pdf | |
| PWC | https://paperswithcode.com/paper/convolutional-phase-retrieval-via-gradient |
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The Do’s and Don’ts for CNN-based Face Verification
| Title | The Do’s and Don’ts for CNN-based Face Verification |
| Authors | Ankan Bansal, Carlos Castillo, Rajeev Ranjan, Rama Chellappa |
| Abstract | While the research community appears to have developed a consensus on the methods of acquiring annotated data, design and training of CNNs, many questions still remain to be answered. In this paper, we explore the following questions that are critical to face recognition research: (i) Can we train on still images and expect the systems to work on videos? (ii) Are deeper datasets better than wider datasets? (iii) Does adding label noise lead to improvement in performance of deep networks? (iv) Is alignment needed for face recognition? We address these questions by training CNNs using CASIA-WebFace, UMDFaces, and a new video dataset and testing on YouTube- Faces, IJB-A and a disjoint portion of UMDFaces datasets. Our new data set, which will be made publicly available, has 22,075 videos and 3,735,476 human annotated frames extracted from them. |
| Tasks | Face Recognition, Face Verification |
| Published | 2017-05-21 |
| URL | http://arxiv.org/abs/1705.07426v2 |
| http://arxiv.org/pdf/1705.07426v2.pdf | |
| PWC | https://paperswithcode.com/paper/the-dos-and-donts-for-cnn-based-face |
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