Paper Group ANR 48
Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication. WebSeg: Learning Semantic Segmentation from Web Searches. A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things. Quick Best Action Identification in Linear Bandit Problems. Document Chunking and Learning Objective Generation for …
Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication
| Title | Supervised and Semi-Supervised Deep Neural Networks for CSI-Based Authentication |
| Authors | Qian Wang, Hang Li, Zhi Chen, Dou Zhao, Shuang Ye, Jiansheng Cai |
| Abstract | From the viewpoint of physical-layer authentication, spoofing attacks can be foiled by checking channel state information (CSI). Existing CSI-based authentication algorithms mostly require a deep knowledge of the channel to deliver decent performance. In this paper, we investigate CSI-based authenticators that can spare the effort to predetermine channel properties by utilizing deep neural networks (DNNs). We first propose a convolutional neural network (CNN)-enabled authenticator that is able to extract the local features in CSI. Next, we employ the recurrent neural network (RNN) to capture the dependencies between different frequencies in CSI. In addition, we propose to use the convolutional recurrent neural network (CRNN)—a combination of the CNN and the RNN—to learn local and contextual information in CSI for user authentication. To effectively train these DNNs, one needs a large amount of labeled channel records. However, it is often expensive to label large channel observations in the presence of a spoofer. In view of this, we further study a case in which only a small part of the the channel observations are labeled. To handle it, we extend these DNNs-enabled approaches into semi-supervised ones. This extension is based on a semi-supervised learning technique that employs both the labeled and unlabeled data to train a DNN. To be specific, our semi-supervised method begins by generating pseudo labels for the unlabeled channel samples through implementing the K-means algorithm in a semi-supervised manner. Subsequently, both the labeled and pseudo labeled data are exploited to pre-train a DNN, which is then fine-tuned based on the labeled channel records. |
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| Published | 2018-07-25 |
| URL | http://arxiv.org/abs/1807.09469v1 |
| http://arxiv.org/pdf/1807.09469v1.pdf | |
| PWC | https://paperswithcode.com/paper/supervised-and-semi-supervised-deep-neural |
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WebSeg: Learning Semantic Segmentation from Web Searches
| Title | WebSeg: Learning Semantic Segmentation from Web Searches |
| Authors | Qibin Hou, Ming-Ming Cheng, Jiangjiang Liu, Philip H. S. Torr |
| Abstract | In this paper, we improve semantic segmentation by automatically learning from Flickr images associated with a particular keyword, without relying on any explicit user annotations, thus substantially alleviating the dependence on accurate annotations when compared to previous weakly supervised methods. To solve such a challenging problem, we leverage several low-level cues (such as saliency, edges, etc.) to help generate a proxy ground truth. Due to the diversity of web-crawled images, we anticipate a large amount of ‘label noise’ in which other objects might be present. We design an online noise filtering scheme which is able to deal with this label noise, especially in cluttered images. We use this filtering strategy as an auxiliary module to help assist the segmentation network in learning cleaner proxy annotations. Extensive experiments on the popular PASCAL VOC 2012 semantic segmentation benchmark show surprising good results in both our WebSeg (mIoU = 57.0%) and weakly supervised (mIoU = 63.3%) settings. |
| Tasks | Semantic Segmentation |
| Published | 2018-03-27 |
| URL | http://arxiv.org/abs/1803.09859v1 |
| http://arxiv.org/pdf/1803.09859v1.pdf | |
| PWC | https://paperswithcode.com/paper/webseg-learning-semantic-segmentation-from |
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A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things
| Title | A Unified Knowledge Representation and Context-aware Recommender System in Internet of Things |
| Authors | Yinhao Li, Awa Alqahtani, Ellis Solaiman, Charith Perera, Prem Prakash Jayaraman, Boualem Benatallah, Rajiv Ranjan |
| Abstract | Within the rapidly developing Internet of Things (IoT), numerous and diverse physical devices, Edge devices, Cloud infrastructure, and their quality of service requirements (QoS), need to be represented within a unified specification in order to enable rapid IoT application development, monitoring, and dynamic reconfiguration. But heterogeneities among different configuration knowledge representation models pose limitations for acquisition, discovery and curation of configuration knowledge for coordinated IoT applications. This paper proposes a unified data model to represent IoT resource configuration knowledge artifacts. It also proposes IoT-CANE (Context-Aware recommendatioN systEm) to facilitate incremental knowledge acquisition and declarative context driven knowledge recommendation. |
| Tasks | Recommendation Systems |
| Published | 2018-05-10 |
| URL | http://arxiv.org/abs/1805.04007v2 |
| http://arxiv.org/pdf/1805.04007v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-unified-knowledge-representation-and |
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Quick Best Action Identification in Linear Bandit Problems
| Title | Quick Best Action Identification in Linear Bandit Problems |
| Authors | Jun Geng, Lifeng Lai |
| Abstract | In this paper, we consider a best action identification problem in the stochastic linear bandit setup with a fixed confident constraint. In the considered best action identification problem, instead of minimizing the accumulative regret as done in existing works, the learner aims to obtain an accurate estimate of the underlying parameter based on his action and reward sequences. To improve the estimation efficiency, the learner is allowed to select his action based his historical information; hence the whole procedure is designed in a sequential adaptive manner. We first show that the existing algorithms designed to minimize the accumulative regret is not a consistent estimator and hence is not a good policy for our problem. We then characterize a lower bound on the estimation error for any policy. We further design a simple policy and show that the estimation error of the designed policy achieves the same scaling order as that of the derived lower bound. |
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| Published | 2018-12-02 |
| URL | http://arxiv.org/abs/1812.00365v1 |
| http://arxiv.org/pdf/1812.00365v1.pdf | |
| PWC | https://paperswithcode.com/paper/quick-best-action-identification-in-linear |
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Document Chunking and Learning Objective Generation for Instruction Design
| Title | Document Chunking and Learning Objective Generation for Instruction Design |
| Authors | Khoi-Nguyen Tran, Jey Han Lau, Danish Contractor, Utkarsh Gupta, Bikram Sengupta, Christopher J. Butler, Mukesh Mohania |
| Abstract | Instructional Systems Design is the practice of creating of instructional experiences that make the acquisition of knowledge and skill more efficient, effective, and appealing. Specifically in designing courses, an hour of training material can require between 30 to 500 hours of effort in sourcing and organizing reference data for use in just the preparation of course material. In this paper, we present the first system of its kind that helps reduce the effort associated with sourcing reference material and course creation. We present algorithms for document chunking and automatic generation of learning objectives from content, creating descriptive content metadata to improve content-discoverability. Unlike existing methods, the learning objectives generated by our system incorporate pedagogically motivated Bloom’s verbs. We demonstrate the usefulness of our methods using real world data from the banking industry and through a live deployment at a large pharmaceutical company. |
| Tasks | Chunking |
| Published | 2018-06-01 |
| URL | http://arxiv.org/abs/1806.01351v2 |
| http://arxiv.org/pdf/1806.01351v2.pdf | |
| PWC | https://paperswithcode.com/paper/document-chunking-and-learning-objective |
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Dynamic Environment Mapping for Augmented Reality Applications on Mobile Devices
| Title | Dynamic Environment Mapping for Augmented Reality Applications on Mobile Devices |
| Authors | Rafael Monroy, Matis Hudon, Aljosa Smolic |
| Abstract | Augmented Reality is a topic of foremost interest nowadays. Its main goal is to seamlessly blend virtual content in real-world scenes. Due to the lack of computational power in mobile devices, rendering a virtual object with high-quality, coherent appearance and in real-time, remains an area of active research. In this work, we present a novel pipeline that allows for coupled environment acquisition and virtual object rendering on a mobile device equipped with a depth sensor. While keeping human interaction to a minimum, our system can scan a real scene and project it onto a two-dimensional environment map containing RGB+Depth data. Furthermore, we define a set of criteria that allows for an adaptive update of the environment map to account for dynamic changes in the scene. Then, under the assumption of diffuse surfaces and distant illumination, our method exploits an analytic expression for the irradiance in terms of spherical harmonic coefficients, which leads to a very efficient rendering algorithm. We show that all the processes in our pipeline can be executed while maintaining an average frame rate of 31Hz on a mobile device. |
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| Published | 2018-09-21 |
| URL | http://arxiv.org/abs/1809.08134v1 |
| http://arxiv.org/pdf/1809.08134v1.pdf | |
| PWC | https://paperswithcode.com/paper/dynamic-environment-mapping-for-augmented |
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Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing
| Title | Bridging the Gap: Simultaneous Fine Tuning for Data Re-Balancing |
| Authors | John McKay, Isaac Gerg, Vishal Monga |
| Abstract | There are many real-world classification problems wherein the issue of data imbalance (the case when a data set contains substantially more samples for one/many classes than the rest) is unavoidable. While under-sampling the problematic classes is a common solution, this is not a compelling option when the large data class is itself diverse and/or the limited data class is especially small. We suggest a strategy based on recent work concerning limited data problems which utilizes a supplemental set of images with similar properties to the limited data class to aid in the training of a neural network. We show results for our model against other typical methods on a real-world synthetic aperture sonar data set. Code can be found at github.com/JohnMcKay/dataImbalance. |
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| Published | 2018-01-08 |
| URL | http://arxiv.org/abs/1801.02548v1 |
| http://arxiv.org/pdf/1801.02548v1.pdf | |
| PWC | https://paperswithcode.com/paper/bridging-the-gap-simultaneous-fine-tuning-for |
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A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping
| Title | A Comparative Study on Unsupervised Domain Adaptation Approaches for Coffee Crop Mapping |
| Authors | Edemir Ferreira, Mário S. Alvim, Jefersson A. dos Santos |
| Abstract | In this work, we investigate the application of existing unsupervised domain adaptation (UDA) approaches to the task of transferring knowledge between crop regions having different coffee patterns. Given a geographical region with fully mapped coffee plantations, we observe that this knowledge can be used to train a classifier and to map a new county with no need of samples indicated in the target region. Experimental results show that transferring knowledge via some UDA strategies performs better than just applying a classifier trained in a region to predict coffee crops in a new one. However, UDA methods may lead to negative transfer, which may indicate that domains are too different that transferring knowledge is not appropriate. We also verify that normalization affect significantly some UDA methods; we observe a meaningful complementary contribution between coffee crops data; and a visual behavior suggests an existent of a cluster of samples that are more likely to be drawn from a specific data. |
| Tasks | Domain Adaptation, Unsupervised Domain Adaptation |
| Published | 2018-06-06 |
| URL | http://arxiv.org/abs/1806.02400v1 |
| http://arxiv.org/pdf/1806.02400v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-comparative-study-on-unsupervised-domain |
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Robust descent using smoothed multiplicative noise
| Title | Robust descent using smoothed multiplicative noise |
| Authors | Matthew J. Holland |
| Abstract | To improve the off-sample generalization of classical procedures minimizing the empirical risk under potentially heavy-tailed data, new robust learning algorithms have been proposed in recent years, with generalized median-of-means strategies being particularly salient. These procedures enjoy performance guarantees in the form of sharp risk bounds under weak moment assumptions on the underlying loss, but typically suffer from a large computational overhead and substantial bias when the data happens to be sub-Gaussian, limiting their utility. In this work, we propose a novel robust gradient descent procedure which makes use of a smoothed multiplicative noise applied directly to observations before constructing a sum of soft-truncated gradient coordinates. We show that the procedure has competitive theoretical guarantees, with the major advantage of a simple implementation that does not require an iterative sub-routine for robustification. Empirical tests reinforce the theory, showing more efficient generalization over a much wider class of data distributions. |
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| Published | 2018-10-15 |
| URL | http://arxiv.org/abs/1810.06207v1 |
| http://arxiv.org/pdf/1810.06207v1.pdf | |
| PWC | https://paperswithcode.com/paper/robust-descent-using-smoothed-multiplicative |
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Image Restoration using Total Variation Regularized Deep Image Prior
| Title | Image Restoration using Total Variation Regularized Deep Image Prior |
| Authors | Jiaming Liu, Yu Sun, Xiaojian Xu, Ulugbek S. Kamilov |
| Abstract | In the past decade, sparsity-driven regularization has led to significant improvements in image reconstruction. Traditional regularizers, such as total variation (TV), rely on analytical models of sparsity. However, increasingly the field is moving towards trainable models, inspired from deep learning. Deep image prior (DIP) is a recent regularization framework that uses a convolutional neural network (CNN) architecture without data-driven training. This paper extends the DIP framework by combining it with the traditional TV regularization. We show that the inclusion of TV leads to considerable performance gains when tested on several traditional restoration tasks such as image denoising and deblurring. |
| Tasks | Deblurring, Denoising, Image Denoising, Image Reconstruction, Image Restoration |
| Published | 2018-10-30 |
| URL | http://arxiv.org/abs/1810.12864v1 |
| http://arxiv.org/pdf/1810.12864v1.pdf | |
| PWC | https://paperswithcode.com/paper/image-restoration-using-total-variation |
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VQA with no questions-answers training
| Title | VQA with no questions-answers training |
| Authors | Ben-Zion Vatashsky, Shimon Ullman |
| Abstract | Methods for teaching machines to answer visual questions have made significant progress in the last few years, but although demonstrating impressive results on particular datasets, these methods lack some important human capabilities, including integrating new visual classes and concepts in a modular manner, providing explanations for the answer and handling new domains without new examples. In this paper we present a system that achieves state-of-the-art results on the CLEVR dataset without any questions-answers training, utilizes real visual estimators and explains the answer. The system includes a question representation stage followed by an answering procedure, which invokes an extendable set of visual estimators. It can explain the answer, including its failures, and provide alternatives to negative answers. The scheme builds upon a framework proposed recently, with extensions allowing the system to deal with novel domains without relying on training examples. |
| Tasks | Visual Question Answering |
| Published | 2018-11-20 |
| URL | http://arxiv.org/abs/1811.08481v1 |
| http://arxiv.org/pdf/1811.08481v1.pdf | |
| PWC | https://paperswithcode.com/paper/vqa-with-no-questions-answers-training |
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Texture variation adaptive image denoising with nonlocal PCA
| Title | Texture variation adaptive image denoising with nonlocal PCA |
| Authors | Wenzhao Zhao, Qiegen Liu, Yisong Lv, Binjie Qin |
| Abstract | Image textures, as a kind of local variations, provide important information for human visual system. Many image textures, especially the small-scale or stochastic textures are rich in high-frequency variations, and are difficult to be preserved. Current state-of-the-art denoising algorithms typically adopt a nonlocal approach consisting of image patch grouping and group-wise denoising filtering. To achieve a better image denoising while preserving the variations in texture, we first adaptively group high correlated image patches with the same kinds of texture elements (texels) via an adaptive clustering method. This adaptive clustering method is applied in an over-clustering-and-iterative-merging approach, where its noise robustness is improved with a custom merging threshold relating to the noise level and cluster size. For texture-preserving denoising of each cluster, considering that the variations in texture are captured and wrapped in not only the between-dimension energy variations but also the within-dimension variations of PCA transform coefficients, we further propose a PCA-transform-domain variation adaptive filtering method to preserve the local variations in textures. Experiments on natural images show the superiority of the proposed transform-domain variation adaptive filtering to traditional PCA-based hard or soft threshold filtering. As a whole, the proposed denoising method achieves a favorable texture preserving performance both quantitatively and visually, especially for stochastic textures, which is further verified in camera raw image denoising. |
| Tasks | Denoising, Image Denoising |
| Published | 2018-10-26 |
| URL | http://arxiv.org/abs/1810.11282v1 |
| http://arxiv.org/pdf/1810.11282v1.pdf | |
| PWC | https://paperswithcode.com/paper/texture-variation-adaptive-image-denoising |
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The Force of Proof by Which Any Argument Prevails
| Title | The Force of Proof by Which Any Argument Prevails |
| Authors | Brian Shay, Patrick Brazil |
| Abstract | Jakob Bernoulli, working in the late 17th century, identified a gap in contemporary probability theory. He cautioned that it was inadequate to specify force of proof (probability of provability) for some kinds of uncertain arguments. After 300 years, this gap remains in present-day probability theory. We present axioms analogous to Kolmogorov’s axioms for probability, specifying uncertainty that lies in an argument’s inference/implication itself rather than in its premise and conclusion. The axioms focus on arguments spanning two Boolean algebras, but generalize the obligatory: “force of proof of A implies B is the probability of B or not A” in the case that the Boolean algebras are identical. We propose a categorical framework that relies on generalized probabilities (objects) to express uncertainty in premises, to mix with arguments (morphisms) to express uncertainty embedded directly in inference/implication. There is a direct application to Shafer’s evidence theory (Dempster-Shafer theory), greatly expanding its scope for applications. Therefore, we can offer this framework not only as an optimal solution to a difficult historical puzzle, but also to advance the frontiers of contemporary artificial intelligence. Keywords: force of proof, probability of provability, Ars Conjectandi, non additive probabilities, evidence theory. |
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| Published | 2018-09-07 |
| URL | http://arxiv.org/abs/1809.02260v1 |
| http://arxiv.org/pdf/1809.02260v1.pdf | |
| PWC | https://paperswithcode.com/paper/the-force-of-proof-by-which-any-argument |
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On the Complexity of Reconnaissance Blind Chess
| Title | On the Complexity of Reconnaissance Blind Chess |
| Authors | Jared Markowitz, Ryan W. Gardner, Ashley J. Llorens |
| Abstract | This paper provides a complexity analysis for the game of reconnaissance blind chess (RBC), a recently-introduced variant of chess where each player does not know the positions of the opponent’s pieces a priori but may reveal a subset of them through chosen, private sensing actions. In contrast to many commonly studied imperfect information games like poker, an RBC player does not know what the opponent knows or has chosen to learn, exponentially expanding the size of the game’s information sets (i.e., the number of possible game states that are consistent with what a player has observed). Effective RBC sensing and moving strategies must account for the uncertainty of both players, an essential element of many real-world decision-making problems. Here we evaluate RBC from a game theoretic perspective, tracking the proliferation of information sets from the perspective of selected canonical bot players in tournament play. We show that, even for effective sensing strategies, the game sizes of RBC compare to those of Go while the average size of a player’s information set throughout an RBC game is much greater than that of a player in Heads-up Limit Hold ‘Em. We compare these measures of complexity among different playing algorithms and provide cursory assessments of the various sensing and moving strategies. |
| Tasks | Decision Making |
| Published | 2018-11-07 |
| URL | http://arxiv.org/abs/1811.03119v2 |
| http://arxiv.org/pdf/1811.03119v2.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-complexity-of-reconnaissance-blind |
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Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization
| Title | Nonparametric Bayesian label prediction on a large graph using truncated Laplacian regularization |
| Authors | Jarno Hartog, Harry van Zanten |
| Abstract | This article describes an implementation of a nonparametric Bayesian approach to solving binary classification problems on graphs. We consider a hierarchical Bayesian approach with a prior that is constructed by truncating a series expansion of the soft label function using the graph Laplacian eigenfunctions as basis functions. We compare our truncated prior to the untruncated Laplacian based prior in simulated and real data examples to illustrate the improved scalability in terms of size of the underlying graph. |
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| Published | 2018-04-13 |
| URL | http://arxiv.org/abs/1804.07262v1 |
| http://arxiv.org/pdf/1804.07262v1.pdf | |
| PWC | https://paperswithcode.com/paper/nonparametric-bayesian-label-prediction-on-a |
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