Paper Group ANR 399
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes. Guided patch-wise nonlocal SAR despeckling. Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions. Commonsense mining as knowledge base completion? A study on the impact of novelty. Preventing Poisoning Attacks on AI based Threat …
An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes
| Title | An Efficient Optical Flow Based Motion Detection Method for Non-stationary Scenes |
| Authors | Junjie Huang, Wei Zou, Zheng Zhu, Jiagang Zhu |
| Abstract | Real-time motion detection in non-stationary scenes is a difficult task due to dynamic background, changing foreground appearance and limited computational resource. These challenges degrade the performance of the existing methods in practical applications. In this paper, an optical flow based framework is proposed to address this problem. By applying a novel strategy to utilize optical flow, we enable our method being free of model constructing, training or updating and can be performed efficiently. Besides, a dual judgment mechanism with adaptive intervals and adaptive thresholds is designed to heighten the system’s adaptation to different situations. In experiment part, we quantitatively and qualitatively validate the effectiveness and feasibility of our method with videos in various scene conditions. The experimental results show that our method adapts itself to different situations and outperforms the state-of-the-art real-time methods, indicating the advantages of our optical flow based method. |
| Tasks | Motion Detection, Motion Detection In Non-Stationary Scenes, Optical Flow Estimation |
| Published | 2018-11-18 |
| URL | http://arxiv.org/abs/1811.08290v2 |
| http://arxiv.org/pdf/1811.08290v2.pdf | |
| PWC | https://paperswithcode.com/paper/an-efficient-optical-flow-based-motion |
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Guided patch-wise nonlocal SAR despeckling
| Title | Guided patch-wise nonlocal SAR despeckling |
| Authors | Sergio Vitale, Davide Cozzolino, Giuseppe Scarpa, Luisa Verdoliva, Giovanni Poggi |
| Abstract | We propose a new method for SAR image despeckling which leverages information drawn from co-registered optical imagery. Filtering is performed by plain patch-wise nonlocal means, operating exclusively on SAR data. However, the filtering weights are computed by taking into account also the optical guide, which is much cleaner than the SAR data, and hence more discriminative. To avoid injecting optical-domain information into the filtered image, a SAR-domain statistical test is preliminarily performed to reject right away any risky predictor. Experiments on two SAR-optical datasets prove the proposed method to suppress very effectively the speckle, preserving structural details, and without introducing visible filtering artifacts. Overall, the proposed method compares favourably with all state-of-the-art despeckling filters, and also with our own previous optical-guided filter. |
| Tasks | Sar Image Despeckling |
| Published | 2018-11-28 |
| URL | http://arxiv.org/abs/1811.11872v1 |
| http://arxiv.org/pdf/1811.11872v1.pdf | |
| PWC | https://paperswithcode.com/paper/guided-patch-wise-nonlocal-sar-despeckling |
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Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions
| Title | Targeting Solutions in Bayesian Multi-Objective Optimization: Sequential and Batch Versions |
| Authors | David Gaudrie, Rodolphe Le Riche, Victor Picheny, Benoit Enaux, Vincent Herbert |
| Abstract | Multi-objective optimization aims at finding trade-off solutions to conflicting objectives. These constitute the Pareto optimal set. In the context of expensive-to-evaluate functions, it is impossible and often non-informative to look for the entire set. As an end-user would typically prefer a certain part of the objective space, we modify the Bayesian multi-objective optimization algorithm which uses Gaussian Processes to maximize the Expected Hypervolume Improvement, to focus the search in the preferred region. The cumulated effects of the Gaussian Processes and the targeting strategy lead to a particularly efficient convergence to the desired part of the Pareto set. To take advantage of parallel computing, a multi-point extension of the targeting criterion is proposed and analyzed. |
| Tasks | Gaussian Processes |
| Published | 2018-11-09 |
| URL | https://arxiv.org/abs/1811.03862v5 |
| https://arxiv.org/pdf/1811.03862v5.pdf | |
| PWC | https://paperswithcode.com/paper/targeting-solutions-in-bayesian-multi |
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Commonsense mining as knowledge base completion? A study on the impact of novelty
| Title | Commonsense mining as knowledge base completion? A study on the impact of novelty |
| Authors | Stanisław Jastrzębski, Dzmitry Bahdanau, Seyedarian Hosseini, Michael Noukhovitch, Yoshua Bengio, Jackie Chi Kit Cheung |
| Abstract | Commonsense knowledge bases such as ConceptNet represent knowledge in the form of relational triples. Inspired by the recent work by Li et al., we analyse if knowledge base completion models can be used to mine commonsense knowledge from raw text. We propose novelty of predicted triples with respect to the training set as an important factor in interpreting results. We critically analyse the difficulty of mining novel commonsense knowledge, and show that a simple baseline method outperforms the previous state of the art on predicting more novel. |
| Tasks | Knowledge Base Completion |
| Published | 2018-04-24 |
| URL | http://arxiv.org/abs/1804.09259v1 |
| http://arxiv.org/pdf/1804.09259v1.pdf | |
| PWC | https://paperswithcode.com/paper/commonsense-mining-as-knowledge-base |
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Preventing Poisoning Attacks on AI based Threat Intelligence Systems
| Title | Preventing Poisoning Attacks on AI based Threat Intelligence Systems |
| Authors | Nitika Khurana, Sudip Mittal, Anupam Joshi |
| Abstract | As AI systems become more ubiquitous, securing them becomes an emerging challenge. Over the years, with the surge in online social media use and the data available for analysis, AI systems have been built to extract, represent and use this information. The credibility of this information extracted from open sources, however, can often be questionable. Malicious or incorrect information can cause a loss of money, reputation, and resources; and in certain situations, pose a threat to human life. In this paper, we use an ensembled semi-supervised approach to determine the credibility of Reddit posts by estimating their reputation score to ensure the validity of information ingested by AI systems. We demonstrate our approach in the cybersecurity domain, where security analysts utilize these systems to determine possible threats by analyzing the data scattered on social media websites, forums, blogs, etc. |
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| Published | 2018-07-19 |
| URL | http://arxiv.org/abs/1807.07418v1 |
| http://arxiv.org/pdf/1807.07418v1.pdf | |
| PWC | https://paperswithcode.com/paper/preventing-poisoning-attacks-on-ai-based |
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PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data
| Title | PhoneMD: Learning to Diagnose Parkinson’s Disease from Smartphone Data |
| Authors | Patrick Schwab, Walter Karlen |
| Abstract | Parkinson’s disease is a neurodegenerative disease that can affect a person’s movement, speech, dexterity, and cognition. Clinicians primarily diagnose Parkinson’s disease by performing a clinical assessment of symptoms. However, misdiagnoses are common. One factor that contributes to misdiagnoses is that the symptoms of Parkinson’s disease may not be prominent at the time the clinical assessment is performed. Here, we present a machine-learning approach towards distinguishing between people with and without Parkinson’s disease using long-term data from smartphone-based walking, voice, tapping and memory tests. We demonstrate that our attentive deep-learning models achieve significant improvements in predictive performance over strong baselines (area under the receiver operating characteristic curve = 0.85) in data from a cohort of 1853 participants. We also show that our models identify meaningful features in the input data. Our results confirm that smartphone data collected over extended periods of time could in the future potentially be used as a digital biomarker for the diagnosis of Parkinson’s disease. |
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| Published | 2018-10-01 |
| URL | http://arxiv.org/abs/1810.01485v2 |
| http://arxiv.org/pdf/1810.01485v2.pdf | |
| PWC | https://paperswithcode.com/paper/phonemd-learning-to-diagnose-parkinsons |
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Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection
| Title | Phonocardiographic Sensing using Deep Learning for Abnormal Heartbeat Detection |
| Authors | Siddique Latif, Muhammad Usman, Rajib Rana, Junaid Qadir |
| Abstract | Cardiac auscultation involves expert interpretation of abnormalities in heart sounds using stethoscope. Deep learning based cardiac auscultation is of significant interest to the healthcare community as it can help reducing the burden of manual auscultation with automated detection of abnormal heartbeats. However, the problem of automatic cardiac auscultation is complicated due to the requirement of reliability and high accuracy, and due to the presence of background noise in the heartbeat sound. In this work, we propose a Recurrent Neural Networks (RNNs) based automated cardiac auscultation solution. Our choice of RNNs is motivated by the great success of deep learning in medical applications and by the observation that RNNs represent the deep learning configuration most suitable for dealing with sequential or temporal data even in the presence of noise. We explore the use of various RNN models, and demonstrate that these models deliver the abnormal heartbeat classification score with significant improvement. Our proposed approach using RNNs can be potentially be used for real-time abnormal heartbeat detection in the Internet of Medical Things for remote monitoring applications. |
| Tasks | Heartbeat Classification |
| Published | 2018-01-25 |
| URL | http://arxiv.org/abs/1801.08322v3 |
| http://arxiv.org/pdf/1801.08322v3.pdf | |
| PWC | https://paperswithcode.com/paper/phonocardiographic-sensing-using-deep |
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An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm
| Title | An end-to-end Differentially Private Latent Dirichlet Allocation Using a Spectral Algorithm |
| Authors | Christopher DeCarolis, Mukul Ram, Seyed A. Esmaeili, Yu-Xiang Wang, Furong Huang |
| Abstract | We provide an end-to-end differentially private spectral algorithm for learning LDA, based on matrix/tensor decompositions, and establish theoretical guarantees on utility/consistency of the estimated model parameters. The spectral algorithm consists of multiple algorithmic steps, named as “{edges}", to which noise could be injected to obtain differential privacy. We identify \emph{subsets of edges}, named as “{configurations}", such that adding noise to all edges in such a subset guarantees differential privacy of the end-to-end spectral algorithm. We characterize the sensitivity of the edges with respect to the input and thus estimate the amount of noise to be added to each edge for any required privacy level. We then characterize the utility loss for each configuration as a function of injected noise. Overall, by combining the sensitivity and utility characterization, we obtain an end-to-end differentially private spectral algorithm for LDA and identify the corresponding configuration that outperforms others in any specific regime. We are the first to achieve utility guarantees under the required level of differential privacy for learning in LDA. Overall our method systematically outperforms differentially private variational inference. |
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| Published | 2018-05-25 |
| URL | https://arxiv.org/abs/1805.10341v3 |
| https://arxiv.org/pdf/1805.10341v3.pdf | |
| PWC | https://paperswithcode.com/paper/an-end-to-end-differentially-private-latent |
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Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning
| Title | Dynamic Risk Assessment for Vehicles of Higher Automation Levels by Deep Learning |
| Authors | Patrik Feth, Mohammed Naveed Akram, René Schuster, Oliver Wasenmüller |
| Abstract | Vehicles of higher automation levels require the creation of situation awareness. One important aspect of this situation awareness is an understanding of the current risk of a driving situation. In this work, we present a novel approach for the dynamic risk assessment of driving situations based on images of a front stereo camera using deep learning. To this end, we trained a deep neural network with recorded monocular images, disparity maps and a risk metric for diverse traffic scenes. Our approach can be used to create the aforementioned situation awareness of vehicles of higher automation levels and can serve as a heterogeneous channel to systems based on radar or lidar sensors that are used traditionally for the calculation of risk metrics. |
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| Published | 2018-06-20 |
| URL | http://arxiv.org/abs/1806.07635v1 |
| http://arxiv.org/pdf/1806.07635v1.pdf | |
| PWC | https://paperswithcode.com/paper/dynamic-risk-assessment-for-vehicles-of |
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Compensated Integrated Gradients to Reliably Interpret EEG Classification
| Title | Compensated Integrated Gradients to Reliably Interpret EEG Classification |
| Authors | Kazuki Tachikawa, Yuji Kawai, Jihoon Park, Minoru Asada |
| Abstract | Integrated gradients are widely employed to evaluate the contribution of input features in classification models because it satisfies the axioms for attribution of prediction. This method, however, requires an appropriate baseline for reliable determination of the contributions. We propose a compensated integrated gradients method that does not require a baseline. In fact, the method compensates the attributions calculated by integrated gradients at an arbitrary baseline using Shapley sampling. We prove that the method retrieves reliable attributions if the processes of input features in a classifier are mutually independent, and they are identical like shared weights in convolutional neural networks. Using three electroencephalogram datasets, we experimentally demonstrate that the attributions of the proposed method are more reliable than those of the original integrated gradients, and its computational complexity is much lower than that of Shapley sampling. |
| Tasks | EEG |
| Published | 2018-11-21 |
| URL | http://arxiv.org/abs/1811.08633v1 |
| http://arxiv.org/pdf/1811.08633v1.pdf | |
| PWC | https://paperswithcode.com/paper/compensated-integrated-gradients-to-reliably |
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Doubly Nested Network for Resource-Efficient Inference
| Title | Doubly Nested Network for Resource-Efficient Inference |
| Authors | Jaehong Kim, Sungeun Hong, Yongseok Choi, Jiwon Kim |
| Abstract | We propose doubly nested network(DNNet) where all neurons represent their own sub-models that solve the same task. Every sub-model is nested both layer-wise and channel-wise. While nesting sub-models layer-wise is straight-forward with deep-supervision as proposed in \cite{xie2015holistically}, channel-wise nesting has not been explored in the literature to our best knowledge. Channel-wise nesting is non-trivial as neurons between consecutive layers are all connected to each other. In this work, we introduce a technique to solve this problem by sorting channels topologically and connecting neurons accordingly. For the purpose, channel-causal convolutions are used. Slicing doubly nested network gives a working sub-network. The most notable application of our proposed network structure with slicing operation is resource-efficient inference. At test time, computing resources such as time and memory available for running the prediction algorithm can significantly vary across devices and applications. Given a budget constraint, we can slice the network accordingly and use a sub-model for inference within budget, requiring no additional computation such as training or fine-tuning after deployment. We demonstrate the effectiveness of our approach in several practical scenarios of utilizing available resource efficiently. |
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| Published | 2018-06-20 |
| URL | http://arxiv.org/abs/1806.07568v1 |
| http://arxiv.org/pdf/1806.07568v1.pdf | |
| PWC | https://paperswithcode.com/paper/doubly-nested-network-for-resource-efficient |
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On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
| Title | On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction |
| Authors | Karim Said Barsim, Bin Yang |
| Abstract | Recently, and with the growing development of big energy datasets, data-driven learning techniques began to represent a potential solution to the energy disaggregation problem outperforming engineered and hand-crafted models. However, most proposed deep disaggregation models are load-dependent in the sense that either expert knowledge or a hyper-parameter optimization stage is required prior to training and deployment (normally for each load category) even upon acquisition and cleansing of aggregate and sub-metered data. In this paper, we present a feasibility study on the development of a generic disaggregation model based on data-driven learning. Specifically, we present a generic deep disaggregation model capable of achieving state-of-art performance in load monitoring for a variety of load categories. The developed model is evaluated on the publicly available UK-DALE dataset with a moderately low sampling frequency and various domestic loads. |
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| Published | 2018-02-05 |
| URL | http://arxiv.org/abs/1802.02139v1 |
| http://arxiv.org/pdf/1802.02139v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-feasibility-of-generic-deep |
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Neural Entity Reasoner for Global Consistency in NER
| Title | Neural Entity Reasoner for Global Consistency in NER |
| Authors | Xiaoxiao Yin, Daqi Zheng, Zhengdong Lu, Ruifang Liu |
| Abstract | We propose Neural Entity Reasoner (NE-Reasoner), a framework to introduce global consistency of recognized entities into Neural Reasoner over Named Entity Recognition (NER) task. Given an input sentence, the NE-Reasoner layer can infer over multiple entities to increase the global consistency of output labels, which then be transfered into entities for the input of next layer. NE-Reasoner inherits and develops some features from Neural Reasoner 1) a symbolic memory, allowing it to exchange entities between layers. 2) the specific interaction-pooling mechanism, allowing it to connect each local word to multiple global entities, and 3) the deep architecture, allowing it to bootstrap the recognized entity set from coarse to fine. Like human beings, NE-Reasoner is able to accommodate ambiguous words and Name Entities that rarely or never met before. Despite the symbolic information the model introduced, NE-Reasoner can still be trained effectively in an end-to-end manner via parameter sharing strategy. NE-Reasoner can outperform conventional NER models in most cases on both English and Chinese NER datasets. For example, it achieves state-of-art on CoNLL-2003 English NER dataset. |
| Tasks | Named Entity Recognition |
| Published | 2018-09-30 |
| URL | http://arxiv.org/abs/1810.00347v1 |
| http://arxiv.org/pdf/1810.00347v1.pdf | |
| PWC | https://paperswithcode.com/paper/neural-entity-reasoner-for-global-consistency |
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Generalizing, Decoding, and Optimizing Support Vector Machine Classification
| Title | Generalizing, Decoding, and Optimizing Support Vector Machine Classification |
| Authors | Mario Michael Krell |
| Abstract | The classification of complex data usually requires the composition of processing steps. Here, a major challenge is the selection of optimal algorithms for preprocessing and classification (including parameterizations). Nowadays, parts of the optimization process are automized but expert knowledge and manual work are still required. We present three steps to face this process and ease the optimization. Namely, we take a theoretical view on classical classifiers, provide an approach to interpret the classifier together with the preprocessing, and integrate both into one framework which enables a semiautomatic optimization of the processing chain and which interfaces numerous algorithms. |
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| Published | 2018-01-15 |
| URL | http://arxiv.org/abs/1801.04929v1 |
| http://arxiv.org/pdf/1801.04929v1.pdf | |
| PWC | https://paperswithcode.com/paper/generalizing-decoding-and-optimizing-support |
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TEST: A Terminology Extraction System for Technology Related Terms
| Title | TEST: A Terminology Extraction System for Technology Related Terms |
| Authors | Murhaf Hossari, Soumyabrata Dev, John D. Kelleher |
| Abstract | Tracking developments in the highly dynamic data-technology landscape are vital to keeping up with novel technologies and tools, in the various areas of Artificial Intelligence (AI). However, It is difficult to keep track of all the relevant technology keywords. In this paper, we propose a novel system that addresses this problem. This tool is used to automatically detect the existence of new technologies and tools in text, and extract terms used to describe these new technologies. The extracted new terms can be logged as new AI technologies as they are found on-the-fly in the web. It can be subsequently classified into the relevant semantic labels and AI domains. Our proposed tool is based on a two-stage cascading model – the first stage classifies if the sentence contains a technology term or not; and the second stage identifies the technology keyword in the sentence. We obtain a competitive accuracy for both tasks of sentence classification and text identification. |
| Tasks | Sentence Classification |
| Published | 2018-12-22 |
| URL | http://arxiv.org/abs/1812.09541v2 |
| http://arxiv.org/pdf/1812.09541v2.pdf | |
| PWC | https://paperswithcode.com/paper/test-a-terminology-extraction-system-for |
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