July 27, 2019

3018 words 15 mins read

Paper Group ANR 598

Self-Taught Support Vector Machine. Gaussian Process Regression for Arctic Coastal Erosion Forecasting. Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic. Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption. High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portra …

Self-Taught Support Vector Machine


Title	Self-Taught Support Vector Machine
Authors	Parvin Razzaghi
Abstract	In this paper, a new approach for classification of target task using limited labeled target data as well as enormous unlabeled source data is proposed which is called self-taught learning. The target and source data can be drawn from different distributions. In the previous approaches, covariate shift assumption is considered where the marginal distributions p(x) change over domains and the conditional distributions p(yx) remain the same. In our approach, we propose a new objective function which simultaneously learns a common space T(.) where the conditional distributions over domains p(T(x)y) remain the same and learns robust SVM classifiers for target task using both source and target data in the new representation. Hence, in the proposed objective function, the hidden label of the source data is also incorporated. We applied the proposed approach on Caltech-256, MSRC+LMO datasets and compared the performance of our algorithm to the available competing methods. Our method has a superior performance to the successful existing algorithms.
Tasks
Published	2017-10-12
URL	http://arxiv.org/abs/1710.04450v1
PDF	http://arxiv.org/pdf/1710.04450v1.pdf
PWC	https://paperswithcode.com/paper/self-taught-support-vector-machine
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Gaussian Process Regression for Arctic Coastal Erosion Forecasting


Title	Gaussian Process Regression for Arctic Coastal Erosion Forecasting
Authors	Matthew Kupilik, Frank Witmer, Euan-Angus MacLeod, Caixia Wang, Tom Ravens
Abstract	Arctic coastal morphology is governed by multiple factors, many of which are affected by climatological changes. As the season length for shorefast ice decreases and temperatures warm permafrost soils, coastlines are more susceptible to erosion from storm waves. Such coastal erosion is a concern, since the majority of the population centers and infrastructure in the Arctic are located near the coasts. Stakeholders and decision makers increasingly need models capable of scenario-based predictions to assess and mitigate the effects of coastal morphology on infrastructure and land use. Our research uses Gaussian process models to forecast Arctic coastal erosion along the Beaufort Sea near Drew Point, AK. Gaussian process regression is a data-driven modeling methodology capable of extracting patterns and trends from data-sparse environments such as remote Arctic coastlines. To train our model, we use annual coastline positions and near-shore summer temperature averages from existing datasets and extend these data by extracting additional coastlines from satellite imagery. We combine our calibrated models with future climate models to generate a range of plausible future erosion scenarios. Our results show that the Gaussian process methodology substantially improves yearly predictions compared to linear and nonlinear least squares methods, and is capable of generating detailed forecasts suitable for use by decision makers.
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Published	2017-12-04
URL	http://arxiv.org/abs/1712.00867v1
PDF	http://arxiv.org/pdf/1712.00867v1.pdf
PWC	https://paperswithcode.com/paper/gaussian-process-regression-for-arctic
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Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic


Title	Evaluating Noisy Optimisation Algorithms: First Hitting Time is Problematic
Authors	Simon M. Lucas, Jialin Liu, Diego Pérez-Liébana
Abstract	A key part of any evolutionary algorithm is fitness evaluation. When fitness evaluations are corrupted by noise, as happens in many real-world problems as a consequence of various types of uncertainty, a strategy is needed in order to cope with this. Resampling is one of the most common strategies, whereby each solution is evaluated many times in order to reduce the variance of the fitness estimates. When evaluating the performance of a noisy optimisation algorithm, a key consideration is the stopping condition for the algorithm. A frequently used stopping condition in runtime analysis, known as “First Hitting Time”, is to stop the algorithm as soon as it encounters the optimal solution. However, this is unrealistic for real-world problems, as if the optimal solution were already known, there would be no need to search for it. This paper argues that the use of First Hitting Time, despite being a commonly used approach, is significantly flawed and overestimates the quality of many algorithms in real-world cases, where the optimum is not known in advance and has to be genuinely searched for. A better alternative is to measure the quality of the solution an algorithm returns after a fixed evaluation budget, i.e., to focus on final solution quality. This paper argues that focussing on final solution quality is more realistic and demonstrates cases where the results produced by each algorithm evaluation method lead to very different conclusions regarding the quality of each noisy optimisation algorithm.
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Published	2017-06-13
URL	http://arxiv.org/abs/1706.05086v2
PDF	http://arxiv.org/pdf/1706.05086v2.pdf
PWC	https://paperswithcode.com/paper/evaluating-noisy-optimisation-algorithms
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Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption


Title	Privacy-Preserving Visual Learning Using Doubly Permuted Homomorphic Encryption
Authors	Ryo Yonetani, Vishnu Naresh Boddeti, Kris M. Kitani, Yoichi Sato
Abstract	We propose a privacy-preserving framework for learning visual classifiers by leveraging distributed private image data. This framework is designed to aggregate multiple classifiers updated locally using private data and to ensure that no private information about the data is exposed during and after its learning procedure. We utilize a homomorphic cryptosystem that can aggregate the local classifiers while they are encrypted and thus kept secret. To overcome the high computational cost of homomorphic encryption of high-dimensional classifiers, we (1) impose sparsity constraints on local classifier updates and (2) propose a novel efficient encryption scheme named doubly-permuted homomorphic encryption (DPHE) which is tailored to sparse high-dimensional data. DPHE (i) decomposes sparse data into its constituent non-zero values and their corresponding support indices, (ii) applies homomorphic encryption only to the non-zero values, and (iii) employs double permutations on the support indices to make them secret. Our experimental evaluation on several public datasets shows that the proposed approach achieves comparable performance against state-of-the-art visual recognition methods while preserving privacy and significantly outperforms other privacy-preserving methods.
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Published	2017-04-07
URL	http://arxiv.org/abs/1704.02203v2
PDF	http://arxiv.org/pdf/1704.02203v2.pdf
PWC	https://paperswithcode.com/paper/privacy-preserving-visual-learning-using
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High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits


Title	High-Quality Correspondence and Segmentation Estimation for Dual-Lens Smart-Phone Portraits
Authors	Xiaoyong Shen, Hongyun Gao, Xin Tao, Chao Zhou, Jiaya Jia
Abstract	Estimating correspondence between two images and extracting the foreground object are two challenges in computer vision. With dual-lens smart phones, such as iPhone 7Plus and Huawei P9, coming into the market, two images of slightly different views provide us new information to unify the two topics. We propose a joint method to tackle them simultaneously via a joint fully connected conditional random field (CRF) framework. The regional correspondence is used to handle textureless regions in matching and make our CRF system computationally efficient. Our method is evaluated over 2,000 new image pairs, and produces promising results on challenging portrait images.
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Published	2017-04-07
URL	http://arxiv.org/abs/1704.02205v1
PDF	http://arxiv.org/pdf/1704.02205v1.pdf
PWC	https://paperswithcode.com/paper/high-quality-correspondence-and-segmentation
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Learning Feedforward and Recurrent Deterministic Spiking Neuron Network Feedback Controllers


Title	Learning Feedforward and Recurrent Deterministic Spiking Neuron Network Feedback Controllers
Authors	Tae Seung Kang, Arunava Banerjee
Abstract	We address the problem of learning feedback control where the controller is a network constructed solely of deterministic spiking neurons. In contrast to previous investigations that were based on a spike rate model of the neuron, the control signal here is determined by the precise temporal positions of spikes generated by the output neurons of the network. We model the problem formally as a hybrid dynamical system comprised of a closed loop between a plant and a spiking neuron network. We derive a novel synaptic weight update rule via which the spiking neuron network controller learns to hold process variables at desired set points. The controller achieves its learning objective based solely on access to the plant’s process variables and their derivatives with respect to changing control signals; in particular, it requires no internal model of the plant. We demonstrate the efficacy of the rule by applying it to the classical control problem of the cart-pole (inverted pendulum) and a model of fish locomotion. Experiments show that the proposed controller has a stability region comparable to a traditional PID controller, its trajectories differ qualitatively from those of a PID controller, and in many instances the controller achieves its objective using very sparse spike train outputs.
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Published	2017-08-08
URL	http://arxiv.org/abs/1708.02603v2
PDF	http://arxiv.org/pdf/1708.02603v2.pdf
PWC	https://paperswithcode.com/paper/learning-feedforward-and-recurrent
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A double competitive strategy based learning automata algorithm


Title	A double competitive strategy based learning automata algorithm
Authors	Chong Di
Abstract	Learning Automata (LA) are considered as one of the most powerful tools in the field of reinforcement learning. The family of estimator algorithms is proposed to improve the convergence rate of LA and has made great achievements. However, the estimators perform poorly on estimating the reward probabilities of actions in the initial stage of the learning process of LA. In this situation, a lot of rewards would be added to the probabilities of non-optimal actions. Thus, a large number of extra iterations are needed to compensate for these wrong rewards. In order to improve the speed of convergence, we propose a new P-model absorbing learning automaton by utilizing a double competitive strategy which is designed for updating the action probability vector. In this way, the wrong rewards can be corrected instantly. Hence, the proposed Double Competitive Algorithm overcomes the drawbacks of existing estimator algorithms. A refined analysis is presented to show the $\epsilon-optimality$ of the proposed scheme. The extensive experimental results in benchmark environments demonstrate that our proposed learning automata perform more efficiently than the most classic LA $SE_{RI}$ and the current fastest LA $DGCPA^{*}$.
Tasks
Published	2017-12-01
URL	http://arxiv.org/abs/1712.00222v1
PDF	http://arxiv.org/pdf/1712.00222v1.pdf
PWC	https://paperswithcode.com/paper/a-double-competitive-strategy-based-learning
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A Short Survey of Biomedical Relation Extraction Techniques


Title	A Short Survey of Biomedical Relation Extraction Techniques
Authors	Elham Shahab
Abstract	Biomedical information is growing rapidly in the recent years and retrieving useful data through information extraction system is getting more attention. In the current research, we focus on different aspects of relation extraction techniques in biomedical domain and briefly describe the state-of-the-art for relation extraction between a variety of biological elements.
Tasks	Relation Extraction
Published	2017-07-18
URL	http://arxiv.org/abs/1707.05850v3
PDF	http://arxiv.org/pdf/1707.05850v3.pdf
PWC	https://paperswithcode.com/paper/a-short-survey-of-biomedical-relation
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Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling


Title	Bayesian Non-Homogeneous Markov Models via Polya-Gamma Data Augmentation with Applications to Rainfall Modeling
Authors	Tracy Holsclaw, Arthur M. Greene, Andrew W. Robertson, Padhraic Smyth
Abstract	Discrete-time hidden Markov models are a broadly useful class of latent-variable models with applications in areas such as speech recognition, bioinformatics, and climate data analysis. It is common in practice to introduce temporal non-homogeneity into such models by making the transition probabilities dependent on time-varying exogenous input variables via a multinomial logistic parametrization. We extend such models to introduce additional non-homogeneity into the emission distribution using a generalized linear model (GLM), with data augmentation for sampling-based inference. However, the presence of the logistic function in the state transition model significantly complicates parameter inference for the overall model, particularly in a Bayesian context. To address this we extend the recently-proposed Polya-Gamma data augmentation approach to handle non-homogeneous hidden Markov models (NHMMs), allowing the development of an efficient Markov chain Monte Carlo (MCMC) sampling scheme. We apply our model and inference scheme to 30 years of daily rainfall in India, leading to a number of insights into rainfall-related phenomena in the region. Our proposed approach allows for fully Bayesian analysis of relatively complex NHMMs on a scale that was not possible with previous methods. Software implementing the methods described in the paper is available via the R package NHMM.
Tasks	Data Augmentation, Latent Variable Models, Speech Recognition
Published	2017-01-11
URL	http://arxiv.org/abs/1701.02856v2
PDF	http://arxiv.org/pdf/1701.02856v2.pdf
PWC	https://paperswithcode.com/paper/bayesian-non-homogeneous-markov-models-via
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Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection


Title	Combining LiDAR Space Clustering and Convolutional Neural Networks for Pedestrian Detection
Authors	Damien Matti, Hazım Kemal Ekenel, Jean-Philippe Thiran
Abstract	Pedestrian detection is an important component for safety of autonomous vehicles, as well as for traffic and street surveillance. There are extensive benchmarks on this topic and it has been shown to be a challenging problem when applied on real use-case scenarios. In purely image-based pedestrian detection approaches, the state-of-the-art results have been achieved with convolutional neural networks (CNN) and surprisingly few detection frameworks have been built upon multi-cue approaches. In this work, we develop a new pedestrian detector for autonomous vehicles that exploits LiDAR data, in addition to visual information. In the proposed approach, LiDAR data is utilized to generate region proposals by processing the three dimensional point cloud that it provides. These candidate regions are then further processed by a state-of-the-art CNN classifier that we have fine-tuned for pedestrian detection. We have extensively evaluated the proposed detection process on the KITTI dataset. The experimental results show that the proposed LiDAR space clustering approach provides a very efficient way of generating region proposals leading to higher recall rates and fewer misses for pedestrian detection. This indicates that LiDAR data can provide auxiliary information for CNN-based approaches.
Tasks	Autonomous Vehicles, Pedestrian Detection
Published	2017-10-17
URL	http://arxiv.org/abs/1710.06160v1
PDF	http://arxiv.org/pdf/1710.06160v1.pdf
PWC	https://paperswithcode.com/paper/combining-lidar-space-clustering-and
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Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis


Title	Anomaly Detection in Multivariate Non-stationary Time Series for Automatic DBMS Diagnosis
Authors	Doyup Lee
Abstract	Anomaly detection in database management systems (DBMSs) is difficult because of increasing number of statistics (stat) and event metrics in big data system. In this paper, I propose an automatic DBMS diagnosis system that detects anomaly periods with abnormal DB stat metrics and finds causal events in the periods. Reconstruction error from deep autoencoder and statistical process control approach are applied to detect time period with anomalies. Related events are found using time series similarity measures between events and abnormal stat metrics. After training deep autoencoder with DBMS metric data, efficacy of anomaly detection is investigated from other DBMSs containing anomalies. Experiment results show effectiveness of proposed model, especially, batch temporal normalization layer. Proposed model is used for publishing automatic DBMS diagnosis reports in order to determine DBMS configuration and SQL tuning.
Tasks	Anomaly Detection, Time Series
Published	2017-08-08
URL	http://arxiv.org/abs/1708.02635v2
PDF	http://arxiv.org/pdf/1708.02635v2.pdf
PWC	https://paperswithcode.com/paper/anomaly-detection-in-multivariate-non
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Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling


Title	Multiobjective Optimization of Solar Powered Irrigation System with Fuzzy Type-2 Noise Modelling
Authors	T. Ganesan, P. Vasant, I. Elamvazuthi
Abstract	Optimization is becoming a crucial element in industrial applications involving sustainable alternative energy systems. During the design of such systems, the engineer/decision maker would often encounter noise factors (e.g. solar insolation and ambient temperature fluctuations) when their system interacts with the environment. In this chapter, the sizing and design optimization of the solar powered irrigation system was considered. This problem is multivariate, noisy, nonlinear and multiobjective. This design problem was tackled by first using the Fuzzy Type II approach to model the noise factors. Consequently, the Bacterial Foraging Algorithm (BFA) (in the context of a weighted sum framework) was employed to solve this multiobjective fuzzy design problem. This method was then used to construct the approximate Pareto frontier as well as to identify the best solution option in a fuzzy setting. Comprehensive analyses and discussions were performed on the generated numerical results with respect to the implemented solution methods.
Tasks	Multiobjective Optimization
Published	2017-01-17
URL	http://arxiv.org/abs/1701.04569v1
PDF	http://arxiv.org/pdf/1701.04569v1.pdf
PWC	https://paperswithcode.com/paper/multiobjective-optimization-of-solar-powered
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An Out-of-the-box Full-network Embedding for Convolutional Neural Networks


Title	An Out-of-the-box Full-network Embedding for Convolutional Neural Networks
Authors	Dario Garcia-Gasulla, Armand Vilalta, Ferran Parés, Jonatan Moreno, Eduard Ayguadé, Jesus Labarta, Ulises Cortés, Toyotaro Suzumura
Abstract	Transfer learning for feature extraction can be used to exploit deep representations in contexts where there is very few training data, where there are limited computational resources, or when tuning the hyper-parameters needed for training is not an option. While previous contributions to feature extraction propose embeddings based on a single layer of the network, in this paper we propose a full-network embedding which successfully integrates convolutional and fully connected features, coming from all layers of a deep convolutional neural network. To do so, the embedding normalizes features in the context of the problem, and discretizes their values to reduce noise and regularize the embedding space. Significantly, this also reduces the computational cost of processing the resultant representations. The proposed method is shown to outperform single layer embeddings on several image classification tasks, while also being more robust to the choice of the pre-trained model used for obtaining the initial features. The performance gap in classification accuracy between thoroughly tuned solutions and the full-network embedding is also reduced, which makes of the proposed approach a competitive solution for a large set of applications.
Tasks	Image Classification, Network Embedding, Transfer Learning
Published	2017-05-22
URL	http://arxiv.org/abs/1705.07706v1
PDF	http://arxiv.org/pdf/1705.07706v1.pdf
PWC	https://paperswithcode.com/paper/an-out-of-the-box-full-network-embedding-for
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A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks


Title	A Multimodal Corpus of Expert Gaze and Behavior during Phonetic Segmentation Tasks
Authors	Arif Khan, Ingmar Steiner, Yusuke Sugano, Andreas Bulling, Ross Macdonald
Abstract	Phonetic segmentation is the process of splitting speech into distinct phonetic units. Human experts routinely perform this task manually by analyzing auditory and visual cues using analysis software, which is an extremely time-consuming process. Methods exist for automatic segmentation, but these are not always accurate enough. In order to improve automatic segmentation, we need to model it as close to the manual segmentation as possible. This corpus is an effort to capture the human segmentation behavior by recording experts performing a segmentation task. We believe that this data will enable us to highlight the important aspects of manual segmentation, which can be used in automatic segmentation to improve its accuracy.
Tasks
Published	2017-12-13
URL	http://arxiv.org/abs/1712.04798v3
PDF	http://arxiv.org/pdf/1712.04798v3.pdf
PWC	https://paperswithcode.com/paper/a-multimodal-corpus-of-expert-gaze-and
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Predicting Severe Sepsis Using Text from the Electronic Health Record


Title	Predicting Severe Sepsis Using Text from the Electronic Health Record
Authors	Phil Culliton, Michael Levinson, Alice Ehresman, Joshua Wherry, Jay S. Steingrub, Stephen I. Gallant
Abstract	Employing a machine learning approach we predict, up to 24 hours prior, a diagnosis of severe sepsis. Strongly predictive models are possible that use only text reports from the Electronic Health Record (EHR), and omit structured numerical data. Unstructured text alone gives slightly better performance than structured data alone, and the combination further improves performance. We also discuss advantages of using unstructured EHR text for modeling, as compared to structured EHR data.
Tasks
Published	2017-11-30
URL	http://arxiv.org/abs/1711.11536v1
PDF	http://arxiv.org/pdf/1711.11536v1.pdf
PWC	https://paperswithcode.com/paper/predicting-severe-sepsis-using-text-from-the
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