January 28, 2020

3095 words 15 mins read

Paper Group ANR 938

A machine learning framework for computationally expensive transient models. Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting. GarmNet: Improving Global with Local Perception for Robotic Laundry Folding. Adversarial Model Extraction on Graph Neural Networks. Federated Uncertainty-Aware Learning for …

A machine learning framework for computationally expensive transient models


Title	A machine learning framework for computationally expensive transient models
Authors	Prashant Kumar, Kushal Sinha, Nandkishor Nere, Yujin Shin, Raimundo Ho, Ahmad Sheikh, Laurie Mlinar
Abstract	The promise of machine learning has been explored in a variety of scientific disciplines in the last few years, however, its application on first-principles based computationally expensive tools is still in nascent stage. Even with the advances in computational resources and power, transient simulations of large-scale dynamic systems using a variety of the first-principles based computational tools are still limited. In this work, we propose an ensemble approach where we combine one such computationally expensive tool, called discrete element method (DEM), with a time-series forecasting method called auto-regressive integrated moving average (ARIMA) and machine-learning methods to significantly reduce the computational burden while retaining model accuracy and performance. The developed machine-learning model shows good predictability and agreement with the literature, demonstrating its tremendous potential in scientific computing.
Tasks	Time Series, Time Series Forecasting
Published	2019-07-12
URL	https://arxiv.org/abs/1907.05928v1
PDF	https://arxiv.org/pdf/1907.05928v1.pdf
PWC	https://paperswithcode.com/paper/a-machine-learning-framework-for
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Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting


Title	Analysis of Wide and Deep Echo State Networks for Multiscale Spatiotemporal Time Series Forecasting
Authors	Zachariah Carmichael, Humza Syed, Dhireesha Kudithipudi
Abstract	Echo state networks are computationally lightweight reservoir models inspired by the random projections observed in cortical circuitry. As interest in reservoir computing has grown, networks have become deeper and more intricate. While these networks are increasingly applied to nontrivial forecasting tasks, there is a need for comprehensive performance analysis of deep reservoirs. In this work, we study the influence of partitioning neurons given a budget and the effect of parallel reservoir pathways across different datasets exhibiting multi-scale and nonlinear dynamics.
Tasks	Time Series, Time Series Forecasting
Published	2019-07-01
URL	https://arxiv.org/abs/1908.08380v1
PDF	https://arxiv.org/pdf/1908.08380v1.pdf
PWC	https://paperswithcode.com/paper/analysis-of-wide-and-deep-echo-state-networks
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GarmNet: Improving Global with Local Perception for Robotic Laundry Folding


Title	GarmNet: Improving Global with Local Perception for Robotic Laundry Folding
Authors	Daniel Fernandes Gomes, Shan Luo, Luis F. Teixeira
Abstract	Developing autonomous assistants to help with domestic tasks is a vital topic in robotics research. Among these tasks, garment folding is one of them that is still far from being achieved mainly due to the large number of possible configurations that a crumpled piece of clothing may exhibit. Research has been done on either estimating the pose of the garment as a whole or detecting the landmarks for grasping separately. However, such works constrain the capability of the robots to perceive the states of the garment by limiting the representations for one single task. In this paper, we propose a novel end-to-end deep learning model named GarmNet that is able to simultaneously localize the garment and detect landmarks for grasping. The localization of the garment represents the global information for recognising the category of the garment, whereas the detection of landmarks can facilitate subsequent grasping actions. We train and evaluate our proposed GarmNet model using the CloPeMa Garment dataset that contains 3,330 images of different garment types in different poses. The experiments show that the inclusion of landmark detection (GarmNet-B) can largely improve the garment localization, with an error rate of 24.7% lower. Solutions as ours are important for robotics applications, as these offer scalable to many classes, memory and processing efficient solutions.
Tasks
Published	2019-06-30
URL	https://arxiv.org/abs/1907.00408v1
PDF	https://arxiv.org/pdf/1907.00408v1.pdf
PWC	https://paperswithcode.com/paper/garmnet-improving-global-with-local
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Adversarial Model Extraction on Graph Neural Networks


Title	Adversarial Model Extraction on Graph Neural Networks
Authors	David DeFazio, Arti Ramesh
Abstract	Along with the advent of deep neural networks came various methods of exploitation, such as fooling the classifier or contaminating its training data. Another such attack is known as model extraction, where provided API access to some black box neural network, the adversary extracts the underlying model. This is done by querying the model in such a way that the underlying neural network provides enough information to the adversary to be reconstructed. While several works have achieved impressive results with neural network extraction in the propositional domain, this problem has not yet been considered over the relational domain, where data samples are no longer considered to be independent and identically distributed (iid). Graph Neural Networks (GNNs) are a popular deep learning framework to perform machine learning tasks over relational data. In this work, we formalize an instance of GNN extraction, present a solution with preliminary results, and discuss our assumptions and future directions.
Tasks
Published	2019-12-16
URL	https://arxiv.org/abs/1912.07721v1
PDF	https://arxiv.org/pdf/1912.07721v1.pdf
PWC	https://paperswithcode.com/paper/adversarial-model-extraction-on-graph-neural
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Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data


Title	Federated Uncertainty-Aware Learning for Distributed Hospital EHR Data
Authors	Sabri Boughorbel, Fethi Jarray, Neethu Venugopal, Shabir Moosa, Haithum Elhadi, Michel Makhlouf
Abstract	Recent works have shown that applying Machine Learning to Electronic Health Records (EHR) can strongly accelerate precision medicine. This requires developing models based on diverse EHR sources. Federated Learning (FL) has enabled predictive modeling using distributed training which lifted the need of sharing data and compromising privacy. Since models are distributed in FL, it is attractive to devise ensembles of Deep Neural Networks that also assess model uncertainty. We propose a new FL model called Federated Uncertainty-Aware Learning Algorithm (FUALA) that improves on Federated Averaging (FedAvg) in the context of EHR. FUALA embeds uncertainty information in two ways: It reduces the contribution of models with high uncertainty in the aggregated model. It also introduces model ensembling at prediction time by keeping the last layers of each hospital from the final round. In FUALA, the Federator (central node) sends at each round the average model to all hospitals as well as a randomly assigned hospital model update to estimate its generalization on that hospital own data. Each hospital sends back its model update as well a generalization estimation of the assigned model. At prediction time, the model outputs C predictions for each sample where C is the number of hospital models. The experimental analysis conducted on a cohort of 87K deliveries for the task of preterm-birth prediction showed that the proposed approach outperforms FedAvg when evaluated on out-of-distribution data. We illustrated how uncertainty could be measured using the proposed approach.
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Published	2019-10-27
URL	https://arxiv.org/abs/1910.12191v1
PDF	https://arxiv.org/pdf/1910.12191v1.pdf
PWC	https://paperswithcode.com/paper/federated-uncertainty-aware-learning-for
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Memorize, Then Recall: A Generative Framework for Low Bit-rate Surveillance Video Compression


Title	Memorize, Then Recall: A Generative Framework for Low Bit-rate Surveillance Video Compression
Authors	Yaojun Wu, Tianyu He, Zhibo Chen
Abstract	Surveillance video applications grow dramatically in public safety and daily life, which often detect and recognize moving objects inside video signals. Existing surveillance video compression schemes are still based on traditional hybrid coding frameworks handling temporal redundancy by block-wise motion compensation mechanism, lacking the extraction and utilization of inherent structure information. In this paper, we alleviate this issue by decomposing surveillance video signals into the structure of a global spatio-temporal feature (memory) and skeleton for each frame (clue). The memory is abstracted by a recurrent neural network across Group of Pictures (GoP) inside one video sequence, representing appearance for elements that appeared inside GoP. While the skeleton is obtained by the specific pose estimator, it served as a clue for recalling memory. In addition, we introduce an attention mechanism to learn the relationships between appearance and skeletons. And we reconstruct each frame with an adversarial training process. Experimental results demonstrate that our approach can effectively generate realistic frames from appearance and skeleton accordingly. Compared with the latest video compression standard H.265, it shows much higher compression performance on surveillance video.
Tasks	Motion Compensation, Video Compression
Published	2019-12-30
URL	https://arxiv.org/abs/1912.12847v1
PDF	https://arxiv.org/pdf/1912.12847v1.pdf
PWC	https://paperswithcode.com/paper/memorize-then-recall-a-generative-framework
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Generating Difficult SAT Instances by Preventing Triangles


Title	Generating Difficult SAT Instances by Preventing Triangles
Authors	Guillaume Escamocher, Barry O’Sullivan, Steven David Prestwich
Abstract	When creating benchmarks for SAT solvers, we need SAT instances that are easy to build but hard to solve. A recent development in the search for such methods has led to the Balanced SAT algorithm, which can create k-SAT instances with m clauses of high difficulty, for arbitrary k and m. In this paper we introduce the No-Triangle SAT algorithm, a SAT instance generator based on the cluster coefficient graph statistic. We empirically compare the two algorithms by fixing the arity and the number of variables, but varying the number of clauses. The hardest instances that we find are produced by No-Triangle SAT. Furthermore, difficult instances from No-Triangle SAT have a different number of clauses than difficult instances from Balanced SAT, potentially allowing a combination of the two methods to find hard SAT instances for a larger array of parameters.
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Published	2019-03-08
URL	http://arxiv.org/abs/1903.03592v1
PDF	http://arxiv.org/pdf/1903.03592v1.pdf
PWC	https://paperswithcode.com/paper/generating-difficult-sat-instances-by
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A Document Skew Detection Method Using Fast Hough Transform


Title	A Document Skew Detection Method Using Fast Hough Transform
Authors	Pavel Bezmaternykh, Dmitry Nikolaev
Abstract	The majority of document image analysis systems use a document skew detection algorithm to simplify all its further processing stages. A huge amount of such algorithms based on Hough transform (HT) analysis has already been proposed. Despite this, we managed to find only one work where the Fast Hough Transform (FHT) usage was suggested to solve the indicated problem. Unfortunately, no study of that method was provided. In this work, we propose and study a skew detection algorithm for the document images which relies on FHT analysis. To measure this algorithm quality we use the dataset from the problem oriented DISEC’13 contest and its evaluation methodology. Obtained values for AED, TOP80, and CE criteria are equal to 0.086, 0.056, 68.80 respectively.
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Published	2019-12-05
URL	https://arxiv.org/abs/1912.02504v1
PDF	https://arxiv.org/pdf/1912.02504v1.pdf
PWC	https://paperswithcode.com/paper/a-document-skew-detection-method-using-fast
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Research on dynamic target detection and tracking system of hexapod robot


Title	Research on dynamic target detection and tracking system of hexapod robot
Authors	Dexin Wang
Abstract	Dynamic target detection and target tracking are hot issues in the field of image. In order to explore its application value in the field of mobile robot, a dynamic target detection and tracking system is designed based on hexapod robot. Firstly, the dynamic target detection method is introduced with region merging and adaptive external point filtering based on motion compensation method. This method achieves the accurate compensation of the moving background through symmetric matching and adaptive external point filtering, and achieves complete detection of non-rigid objects by region merging. Secondly, the application of target tracking algorithm based on KCF in hexapod robot platform is studied, and the Angle tracking of moving target is realized by adaptive adjustment of tracking speed. The last, the architecture of robot monitoring system is designed, which consists of operator, processor, hexapod robot and vision sensor, and the moving object detection and tracking algorithm proposed in this paper is applied to the system. The experimental results show that the improved algorithm can effectively detect and track the moving target when applied to the system of the mobile hexapod robot.
Tasks	Motion Compensation, Object Detection
Published	2019-12-04
URL	https://arxiv.org/abs/1912.01992v1
PDF	https://arxiv.org/pdf/1912.01992v1.pdf
PWC	https://paperswithcode.com/paper/research-on-dynamic-target-detection-and
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Comparison of Neural Network Architectures for Spectrum Sensing


Title	Comparison of Neural Network Architectures for Spectrum Sensing
Authors	Ziyu Ye, Andrew Gilman, Qihang Peng, Kelly Levick, Pamela Cosman, Larry Milstein
Abstract	Different neural network (NN) architectures have different advantages. Convolutional neural networks (CNNs) achieved enormous success in computer vision, while recurrent neural networks (RNNs) gained popularity in speech recognition. It is not known which type of NN architecture is the best fit for classification of communication signals. In this work, we compare the behavior of fully-connected NN (FC), CNN, RNN, and bi-directional RNN (BiRNN) in a spectrum sensing task. The four NN architectures are compared on their detection performance, requirement of training data, computational complexity, and memory requirement. Given abundant training data and computational and memory resources, CNN, RNN, and BiRNN are shown to achieve similar performance. The performance of FC is worse than that of the other three types, except in the case where computational complexity is stringently limited.
Tasks	Speech Recognition
Published	2019-07-15
URL	https://arxiv.org/abs/1907.07321v1
PDF	https://arxiv.org/pdf/1907.07321v1.pdf
PWC	https://paperswithcode.com/paper/comparison-of-neural-network-architectures
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Multi-Attribute Bayesian Optimization With Interactive Preference Learning


Title	Multi-Attribute Bayesian Optimization With Interactive Preference Learning
Authors	Raul Astudillo, Peter I. Frazier
Abstract	We consider black-box global optimization of time-consuming-to-evaluate functions on behalf of a decision-maker (DM) whose preferences must be learned. Each feasible design is associated with a time-consuming-to-evaluate vector of attributes and each vector of attributes is assigned a utility by the DM’s utility function, which may be learned approximately using preferences expressed over pairs of attribute vectors. Past work has used a point estimate of this utility function as if it were error-free within single-objective optimization. However, utility estimation errors may yield a poor suggested design. Furthermore, this approach produces a single suggested “best” design, whereas DMs often prefer to choose from a menu. We propose a novel multi-attribute Bayesian optimization with preference learning approach. Our approach acknowledges the uncertainty in preference estimation and implicitly chooses designs to evaluate that are good not just for a single estimated utility function but a range of likely ones. The outcome of our approach is a menu of designs and evaluated attributes from which the DM makes a final selection. We demonstrate the value and flexibility of our approach in a variety of experiments.
Tasks
Published	2019-11-14
URL	https://arxiv.org/abs/1911.05934v2
PDF	https://arxiv.org/pdf/1911.05934v2.pdf
PWC	https://paperswithcode.com/paper/bayesian-optimization-with-uncertain
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Mathematical Reasoning in Latent Space


Title	Mathematical Reasoning in Latent Space
Authors	Dennis Lee, Christian Szegedy, Markus N. Rabe, Sarah M. Loos, Kshitij Bansal
Abstract	We design and conduct a simple experiment to study whether neural networks can perform several steps of approximate reasoning in a fixed dimensional latent space. The set of rewrites (i.e. transformations) that can be successfully performed on a statement represents essential semantic features of the statement. We can compress this information by embedding the formula in a vector space, such that the vector associated with a statement can be used to predict whether a statement can be rewritten by other theorems. Predicting the embedding of a formula generated by some rewrite rule is naturally viewed as approximate reasoning in the latent space. In order to measure the effectiveness of this reasoning, we perform approximate deduction sequences in the latent space and use the resulting embedding to inform the semantic features of the corresponding formal statement (which is obtained by performing the corresponding rewrite sequence using real formulas). Our experiments show that graph neural networks can make non-trivial predictions about the rewrite-success of statements, even when they propagate predicted latent representations for several steps. Since our corpus of mathematical formulas includes a wide variety of mathematical disciplines, this experiment is a strong indicator for the feasibility of deduction in latent space in general.
Tasks
Published	2019-09-26
URL	https://arxiv.org/abs/1909.11851v1
PDF	https://arxiv.org/pdf/1909.11851v1.pdf
PWC	https://paperswithcode.com/paper/mathematical-reasoning-in-latent-space
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Data Aggregation Techniques for Internet of Things


Title	Data Aggregation Techniques for Internet of Things
Authors	Sunny Sanyal
Abstract	The goal of this dissertation is to design efficient data aggregation frameworks for massive IoT networks in different scenarios to support the proper functioning of IoT analytics layer. This dissertation includes modern algorithmic frameworks such as non convex optimization, machine learning, stochastic matrix perturbation theory and federated filtering along with modern computing infrastructure such as fog computing and cloud computing. The development of such an ambitious design involves many open challenges, this proposal envisions three major open challenges for IoT data aggregation: first, severe resource constraints of IoT nodes due to limited power and computational ability, second, the highly uncertain (unreliable) raw IoT data is not fit for decisionmaking and third, network latency and privacy issue for critical applications. This dissertation presents three independent novel approaches for distinct scenarios to solve one or more aforementioned open challenges. The first approach focuses on energy efficient routing; discusses a clustering protocol based on device to device communication for both stationary and mobile IoT nodes. The second approach focuses on processing uncertain raw IoT data; presents an IoT data aggregation scheme to improve the quality of raw IoT data. Finally, the third approach focuses on power loss due to communication overhead and privacy issues for medical IoT devices (IoMT); describes a prediction based data aggregation framework for massive IoMT devices.
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Published	2019-07-24
URL	https://arxiv.org/abs/1907.11367v1
PDF	https://arxiv.org/pdf/1907.11367v1.pdf
PWC	https://paperswithcode.com/paper/data-aggregation-techniques-for-internet-of
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Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning


Title	Coordination in Adversarial Sequential Team Games via Multi-Agent Deep Reinforcement Learning
Authors	Andrea Celli, Marco Ciccone, Raffaele Bongo, Nicola Gatti
Abstract	Many real-world applications involve teams of agents that have to coordinate their actions to reach a common goal against potential adversaries. This paper focuses on zero-sum games where a team of players faces an opponent, as is the case, for example, in Bridge, collusion in poker, and collusion in bidding. The possibility for the team members to communicate before gameplay—that is, coordinate their strategies ex ante—makes the use of behavioral strategies unsatisfactory. We introduce Soft Team Actor-Critic (STAC) as a solution to the team’s coordination problem that does not require any prior domain knowledge. STAC allows team members to effectively exploit ex ante communication via exogenous signals that are shared among the team. STAC reaches near-optimal coordinated strategies both in perfectly observable and partially observable games, where previous deep RL algorithms fail to reach optimal coordinated behaviors.
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Published	2019-12-16
URL	https://arxiv.org/abs/1912.07712v1
PDF	https://arxiv.org/pdf/1912.07712v1.pdf
PWC	https://paperswithcode.com/paper/coordination-in-adversarial-sequential-team
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Diffusion and Auction on Graphs


Title	Diffusion and Auction on Graphs
Authors	Bin Li, Dong Hao, Dengji Zhao, Makoto Yokoo
Abstract	Auction is the common paradigm for resource allocation which is a fundamental problem in human society. Existing research indicates that the two primary objectives, the seller’s revenue and the allocation efficiency, are generally conflicting in auction design. For the first time, we expand the domain of the classic auction to a social graph and formally identify a new class of auction mechanisms on graphs. All mechanisms in this class are incentive-compatible and also promote all buyers to diffuse the auction information to others, whereby both the seller’s revenue and the allocation efficiency are significantly improved comparing with the Vickrey auction. It is found that the recently proposed information diffusion mechanism is an extreme case with the lowest revenue in this new class. Our work could potentially inspire a new perspective for the efficient and optimal auction design and could be applied into the prevalent online social and economic networks.
Tasks
Published	2019-05-23
URL	https://arxiv.org/abs/1905.09604v2
PDF	https://arxiv.org/pdf/1905.09604v2.pdf
PWC	https://paperswithcode.com/paper/diffusion-and-auction-on-graphs
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