Paper Group ANR 597
A Sport Tournament Scheduling by Genetic Algorithm with Swapping Method. Realistic Traffic Generation for Web Robots. Bandit Convex Optimization for Scalable and Dynamic IoT Management. An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges. Towards a More Reliable Privacy-preserving Recommender System. Variance …
A Sport Tournament Scheduling by Genetic Algorithm with Swapping Method
| Title | A Sport Tournament Scheduling by Genetic Algorithm with Swapping Method |
| Authors | Tinnaluk Rutjanisarakul, Thiradet Jiarasuksakun |
| Abstract | A sport tournament problem is considered the Traveling Tournament Problem (TTP). One interesting type is the mirrored Traveling Tournament Problem (mTTP). The objective of the problem is to minimize either the total number of traveling or the total distances of traveling or both. This research aims to find an optimized solution of the mirrored Traveling Tournament Problem with minimum total number of traveling. The solutions consisting of traveling and scheduling tables are solved by using genetic algorithm (GA) with swapping method. The number of traveling of all teams from obtained solutions are close to the lower bound theory of number of traveling. Moreover, this algorithm generates better solutions than known results for most cases. |
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| Published | 2017-04-17 |
| URL | http://arxiv.org/abs/1704.04879v1 |
| http://arxiv.org/pdf/1704.04879v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-sport-tournament-scheduling-by-genetic |
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Realistic Traffic Generation for Web Robots
| Title | Realistic Traffic Generation for Web Robots |
| Authors | Kyle Brown, Derek Doran |
| Abstract | Critical to evaluating the capacity, scalability, and availability of web systems are realistic web traffic generators. Web traffic generation is a classic research problem, no generator accounts for the characteristics of web robots or crawlers that are now the dominant source of traffic to a web server. Administrators are thus unable to test, stress, and evaluate how their systems perform in the face of ever increasing levels of web robot traffic. To resolve this problem, this paper introduces a novel approach to generate synthetic web robot traffic with high fidelity. It generates traffic that accounts for both the temporal and behavioral qualities of robot traffic by statistical and Bayesian models that are fitted to the properties of robot traffic seen in web logs from North America and Europe. We evaluate our traffic generator by comparing the characteristics of generated traffic to those of the original data. We look at session arrival rates, inter-arrival times and session lengths, comparing and contrasting them between generated and real traffic. Finally, we show that our generated traffic affects cache performance similarly to actual traffic, using the common LRU and LFU eviction policies. |
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| Published | 2017-12-15 |
| URL | http://arxiv.org/abs/1712.05813v1 |
| http://arxiv.org/pdf/1712.05813v1.pdf | |
| PWC | https://paperswithcode.com/paper/realistic-traffic-generation-for-web-robots |
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Bandit Convex Optimization for Scalable and Dynamic IoT Management
| Title | Bandit Convex Optimization for Scalable and Dynamic IoT Management |
| Authors | Tianyi Chen, Georgios B. Giannakis |
| Abstract | The present paper deals with online convex optimization involving both time-varying loss functions, and time-varying constraints. The loss functions are not fully accessible to the learner, and instead only the function values (a.k.a. bandit feedback) are revealed at queried points. The constraints are revealed after making decisions, and can be instantaneously violated, yet they must be satisfied in the long term. This setting fits nicely the emerging online network tasks such as fog computing in the Internet-of-Things (IoT), where online decisions must flexibly adapt to the changing user preferences (loss functions), and the temporally unpredictable availability of resources (constraints). Tailored for such human-in-the-loop systems where the loss functions are hard to model, a family of bandit online saddle-point (BanSaP) schemes are developed, which adaptively adjust the online operations based on (possibly multiple) bandit feedback of the loss functions, and the changing environment. Performance here is assessed by: i) dynamic regret that generalizes the widely used static regret; and, ii) fit that captures the accumulated amount of constraint violations. Specifically, BanSaP is proved to simultaneously yield sub-linear dynamic regret and fit, provided that the best dynamic solutions vary slowly over time. Numerical tests in fog computation offloading tasks corroborate that our proposed BanSaP approach offers competitive performance relative to existing approaches that are based on gradient feedback. |
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| Published | 2017-07-27 |
| URL | http://arxiv.org/abs/1707.09060v1 |
| http://arxiv.org/pdf/1707.09060v1.pdf | |
| PWC | https://paperswithcode.com/paper/bandit-convex-optimization-for-scalable-and |
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An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges
| Title | An Asynchronous Distributed Framework for Large-scale Learning Based on Parameter Exchanges |
| Authors | Bikash Joshi, Franck Iutzeler, Massih-Reza Amini |
| Abstract | In many distributed learning problems, the heterogeneous loading of computing machines may harm the overall performance of synchronous strategies. In this paper, we propose an effective asynchronous distributed framework for the minimization of a sum of smooth functions, where each machine performs iterations in parallel on its local function and updates a shared parameter asynchronously. In this way, all machines can continuously work even though they do not have the latest version of the shared parameter. We prove the convergence of the consistency of this general distributed asynchronous method for gradient iterations then show its efficiency on the matrix factorization problem for recommender systems and on binary classification. |
| Tasks | Recommendation Systems |
| Published | 2017-05-22 |
| URL | http://arxiv.org/abs/1705.07751v1 |
| http://arxiv.org/pdf/1705.07751v1.pdf | |
| PWC | https://paperswithcode.com/paper/an-asynchronous-distributed-framework-for |
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Towards a More Reliable Privacy-preserving Recommender System
| Title | Towards a More Reliable Privacy-preserving Recommender System |
| Authors | Jia-Yun Jiang, Cheng-Te Li, Shou-De Lin |
| Abstract | This paper proposes a privacy-preserving distributed recommendation framework, Secure Distributed Collaborative Filtering (SDCF), to preserve the privacy of value, model and existence altogether. That says, not only the ratings from the users to the items, but also the existence of the ratings as well as the learned recommendation model are kept private in our framework. Our solution relies on a distributed client-server architecture and a two-stage Randomized Response algorithm, along with an implementation on the popular recommendation model, Matrix Factorization (MF). We further prove SDCF to meet the guarantee of Differential Privacy so that clients are allowed to specify arbitrary privacy levels. Experiments conducted on numerical rating prediction and one-class rating action prediction exhibit that SDCF does not sacrifice too much accuracy for privacy. |
| Tasks | Recommendation Systems |
| Published | 2017-11-21 |
| URL | http://arxiv.org/abs/1711.07638v2 |
| http://arxiv.org/pdf/1711.07638v2.pdf | |
| PWC | https://paperswithcode.com/paper/towards-a-more-reliable-privacy-preserving |
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Variance Based Moving K-Means Algorithm
| Title | Variance Based Moving K-Means Algorithm |
| Authors | Vibin Vijay, Raghunath Vp, Amarjot Singh, SN Omar |
| Abstract | Clustering is a useful data exploratory method with its wide applicability in multiple fields. However, data clustering greatly relies on initialization of cluster centers that can result in large intra-cluster variance and dead centers, therefore leading to sub-optimal solutions. This paper proposes a novel variance based version of the conventional Moving K-Means (MKM) algorithm called Variance Based Moving K-Means (VMKM) that can partition data into optimal homogeneous clusters, irrespective of cluster initialization. The algorithm utilizes a novel distance metric and a unique data element selection criteria to transfer the selected elements between clusters to achieve low intra-cluster variance and subsequently avoid dead centers. Quantitative and qualitative comparison with various clustering techniques is performed on four datasets selected from image processing, bioinformatics, remote sensing and the stock market respectively. An extensive analysis highlights the superior performance of the proposed method over other techniques. |
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| Published | 2017-04-07 |
| URL | http://arxiv.org/abs/1704.02197v2 |
| http://arxiv.org/pdf/1704.02197v2.pdf | |
| PWC | https://paperswithcode.com/paper/variance-based-moving-k-means-algorithm |
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Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector
| Title | Adaptive compressed 3D imaging based on wavelet trees and Hadamard multiplexing with a single photon counting detector |
| Authors | Huidong Dai, Weiji He, Guohua Gu, Ling Ye, Tianyi Mao, Qian Chen |
| Abstract | Photon counting 3D imaging allows to obtain 3D images with single-photon sensitivity and sub-ns temporal resolution. However, it is challenging to scale to high spatial resolution. In this work, we demonstrate a photon counting 3D imaging technique with short-pulsed structured illumination and a single-pixel photon counting detector. The proposed multi-resolution photon counting 3D imaging technique acquires a high-resolution 3D image from a coarse image and edges at successfully finer resolution sampled by Hadamard multiplexing along the wavelet trees. The detected power is significantly increased thanks to the Hadamard multiplexing. Both the required measurements and the reconstruction time can be significantly reduced by performing wavelet-tree-based regions of edges predication and Hadamard demultiplexing, which makes the proposed technique suitable for scenes with high spatial resolution. The experimental results indicate that a 3D image at resolution up to 512*512 pixels can be acquired and retrieved with practical time as low as 17 seconds. |
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| Published | 2017-09-15 |
| URL | http://arxiv.org/abs/1709.05961v1 |
| http://arxiv.org/pdf/1709.05961v1.pdf | |
| PWC | https://paperswithcode.com/paper/adaptive-compressed-3d-imaging-based-on |
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Reflection Separation and Deblurring of Plenoptic Images
| Title | Reflection Separation and Deblurring of Plenoptic Images |
| Authors | Paramanand Chandramouli, Mehdi Noroozi, Paolo Favaro |
| Abstract | In this paper, we address the problem of reflection removal and deblurring from a single image captured by a plenoptic camera. We develop a two-stage approach to recover the scene depth and high resolution textures of the reflected and transmitted layers. For depth estimation in the presence of reflections, we train a classifier through convolutional neural networks. For recovering high resolution textures, we assume that the scene is composed of planar regions and perform the reconstruction of each layer by using an explicit form of the plenoptic camera point spread function. The proposed framework also recovers the sharp scene texture with different motion blurs applied to each layer. We demonstrate our method on challenging real and synthetic images. |
| Tasks | Deblurring, Depth Estimation |
| Published | 2017-08-22 |
| URL | http://arxiv.org/abs/1708.06779v1 |
| http://arxiv.org/pdf/1708.06779v1.pdf | |
| PWC | https://paperswithcode.com/paper/reflection-separation-and-deblurring-of |
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Exponential convergence of testing error for stochastic gradient methods
| Title | Exponential convergence of testing error for stochastic gradient methods |
| Authors | Loucas Pillaud-Vivien, Alessandro Rudi, Francis Bach |
| Abstract | We consider binary classification problems with positive definite kernels and square loss, and study the convergence rates of stochastic gradient methods. We show that while the excess testing loss (squared loss) converges slowly to zero as the number of observations (and thus iterations) goes to infinity, the testing error (classification error) converges exponentially fast if low-noise conditions are assumed. |
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| Published | 2017-12-13 |
| URL | http://arxiv.org/abs/1712.04755v4 |
| http://arxiv.org/pdf/1712.04755v4.pdf | |
| PWC | https://paperswithcode.com/paper/exponential-convergence-of-testing-error-for |
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Building Data-driven Models with Microstructural Images: Generalization and Interpretability
| Title | Building Data-driven Models with Microstructural Images: Generalization and Interpretability |
| Authors | Julia Ling, Maxwell Hutchinson, Erin Antono, Brian DeCost, Elizabeth A. Holm, Bryce Meredig |
| Abstract | As data-driven methods rise in popularity in materials science applications, a key question is how these machine learning models can be used to understand microstructure. Given the importance of process-structure-property relations throughout materials science, it seems logical that models that can leverage microstructural data would be more capable of predicting property information. While there have been some recent attempts to use convolutional neural networks to understand microstructural images, these early studies have focused only on which featurizations yield the highest machine learning model accuracy for a single data set. This paper explores the use of convolutional neural networks for classifying microstructure with a more holistic set of objectives in mind: generalization between data sets, number of features required, and interpretability. |
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| Published | 2017-11-01 |
| URL | http://arxiv.org/abs/1711.00404v1 |
| http://arxiv.org/pdf/1711.00404v1.pdf | |
| PWC | https://paperswithcode.com/paper/building-data-driven-models-with |
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Identifying 3 moss species by deep learning, using the “chopped picture” method
| Title | Identifying 3 moss species by deep learning, using the “chopped picture” method |
| Authors | Takeshi Ise, Mari Minagawa, Masanori Onishi |
| Abstract | In general, object identification tends not to work well on ambiguous, amorphous objects such as vegetation. In this study, we developed a simple but effective approach to identify ambiguous objects and applied the method to several moss species. As a result, the model correctly classified test images with accuracy more than 90%. Using this approach will help progress in computer vision studies. |
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| Published | 2017-08-07 |
| URL | http://arxiv.org/abs/1708.01986v2 |
| http://arxiv.org/pdf/1708.01986v2.pdf | |
| PWC | https://paperswithcode.com/paper/identifying-3-moss-species-by-deep-learning |
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Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images
| Title | Where computer vision can aid physics: dynamic cloud motion forecasting from satellite images |
| Authors | Sergiy Zhuk, Tigran Tchrakian, Albert Akhriev, Siyuan Lu, Hendrik Hamann |
| Abstract | This paper describes a new algorithm for solar energy forecasting from a sequence of Cloud Optical Depth (COD) images. The algorithm is based on the following simple observation: the dynamics of clouds represented by COD images resembles the motion (transport) of a density in a fluid flow. This suggests that, to forecast the motion of COD images, it is sufficient to forecast the flow. The latter, in turn, can be accomplished by fitting a parametric model of the fluid flow to the COD images observed in the past. Namely, the learning phase of the algorithm is composed of the following steps: (i) given a sequence of COD images, the snapshots of the optical flow are estimated from two consecutive COD images; (ii) these snapshots are then assimilated into a Navier-Stokes Equation (NSE), i.e. an initial velocity field for NSE is selected so that the corresponding NSE’ solution is as close as possible to the optical flow snapshots. The prediction phase consists of utilizing a linear transport equation, which describes the propagation of COD images in the fluid flow predicted by NSE, to estimate the future motion of the COD images. The algorithm has been tested on COD images provided by two geostationary operational environmental satellites from NOAA serving the west-hemisphere. |
| Tasks | Motion Forecasting, Optical Flow Estimation |
| Published | 2017-09-30 |
| URL | http://arxiv.org/abs/1710.00194v1 |
| http://arxiv.org/pdf/1710.00194v1.pdf | |
| PWC | https://paperswithcode.com/paper/where-computer-vision-can-aid-physics-dynamic |
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AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control
| Title | AMBER: Adaptive Multi-Batch Experience Replay for Continuous Action Control |
| Authors | Seungyul Han, Youngchul Sung |
| Abstract | In this paper, a new adaptive multi-batch experience replay scheme is proposed for proximal policy optimization (PPO) for continuous action control. On the contrary to original PPO, the proposed scheme uses the batch samples of past policies as well as the current policy for the update for the next policy, where the number of the used past batches is adaptively determined based on the oldness of the past batches measured by the average importance sampling (IS) weight. The new algorithm constructed by combining PPO with the proposed multi-batch experience replay scheme maintains the advantages of original PPO such as random mini-batch sampling and small bias due to low IS weights by storing the pre-computed advantages and values and adaptively determining the mini-batch size. Numerical results show that the proposed method significantly increases the speed and stability of convergence on various continuous control tasks compared to original PPO. |
| Tasks | Continuous Control |
| Published | 2017-10-12 |
| URL | http://arxiv.org/abs/1710.04423v2 |
| http://arxiv.org/pdf/1710.04423v2.pdf | |
| PWC | https://paperswithcode.com/paper/amber-adaptive-multi-batch-experience-replay |
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On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search
| Title | On the Discrimination Power and Effective Utilization of Active Learning Measures in Version Space Search |
| Authors | Patrick Rodler |
| Abstract | Active Learning (AL) methods have proven cost-saving against passive supervised methods in many application domains. An active learner, aiming to find some target hypothesis, formulates sequential queries to some oracle. The set of hypotheses consistent with the already answered queries is called version space. Several query selection measures (QSMs) for determining the best query to ask next have been proposed. Assuming binaryoutcome queries, we analyze various QSMs wrt. to the discrimination power of their selected queries within the current version space. As a result, we derive superiority and equivalence relations between these QSMs and introduce improved versions of existing QSMs to overcome identified issues. The obtained picture gives a hint about which QSMs should preferably be used in pool-based AL scenarios. Moreover, we deduce properties optimal queries wrt. QSMs must satisfy. Based on these, we demonstrate how efficient heuristic search methods for optimal queries in query synthesis AL scenarios can be devised. |
| Tasks | Active Learning |
| Published | 2017-09-22 |
| URL | http://arxiv.org/abs/1709.07899v1 |
| http://arxiv.org/pdf/1709.07899v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-the-discrimination-power-and-effective |
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GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data
| Title | GMM-Based Synthetic Samples for Classification of Hyperspectral Images With Limited Training Data |
| Authors | AmirAbbas Davari, Erchan Aptoula, Berrin Yanikoglu, Andreas Maier, Christian Riess |
| Abstract | The amount of training data that is required to train a classifier scales with the dimensionality of the feature data. In hyperspectral remote sensing, feature data can potentially become very high dimensional. However, the amount of training data is oftentimes limited. Thus, one of the core challenges in hyperspectral remote sensing is how to perform multi-class classification using only relatively few training data points. In this work, we address this issue by enriching the feature matrix with synthetically generated sample points. This synthetic data is sampled from a GMM fitted to each class of the limited training data. Although, the true distribution of features may not be perfectly modeled by the fitted GMM, we demonstrate that a moderate augmentation by these synthetic samples can effectively replace a part of the missing training samples. We show the efficacy of the proposed approach on two hyperspectral datasets. The median gain in classification performance is $5%$. It is also encouraging that this performance gain is remarkably stable for large variations in the number of added samples, which makes it much easier to apply this method to real-world applications. |
| Tasks | Classification Of Hyperspectral Images |
| Published | 2017-12-13 |
| URL | http://arxiv.org/abs/1712.04778v1 |
| http://arxiv.org/pdf/1712.04778v1.pdf | |
| PWC | https://paperswithcode.com/paper/gmm-based-synthetic-samples-for |
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