January 27, 2020

3009 words 15 mins read

Paper Group ANR 1268

Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering. Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography. Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches. A Deep Neural Network for Short-Segment Speaker Recognition. From Sp …

Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering


Title	Improving the Scalability of a Prosumer Cooperative Game with K-Means Clustering
Authors	Liyang Han, Thomas Morstyn, Constance Crozier, Malcolm McCulloch
Abstract	Among the various market structures under peer-to-peer energy sharing, one model based on cooperative game theory provides clear incentives for prosumers to collaboratively schedule their energy resources. The computational complexity of this model, however, increases exponentially with the number of participants. To address this issue, this paper proposes the application of K-means clustering to the energy profiles following the grand coalition optimization. The cooperative model is run with the “clustered players” to compute their payoff allocations, which are then further distributed among the prosumers within each cluster. Case studies show that the proposed method can significantly improve the scalability of the cooperative scheme while maintaining a high level of financial incentives for the prosumers.
Tasks
Published	2019-03-26
URL	http://arxiv.org/abs/1903.10965v1
PDF	http://arxiv.org/pdf/1903.10965v1.pdf
PWC	https://paperswithcode.com/paper/improving-the-scalability-of-a-prosumer
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Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography


Title	Corticospinal Tract (CST) reconstruction based on fiber orientation distributions(FODs) tractography
Authors	Youshan Zhang
Abstract	The Corticospinal Tract (CST) is a part of pyramidal tract (PT), and it can innervate the voluntary movement of skeletal muscle through spinal interneurons (the 4th layer of the Rexed gray board layers), and anterior horn motorneurons (which control trunk and proximal limb muscles). Spinal cord injury (SCI) is a highly disabling disease often caused by traffic accidents. The recovery of CST and the functional reconstruction of spinal anterior horn motor neurons play an essential role in the treatment of SCI. However, the localization and reconstruction of CST are still challenging issues; the accuracy of the geometric reconstruction can directly affect the results of the surgery. The main contribution of this paper is the reconstruction of the CST based on the fiber orientation distributions (FODs) tractography. Differing from tensor-based tractography in which the primary direction is a determined orientation, the direction of FODs tractography is determined by the probability. The spherical harmonics (SPHARM) can be used to approximate the efficiency of FODs tractography. We manually delineate the three ROIs (the posterior limb of the internal capsule, the cerebral peduncle, and the anterior pontine area) by the ITK-SNAP software, and use the pipeline software to reconstruct both the left and right sides of the CST fibers. Our results demonstrate that FOD-based tractography can show more and correct anatomical CST fiber bundles.
Tasks
Published	2019-04-23
URL	http://arxiv.org/abs/1904.11136v1
PDF	http://arxiv.org/pdf/1904.11136v1.pdf
PWC	https://paperswithcode.com/paper/corticospinal-tract-cst-reconstruction-based
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Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches


Title	Low-Shot Classification: A Comparison of Classical and Deep Transfer Machine Learning Approaches
Authors	Peter Usherwood, Steven Smit
Abstract	Despite the recent success of deep transfer learning approaches in NLP, there is a lack of quantitative studies demonstrating the gains these models offer in low-shot text classification tasks over existing paradigms. Deep transfer learning approaches such as BERT and ULMFiT demonstrate that they can beat state-of-the-art results on larger datasets, however when one has only 100-1000 labelled examples per class, the choice of approach is less clear, with classical machine learning and deep transfer learning representing valid options. This paper compares the current best transfer learning approach with top classical machine learning approaches on a trinary sentiment classification task to assess the best paradigm. We find that BERT, representing the best of deep transfer learning, is the best performing approach, outperforming top classical machine learning algorithms by 9.7% on average when trained with 100 examples per class, narrowing to 1.8% at 1000 labels per class. We also show the robustness of deep transfer learning in moving across domains, where the maximum loss in accuracy is only 0.7% in similar domain tasks and 3.2% cross domain, compared to classical machine learning which loses up to 20.6%.
Tasks	Sentiment Analysis, Text Classification, Transfer Learning
Published	2019-07-17
URL	https://arxiv.org/abs/1907.07543v1
PDF	https://arxiv.org/pdf/1907.07543v1.pdf
PWC	https://paperswithcode.com/paper/low-shot-classification-a-comparison-of
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A Deep Neural Network for Short-Segment Speaker Recognition


Title	A Deep Neural Network for Short-Segment Speaker Recognition
Authors	Amirhossein Hajavi, Ali Etemad
Abstract	Todays interactive devices such as smart-phone assistants and smart speakers often deal with short-duration speech segments. As a result, speaker recognition systems integrated into such devices will be much better suited with models capable of performing the recognition task with short-duration utterances. In this paper, a new deep neural network, UtterIdNet, capable of performing speaker recognition with short speech segments is proposed. Our proposed model utilizes a novel architecture that makes it suitable for short-segment speaker recognition through an efficiently increased use of information in short speech segments. UtterIdNet has been trained and tested on the VoxCeleb datasets, the latest benchmarks in speaker recognition. Evaluations for different segment durations show consistent and stable performance for short segments, with significant improvement over the previous models for segments of 2 seconds, 1 second, and especially sub-second durations (250 ms and 500 ms).
Tasks	Speaker Recognition
Published	2019-07-22
URL	https://arxiv.org/abs/1907.10420v1
PDF	https://arxiv.org/pdf/1907.10420v1.pdf
PWC	https://paperswithcode.com/paper/a-deep-neural-network-for-short-segment
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From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images


Title	From Species to Cultivar: Soybean Cultivar Recognition using Multiscale Sliding Chord Matching of Leaf Images
Authors	Bin Wang, Yongsheng Gao, Xiaohan Yu, Xiaohui Yuan, Shengwu Xiong, Xianzhong Feng
Abstract	Leaf image recognition techniques have been actively researched for plant species identification. However it remains unclear whether leaf patterns can provide sufficient information for cultivar recognition. This paper reports the first attempt on soybean cultivar recognition from plant leaves which is not only a challenging research problem but also important for soybean cultivar evaluation, selection and production in agriculture. In this paper, we propose a novel multiscale sliding chord matching (MSCM) approach to extract leaf patterns that are distinctive for soybean cultivar identification. A chord is defined to slide along the contour for measuring the synchronised patterns of exterior shape and interior appearance of soybean leaf images. A multiscale sliding chord strategy is developed to extract features in a coarse-to-fine hierarchical order. A joint description that integrates the leaf descriptors from different parts of a soybean plant is proposed for further enhancing the discriminative power of cultivar description. We built a cultivar leaf image database, SoyCultivar, consisting of 1200 sample leaf images from 200 soybean cultivars for performance evaluation. Encouraging experimental results of the proposed method in comparison to the state-of-the-art leaf species recognition methods demonstrate the availability of cultivar information in soybean leaves and effectiveness of the proposed MSCM for soybean cultivar identification, which may advance the research in leaf recognition from species to cultivar.
Tasks
Published	2019-10-11
URL	https://arxiv.org/abs/1910.04919v1
PDF	https://arxiv.org/pdf/1910.04919v1.pdf
PWC	https://paperswithcode.com/paper/from-species-to-cultivar-soybean-cultivar
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A Bias Trick for Centered Robust Principal Component Analysis


Title	A Bias Trick for Centered Robust Principal Component Analysis
Authors	Baokun He, Guihong Wan, Haim Schweitzer
Abstract	Outlier based Robust Principal Component Analysis (RPCA) requires centering of the non-outliers. We show a “bias trick” that automatically centers these non-outliers. Using this bias trick we obtain the first RPCA algorithm that is optimal with respect to centering.
Tasks
Published	2019-11-19
URL	https://arxiv.org/abs/1911.08024v1
PDF	https://arxiv.org/pdf/1911.08024v1.pdf
PWC	https://paperswithcode.com/paper/a-bias-trick-for-centered-robust-principal
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Towards end-to-end pulsed eddy current classification and regression with CNN


Title	Towards end-to-end pulsed eddy current classification and regression with CNN
Authors	Xin Fu, Chengkai Zhang, Xiang Peng, Lihua Jian, Zheng Liu
Abstract	Pulsed eddy current (PEC) is an effective electromagnetic non-destructive inspection (NDI) technique for metal materials, which has already been widely adopted in detecting cracking and corrosion in some multi-layer structures. Automatically inspecting the defects in these structures would be conducive to further analysis and treatment of them. In this paper, we propose an effective end-to-end model using convolutional neural networks (CNN) to learn effective features from PEC data. Specifically, we construct a multi-task generic model, based on 1D CNN, to predict both the class and depth of flaws simultaneously. Extensive experiments demonstrate our model is capable of handling both classification and regression tasks on PEC data. Our proposed model obtains higher accuracy and lower error compared to other standard methods.
Tasks
Published	2019-02-22
URL	http://arxiv.org/abs/1902.08553v1
PDF	http://arxiv.org/pdf/1902.08553v1.pdf
PWC	https://paperswithcode.com/paper/towards-end-to-end-pulsed-eddy-current
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Development of verification system of socio-demographic data of virtual community member


Title	Development of verification system of socio-demographic data of virtual community member
Authors	S. S. Fedushko
Abstract	The important task of developing verification system of data of virtual community member on the basis of computer-linguistic analysis of the content of a large sample of Ukrainian virtual communities is solved. The subject of research is methods and tools for verification of web-members socio-demographic characteristics based on computer-linguistic analysis of their communicative interaction results. The aim of paper is to verifying web-user personal data on the basis of computer-linguistic analysis of web-members information tracks. The structure of verification software for web-user profile is designed for a practical implementation of assigned tasks. The method of personal data verification of web-members by analyzing information track of virtual community member is conducted. For the first time the method for checking the authenticity of web members personal data, which helped to design of verification tool for socio-demographic characteristics of web-member is developed. The verification system of data of web-members, which forms the verified socio-demographic profiles of web-members, is developed as a result of conducted experiments. Also the user interface of the developed verification system web-members data is presented. Effectiveness and efficiency of use of the developed methods and means for solving tasks in web-communities administration is proved by their approbation. The number of false results of verification system is 18%.
Tasks
Published	2019-01-21
URL	http://arxiv.org/abs/1901.07067v1
PDF	http://arxiv.org/pdf/1901.07067v1.pdf
PWC	https://paperswithcode.com/paper/development-of-verification-system-of-socio
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DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage


Title	DeepVS: An Efficient and Generic Approach for Source Code Modeling Usage
Authors	Yasir Hussain, Zhiqiu Huang, Yu Zhou, Senzhang Wang
Abstract	Recently deep learning-based approaches have shown great potential in the modeling of source code for various software engineering tasks. These techniques lack adequate generalization and resistance to acclimate the use of such models in a real-world software development environment. In this work, we propose a novel general framework that combines cloud computing and deep learning in an integrated development environment (IDE) to assist software developers in various source code modeling tasks. Additionally, we present DeepVS, an end-to-end deep learning-based source code suggestion tool that shows a real-world implementation of our proposed framework. The DeepVS tool is capable of providing source code suggestions instantly in an IDE by using a pre-trained source code model. Moreover, the DeepVS tool is also capable of suggesting zero-day (unseen) code tokens. The DeepVS tool illustrates the effectiveness of the proposed framework and shows how it can help to link the gap between developers and researchers.
Tasks
Published	2019-10-15
URL	https://arxiv.org/abs/1910.06500v1
PDF	https://arxiv.org/pdf/1910.06500v1.pdf
PWC	https://paperswithcode.com/paper/deepvs-an-efficient-and-generic-approach-for
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A Dual-hierarchy Semantic Graph for Robust Object Recognition


Title	A Dual-hierarchy Semantic Graph for Robust Object Recognition
Authors	Isaac Weiss
Abstract	We present a system for object recognition based on a semantic model graph, which the system can build by learning from image examples. This model graph is based on intrinsic properties of objects such as structure and geometry, so it is more robust than the current machine learning methods that can be fooled by changing a few pixels. Current methods have proved to be powerful but fragile because they ignore the structure and semantics of the objects. We define semantics as a form of abstraction, in terms of the intrinsic properties of the object, not in terms of human perception. Thus it can be learned automatically. Our model graph is more accurate and versatile than previous ones because it uses two distinct hierarchies: parts and abstraction. Previous semantic networks used only one amorphous hierarchy and were hard to build and traverse. Our system performs both the learning and recognition by an algorithm that moves in both hierarchies at the some time, combining the advantages of top-down and bottom-up strategies. This reduces dimensionality and obviates the need for the brute force of big data training.
Tasks	Object Recognition
Published	2019-09-15
URL	https://arxiv.org/abs/1909.06867v2
PDF	https://arxiv.org/pdf/1909.06867v2.pdf
PWC	https://paperswithcode.com/paper/a-dual-hierarchy-semantic-graph-for-robust
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Multimodal Attention Branch Network for Perspective-Free Sentence Generation


Title	Multimodal Attention Branch Network for Perspective-Free Sentence Generation
Authors	Aly Magassouba, Komei Sugiura, Hisashi Kawai
Abstract	In this paper, we address the automatic sentence generation of fetching instructions for domestic service robots. Typical fetching commands such as “bring me the yellow toy from the upper part of the white shelf” includes referring expressions, i.e., “from the white upper part of the white shelf”. To solve this task, we propose a multimodal attention branch network (Multi-ABN) which generates natural sentences in an end-to-end manner. Multi-ABN uses multiple images of the same fixed scene to generate sentences that are not tied to a particular viewpoint. This approach combines a linguistic attention branch mechanism with several attention branch mechanisms. We evaluated our approach, which outperforms the state-of-the-art method on a standard metrics. Our method also allows us to visualize the alignment between the linguistic input and the visual features.
Tasks
Published	2019-09-10
URL	https://arxiv.org/abs/1909.05664v1
PDF	https://arxiv.org/pdf/1909.05664v1.pdf
PWC	https://paperswithcode.com/paper/multimodal-attention-branch-network-for
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Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation


Title	Generating Training Data for Denoising Real RGB Images via Camera Pipeline Simulation
Authors	Ronnachai Jaroensri, Camille Biscarrat, Miika Aittala, Frédo Durand
Abstract	Image reconstruction techniques such as denoising often need to be applied to the RGB output of cameras and cellphones. Unfortunately, the commonly used additive white noise (AWGN) models do not accurately reproduce the noise and the degradation encountered on these inputs. This is particularly important for learning-based techniques, because the mismatch between training and real world data will hurt their generalization. This paper aims to accurately simulate the degradation and noise transformation performed by camera pipelines. This allows us to generate realistic degradation in RGB images that can be used to train machine learning models. We use our simulation to study the importance of noise modeling for learning-based denoising. Our study shows that a realistic noise model is required for learning to denoise real JPEG images. A neural network trained on realistic noise outperforms the one trained with AWGN by 3 dB. An ablation study of our pipeline shows that simulating denoising and demosaicking is important to this improvement and that realistic demosaicking algorithms, which have been rarely considered, is needed. We believe this simulation will also be useful for other image reconstruction tasks, and we will distribute our code publicly.
Tasks	Demosaicking, Denoising, Image Reconstruction
Published	2019-04-18
URL	http://arxiv.org/abs/1904.08825v1
PDF	http://arxiv.org/pdf/1904.08825v1.pdf
PWC	https://paperswithcode.com/paper/generating-training-data-for-denoising-real
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Unified Underwater Structure-from-Motion


Title	Unified Underwater Structure-from-Motion
Authors	Kazuto Ichimaru, Yuichi Taguchi, Hiroshi Kawasaki
Abstract	This paper shows that accurate underwater 3D shape reconstruction is possible using a single camera, observing a target through a refractive interface. We provide unified reconstruction techniques for a variety of scenarios such as single static camera and moving refractive interface, single moving camera and static refractive interface, and single moving camera and moving refractive interface. In our basic setup, we assume that the refractive interface is planar, and simultaneously estimate the unknown transformations of the planar interface and the camera, and the unknown target shape using bundle adjustment. We also extend it to relax the planarity assumption, which enables us to use waves of the refractive interface for the reconstruction task. Experiments with real data show the superiority of our method to existing methods.
Tasks
Published	2019-09-09
URL	https://arxiv.org/abs/1909.03583v1
PDF	https://arxiv.org/pdf/1909.03583v1.pdf
PWC	https://paperswithcode.com/paper/unified-underwater-structure-from-motion
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Deep Demosaicing for Edge Implementation


Title	Deep Demosaicing for Edge Implementation
Authors	Ramchalam Kinattinkara Ramakrishnan, Shangling Jui, Vahid Patrovi Nia
Abstract	Most digital cameras use sensors coated with a Color Filter Array (CFA) to capture channel components at every pixel location, resulting in a mosaic image that does not contain pixel values in all channels. Current research on reconstructing these missing channels, also known as demosaicing, introduces many artifacts, such as zipper effect and false color. Many deep learning demosaicing techniques outperform other classical techniques in reducing the impact of artifacts. However, most of these models tend to be over-parametrized. Consequently, edge implementation of the state-of-the-art deep learning-based demosaicing algorithms on low-end edge devices is a major challenge. We provide an exhaustive search of deep neural network architectures and obtain a pareto front of Color Peak Signal to Noise Ratio (CPSNR) as the performance criterion versus the number of parameters as the model complexity that beats the state-of-the-art. Architectures on the pareto front can then be used to choose the best architecture for a variety of resource constraints. Simple architecture search methods such as exhaustive search and grid search require some conditions of the loss function to converge to the optimum. We clarify these conditions in a brief theoretical study.
Tasks	Demosaicking, Neural Architecture Search
Published	2019-03-26
URL	https://arxiv.org/abs/1904.00775v3
PDF	https://arxiv.org/pdf/1904.00775v3.pdf
PWC	https://paperswithcode.com/paper/deep-demosaicing-for-edge-implementation
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Megapixel Photon-Counting Color Imaging using Quanta Image Sensor


Title	Megapixel Photon-Counting Color Imaging using Quanta Image Sensor
Authors	Abhiram Gnanasambandam, Omar Elgendy, Jiaju Ma, and Stanley H. Chan
Abstract	Quanta Image Sensor (QIS) is a single-photon detector designed for extremely low light imaging conditions. Majority of the existing QIS prototypes are monochrome based on single-photon avalanche diodes (SPAD). Passive color imaging has not been demonstrated with single-photon detectors due to the intrinsic difficulty of shrinking the pixel size and increasing the spatial resolution while maintaining acceptable intra-pixel cross-talk. In this paper, we present image reconstruction of the first color QIS with a resolution of $1024 \times 1024$ pixels, supporting both single-bit and multi-bit photon counting capability. Our color image reconstruction is enabled by a customized joint demosaicing-denoising algorithm, leveraging truncated Poisson statistics and variance stabilizing transforms. Experimental results of the new sensor and algorithm demonstrate superior color imaging performance for very low-light conditions with a mean exposure of as low as a few photons per pixel in both real and simulated images.
Tasks	Demosaicking, Denoising, Image Reconstruction
Published	2019-03-21
URL	https://arxiv.org/abs/1903.09036v2
PDF	https://arxiv.org/pdf/1903.09036v2.pdf
PWC	https://paperswithcode.com/paper/megapixel-photon-counting-color-imaging-using
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