pwc-archive/papers-with-abstracts huggingface dataset:
Recurrent Squeeze-and-Excitation Context Aggregation Net for Single Image Deraining
Authors: Xia Li, Jianlong Wu, Zhouchen Lin, Hong Liu, Hongbin Zha
Date: 2018-07-16 00:00:00
Proceeding: ECCV 2018 9
Tasks: Rain Removal, Single Image Deraining

Rain streaks can severely degrade the visibility, which causes many current
computer vision algorithms fail to work. So it is necessary to remove the rain
from images. We propose a novel deep network architecture based on deep
convolutional and recurrent neural networks for single image deraining. As
contextual information is very important for rain removal, we first adopt the
dilated convolutional neural network to acquire large receptive field. To
better fit the rain removal task, we also modify the network. In heavy rain,
rain streaks have various directions and shapes, which can be regarded as the
accumulation of multiple rain streak layers. We assign different alpha-values
to various rain streak layers according to the intensity and transparency by
incorporating the squeeze-and-excitation block. Since rain streak layers
overlap with each other, it is not easy to remove the rain in one stage. So we
further decompose the rain removal into multiple stages. Recurrent neural
network is incorporated to preserve the useful information in previous stages
and benefit the rain removal in later stages. We conduct extensive experiments
on both synthetic and real-world datasets. Our proposed method outperforms the
state-of-the-art approaches under all evaluation metrics. Codes and
supplementary material are available at our project webpage:
https://xialipku.github.io/RESCAN .
LineNet: a Zoomable CNN for Crowdsourced High Definition Maps Modeling in Urban Environments
Authors: Dun Liang, Yuanchen Guo, Shaokui Zhang, Song-Hai Zhang, Peter Hall, Min Zhang, Shi-Min Hu
Date: 2018-07-16 00:00:00
Tasks: Lane Detection

High Definition (HD) maps play an important role in modern traffic scenes.
However, the development of HD maps coverage grows slowly because of the cost
limitation. To efficiently model HD maps, we proposed a convolutional neural
network with a novel prediction layer and a zoom module, called LineNet. It is
designed for state-of-the-art lane detection in an unordered crowdsourced image
dataset. And we introduced TTLane, a dataset for efficient lane detection in
urban road modeling applications. Combining LineNet and TTLane, we proposed a
pipeline to model HD maps with crowdsourced data for the first time. And the
maps can be constructed precisely even with inaccurate crowdsourced data.
Teaching machines to understand data science code by semantic enrichment of dataflow graphs
Authors: Evan Patterson, Ioana Baldini, Aleksandra Mojsilovic, Kush R. Varshney
Date: 2018-07-16 00:00:00

Your computer is continuously executing programs, but does it really
understand them? Not in any meaningful sense. That burden falls upon human
knowledge workers, who are increasingly asked to write and understand code.
They deserve to have intelligent tools that reveal the connections between code
and its subject matter. Towards this prospect, we develop an AI system that
forms semantic representations of computer programs, using techniques from
knowledge representation and program analysis. To create the representations,
we introduce an algorithm for enriching dataflow graphs with semantic
information. The semantic enrichment algorithm is undergirded by a new ontology
language for modeling computer programs and a new ontology about data science,
written in this language. Throughout the paper, we focus on code written by
data scientists and we locate our work within a larger movement towards
collaborative, open, and reproducible science.
SCAN: Self-and-Collaborative Attention Network for Video Person Re-identification
Authors: Ruimao Zhang, Hongbin Sun, Jingyu Li, Yuying Ge, Liang Lin, Ping Luo, Xiaogang Wang
Date: 2018-07-16 00:00:00
Tasks: Person Re-Identification, Video-Based Person Re-Identification

Video person re-identification attracts much attention in recent years. It aims to match image sequences of pedestrians from different camera views. Previous approaches usually improve this task from three aspects, including a) selecting more discriminative frames, b) generating more informative temporal representations, and c) developing more effective distance metrics. To address the above issues, we present a novel and practical deep architecture for video person re-identification termed Self-and-Collaborative Attention Network (SCAN). It has several appealing properties. First, SCAN adopts non-parametric attention mechanism to refine the intra-sequence and inter-sequence feature representation of videos, and outputs self-and-collaborative feature representation for each video, making the discriminative frames aligned between the probe and gallery sequences.Second, beyond existing models, a generalized pairwise similarity measurement is proposed to calculate the similarity feature representations of video pairs, enabling computing the matching scores by the binary classifier. Third, a dense clip segmentation strategy is also introduced to generate rich probe-gallery pairs to optimize the model. Extensive experiments demonstrate the effectiveness of SCAN, which outperforms the best-performing baselines on iLIDS-VID, PRID2011 and MARS dataset, respectively.
Scene Learning: Deep Convolutional Networks For Wind Power Prediction by Embedding Turbines into Grid Space
Authors: Ruiguo Yu, Zhi-Qiang Liu, Xuewei Li, Wenhuan Lu, Mei Yu, Jianrong Wang, Bin Li
Date: 2018-07-16 00:00:00
Tasks: Time Series, Time Series Analysis

Wind power prediction is of vital importance in wind power utilization. There
have been a lot of researches based on the time series of the wind power or
speed, but In fact, these time series cannot express the temporal and spatial
changes of wind, which fundamentally hinders the advance of wind power
prediction. In this paper, a new kind of feature that can describe the process
of temporal and spatial variation is proposed, namely, Spatio-Temporal
Features. We first map the data collected at each moment from the wind turbine
to the plane to form the state map, namely, the scene, according to the
relative positions. The scene time series over a period of time is a
multi-channel image, i.e. the Spatio-Temporal Features. Based on the
Spatio-Temporal Features, the deep convolutional network is applied to predict
the wind power, achieving a far better accuracy than the existing methods.
Compared with the starge-of-the-art method, the mean-square error (MSE) in our
method is reduced by 49.83%, and the average time cost for training models can
be shortened by a factor of more than 150.
Learning and Matching Multi-View Descriptors for Registration of Point Clouds
Authors: Lei Zhou, Siyu Zhu, Zixin Luo, Tianwei Shen, Runze Zhang, Mingmin Zhen, Tian Fang, Long Quan
Date: 2018-07-16 00:00:00
Proceeding: ECCV 2018 9

Critical to the registration of point clouds is the establishment of a set of accurate correspondences between points in 3D space. The correspondence problem is generally addressed by the design of discriminative 3D local descriptors on the one hand, and the development of robust matching strategies on the other hand. In this work, we first propose a multi-view local descriptor, which is learned from the images of multiple views, for the description of 3D keypoints. Then, we develop a robust matching approach, aiming at rejecting outlier matches based on the efficient inference via belief propagation on the defined graphical model. We have demonstrated the boost of our approaches to registration on the public scanning and multi-view stereo datasets. The superior performance has been verified by the intensive comparisons against a variety of descriptors and matching methods.
Time Series Deinterleaving of DNS Traffic
Authors: Amir Asiaee, Hardik Goel, Shalini Ghosh, Vinod Yegneswaran, Arindam Banerjee
Date: 2018-07-16 00:00:00
Tasks: BIG-bench Machine Learning, Time Series, Time Series Analysis

Stream deinterleaving is an important problem with various applications in
the cybersecurity domain. In this paper, we consider the specific problem of
deinterleaving DNS data streams using machine-learning techniques, with the
objective of automating the extraction of malware domain sequences. We first
develop a generative model for user request generation and DNS stream
interleaving. Based on these we evaluate various inference strategies for
deinterleaving including augmented HMMs and LSTMs on synthetic datasets. Our
results demonstrate that state-of-the-art LSTMs outperform more traditional
augmented HMMs in this application domain.
Convolutional Sparse Kernel Network for Unsupervised Medical Image Analysis
Authors: Euijoon Ahn, Jinman Kim, Ashnil Kumar, Michael Fulham, Dagan Feng
Date: 2018-07-16 00:00:00
Tasks: Image Retrieval, Medical Image Analysis, Medical Image Retrieval, Retrieval

The availability of large-scale annotated image datasets and recent advances in supervised deep learning methods enable the end-to-end derivation of representative image features that can impact a variety of image analysis problems. Such supervised approaches, however, are difficult to implement in the medical domain where large volumes of labelled data are difficult to obtain due to the complexity of manual annotation and inter- and intra-observer variability in label assignment. We propose a new convolutional sparse kernel network (CSKN), which is a hierarchical unsupervised feature learning framework that addresses the challenge of learning representative visual features in medical image analysis domains where there is a lack of annotated training data. Our framework has three contributions: (i) We extend kernel learning to identify and represent invariant features across image sub-patches in an unsupervised manner. (ii) We initialise our kernel learning with a layer-wise pre-training scheme that leverages the sparsity inherent in medical images to extract initial discriminative features. (iii) We adapt a multi-scale spatial pyramid pooling (SPP) framework to capture subtle geometric differences between learned visual features. We evaluated our framework in medical image retrieval and classification on three public datasets. Our results show that our CSKN had better accuracy when compared to other conventional unsupervised methods and comparable accuracy to methods that used state-of-the-art supervised convolutional neural networks (CNNs). Our findings indicate that our unsupervised CSKN provides an opportunity to leverage unannotated big data in medical imaging repositories.
LATE Ain'T Earley: A Faster Parallel Earley Parser
Authors: Peter Ahrens, John Feser, Robin Hui
Date: 2018-07-16 00:00:00

We present the LATE algorithm, an asynchronous variant of the Earley
algorithm for parsing context-free grammars. The Earley algorithm is naturally
task-based, but is difficult to parallelize because of dependencies between the
tasks. We present the LATE algorithm, which uses additional data structures to
maintain information about the state of the parse so that work items may be
processed in any order. This property allows the LATE algorithm to be sped up
using task parallelism. We show that the LATE algorithm can achieve a 120x
speedup over the Earley algorithm on a natural language task.
Cross Pixel Optical Flow Similarity for Self-Supervised Learning
Authors: Aravindh Mahendran, James Thewlis, Andrea Vedaldi
Date: 2018-07-15 00:00:00
Tasks: image-classification, Image Classification, Image Segmentation, Optical Flow Estimation, Segmentation, Self-Supervised Learning, Semantic Segmentation

We propose a novel method for learning convolutional neural image
representations without manual supervision. We use motion cues in the form of
optical flow, to supervise representations of static images. The obvious
approach of training a network to predict flow from a single image can be
needlessly difficult due to intrinsic ambiguities in this prediction task. We
instead propose a much simpler learning goal: embed pixels such that the
similarity between their embeddings matches that between their optical flow
vectors. At test time, the learned deep network can be used without access to
video or flow information and transferred to tasks such as image
classification, detection, and segmentation. Our method, which significantly
simplifies previous attempts at using motion for self-supervision, achieves
state-of-the-art results in self-supervision using motion cues, competitive
results for self-supervision in general, and is overall state of the art in
self-supervised pretraining for semantic image segmentation, as demonstrated on
standard benchmarks.
NEUZZ: Efficient Fuzzing with Neural Program Smoothing
Authors: Dongdong She, Kexin Pei, Dave Epstein, Junfeng Yang, Baishakhi Ray, Suman Jana
Date: 2018-07-15 00:00:00
Tasks: Evolutionary Algorithms

Fuzzing has become the de facto standard technique for finding software vulnerabilities. However, even state-of-the-art fuzzers are not very efficient at finding hard-to-trigger software bugs. Most popular fuzzers use evolutionary guidance to generate inputs that can trigger different bugs. Such evolutionary algorithms, while fast and simple to implement, often get stuck in fruitless sequences of random mutations. Gradient-guided optimization presents a promising alternative to evolutionary guidance. Gradient-guided techniques have been shown to significantly outperform evolutionary algorithms at solving high-dimensional structured optimization problems in domains like machine learning by efficiently utilizing gradients or higher-order derivatives of the underlying function. However, gradient-guided approaches are not directly applicable to fuzzing as real-world program behaviors contain many discontinuities, plateaus, and ridges where the gradient-based methods often get stuck. We observe that this problem can be addressed by creating a smooth surrogate function approximating the discrete branching behavior of target program. In this paper, we propose a novel program smoothing technique using surrogate neural network models that can incrementally learn smooth approximations of a complex, real-world program's branching behaviors. We further demonstrate that such neural network models can be used together with gradient-guided input generation schemes to significantly improve the fuzzing efficiency. Our extensive evaluations demonstrate that NEUZZ significantly outperforms 10 state-of-the-art graybox fuzzers on 10 real-world programs both at finding new bugs and achieving higher edge coverage. NEUZZ found 31 unknown bugs that other fuzzers failed to find in 10 real world programs and achieved 3X more edge coverage than all of the tested graybox fuzzers for 24 hours running.
Improved Person Re-Identification Based on Saliency and Semantic Parsing with Deep Neural Network Models
Authors: Rodolfo Quispe, Helio Pedrini
Date: 2018-07-15 00:00:00
Tasks: Person Re-Identification, Re-Ranking

Given a video or an image of a person acquired from a camera, person
re-identification is the process of retrieving all instances of the same person
from videos or images taken from a different camera with non-overlapping view.
This task has applications in various fields, such as surveillance, forensics,
robotics, multimedia. In this paper, we present a novel framework, named
Saliency-Semantic Parsing Re-Identification (SSP-ReID), for taking advantage of
the capabilities of both clues: saliency and semantic parsing maps, to guide a
backbone convolutional neural network (CNN) to learn complementary
representations that improves the results over the original backbones. The
insight of fusing multiple clues is based on specific scenarios in which one
response is better than another, thus favoring the combination of them to
increase performance. Due to its definition, our framework can be easily
applied to a wide variety of networks and, in contrast to other competitive
methods, our training process follows simple and standard protocols. We present
extensive evaluation of our approach through five backbones and three
benchmarks. Experimental results demonstrate the effectiveness of our person
re-identification framework. In addition, we combine our framework with
re-ranking techniques to achieve state-of-the-art results on three benchmarks.
The Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule
Authors: Bart Jacobs
Date: 2018-07-15 00:00:00
Tasks: Probabilistic Programming

Evidence in probabilistic reasoning may be 'hard' or 'soft', that is, it may be of yes/no form, or it may involve a strength of belief, in the unit interval [0, 1]. Reasoning with soft, [0, 1]-valued evidence is important in many situations but may lead to different, confusing interpretations. This paper intends to bring more mathematical and conceptual clarity to the field by shifting the existing focus from specification of soft evidence to accomodation of soft evidence. There are two main approaches, known as Jeffrey's rule and Pearl's method; they give different outcomes on soft evidence. This paper argues that they can be understood as correction and as improvement. It describes these two approaches as different ways of updating with soft evidence, highlighting their differences, similarities and applications. This account is based on a novel channel-based approach to Bayesian probability. Proper understanding of these two update mechanisms is highly relevant for inference, decision tools and probabilistic programming languages.
Deep Learning for Semantic Segmentation on Minimal Hardware
Authors: Sander G. van Dijk, Marcus M. Scheunemann
Date: 2018-07-15 00:00:00
Tasks: Deep Learning, Semantic Segmentation

Deep learning has revolutionised many fields, but it is still challenging to
transfer its success to small mobile robots with minimal hardware.
Specifically, some work has been done to this effect in the RoboCup humanoid
football domain, but results that are performant and efficient and still
generally applicable outside of this domain are lacking. We propose an approach
conceptually different from those taken previously. It is based on semantic
segmentation and does achieve these desired properties. In detail, it is being
able to process full VGA images in real-time on a low-power mobile processor.
It can further handle multiple image dimensions without retraining, it does not
require specific domain knowledge for achieving a high frame rate and it is
applicable on a minimal mobile hardware.
Global Optimality in Separable Dictionary Learning with Applications to the Analysis of Diffusion MRI
Authors: Evan Schwab, Benjamin D. Haeffele, René Vidal, Nicolas Charon
Date: 2018-07-15 00:00:00
Tasks: Denoising, Dictionary Learning, Diffusion  MRI

Sparse dictionary learning is a popular method for representing signals as linear combinations of a few elements from a dictionary that is learned from the data. In the classical setting, signals are represented as vectors and the dictionary learning problem is posed as a matrix factorization problem where the data matrix is approximately factorized into a dictionary matrix and a sparse matrix of coefficients. However, in many applications in computer vision and medical imaging, signals are better represented as matrices or tensors (e.g. images or videos), where it may be beneficial to exploit the multi-dimensional structure of the data to learn a more compact representation. One such approach is separable dictionary learning, where one learns separate dictionaries for different dimensions of the data. However, typical formulations involve solving a non-convex optimization problem; thus guaranteeing global optimality remains a challenge. In this work, we propose a framework that builds upon recent developments in matrix factorization to provide theoretical and numerical guarantees of global optimality for separable dictionary learning. We propose an algorithm to find such a globally optimal solution, which alternates between following local descent steps and checking a certificate for global optimality. We illustrate our approach on diffusion magnetic resonance imaging (dMRI) data, a medical imaging modality that measures water diffusion along multiple angular directions in every voxel of an MRI volume. State-of-the-art methods in dMRI either learn dictionaries only for the angular domain of the signals or in some cases learn spatial and angular dictionaries independently. In this work, we apply the proposed separable dictionary learning framework to learn spatial and angular dMRI dictionaries jointly and provide preliminary validation on denoising phantom and real dMRI brain data.
Ontology-Based Query Expansion with Latently Related Named Entities for Semantic Text Search
Authors: Vuong M. Ngo, Tru H. Cao
Date: 2018-07-15 00:00:00
Tasks: Information Retrieval, Retrieval

Traditional information retrieval systems represent documents and queries by
keyword sets. However, the content of a document or a query is mainly defined
by both keywords and named entities occurring in it. Named entities have
ontological features, namely, their aliases, classes, and identifiers, which
are hidden from their textual appearance. Besides, the meaning of a query may
imply latent named entities that are related to the apparent ones in the query.
We propose an ontology-based generalized vector space model to semantic text
search. It exploits ontological features of named entities and their latently
related ones to reveal the semantics of documents and queries. We also propose
a framework to combine different ontologies to take their complementary
advantages for semantic annotation and searching. Experiments on a benchmark
dataset show better search quality of our model to other ones.
WordNet-Based Information Retrieval Using Common Hypernyms and Combined Features
Authors: Vuong M. Ngo, Tru H. Cao, Tuan M. V. Le
Date: 2018-07-15 00:00:00
Tasks: Information Retrieval, Retrieval, Word Sense Disambiguation

Text search based on lexical matching of keywords is not satisfactory due to
polysemous and synonymous words. Semantic search that exploits word meanings,
in general, improves search performance. In this paper, we survey WordNet-based
information retrieval systems, which employ a word sense disambiguation method
to process queries and documents. The problem is that in many cases a word has
more than one possible direct sense, and picking only one of them may give a
wrong sense for the word. Moreover, the previous systems use only word forms to
represent word senses and their hypernyms. We propose a novel approach that
uses the most specific common hypernym of the remaining undisambiguated
multi-senses of a word, as well as combined WordNet features to represent word
meanings. Experiments on a benchmark dataset show that, in terms of the MAP
measure, our search engine is 17.7% better than the lexical search, and at
least 9.4% better than all surveyed search systems using WordNet.
  Keywords Ontology, word sense disambiguation, semantic annotation, semantic
search.