pwc-archive/papers-with-abstracts huggingface dataset:
Stealing Neural Networks via Timing Side Channels
Authors: Vasisht Duddu, Debasis Samanta, D. Vijay Rao, Valentina E. Balas
Date: 2018-12-31 00:00:00
Tasks: Knowledge Distillation, Reinforcement Learning

Deep learning is gaining importance in many applications. However, Neural Networks face several security and privacy threats. This is particularly significant in the scenario where Cloud infrastructures deploy a service with Neural Network model at the back end. Here, an adversary can extract the Neural Network parameters, infer the regularization hyperparameter, identify if a data point was part of the training data, and generate effective transferable adversarial examples to evade classifiers. This paper shows how a Neural Network model is susceptible to timing side channel attack. In this paper, a black box Neural Network extraction attack is proposed by exploiting the timing side channels to infer the depth of the network. Although, constructing an equivalent architecture is a complex search problem, it is shown how Reinforcement Learning with knowledge distillation can effectively reduce the search space to infer a target model. The proposed approach has been tested with VGG architectures on CIFAR10 data set. It is observed that it is possible to reconstruct substitute models with test accuracy close to the target models and the proposed approach is scalable and independent of type of Neural Network architectures.
On the Complexity of the Inverse Semivalue Problem for Weighted Voting Games
Authors: Ilias Diakonikolas, Chrystalla Pavlou
Date: 2018-12-31 00:00:00

Weighted voting games are a family of cooperative games, typically used to
model voting situations where a number of agents (players) vote against or for
a proposal. In such games, a proposal is accepted if an appropriately weighted
sum of the votes exceeds a prespecified threshold. As the influence of a player
over the voting outcome is not in general proportional to her assigned weight,
various power indices have been proposed to measure each player's influence.
The inverse power index problem is the problem of designing a weighted voting
game that achieves a set of target influences according to a predefined power
index. In this work, we study the computational complexity of the inverse
problem when the power index belongs to the class of semivalues. We prove that
the inverse problem is computationally intractable for a broad family of
semivalues, including all regular semivalues. As a special case of our general
result, we establish computational hardness of the inverse problem for the
Banzhaf indices and the Shapley values, arguably the most popular power
indices.
Cross-language Citation Recommendation via Hierarchical Representation Learning on Heterogeneous Graph
Authors: Zhuoren Jiang, Yue Yin, Liangcai Gao, Yao Lu, Xiaozhong Liu
Date: 2018-12-31 00:00:00
Tasks: Citation Recommendation, Representation Learning

While the volume of scholarly publications has increased at a frenetic pace,
accessing and consuming the useful candidate papers, in very large digital
libraries, is becoming an essential and challenging task for scholars.
Unfortunately, because of language barrier, some scientists (especially the
junior ones or graduate students who do not master other languages) cannot
efficiently locate the publications hosted in a foreign language repository. In
this study, we propose a novel solution, cross-language citation recommendation
via Hierarchical Representation Learning on Heterogeneous Graph (HRLHG), to
address this new problem. HRLHG can learn a representation function by mapping
the publications, from multilingual repositories, to a low-dimensional joint
embedding space from various kinds of vertexes and relations on a heterogeneous
graph. By leveraging both global (task specific) plus local (task independent)
information as well as a novel supervised hierarchical random walk algorithm,
the proposed method can optimize the publication representations by maximizing
the likelihood of locating the important cross-language neighborhoods on the
graph. Experiment results show that the proposed method can not only outperform
state-of-the-art baseline models, but also improve the interpretability of the
representation model for cross-language citation recommendation task.
SiamRPN++: Evolution of Siamese Visual Tracking with Very Deep Networks
Authors: Bo Li, Wei Wu, Qiang Wang, Fangyi Zhang, Junliang Xing, Junjie Yan
Date: 2018-12-31 00:00:00
Proceeding: CVPR 2019 6
Tasks: Translation, Visual Object Tracking, Visual Tracking

Siamese network based trackers formulate tracking as convolutional feature
cross-correlation between target template and searching region. However,
Siamese trackers still have accuracy gap compared with state-of-the-art
algorithms and they cannot take advantage of feature from deep networks, such
as ResNet-50 or deeper. In this work we prove the core reason comes from the
lack of strict translation invariance. By comprehensive theoretical analysis
and experimental validations, we break this restriction through a simple yet
effective spatial aware sampling strategy and successfully train a
ResNet-driven Siamese tracker with significant performance gain. Moreover, we
propose a new model architecture to perform depth-wise and layer-wise
aggregations, which not only further improves the accuracy but also reduces the
model size. We conduct extensive ablation studies to demonstrate the
effectiveness of the proposed tracker, which obtains currently the best results
on four large tracking benchmarks, including OTB2015, VOT2018, UAV123, and
LaSOT. Our model will be released to facilitate further studies based on this
problem.
Sex-Classification from Cell-Phones Periocular Iris Images
Authors: Juan Tapia, Claudia Arellano, Ignacio Viedma
Date: 2018-12-31 00:00:00
Tasks: Classification, General Classification, Marketing, Super-Resolution

Selfie soft biometrics has great potential for various applications ranging
from marketing, security and online banking. However, it faces many challenges
since there is limited control in data acquisition conditions. This chapter
presents a Super-Resolution-Convolutional Neural Networks (SRCNNs) approach
that increases the resolution of low quality periocular iris images cropped
from selfie images of subject's faces. This work shows that increasing image
resolution (2x and 3x) can improve the sex-classification rate when using a
Random Forest classifier. The best sex-classification rate was 90.15% for the
right and 87.15% for the left eye. This was achieved when images were upscaled
from 150x150 to 450x450 pixels. These results compare well with the state of
the art and show that when improving image resolution with the SRCNN the
sex-classification rate increases. Additionally, a novel selfie database
captured from 150 subjects with an iPhone X was created (available upon
request).
Deep Residual Learning in the JPEG Transform Domain
Authors: Max Ehrlich, Larry Davis
Date: 2018-12-31 00:00:00
Proceeding: ICCV 2019 10
Tasks: General Classification, image-classification, Image Classification

We introduce a general method of performing Residual Network inference and learning in the JPEG transform domain that allows the network to consume compressed images as input. Our formulation leverages the linearity of the JPEG transform to redefine convolution and batch normalization with a tune-able numerical approximation for ReLu. The result is mathematically equivalent to the spatial domain network up to the ReLu approximation accuracy. A formulation for image classification and a model conversion algorithm for spatial domain networks are given as examples of the method. We show that the sparsity of the JPEG format allows for faster processing of images with little to no penalty in the network accuracy.
K-nearest Neighbor Search by Random Projection Forests
Authors: Donghui Yan, Yingjie Wang, Jin Wang, Honggang Wang, Zhenpeng Li
Date: 2018-12-31 00:00:00

K-nearest neighbor (kNN) search has wide applications in many areas,
including data mining, machine learning, statistics and many applied domains.
Inspired by the success of ensemble methods and the flexibility of tree-based
methodology, we propose random projection forests (rpForests), for kNN search.
rpForests finds kNNs by aggregating results from an ensemble of random
projection trees with each constructed recursively through a series of
carefully chosen random projections. rpForests achieves a remarkable accuracy
in terms of fast decay in the missing rate of kNNs and that of discrepancy in
the kNN distances. rpForests has a very low computational complexity. The
ensemble nature of rpForests makes it easily run in parallel on multicore or
clustered computers; the running time is expected to be nearly inversely
proportional to the number of cores or machines. We give theoretical insights
by showing the exponential decay of the probability that neighboring points
would be separated by ensemble random projection trees when the ensemble size
increases. Our theory can be used to refine the choice of random projections in
the growth of trees, and experiments show that the effect is remarkable.
A dataset of 40K naturalistic 6-degree-of-freedom robotic grasp demonstrations
Authors: Rajan Iyengar, Victor Reyes Osorio, Presish Bhattachan, Adrian Ragobar, Bryan Tripp
Date: 2018-12-31 00:00:00

Modern approaches to grasp planning often involve deep learning. However,
there are only a few large datasets of labelled grasping examples on physical
robots, and available datasets involve relatively simple planar grasps with
two-fingered grippers. Here we present: 1) a new human grasp demonstration
method that facilitates rapid collection of naturalistic grasp examples, with
full six-degree-of-freedom gripper positioning; and 2) a dataset of roughly
forty thousand successful grasps on 109 different rigid objects with the
RightHand Robotics three-fingered ReFlex gripper.
ADMM-NN: An Algorithm-Hardware Co-Design Framework of DNNs Using Alternating Direction Method of Multipliers
Authors: Ao Ren, Tianyun Zhang, Shaokai Ye, Jiayu Li, Wenyao Xu, Xuehai Qian, Xue Lin, Yanzhi Wang
Date: 2018-12-31 00:00:00
Tasks: Model Compression, Quantization

To facilitate efficient embedded and hardware implementations of deep neural
networks (DNNs), two important categories of DNN model compression techniques:
weight pruning and weight quantization are investigated. The former leverages
the redundancy in the number of weights, whereas the latter leverages the
redundancy in bit representation of weights. However, there lacks a systematic
framework of joint weight pruning and quantization of DNNs, thereby limiting
the available model compression ratio. Moreover, the computation reduction,
energy efficiency improvement, and hardware performance overhead need to be
accounted for besides simply model size reduction.
  To address these limitations, we present ADMM-NN, the first
algorithm-hardware co-optimization framework of DNNs using Alternating
Direction Method of Multipliers (ADMM), a powerful technique to deal with
non-convex optimization problems with possibly combinatorial constraints. The
first part of ADMM-NN is a systematic, joint framework of DNN weight pruning
and quantization using ADMM. It can be understood as a smart regularization
technique with regularization target dynamically updated in each ADMM
iteration, thereby resulting in higher performance in model compression than
prior work. The second part is hardware-aware DNN optimizations to facilitate
hardware-level implementations.
  Without accuracy loss, we can achieve 85$\times$ and 24$\times$ pruning on
LeNet-5 and AlexNet models, respectively, significantly higher than prior work.
The improvement becomes more significant when focusing on computation
reductions. Combining weight pruning and quantization, we achieve 1,910$\times$
and 231$\times$ reductions in overall model size on these two benchmarks, when
focusing on data storage. Highly promising results are also observed on other
representative DNNs such as VGGNet and ResNet-50.
Soft Autoencoder and Its Wavelet Adaptation Interpretation
Authors: Fenglei Fan, Mengzhou Li, Yueyang Teng, Ge Wang
Date: 2018-12-31 00:00:00
Tasks: Deblurring, Deep Learning, Denoising

Recently, deep learning becomes the main focus of machine learning research and has greatly impacted many important fields. However, deep learning is criticized for lack of interpretability. As a successful unsupervised model in deep learning, the autoencoder embraces a wide spectrum of applications, yet it suffers from the model opaqueness as well. In this paper, we propose a new type of convolutional autoencoders, termed as Soft Autoencoder (Soft-AE), in which the activation functions of encoding layers are implemented with adaptable soft-thresholding units while decoding layers are realized with linear units. Consequently, Soft-AE can be naturally interpreted as a learned cascaded wavelet shrinkage system. Our denoising experiments demonstrate that Soft-AE not only is interpretable but also offers a competitive performance relative to its counterparts. Furthermore, we propose a generalized linear unit (GenLU) to make an autoencoder more adaptive in nonlinearly filtering images and data, such as denoising and deblurring.
Unsupervised monocular stereo matching
Authors: Zhimin Zhang, Jianzhong Qiao, Shukuan Lin
Date: 2018-12-31 00:00:00
Tasks: Depth Estimation, Monocular Depth Estimation, Stereo Matching, Stereo Matching Hand

At present, deep learning has been applied more and more in monocular image
depth estimation and has shown promising results. The current more ideal method
for monocular depth estimation is the supervised learning based on ground truth
depth, but this method requires an abundance of expensive ground truth depth as
the supervised labels. Therefore, researchers began to work on unsupervised
depth estimation methods. Although the accuracy of unsupervised depth
estimation method is still lower than that of supervised method, it is a
promising research direction.
  In this paper, Based on the experimental results that the stereo matching
models outperforms monocular depth estimation models under the same
unsupervised depth estimation model, we proposed an unsupervised monocular
vision stereo matching method. In order to achieve the monocular stereo
matching, we constructed two unsupervised deep convolution network models, one
was to reconstruct the right view from the left view, and the other was to
estimate the depth map using the reconstructed right view and the original left
view. The two network models are piped together during the test phase. The
output results of this method outperforms the current mainstream unsupervised
depth estimation method in the challenging KITTI dataset.
Predicting Aircraft Trajectories: A Deep Generative Convolutional Recurrent Neural Networks Approach
Authors: Yulin Liu, Mark Hansen
Date: 2018-12-31 00:00:00
Tasks: Decoder, Trajectory Prediction

Reliable 4D aircraft trajectory prediction, whether in a real-time setting or
for analysis of counterfactuals, is important to the efficiency of the aviation
system. Toward this end, we first propose a highly generalizable efficient
tree-based matching algorithm to construct image-like feature maps from
high-fidelity meteorological datasets - wind, temperature and convective
weather. We then model the track points on trajectories as conditional Gaussian
mixtures with parameters to be learned from our proposed deep generative model,
which is an end-to-end convolutional recurrent neural network that consists of
a long short-term memory (LSTM) encoder network and a mixture density LSTM
decoder network. The encoder network embeds last-filed flight plan information
into fixed-size hidden state variables and feeds the decoder network, which
further learns the spatiotemporal correlations from the historical flight
tracks and outputs the parameters of Gaussian mixtures. Convolutional layers
are integrated into the pipeline to learn representations from the
high-dimension weather features. During the inference process, beam search,
adaptive Kalman filter, and Rauch-Tung-Striebel smoother algorithms are used to
prune the variance of generated trajectories.
Path-Invariant Map Networks
Authors: Zaiwei Zhang, Zhenxiao Liang, Lemeng Wu, Xiaowei Zhou, Qi-Xing Huang
Date: 2018-12-31 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Semantic Segmentation, Scene Segmentation, Segmentation, Semantic Segmentation

Optimizing a network of maps among a collection of objects/domains (or map synchronization) is a central problem across computer vision and many other relevant fields. Compared to optimizing pairwise maps in isolation, the benefit of map synchronization is that there are natural constraints among a map network that can improve the quality of individual maps. While such self-supervision constraints are well-understood for undirected map networks (e.g., the cycle-consistency constraint), they are under-explored for directed map networks, which naturally arise when maps are given by parametric maps (e.g., a feed-forward neural network). In this paper, we study a natural self-supervision constraint for directed map networks called path-invariance, which enforces that composite maps along different paths between a fixed pair of source and target domains are identical. We introduce path-invariance bases for efficient encoding of the path-invariance constraint and present an algorithm that outputs a path-variance basis with polynomial time and space complexities. We demonstrate the effectiveness of our approach on optimizing object correspondences, estimating dense image maps via neural networks, and semantic segmentation of 3D scenes via map networks of diverse 3D representations. In particular, for 3D semantic segmentation, our approach only requires 8% labeled data from ScanNet to achieve the same performance as training a single 3D segmentation network with 30% to 100% labeled data.
Actor Conditioned Attention Maps for Video Action Detection
Authors: Oytun Ulutan, Swati Rallapalli, Mudhakar Srivatsa, Carlos Torres, B. S. Manjunath
Date: 2018-12-30 00:00:00
Tasks: Action Detection, Video Action Detection

While observing complex events with multiple actors, humans do not assess each actor separately, but infer from the context. The surrounding context provides essential information for understanding actions. To this end, we propose to replace region of interest(RoI) pooling with an attention module, which ranks each spatio-temporal region's relevance to a detected actor instead of cropping. We refer to these as Actor-Conditioned Attention Maps (ACAM), which amplify/dampen the features extracted from the entire scene. The resulting actor-conditioned features focus the model on regions that are relevant to the conditioned actor. For actor localization, we leverage pre-trained object detectors, which transfer better. The proposed model is efficient and our action detection pipeline achieves near real-time performance. Experimental results on AVA 2.1 and JHMDB demonstrate the effectiveness of attention maps, with improvements of 7 mAP on AVA and 4 mAP on JHMDB.
State-of-the-Art Economic Load Dispatch of Power Systems Using Particle Swarm Optimization
Authors: Mahamad Nabab Alam
Date: 2018-12-30 00:00:00
Tasks: Electrical Engineering

Metaheuristic particle swarm optimization (PSO) algorithm has emerged as one
of the most promising optimization techniques in solving highly constrained
non-linear and non-convex optimization problems in different areas of
electrical engineering. Economic operation of the power system is one of the
most important areas of electrical engineering where PSO has been used
efficiently in solving various issues of practical systems. In this paper, a
comprehensive survey of research works in solving various aspects of economic
load dispatch (ELD) problems of power system engineering using different types
of PSO algorithms is presented. Five important areas of ELD problems have been
identified, and the papers published in the general area of ELD using PSO have
been classified into these five sections. These five areas are (i) single
objective economic load dispatch, (ii) dynamic economic load dispatch, (iii)
economic load dispatch with non-conventional sources, (iv) multi-objective
environmental/economic dispatch, and (v) economic load dispatch of microgrids.
At the end of each category, a table is provided which describes the main
features of the papers in brief. The promising future works are given at the
conclusion of the review.
Solar Potential Analysis of Rooftops Using Satellite Imagery
Authors: Akash Kumar
Date: 2018-12-30 00:00:00

Solar energy is one of the most important sources of renewable energy and the cleanest form of energy. In India, where solar energy could produce power around trillion kilowatt-hours in a year, our country is only able to produce power of around in gigawatts only. Many people are not aware of the solar potential of their rooftop, and hence they always think that installing solar panels is very much expensive. In this work, we introduce an approach through which we can generate a report remotely that provides the amount of solar potential of a building using only its latitude and longitude. We further evaluated various types of rooftops to make our solution more robust. We also provide an approximate area of rooftop that can be used for solar panels placement and a visual analysis of how solar panels can be placed to maximize the output of solar power at a location.
Cascaded V-Net using ROI masks for brain tumor segmentation
Authors: Adrià Casamitjana, Marcel Catà, Irina Sánchez, Marc Combalia, Verónica Vilaplana
Date: 2018-12-30 00:00:00
Tasks: Brain Tumor Segmentation, Segmentation, Tumor Segmentation

In this work we approach the brain tumor segmentation problem with a cascade
of two CNNs inspired in the V-Net architecture \cite{VNet}, reformulating
residual connections and making use of ROI masks to constrain the networks to
train only on relevant voxels. This architecture allows dense training on
problems with highly skewed class distributions, such as brain tumor
segmentation, by focusing training only on the vecinity of the tumor area. We
report results on BraTS2017 Training and Validation sets.
Sentiment Classification of Customer Reviews about Automobiles in Roman Urdu
Authors: Moin Khan, Kamran Malik
Date: 2018-12-30 00:00:00
Tasks: Attribute, Classification, General Classification, Sentiment Analysis, Sentiment Classification, text-classification, Text Classification

Text mining is a broad field having sentiment mining as its important
constituent in which we try to deduce the behavior of people towards a specific
item, merchandise, politics, sports, social media comments, review sites etc.
Out of many issues in sentiment mining, analysis and classification, one major
issue is that the reviews and comments can be in different languages like
English, Arabic, Urdu etc. Handling each language according to its rules is a
difficult task. A lot of research work has been done in English Language for
sentiment analysis and classification but limited sentiment analysis work is
being carried out on other regional languages like Arabic, Urdu and Hindi. In
this paper, Waikato Environment for Knowledge Analysis (WEKA) is used as a
platform to execute different classification models for text classification of
Roman Urdu text. Reviews dataset has been scrapped from different automobiles
sites. These extracted Roman Urdu reviews, containing 1000 positive and 1000
negative reviews, are then saved in WEKA attribute-relation file format (arff)
as labeled examples. Training is done on 80% of this data and rest of it is
used for testing purpose which is done using different models and results are
analyzed in each case. The results show that Multinomial Naive Bayes
outperformed Bagging, Deep Neural Network, Decision Tree, Random Forest,
AdaBoost, k-NN and SVM Classifiers in terms of more accuracy, precision, recall
and F-measure.