pwc-archive/papers-with-abstracts huggingface dataset:
Catching Anomalous Distributed Photovoltaics: An Edge-based Multi-modal Anomaly Detection
Authors: Devu Manikantan Shilay, Kin Gwn Lorey, Tianshu Weiz, Teems Lovetty, Yu Cheng
Date: 2017-09-26 00:00:00
Tasks: Anomaly Detection, Time Series Analysis

A significant challenge in energy system cyber security is the current
inability to detect cyber-physical attacks targeting and originating from
distributed grid-edge devices such as photovoltaics (PV) panels, smart flexible
loads, and electric vehicles. We address this concern by designing and
developing a distributed, multi-modal anomaly detection approach that can sense
the health of the device and the electric power grid from the edge. This is
realized by exploiting unsupervised machine learning algorithms on multiple
sources of time-series data, fusing these multiple local observations and
flagging anomalies when a deviation from the normal behavior is observed.
  We particularly focus on the cyber-physical threats to the distributed PVs
that has the potential to cause local disturbances or grid instabilities by
creating supply-demand mismatch, reverse power flow conditions etc. We use an
open source power system simulation tool called GridLAB-D, loaded with real
smart home and solar datasets to simulate the smart grid scenarios and to
illustrate the impact of PV attacks on the power system. Various attacks
targeting PV panels that create voltage fluctuations, reverse power flow etc
were designed and performed. We observe that while individual unsupervised
learning algorithms such as OCSVMs, Corrupt RF and PCA surpasses in identifying
particular attack type, PCA with Convex Hull outperforms all algorithms in
identifying all designed attacks with a true positive rate of 83.64% and an
accuracy of 95.78%. Our key insight is that due to the heterogeneous nature of
the distribution grid and the uncertainty in the type of the attack being
launched, relying on single mode of information for defense can lead to
increased false alarms and missed detection rates as one can design attacks to
hide within those uncertainties and remain stealthy.
Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks
Authors: Hui Li, Peng Wang, Chunhua Shen
Date: 2017-09-26 00:00:00
Tasks: License Plate Detection

In this work, we tackle the problem of car license plate detection and
recognition in natural scene images. We propose a unified deep neural network
which can localize license plates and recognize the letters simultaneously in a
single forward pass. The whole network can be trained end-to-end. In contrast
to existing approaches which take license plate detection and recognition as
two separate tasks and settle them step by step, our method jointly solves
these two tasks by a single network. It not only avoids intermediate error
accumulation, but also accelerates the processing speed. For performance
evaluation, three datasets including images captured from various scenes under
different conditions are tested. Extensive experiments show the effectiveness
and efficiency of our proposed approach.
Converting Your Thoughts to Texts: Enabling Brain Typing via Deep Feature Learning of EEG Signals
Authors: Xiang Zhang, Lina Yao, Quan Z. Sheng, Salil S. Kanhere, Tao Gu, Dalin Zhang
Date: 2017-09-26 00:00:00
Tasks: Brain Computer Interface, EEG, Electroencephalogram (EEG), General Classification, Motor Imagery

An electroencephalography (EEG) based Brain Computer Interface (BCI) enables
people to communicate with the outside world by interpreting the EEG signals of
their brains to interact with devices such as wheelchairs and intelligent
robots. More specifically, motor imagery EEG (MI-EEG), which reflects a
subjects active intent, is attracting increasing attention for a variety of BCI
applications. Accurate classification of MI-EEG signals while essential for
effective operation of BCI systems, is challenging due to the significant noise
inherent in the signals and the lack of informative correlation between the
signals and brain activities. In this paper, we propose a novel deep neural
network based learning framework that affords perceptive insights into the
relationship between the MI-EEG data and brain activities. We design a joint
convolutional recurrent neural network that simultaneously learns robust
high-level feature presentations through low-dimensional dense embeddings from
raw MI-EEG signals. We also employ an Autoencoder layer to eliminate various
artifacts such as background activities. The proposed approach has been
evaluated extensively on a large- scale public MI-EEG dataset and a limited but
easy-to-deploy dataset collected in our lab. The results show that our approach
outperforms a series of baselines and the competitive state-of-the- art
methods, yielding a classification accuracy of 95.53%. The applicability of our
proposed approach is further demonstrated with a practical BCI system for
typing.
A Deep Learning Model for Traffic Flow State Classification Based on Smart Phone Sensor Data
Authors: Wenwen Tu, Feng Xiao, Liping Fu, Guangyuan Pan
Date: 2017-09-26 00:00:00
Tasks: Classification, Computational Efficiency, General Classification

This study proposes a Deep Belief Network model to classify traffic flow
states. The model is capable of processing massive, high-density, and
noise-contaminated data sets generated from smartphone sensors. The statistical
features of Vehicle acceleration, angular acceleration, and GPS speed data,
recorded by smartphone software, are analyzed, and then used as input for
traffic flow state classification. Data from a five-day experiment is used to
train and test the proposed model. A total of 747,856 sets of data are
generated and used for both traffic flow states classification and sensitivity
analysis of input variables. The result shows that the proposed Deep Belief
Network model is superior to traditional machine learning methods in both
classification performance and computational efficiency.
APE-GAN: Adversarial Perturbation Elimination with GAN
Authors: Shiwei Shen, Guoqing Jin, Ke Gao, Yongdong Zhang
Date: 2017-07-18 00:00:00

Although neural networks could achieve state-of-the-art performance while
recongnizing images, they often suffer a tremendous defeat from adversarial
examples--inputs generated by utilizing imperceptible but intentional
perturbation to clean samples from the datasets. How to defense against
adversarial examples is an important problem which is well worth researching.
So far, very few methods have provided a significant defense to adversarial
examples. In this paper, a novel idea is proposed and an effective framework
based Generative Adversarial Nets named APE-GAN is implemented to defense
against the adversarial examples. The experimental results on three benchmark
datasets including MNIST, CIFAR10 and ImageNet indicate that APE-GAN is
effective to resist adversarial examples generated from five attacks.
Ensemble Classifier for Eye State Classification using EEG Signals
Authors: Ali Al-Taei
Date: 2017-09-25 00:00:00
Tasks: Brain Computer Interface, Classification, EEG, Electroencephalogram (EEG), General Classification

The growing importance and utilization of measuring brain waves (e.g. EEG
signals of eye state) in brain-computer interface (BCI) applications
highlighted the need for suitable classification methods. In this paper, a
comparison between three of well-known classification methods (i.e. support
vector machine (SVM), hidden Markov map (HMM), and radial basis function (RBF))
for EEG based eye state classification was achieved. Furthermore, a suggested
method that is based on ensemble model was tested. The suggested (ensemble
system) method based on a voting algorithm with two kernels: random forest (RF)
and Kstar classification methods. The performance was tested using three
measurement parameters: accuracy, mean absolute error (MAE), and confusion
matrix. Results showed that the proposed method outperforms the other tested
methods. For instance, the suggested method's performance was 97.27% accuracy
and 0.13 MAE.
On Stein's Identity and Near-Optimal Estimation in High-dimensional Index Models
Authors: Zhuoran Yang, Krishnakumar Balasubramanian, Han Liu
Date: 2017-09-26 00:00:00

We consider estimating the parametric components of semi-parametric multiple
index models in a high-dimensional and non-Gaussian setting. Such models form a
rich class of non-linear models with applications to signal processing, machine
learning and statistics. Our estimators leverage the score function based first
and second-order Stein's identities and do not require the covariates to
satisfy Gaussian or elliptical symmetry assumptions common in the literature.
Moreover, to handle score functions and responses that are heavy-tailed, our
estimators are constructed via carefully thresholding their empirical
counterparts. We show that our estimator achieves near-optimal statistical rate
of convergence in several settings. We supplement our theoretical results via
simulation experiments that confirm the theory.
Cross-lingual Entity Alignment via Joint Attribute-Preserving Embedding
Authors: Zequn Sun, Wei Hu, Chengkai Li
Date: 2017-08-16 00:00:00
Tasks: Attribute, Entity Alignment, Machine Translation, Translation

Entity alignment is the task of finding entities in two knowledge bases (KBs)
that represent the same real-world object. When facing KBs in different natural
languages, conventional cross-lingual entity alignment methods rely on machine
translation to eliminate the language barriers. These approaches often suffer
from the uneven quality of translations between languages. While recent
embedding-based techniques encode entities and relationships in KBs and do not
need machine translation for cross-lingual entity alignment, a significant
number of attributes remain largely unexplored. In this paper, we propose a
joint attribute-preserving embedding model for cross-lingual entity alignment.
It jointly embeds the structures of two KBs into a unified vector space and
further refines it by leveraging attribute correlations in the KBs. Our
experimental results on real-world datasets show that this approach
significantly outperforms the state-of-the-art embedding approaches for
cross-lingual entity alignment and could be complemented with methods based on
machine translation.
On the Model Shrinkage Effect of Gamma Process Edge Partition Models
Authors: Iku Ohama, Issei Sato, Takuya Kida, Hiroki Arimura
Date: 2017-09-26 00:00:00
Proceeding: NeurIPS 2017 12
Tasks: Link Prediction

The edge partition model (EPM) is a fundamental Bayesian nonparametric model
for extracting an overlapping structure from binary matrix. The EPM adopts a
gamma process ($\Gamma$P) prior to automatically shrink the number of active
atoms. However, we empirically found that the model shrinkage of the EPM does
not typically work appropriately and leads to an overfitted solution. An
analysis of the expectation of the EPM's intensity function suggested that the
gamma priors for the EPM hyperparameters disturb the model shrinkage effect of
the internal $\Gamma$P. In order to ensure that the model shrinkage effect of
the EPM works in an appropriate manner, we proposed two novel generative
constructions of the EPM: CEPM incorporating constrained gamma priors, and DEPM
incorporating Dirichlet priors instead of the gamma priors. Furthermore, all
DEPM's model parameters including the infinite atoms of the $\Gamma$P prior
could be marginalized out, and thus it was possible to derive a truly infinite
DEPM (IDEPM) that can be efficiently inferred using a collapsed Gibbs sampler.
We experimentally confirmed that the model shrinkage of the proposed models
works well and that the IDEPM indicated state-of-the-art performance in
generalization ability, link prediction accuracy, mixing efficiency, and
convergence speed.
Image similarity using Deep CNN and Curriculum Learning
Authors: Srikar Appalaraju, Vineet Chaoji
Date: 2017-09-26 00:00:00

Image similarity involves fetching similar looking images given a reference
image. Our solution called SimNet, is a deep siamese network which is trained
on pairs of positive and negative images using a novel online pair mining
strategy inspired by Curriculum learning. We also created a multi-scale CNN,
where the final image embedding is a joint representation of top as well as
lower layer embedding's. We go on to show that this multi-scale siamese network
is better at capturing fine grained image similarities than traditional CNN's.
Question-Answering with Grammatically-Interpretable Representations
Authors: Hamid Palangi, Paul Smolensky, Xiaodong He, Li Deng
Date: 2017-05-23 00:00:00
Tasks: Inductive Bias, Question Answering

We introduce an architecture, the Tensor Product Recurrent Network (TPRN). In
our application of TPRN, internal representations learned by end-to-end
optimization in a deep neural network performing a textual question-answering
(QA) task can be interpreted using basic concepts from linguistic theory. No
performance penalty need be paid for this increased interpretability: the
proposed model performs comparably to a state-of-the-art system on the SQuAD QA
task. The internal representation which is interpreted is a Tensor Product
Representation: for each input word, the model selects a symbol to encode the
word, and a role in which to place the symbol, and binds the two together. The
selection is via soft attention. The overall interpretation is built from
interpretations of the symbols, as recruited by the trained model, and
interpretations of the roles as used by the model. We find support for our
initial hypothesis that symbols can be interpreted as lexical-semantic word
meanings, while roles can be interpreted as approximations of grammatical roles
(or categories) such as subject, wh-word, determiner, etc. Fine-grained
analysis reveals specific correspondences between the learned roles and parts
of speech as assigned by a standard tagger (Toutanova et al. 2003), and finds
several discrepancies in the model's favor. In this sense, the model learns
significant aspects of grammar, after having been exposed solely to
linguistically unannotated text, questions, and answers: no prior linguistic
knowledge is given to the model. What is given is the means to build
representations using symbols and roles, with an inductive bias favoring use of
these in an approximately discrete manner.
Camera-Aware Multi-Resolution Analysis (CAMRA) for Raw Sensor Data Compression
Authors: Y. Lee, K. Hirakawa, T. Nguyen
Date: 2017-09-25 00:00:00
Tasks: Data Compression

We propose a novel lossless and lossy compression scheme for color filter
array~(CFA) sampled images based on the wavelet transform of them. Our analysis
suggests that the wavelet coefficients of HL and LH subbands are highly
correlated. Hence, we decorrelate Mallat wavelet packet decomposition to
further sparsify the coefficients. In addition, we develop a camera processing
pipeline for compressing CFA sampled images aimed at maximizing the quality of
the color images constructed from the compressed CFA sampled images. We
validated our theoretical analysis and the performance of the proposed
compression scheme using images of natural scenes captured in a raw format. The
experimental results verify that our proposed method improves coding efficiency
relative to the standard and the state-of-the-art compression schemes CFA
sampled images.
Active learning in annotating micro-blogs dealing with e-reputation
Authors: Jean-Valère Cossu, Alejandro Molina-Villegas, Mariana Tello-Signoret
Date: 2017-06-16 00:00:00
Tasks: Active Learning, Information Retrieval, Opinion Mining, Retrieval, Topic Classification

Elections unleash strong political views on Twitter, but what do people
really think about politics? Opinion and trend mining on micro blogs dealing
with politics has recently attracted researchers in several fields including
Information Retrieval and Machine Learning (ML). Since the performance of ML
and Natural Language Processing (NLP) approaches are limited by the amount and
quality of data available, one promising alternative for some tasks is the
automatic propagation of expert annotations. This paper intends to develop a
so-called active learning process for automatically annotating French language
tweets that deal with the image (i.e., representation, web reputation) of
politicians. Our main focus is on the methodology followed to build an original
annotated dataset expressing opinion from two French politicians over time. We
therefore review state of the art NLP-based ML algorithms to automatically
annotate tweets using a manual initiation step as bootstrap. This paper focuses
on key issues about active learning while building a large annotated data set
from noise. This will be introduced by human annotators, abundance of data and
the label distribution across data and entities. In turn, we show that Twitter
characteristics such as the author's name or hashtags can be considered as the
bearing point to not only improve automatic systems for Opinion Mining (OM) and
Topic Classification but also to reduce noise in human annotations. However, a
later thorough analysis shows that reducing noise might induce the loss of
crucial information.
Understanding a Version of Multivariate Symmetric Uncertainty to assist in Feature Selection
Authors: Gustavo Sosa-Cabrera, Miguel García-Torres, Santiago Gómez, Christian Schaerer, Federico Divina
Date: 2017-09-25 00:00:00
Tasks: Dimensionality Reduction, feature selection

In this paper, we analyze the behavior of the multivariate symmetric
uncertainty (MSU) measure through the use of statistical simulation techniques
under various mixes of informative and non-informative randomly generated
features. Experiments show how the number of attributes, their cardinalities,
and the sample size affect the MSU. We discovered a condition that preserves
good quality in the MSU under different combinations of these three factors,
providing a new useful criterion to help drive the process of dimension
reduction.
Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
Authors: Konstantinos Bousmalis, Alex Irpan, Paul Wohlhart, Yunfei Bai, Matthew Kelcey, Mrinal Kalakrishnan, Laura Downs, Julian Ibarz, Peter Pastor, Kurt Konolige, Sergey Levine, Vincent Vanhoucke
Date: 2017-09-22 00:00:00
Tasks: Domain Adaptation, Industrial Robots, Robotic Grasping

Instrumenting and collecting annotated visual grasping datasets to train
modern machine learning algorithms can be extremely time-consuming and
expensive. An appealing alternative is to use off-the-shelf simulators to
render synthetic data for which ground-truth annotations are generated
automatically. Unfortunately, models trained purely on simulated data often
fail to generalize to the real world. We study how randomized simulated
environments and domain adaptation methods can be extended to train a grasping
system to grasp novel objects from raw monocular RGB images. We extensively
evaluate our approaches with a total of more than 25,000 physical test grasps,
studying a range of simulation conditions and domain adaptation methods,
including a novel extension of pixel-level domain adaptation that we term the
GraspGAN. We show that, by using synthetic data and domain adaptation, we are
able to reduce the number of real-world samples needed to achieve a given level
of performance by up to 50 times, using only randomly generated simulated
objects. We also show that by using only unlabeled real-world data and our
GraspGAN methodology, we obtain real-world grasping performance without any
real-world labels that is similar to that achieved with 939,777 labeled
real-world samples.
DOC: Deep Open Classification of Text Documents
Authors: Lei Shu, Hu Xu, Bing Liu
Date: 2017-09-25 00:00:00
Proceeding: EMNLP 2017 9
Tasks: Classification, General Classification, text-classification, Text Classification

Traditional supervised learning makes the closed-world assumption that the
classes appeared in the test data must have appeared in training. This also
applies to text learning or text classification. As learning is used
increasingly in dynamic open environments where some new/test documents may not
belong to any of the training classes, identifying these novel documents during
classification presents an important problem. This problem is called open-world
classification or open classification. This paper proposes a novel deep
learning based approach. It outperforms existing state-of-the-art techniques
dramatically.
Tweeting AI: Perceptions of Lay vs Expert Twitterati
Authors: Lydia Manikonda, Subbarao Kambhampati
Date: 2017-09-25 00:00:00
Tasks: Emotion Recognition

With the recent advancements in Artificial Intelligence (AI), various
organizations and individuals are debating about the progress of AI as a
blessing or a curse for the future of the society. This paper conducts an
investigation on how the public perceives the progress of AI by utilizing the
data shared on Twitter. Specifically, this paper performs a comparative
analysis on the understanding of users belonging to two categories -- general
AI-Tweeters (AIT) and expert AI-Tweeters (EAIT) who share posts about AI on
Twitter. Our analysis revealed that users from both the categories express
distinct emotions and interests towards AI. Users from both the categories
regard AI as positive and are optimistic about the progress of AI but the
experts are more negative than the general AI-Tweeters. Expert AI-Tweeters
share relatively large percentage of tweets about their personal news compared
to technical aspects of AI. However, the effects of automation on the future
are of primary concern to AIT than to EAIT. When the expert category is
sub-categorized, the emotion analysis revealed that students and industry
professionals have more insights in their tweets about AI than academicians.
Glass-Box Program Synthesis: A Machine Learning Approach
Authors: Konstantina Christakopoulou, Adam Tauman Kalai
Date: 2017-09-25 00:00:00
Tasks: BIG-bench Machine Learning, Program Synthesis

Recently proposed models which learn to write computer programs from data use
either input/output examples or rich execution traces. Instead, we argue that a
novel alternative is to use a glass-box loss function, given as a program
itself that can be directly inspected. Glass-box optimization covers a wide
range of problems, from computing the greatest common divisor of two integers,
to learning-to-learn problems.
  In this paper, we present an intelligent search system which learns, given
the partial program and the glass-box problem, the probabilities over the space
of programs. We empirically demonstrate that our informed search procedure
leads to significant improvements compared to brute-force program search, both
in terms of accuracy and time. For our experiments we use rich context free
grammars inspired by number theory, text processing, and algebra. Our results
show that (i) performing 4 rounds of our framework typically solves about 70%
of the target problems, (ii) our framework can improve itself even in domain
agnostic scenarios, and (iii) it can solve problems that would be otherwise too
slow to solve with brute-force search.
Fast Vehicle Detection in Aerial Imagery
Authors: Jennifer Carlet, Bernard Abayowa
Date: 2017-09-25 00:00:00
Tasks: Fast Vehicle Detection, Object, vehicle detection

In recent years, several real-time or near real-time object detectors have
been developed. However these object detectors are typically designed for
first-person view images where the subject is large in the image and do not
directly apply well to detecting vehicles in aerial imagery. Though some
detectors have been developed for aerial imagery, these are either slow or do
not handle multi-scale imagery very well. Here the popular YOLOv2 detector is
modified to vastly improve it's performance on aerial data. The modified
detector is compared to Faster RCNN on several aerial imagery datasets. The
proposed detector gives near state of the art performance at more than 4x the
speed.