pwc-archive/papers-with-abstracts huggingface dataset:
Water Disaggregation via Shape Features based Bayesian Discriminative Sparse Coding
Authors: Bingsheng Wang, Xuchao Zhang, Chang-Tien Lu, Feng Chen
Date: 2018-08-26 00:00:00

As the issue of freshwater shortage is increasing daily, it is critical to
take effective measures for water conservation. According to previous studies,
device level consumption could lead to significant freshwater conservation.
Existing water disaggregation methods focus on learning the signatures for
appliances; however, they are lack of the mechanism to accurately discriminate
parallel appliances' consumption. In this paper, we propose a Bayesian
Discriminative Sparse Coding model using Laplace Prior (BDSC-LP) to extensively
enhance the disaggregation performance. To derive discriminative basis
functions, shape features are presented to describe the low-sampling-rate water
consumption patterns. A Gibbs sampling based inference method is designed to
extend the discriminative capability of the disaggregation dictionaries.
Extensive experiments were performed to validate the effectiveness of the
proposed model using both real-world and synthetic datasets.
A Summary Description of the A2RD Project
Authors: Juliao Braga, Joao Nuno Silva, Patricia Takako Endo, Nizam Omar
Date: 2018-08-26 00:00:00

This paper describes the Autonomous Architecture Over Restricted Domains
project. It begins with the description of the context upon which the project
is focused, and in the sequence describes the project and implementation
models. It finish by presenting the environment conceptual model, showing where
stand the components, inputs and facilities required to interact among the
intelligent agents of the various implementations in their respective and
restricted, routing domains (Autonomous Systems) which together make the
Internet work.
Guiding Deep Learning System Testing using Surprise Adequacy
Authors: Jinhan Kim, Robert Feldt, Shin Yoo
Date: 2018-08-25 00:00:00
Tasks: Autonomous Driving, Deep Learning

Deep Learning (DL) systems are rapidly being adopted in safety and security
critical domains, urgently calling for ways to test their correctness and
robustness. Testing of DL systems has traditionally relied on manual collection
and labelling of data. Recently, a number of coverage criteria based on neuron
activation values have been proposed. These criteria essentially count the
number of neurons whose activation during the execution of a DL system
satisfied certain properties, such as being above predefined thresholds.
However, existing coverage criteria are not sufficiently fine grained to
capture subtle behaviours exhibited by DL systems. Moreover, evaluations have
focused on showing correlation between adversarial examples and proposed
criteria rather than evaluating and guiding their use for actual testing of DL
systems. We propose a novel test adequacy criterion for testing of DL systems,
called Surprise Adequacy for Deep Learning Systems (SADL), which is based on
the behaviour of DL systems with respect to their training data. We measure the
surprise of an input as the difference in DL system's behaviour between the
input and the training data (i.e., what was learnt during training), and
subsequently develop this as an adequacy criterion: a good test input should be
sufficiently but not overtly surprising compared to training data. Empirical
evaluation using a range of DL systems from simple image classifiers to
autonomous driving car platforms shows that systematic sampling of inputs based
on their surprise can improve classification accuracy of DL systems against
adversarial examples by up to 77.5% via retraining.
Neural Task Planning with And-Or Graph Representations
Authors: Tianshui Chen, Riquan Chen, Lin Nie, Xiaonan Luo, Xiaobai Liu, Liang Lin
Date: 2018-08-25 00:00:00
Tasks: Common Sense Reasoning, Task Planning, valid

This paper focuses on semantic task planning, i.e., predicting a sequence of
actions toward accomplishing a specific task under a certain scene, which is a
new problem in computer vision research. The primary challenges are how to
model task-specific knowledge and how to integrate this knowledge into the
learning procedure. In this work, we propose training a recurrent long
short-term memory (LSTM) network to address this problem, i.e., taking a scene
image (including pre-located objects) and the specified task as input and
recurrently predicting action sequences. However, training such a network
generally requires large numbers of annotated samples to cover the semantic
space (e.g., diverse action decomposition and ordering). To overcome this
issue, we introduce a knowledge and-or graph (AOG) for task description, which
hierarchically represents a task as atomic actions. With this AOG
representation, we can produce many valid samples (i.e., action sequences
according to common sense) by training another auxiliary LSTM network with a
small set of annotated samples. Furthermore, these generated samples (i.e.,
task-oriented action sequences) effectively facilitate training of the model
for semantic task planning. In our experiments, we create a new dataset that
contains diverse daily tasks and extensively evaluate the effectiveness of our
approach.
A model of diffuse Galactic Radio Emission from 10 MHz to 100 GHz
Authors: Angelica de Oliveira-Costa, Max Tegmark, B. M. Gaensler, Justin Jonas, T. L. Landecker, Patricia Reich
Date: 2008-02-12 00:00:00
Tasks: Optical Character Recognition, Optical Character Recognition (OCR)

Understanding diffuse Galactic radio emission is interesting both in its own
right and for minimizing foreground contamination of cosmological measurements.
Cosmic Microwave Background experiments have focused on frequencies > 10 GHz,
whereas 21 cm tomography of the high redshift universe will mainly focus on <
0.2 GHz, for which less is currently known about Galactic emission. Motivated
by this, we present a global sky model derived from all publicly available
total power large-area radio surveys, digitized with optical character
recognition when necessary and compiled into a uniform format, as well as the
new Villa Elisa data extending the 1.4 GHz map to the entire sky. We quantify
statistical and systematic uncertainties in these surveys by comparing them
with various global multi-frequency model fits. We find that a principal
component based model with only three components can fit the 11 most accurate
data sets (at 10, 22, 45 & 408 MHz and 1.4, 2.3, 23, 33, 41, 61, 94 GHz) to an
accuracy around 1%-10% depending on frequency and sky region. Both our data
compilation and our software returning a predicted all-sky map at any frequency
from 10 MHz to 100 GHz are publicly available at
http://space.mit.edu/home/angelica/gsm .
Adversarial Deep Reinforcement Learning in Portfolio Management
Authors: Zhipeng Liang, Hao Chen, Junhao Zhu, Kangkang Jiang, Yan-ran Li
Date: 2018-08-29 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we implement three state-of-art continuous reinforcement
learning algorithms, Deep Deterministic Policy Gradient (DDPG), Proximal Policy
Optimization (PPO) and Policy Gradient (PG)in portfolio management. All of them
are widely-used in game playing and robot control. What's more, PPO has
appealing theoretical propeties which is hopefully potential in portfolio
management. We present the performances of them under different settings,
including different learning rates, objective functions, feature combinations,
in order to provide insights for parameters tuning, features selection and data
preparation. We also conduct intensive experiments in China Stock market and
show that PG is more desirable in financial market than DDPG and PPO, although
both of them are more advanced. What's more, we propose a so called Adversarial
Training method and show that it can greatly improve the training efficiency
and significantly promote average daily return and sharpe ratio in back test.
Based on this new modification, our experiments results show that our agent
based on Policy Gradient can outperform UCRP.
Fixed-Point Convolutional Neural Network for Real-Time Video Processing in FPGA
Authors: Roman Solovyev, Alexander Kustov, Dmitry Telpukhov, Vladimir Rukhlov, Alexandr Kalinin
Date: 2018-08-29 00:00:00
Tasks: image-classification, Image Classification

Modern mobile neural networks with a reduced number of weights and parameters do a good job with image classification tasks, but even they may be too complex to be implemented in an FPGA for video processing tasks. The article proposes neural network architecture for the practical task of recognizing images from a camera, which has several advantages in terms of speed. This is achieved by reducing the number of weights, moving from a floating-point to a fixed-point arithmetic, and due to a number of hardware-level optimizations associated with storing weights in blocks, a shift register, and an adjustable number of convolutional blocks that work in parallel. The article also proposed methods for adapting the existing data set for solving a different task. As the experiments showed, the proposed neural network copes well with real-time video processing even on the cheap FPGAs.
Revisiting Character-Based Neural Machine Translation with Capacity and Compression
Authors: Colin Cherry, George Foster, Ankur Bapna, Orhan Firat, Wolfgang Macherey
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Feature Engineering, Machine Translation, NMT, Translation

Translating characters instead of words or word-fragments has the potential
to simplify the processing pipeline for neural machine translation (NMT), and
improve results by eliminating hyper-parameters and manual feature engineering.
However, it results in longer sequences in which each symbol contains less
information, creating both modeling and computational challenges. In this
paper, we show that the modeling problem can be solved by standard
sequence-to-sequence architectures of sufficient depth, and that deep models
operating at the character level outperform identical models operating over
word fragments. This result implies that alternative architectures for handling
character input are better viewed as methods for reducing computation time than
as improved ways of modeling longer sequences. From this perspective, we
evaluate several techniques for character-level NMT, verify that they do not
match the performance of our deep character baseline model, and evaluate the
performance versus computation time tradeoffs they offer. Within this
framework, we also perform the first evaluation for NMT of conditional
computation over time, in which the model learns which timesteps can be
skipped, rather than having them be dictated by a fixed schedule specified
before training begins.
Neural Compositional Denotational Semantics for Question Answering
Authors: Nitish Gupta, Mike Lewis
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Question Answering, Semantic Parsing, Sentence

Answering compositional questions requiring multi-step reasoning is
challenging. We introduce an end-to-end differentiable model for interpreting
questions about a knowledge graph (KG), which is inspired by formal approaches
to semantics. Each span of text is represented by a denotation in a KG and a
vector that captures ungrounded aspects of meaning. Learned composition modules
recursively combine constituent spans, culminating in a grounding for the
complete sentence which answers the question. For example, to interpret "not
green", the model represents "green" as a set of KG entities and "not" as a
trainable ungrounded vector---and then uses this vector to parameterize a
composition function that performs a complement operation. For each sentence,
we build a parse chart subsuming all possible parses, allowing the model to
jointly learn both the composition operators and output structure by gradient
descent from end-task supervision. The model learns a variety of challenging
semantic operators, such as quantifiers, disjunctions and composed relations,
and infers latent syntactic structure. It also generalizes well to longer
questions than seen in its training data, in contrast to RNN, its tree-based
variants, and semantic parsing baselines.
Attention-based Neural Text Segmentation
Authors: Pinkesh Badjatiya, Litton J Kurisinkel, Manish Gupta, Vasudeva Varma
Date: 2018-08-29 00:00:00
Tasks: Feature Engineering, Segmentation, Sentence, Sentence Embeddings, Text Segmentation

Text segmentation plays an important role in various Natural Language
Processing (NLP) tasks like summarization, context understanding, document
indexing and document noise removal. Previous methods for this task require
manual feature engineering, huge memory requirements and large execution times.
To the best of our knowledge, this paper is the first one to present a novel
supervised neural approach for text segmentation. Specifically, we propose an
attention-based bidirectional LSTM model where sentence embeddings are learned
using CNNs and the segments are predicted based on contextual information. This
model can automatically handle variable sized context information. Compared to
the existing competitive baselines, the proposed model shows a performance
improvement of ~7% in WinDiff score on three benchmark datasets.
Certified Mapper: Repeated testing for acyclicity and obstructions to the nerve lemma
Authors: Mikael Vejdemo-Johansson, Alisa Leshchenko
Date: 2018-08-29 00:00:00
Tasks: LEMMA

The Mapper algorithm does not include a check for whether the cover produced
conforms to the requirements of the nerve lemma. To perform a check for
obstructions to the nerve lemma, statistical considerations of multiple testing
quickly arise.
  In this paper, we propose several statistical approaches to finding
obstructions: through a persistent nerve lemma, through simulation testing, and
using a parametric refinement of simulation tests.
  We suggest Certified Mapper -- a method built from these approaches to
generate certificates of non-obstruction, or identify specific obstructions to
the nerve lemma -- and we give recommendations for which statistical approaches
are most appropriate for the task.
A Neural Model of Adaptation in Reading
Authors: Marten van Schijndel, Tal Linzen
Date: 2018-08-29 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Language Modeling, Language Modelling, model

It has been argued that humans rapidly adapt their lexical and syntactic
expectations to match the statistics of the current linguistic context. We
provide further support to this claim by showing that the addition of a simple
adaptation mechanism to a neural language model improves our predictions of
human reading times compared to a non-adaptive model. We analyze the
performance of the model on controlled materials from psycholinguistic
experiments and show that it adapts not only to lexical items but also to
abstract syntactic structures.
Question Answering by Reasoning Across Documents with Graph Convolutional Networks
Authors: Nicola De Cao, Wilker Aziz, Ivan Titov
Date: 2018-08-29 00:00:00
Proceeding: NAACL 2019 6
Tasks: Question Answering, Reading Comprehension

Most research in reading comprehension has focused on answering questions based on individual documents or even single paragraphs. We introduce a neural model which integrates and reasons relying on information spread within documents and across multiple documents. We frame it as an inference problem on a graph. Mentions of entities are nodes of this graph while edges encode relations between different mentions (e.g., within- and cross-document co-reference). Graph convolutional networks (GCNs) are applied to these graphs and trained to perform multi-step reasoning. Our Entity-GCN method is scalable and compact, and it achieves state-of-the-art results on a multi-document question answering dataset, WikiHop (Welbl et al., 2018).
Autoencoders, Kernels, and Multilayer Perceptrons for Electron Micrograph Restoration and Compression
Authors: Jeffrey M. Ede
Date: 2018-08-29 00:00:00
Tasks: Denoising

We present 14 autoencoders, 15 kernels and 14 multilayer perceptrons for
electron micrograph restoration and compression. These have been trained for
transmission electron microscopy (TEM), scanning transmission electron
microscopy (STEM) and for both (TEM+STEM). TEM autoencoders have been trained
for 1$\times$, 4$\times$, 16$\times$ and 64$\times$ compression, STEM
autoencoders for 1$\times$, 4$\times$ and 16$\times$ compression and TEM+STEM
autoencoders for 1$\times$, 2$\times$, 4$\times$, 8$\times$, 16$\times$,
32$\times$ and 64$\times$ compression. Kernels and multilayer perceptrons have
been trained to approximate the denoising effect of the 4$\times$ compression
autoencoders. Kernels for input sizes of 3, 5, 7, 11 and 15 have been fitted
for TEM, STEM and TEM+STEM. TEM multilayer perceptrons have been trained with 1
hidden layer for input sizes of 3, 5 and 7 and with 2 hidden layers for input
sizes of 5 and 7. STEM multilayer perceptrons have been trained with 1 hidden
layer for input sizes of 3, 5 and 7. TEM+STEM multilayer perceptrons have been
trained with 1 hidden layer for input sizes of 3, 5, 7 and 11 and with 2 hidden
layers for input sizes of 3 and 7. Our code, example usage and pre-trained
models are available at
https://github.com/Jeffrey-Ede/Denoising-Kernels-MLPs-Autoencoders
Dropout with Tabu Strategy for Regularizing Deep Neural Networks
Authors: Zongjie Ma, Abdul Sattar, Jun Zhou, Qingliang Chen, Kaile Su
Date: 2018-08-29 00:00:00

Dropout has proven to be an effective technique for regularization and
preventing the co-adaptation of neurons in deep neural networks (DNN). It
randomly drops units with a probability $p$ during the training stage of DNN.
Dropout also provides a way of approximately combining exponentially many
different neural network architectures efficiently. In this work, we add a
diversification strategy into dropout, which aims at generating more different
neural network architectures in a proper times of iterations. The dropped units
in last forward propagation will be marked. Then the selected units for
dropping in the current FP will be kept if they have been marked in the last
forward propagation. We only mark the units from the last forward propagation.
We call this new technique Tabu Dropout. Tabu Dropout has no extra parameters
compared with the standard Dropout and also it is computationally cheap. The
experiments conducted on MNIST, Fashion-MNIST datasets show that Tabu Dropout
improves the performance of the standard dropout.
Extreme Value Theory for Open Set Classification -- GPD and GEV Classifiers
Authors: Edoardo Vignotto, Sebastian Engelke
Date: 2018-08-29 00:00:00
Tasks: General Classification, open-set classification

Classification tasks usually assume that all possible classes are present during the training phase. This is restrictive if the algorithm is used over a long time and possibly encounters samples from unknown classes. The recently introduced extreme value machine, a classifier motivated by extreme value theory, addresses this problem and achieves competitive performance in specific cases. We show that this algorithm can fail when the geometries of known and unknown classes differ. To overcome this problem, we propose two new algorithms relying on approximations from extreme value theory. We show the effectiveness of our classifiers in simulations and on the LETTER and MNIST data sets.
Towards security defect prediction with AI
Authors: Carson D. Sestili, William S. Snavely, Nathan M. VanHoudnos
Date: 2018-08-29 00:00:00
Tasks: Prediction

In this study, we investigate the limits of the current state of the art AI
system for detecting buffer overflows and compare it with current static
analysis tools. To do so, we developed a code generator, s-bAbI, capable of
producing an arbitrarily large number of code samples of controlled complexity.
We found that the static analysis engines we examined have good precision, but
poor recall on this dataset, except for a sound static analyzer that has good
precision and recall. We found that the state of the art AI system, a memory
network modeled after Choi et al. [1], can achieve similar performance to the
static analysis engines, but requires an exhaustive amount of training data in
order to do so. Our work points towards future approaches that may solve these
problems; namely, using representations of code that can capture appropriate
scope information and using deep learning methods that are able to perform
arithmetic operations.
Review Helpfulness Prediction with Embedding-Gated CNN
Authors: Cen Chen, Minghui Qiu, Yinfei Yang, Jun Zhou, Jun Huang, Xiaolong Li, Forrest Bao
Date: 2018-08-29 00:00:00
Tasks: Prediction, Sentence

Product reviews, in the form of texts dominantly, significantly help
consumers finalize their purchasing decisions. Thus, it is important for
e-commerce companies to predict review helpfulness to present and recommend
reviews in a more informative manner. In this work, we introduce a
convolutional neural network model that is able to extract abstract features
from multi-granularity representations. Inspired by the fact that different
words contribute to the meaning of a sentence differently, we consider to learn
word-level embedding-gates for all the representations. Furthermore, as it is
common that some product domains/categories have rich user reviews, other
domains not. To help domains with less sufficient data, we integrate our model
into a cross-domain relationship learning framework for effectively
transferring knowledge from other domains. Extensive experiments show that our
model yields better performance than the existing methods.
Top-down Attention Recurrent VLAD Encoding for Action Recognition in Videos
Authors: Swathikiran Sudhakaran, Oswald Lanz
Date: 2018-08-29 00:00:00
Tasks: Action Recognition, Action Recognition In Videos, General Classification, image-classification, Image Classification, Temporal Action Localization

Most recent approaches for action recognition from video leverage deep
architectures to encode the video clip into a fixed length representation
vector that is then used for classification. For this to be successful, the
network must be capable of suppressing irrelevant scene background and extract
the representation from the most discriminative part of the video. Our
contribution builds on the observation that spatio-temporal patterns
characterizing actions in videos are highly correlated with objects and their
location in the video. We propose Top-down Attention Action VLAD (TA-VLAD), a
deep recurrent architecture with built-in spatial attention that performs
temporally aggregated VLAD encoding for action recognition from videos. We
adopt a top-down approach of attention, by using class specific activation maps
obtained from a deep CNN pre-trained for image classification, to weight
appearance features before encoding them into a fixed-length video descriptor
using Gated Recurrent Units. Our method achieves state of the art recognition
accuracy on HMDB51 and UCF101 benchmarks.
PanoRoom: From the Sphere to the 3D Layout
Authors: Clara Fernandez-Labrador, Jose M. Facil, Alejandro Perez-Yus, Cedric Demonceaux, Jose J. Guerrero
Date: 2018-08-29 00:00:00

We propose a novel FCN able to work with omnidirectional images that outputs
accurate probability maps representing the main structure of indoor scenes,
which is able to generalize on different data. Our approach handles occlusions
and recovers complex shaped rooms more faithful to the actual shape of the real
scenes. We outperform the state of the art not only in accuracy of the 3D
models but also in speed.