pwc-archive/papers-with-abstracts huggingface dataset:
Anti-drift in electronic nose via dimensionality reduction: a discriminative subspace projection approach
Authors: Zhengkun Yi, Cheng Li
Date: 2018-12-14 00:00:00
Tasks: Dimensionality Reduction

Sensor drift is a well-known issue in the field of sensors and measurement
and has plagued the sensor community for many years. In this paper, we propose
a sensor drift correction method to deal with the sensor drift problem.
Specifically, we propose a discriminative subspace projection approach for
sensor drift reduction in electronic noses. The proposed method inherits the
merits of the subspace projection method called domain regularized component
analysis. Moreover, the proposed method takes the source data label information
into consideration, which minimizes the within-class variance of the projected
source samples and at the same time maximizes the between-class variance. The
label information is exploited to avoid overlapping of samples with different
labels in the subspace. Experiments on two sensor drift datasets have shown the
effectiveness of the proposed approach.
Sparse One-Time Grab Sampling of Inliers
Authors: Maryam Jaberi, Marianna Pensky, Hassan Foroosh
Date: 2018-12-21 00:00:00
Tasks: Clustering

Estimating structures in "big data" and clustering them are among the most
fundamental problems in computer vision, pattern recognition, data mining, and
many other other research fields. Over the past few decades, many studies have
been conducted focusing on different aspects of these problems. One of the main
approaches that is explored in the literature to tackle the problems of size
and dimensionality is sampling subsets of the data in order to estimate the
characteristics of the whole population, e.g. estimating the underlying
clusters or structures in the data. In this paper, we propose a `one-time-grab'
sampling algorithm\cite{jaberi2015swift,jaberi2018sparse}. This method can be
used as the front end to any supervised or unsupervised clustering method.
Rather than focusing on the strategy of maximizing the probability of sampling
inliers, our goal is to minimize the number of samples needed to instantiate
all underlying model instances. More specifically, our goal is to answer the
following question: {\em `Given a very large population of points with $C$
embedded structures and gross outliers, what is the minimum number of points
$r$ to be selected randomly in one grab in order to make sure with probability
$P$ that at least $\varepsilon$ points are selected on each structure, where
$\varepsilon$ is the number of degrees of freedom of each structure.'}
Detecting the Trend in Musical Taste over the Decade -- A Novel Feature Extraction Algorithm to Classify Musical Content with Simple Features
Authors: Anish Acharya
Date: 2018-12-19 00:00:00
Tasks: Classification, Dimensionality Reduction, feature selection, General Classification

This work proposes a novel feature selection algorithm to classify Songs into
different groups. Classification of musical content is often a non-trivial job
and still relatively less explored area. The main idea conveyed in this article
is to come up with a new feature selection scheme that does the classification
job elegantly and with high accuracy but with simpler but wisely chosen small
number of features thus being less prone to over-fitting. This uses a very
basic general idea about the structure of the audio signal which is generally
in the shape of a trapezium. So, using this general idea of the Musical
Community we propose three frames to be considered and analyzed for feature
extraction for each of the audio signal -- opening, stanzas and closing -- and
it has been established with the help of a lot of experiments that this scheme
leads to much efficient classification with less complex features in a low
dimensional feature space thus is also a computationally less expensive method.
Step by step analysis of feature extraction, feature ranking, dimensionality
reduction using PCA has been carried in this article. Sequential Forward
selection (SFS) algorithm is used to explore the most significant features both
with the raw Fisher Discriminant Ratio (FDR) and also with the significant
eigen-values after PCA. Also during classification extensive validation and
cross validation has been done in a monte-carlo manner to ensure validity of
the claims.
Performance Comparison of Convolutional AutoEncoders, Generative Adversarial Networks and Super-Resolution for Image Compression
Authors: Zhengxue Cheng, Heming Sun, Masaru Takeuchi, Jiro Katto
Date: 2018-07-01 00:00:00
Tasks: Image Compression, Super-Resolution

Image compression has been investigated for many decades. Recently, deep
learning approaches have achieved a great success in many computer vision
tasks, and are gradually used in image compression. In this paper, we develop
three overall compression architectures based on convolutional autoencoders
(CAEs), generative adversarial networks (GANs) as well as super-resolution
(SR), and present a comprehensive performance comparison. According to
experimental results, CAEs achieve better coding efficiency than JPEG by
extracting compact features. GANs show potential advantages on large
compression ratio and high subjective quality reconstruction. Super-resolution
achieves the best rate-distortion (RD) performance among them, which is
comparable to BPG.
Cross-Modality Distillation: A case for Conditional Generative Adversarial Networks
Authors: Siddharth Roheda, Benjamin S. Riggan, Hamid Krim, Liyi Dai
Date: 2018-07-20 00:00:00
Tasks: Generative Adversarial Network

In this paper, we propose to use a Conditional Generative Adversarial Network
(CGAN) for distilling (i.e. transferring) knowledge from sensor data and
enhancing low-resolution target detection. In unconstrained surveillance
settings, sensor measurements are often noisy, degraded, corrupted, and even
missing/absent, thereby presenting a significant problem for multi-modal
fusion. We therefore specifically tackle the problem of a missing modality in
our attempt to propose an algorithm based on CGANs to generate representative
information from the missing modalities when given some other available
modalities. Despite modality gaps, we show that one can distill knowledge from
one set of modalities to another. Moreover, we demonstrate that it achieves
better performance than traditional approaches and recent teacher-student
models.
Fairness of Exposure in Rankings
Authors: Ashudeep Singh, Thorsten Joachims
Date: 2018-02-20 00:00:00
Tasks: Fairness

Rankings are ubiquitous in the online world today. As we have transitioned
from finding books in libraries to ranking products, jobs, job applicants,
opinions and potential romantic partners, there is a substantial precedent that
ranking systems have a responsibility not only to their users but also to the
items being ranked. To address these often conflicting responsibilities, we
propose a conceptual and computational framework that allows the formulation of
fairness constraints on rankings in terms of exposure allocation. As part of
this framework, we develop efficient algorithms for finding rankings that
maximize the utility for the user while provably satisfying a specifiable
notion of fairness. Since fairness goals can be application specific, we show
how a broad range of fairness constraints can be implemented using our
framework, including forms of demographic parity, disparate treatment, and
disparate impact constraints. We illustrate the effect of these constraints by
providing empirical results on two ranking problems.
Risk-Sensitive Reinforcement Learning Applied to Control under Constraints
Authors: P. Geibel, F. Wysotzki
Date: 2011-09-09 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In this paper, we consider Markov Decision Processes (MDPs) with error
states. Error states are those states entering which is undesirable or
dangerous. We define the risk with respect to a policy as the probability of
entering such a state when the policy is pursued. We consider the problem of
finding good policies whose risk is smaller than some user-specified threshold,
and formalize it as a constrained MDP with two criteria. The first criterion
corresponds to the value function originally given. We will show that the risk
can be formulated as a second criterion function based on a cumulative return,
whose definition is independent of the original value function. We present a
model free, heuristic reinforcement learning algorithm that aims at finding
good deterministic policies. It is based on weighting the original value
function and the risk. The weight parameter is adapted in order to find a
feasible solution for the constrained problem that has a good performance with
respect to the value function. The algorithm was successfully applied to the
control of a feed tank with stochastic inflows that lies upstream of a
distillation column. This control task was originally formulated as an optimal
control problem with chance constraints, and it was solved under certain
assumptions on the model to obtain an optimal solution. The power of our
learning algorithm is that it can be used even when some of these restrictive
assumptions are relaxed.
A Constructive Approach for One-Shot Training of Neural Networks Using Hypercube-Based Topological Coverings
Authors: W. Brent Daniel, Enoch Yeung
Date: 2019-01-09 00:00:00

In this paper we presented a novel constructive approach for training deep
neural networks using geometric approaches. We show that a topological covering
can be used to define a class of distributed linear matrix inequalities, which
in turn directly specify the shape and depth of a neural network architecture.
The key insight is a fundamental relationship between linear matrix
inequalities and their ability to bound the shape of data, and the rectified
linear unit (ReLU) activation function employed in modern neural networks. We
show that unit cover geometry and cover porosity are two design variables in
cover-constructive learning that play a critical role in defining the
complexity of the model and generalizability of the resulting neural network
classifier. In the context of cover-constructive learning, these findings
underscore the age old trade-off between model complexity and overfitting (as
quantified by the number of elements in the data cover) and generalizability on
test data. Finally, we benchmark on algorithm on the Iris, MNIST, and Wine
dataset and show that the constructive algorithm is able to train a deep neural
network classifier in one shot, achieving equal or superior levels of training
and test classification accuracy with reduced training time.
Learning to Infer and Execute 3D Shape Programs
Authors: Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, Jiajun Wu
Date: 2019-01-09 00:00:00
Proceeding: ICLR 2019 5

Human perception of 3D shapes goes beyond reconstructing them as a set of points or a composition of geometric primitives: we also effortlessly understand higher-level shape structure such as the repetition and reflective symmetry of object parts. In contrast, recent advances in 3D shape sensing focus more on low-level geometry but less on these higher-level relationships. In this paper, we propose 3D shape programs, integrating bottom-up recognition systems with top-down, symbolic program structure to capture both low-level geometry and high-level structural priors for 3D shapes. Because there are no annotations of shape programs for real shapes, we develop neural modules that not only learn to infer 3D shape programs from raw, unannotated shapes, but also to execute these programs for shape reconstruction. After initial bootstrapping, our end-to-end differentiable model learns 3D shape programs by reconstructing shapes in a self-supervised manner. Experiments demonstrate that our model accurately infers and executes 3D shape programs for highly complex shapes from various categories. It can also be integrated with an image-to-shape module to infer 3D shape programs directly from an RGB image, leading to 3D shape reconstructions that are both more accurate and more physically plausible.
The Lingering of Gradients: Theory and Applications
Authors: Zeyuan Allen-Zhu, David Simchi-Levi, Xinshang Wang
Date: 2019-01-09 00:00:00
Proceeding: NeurIPS 2018 12
Tasks: Management

Classically, the time complexity of a first-order method is estimated by its number of gradient computations. In this paper, we study a more refined complexity by taking into account the `lingering' of gradients: once a gradient is computed at $x_k$, the additional time to compute gradients at $x_{k+1},x_{k+2},\dots$ may be reduced. We show how this improves the running time of several first-order methods. For instance, if the `additional time' scales linearly with respect to the traveled distance, then the `convergence rate' of gradient descent can be improved from $1/T$ to $\exp(-T^{1/3})$. On the application side, we solve a hypothetical revenue management problem on the Yahoo! Front Page Today Module with 4.6m users to $10^{-6}$ error using only 6 passes of the dataset; and solve a real-life support vector machine problem to an accuracy that is two orders of magnitude better comparing to the state-of-the-art algorithm.
Fuzzy neural networks to create an expert system for detecting attacks by SQL Injection
Authors: Lucas Oliveira Batista, Gabriel Adriano de Silva, Vanessa Souza Araújo, Vinícius Jonathan Silva Araújo, Thiago Silva Rezende, Augusto Junio Guimarães, Paulo Vitor de Campos Souza
Date: 2019-01-09 00:00:00

Its constant technological evolution characterizes the contemporary world,
and every day the processes, once manual, become computerized. Data are stored
in the cyberspace, and as a consequence, one must increase the concern with the
security of this environment. Cyber-attacks are represented by a growing
worldwide scale and are characterized as one of the significant challenges of
the century. This article aims to propose a computational system based on
intelligent hybrid models, which through fuzzy rules allows the construction of
expert systems in cybernetic data attacks, focusing on the SQL Injection
attack. The tests were performed with real bases of SQL Injection attacks on
government computers, using fuzzy neural networks. According to the results
obtained, the feasibility of constructing a system based on fuzzy rules, with
the classification accuracy of cybernetic invasions within the margin of the
standard deviation (compared to the state-of-the-art model in solving this type
of problem) is real. The model helps countries prepare to protect their data
networks and information systems, as well as create opportunities for expert
systems to automate the identification of attacks in cyberspace.
Transformer-XL: Attentive Language Models Beyond a Fixed-Length Context
Authors: Zihang Dai, Zhilin Yang, Yiming Yang, Jaime Carbonell, Quoc V. Le, Ruslan Salakhutdinov
Date: 2019-01-09 00:00:00
Proceeding: ACL 2019 7
Tasks: Articles, Language Modeling, Language Modelling

Transformers have a potential of learning longer-term dependency, but are limited by a fixed-length context in the setting of language modeling. We propose a novel neural architecture Transformer-XL that enables learning dependency beyond a fixed length without disrupting temporal coherence. It consists of a segment-level recurrence mechanism and a novel positional encoding scheme. Our method not only enables capturing longer-term dependency, but also resolves the context fragmentation problem. As a result, Transformer-XL learns dependency that is 80% longer than RNNs and 450% longer than vanilla Transformers, achieves better performance on both short and long sequences, and is up to 1,800+ times faster than vanilla Transformers during evaluation. Notably, we improve the state-of-the-art results of bpc/perplexity to 0.99 on enwiki8, 1.08 on text8, 18.3 on WikiText-103, 21.8 on One Billion Word, and 54.5 on Penn Treebank (without finetuning). When trained only on WikiText-103, Transformer-XL manages to generate reasonably coherent, novel text articles with thousands of tokens. Our code, pretrained models, and hyperparameters are available in both Tensorflow and PyTorch.
Adaptive Feature Processing for Robust Human Activity Recognition on a Novel Multi-Modal Dataset
Authors: Mirco Moencks, Varuna De Silva, Jamie Roche, Ahmet Kondoz
Date: 2019-01-09 00:00:00
Tasks: Activity Recognition, Autonomous Vehicles, BIG-bench Machine Learning, Human Activity Recognition, Multimodal Activity Recognition, Sports Analytics

Human Activity Recognition (HAR) is a key building block of many emerging
applications such as intelligent mobility, sports analytics, ambient-assisted
living and human-robot interaction. With robust HAR, systems will become more
human-aware, leading towards much safer and empathetic autonomous systems.
While human pose detection has made significant progress with the dawn of deep
convolutional neural networks (CNNs), the state-of-the-art research has almost
exclusively focused on a single sensing modality, especially video. However, in
safety critical applications it is imperative to utilize multiple sensor
modalities for robust operation. To exploit the benefits of state-of-the-art
machine learning techniques for HAR, it is extremely important to have
multimodal datasets. In this paper, we present a novel, multi-modal sensor
dataset that encompasses nine indoor activities, performed by 16 participants,
and captured by four types of sensors that are commonly used in indoor
applications and autonomous vehicles. This multimodal dataset is the first of
its kind to be made openly available and can be exploited for many applications
that require HAR, including sports analytics, healthcare assistance and indoor
intelligent mobility. We propose a novel data preprocessing algorithm to enable
adaptive feature extraction from the dataset to be utilized by different
machine learning algorithms. Through rigorous experimental evaluations, this
paper reviews the performance of machine learning approaches to posture
recognition, and analyses the robustness of the algorithms. When performing HAR
with the RGB-Depth data from our new dataset, machine learning algorithms such
as a deep neural network reached a mean accuracy of up to 96.8% for
classification across all stationary and dynamic activities
GIF2Video: Color Dequantization and Temporal Interpolation of GIF images
Authors: Yang Wang, Haibin Huang, Chuan Wang, Tong He, Jue Wang, Minh Hoai
Date: 2019-01-09 00:00:00
Proceeding: CVPR 2019 6
Tasks: Quantization

Graphics Interchange Format (GIF) is a highly portable graphics format that
is ubiquitous on the Internet. Despite their small sizes, GIF images often
contain undesirable visual artifacts such as flat color regions, false
contours, color shift, and dotted patterns. In this paper, we propose
GIF2Video, the first learning-based method for enhancing the visual quality of
GIFs in the wild. We focus on the challenging task of GIF restoration by
recovering information lost in the three steps of GIF creation: frame sampling,
color quantization, and color dithering. We first propose a novel CNN
architecture for color dequantization. It is built upon a compositional
architecture for multi-step color correction, with a comprehensive loss
function designed to handle large quantization errors. We then adapt the
SuperSlomo network for temporal interpolation of GIF frames. We introduce two
large datasets, namely GIF-Faces and GIF-Moments, for both training and
evaluation. Experimental results show that our method can significantly improve
the visual quality of GIFs, and outperforms direct baseline and
state-of-the-art approaches.
SEWA DB: A Rich Database for Audio-Visual Emotion and Sentiment Research in the Wild
Authors: Jean Kossaifi, Robert Walecki, Yannis Panagakis, Jie Shen, Maximilian Schmitt, Fabien Ringeval, Jing Han, Vedhas Pandit, Antoine Toisoul, Bjorn Schuller, Kam Star, Elnar Hajiyev, Maja Pantic
Date: 2019-01-09 00:00:00

Natural human-computer interaction and audio-visual human behaviour sensing systems, which would achieve robust performance in-the-wild are more needed than ever as digital devices are increasingly becoming an indispensable part of our life. Accurately annotated real-world data are the crux in devising such systems. However, existing databases usually consider controlled settings, low demographic variability, and a single task. In this paper, we introduce the SEWA database of more than 2000 minutes of audio-visual data of 398 people coming from six cultures, 50% female, and uniformly spanning the age range of 18 to 65 years old. Subjects were recorded in two different contexts: while watching adverts and while discussing adverts in a video chat. The database includes rich annotations of the recordings in terms of facial landmarks, facial action units (FAU), various vocalisations, mirroring, and continuously valued valence, arousal, liking, agreement, and prototypic examples of (dis)liking. This database aims to be an extremely valuable resource for researchers in affective computing and automatic human sensing and is expected to push forward the research in human behaviour analysis, including cultural studies. Along with the database, we provide extensive baseline experiments for automatic FAU detection and automatic valence, arousal and (dis)liking intensity estimation.
Learnable Manifold Alignment (LeMA) : A Semi-supervised Cross-modality Learning Framework for Land Cover and Land Use Classification
Authors: Danfeng Hong, Naoto Yokoya, Nan Ge, Jocelyn Chanussot, Xiao Xiang Zhu
Date: 2019-01-09 00:00:00
Tasks: General Classification

In this paper, we aim at tackling a general but interesting cross-modality
feature learning question in remote sensing community --- can a limited amount
of highly-discrimin-ative (e.g., hyperspectral) training data improve the
performance of a classification task using a large amount of
poorly-discriminative (e.g., multispectral) data? Traditional semi-supervised
manifold alignment methods do not perform sufficiently well for such problems,
since the hyperspectral data is very expensive to be largely collected in a
trade-off between time and efficiency, compared to the multispectral data. To
this end, we propose a novel semi-supervised cross-modality learning framework,
called learnable manifold alignment (LeMA). LeMA learns a joint graph structure
directly from the data instead of using a given fixed graph defined by a
Gaussian kernel function. With the learned graph, we can further capture the
data distribution by graph-based label propagation, which enables finding a
more accurate decision boundary. Additionally, an optimization strategy based
on the alternating direction method of multipliers (ADMM) is designed to solve
the proposed model. Extensive experiments on two hyperspectral-multispectral
datasets demonstrate the superiority and effectiveness of the proposed method
in comparison with several state-of-the-art methods.