pwc-archive/papers-with-abstracts huggingface dataset:
Actions Speak Louder Than (Pass)words: Passive Authentication of Smartphone Users via Deep Temporal Features
Authors: Debayan Deb, Arun Ross, Anil K. Jain, Kwaku Prakah-Asante, K. Venkatesh Prasad
Date: 2019-01-16 00:00:00

Prevailing user authentication schemes on smartphones rely on explicit user
interaction, where a user types in a passcode or presents a biometric cue such
as face, fingerprint, or iris. In addition to being cumbersome and obtrusive to
the users, such authentication mechanisms pose security and privacy concerns.
Passive authentication systems can tackle these challenges by frequently and
unobtrusively monitoring the user's interaction with the device. In this paper,
we propose a Siamese Long Short-Term Memory network architecture for passive
authentication, where users can be verified without requiring any explicit
authentication step. We acquired a dataset comprising of measurements from 30
smartphone sensor modalities for 37 users. We evaluate our approach on 8
dominant modalities, namely, keystroke dynamics, GPS location, accelerometer,
gyroscope, magnetometer, linear accelerometer, gravity, and rotation sensors.
Experimental results find that, within 3 seconds, a genuine user can be
correctly verified 97.15% of the time at a false accept rate of 0.1%.
Uncertainty-Aware Driver Trajectory Prediction at Urban Intersections
Authors: Xin Huang, Stephen McGill, Brian C. Williams, Luke Fletcher, Guy Rosman
Date: 2019-01-16 00:00:00
Tasks: Mixture-of-Experts, Prediction, Trajectory Prediction

Predicting the motion of a driver's vehicle is crucial for advanced driving
systems, enabling detection of potential risks towards shared control between
the driver and automation systems. In this paper, we propose a variational
neural network approach that predicts future driver trajectory distributions
for the vehicle based on multiple sensors. Our predictor generates both a
conditional variational distribution of future trajectories, as well as a
confidence estimate for different time horizons. Our approach allows us to
handle inherently uncertain situations, and reason about information gain from
each input, as well as combine our model with additional predictors, creating a
mixture of experts. We show how to augment the variational predictor with a
physics-based predictor, and based on their confidence estimations, improve
overall system performance. The resulting combined model is aware of the
uncertainty associated with its predictions, which can help the vehicle
autonomy to make decisions with more confidence. The model is validated on
real-world urban driving data collected in multiple locations. This validation
demonstrates that our approach improves the prediction error of a physics-based
model by 25% while successfully identifying the uncertain cases with 82%
accuracy.
A Comprehensive Performance Evaluation for 3D Transformation Estimation Techniques
Authors: Bao Zhao, Xiaobo Chen, Xinyi Le, Juntong Xi
Date: 2019-01-16 00:00:00
Tasks: Retrieval

3D local feature extraction and matching is the basis for solving many tasks
in the area of computer vision, such as 3D registration, modeling, recognition
and retrieval. However, this process commonly draws into false correspondences,
due to noise, limited features, occlusion, incomplete surface and etc. In order
to estimate accurate transformation based on these corrupted correspondences,
numerous transformation estimation techniques have been proposed. However, the
merits, demerits and appropriate application for these methods are unclear
owing to that no comprehensive evaluation for the performance of these methods
has been conducted. This paper evaluates eleven state-of-the-art transformation
estimation proposals on both descriptor based and synthetic correspondences. On
descriptor based correspondences, several evaluation items (including the
performance on different datasets, robustness to different overlap ratios and
the performance of these technique combined with Iterative Closest Point (ICP),
different local features and LRF/A techniques) of these methods are tested on
four popular datasets acquired with different devices. On synthetic
correspondences, the robustness of these methods to varying percentages of
correct correspondences (PCC) is evaluated. In addition, we also evaluate the
efficiencies of these methods. Finally, the merits, demerits and application
guidance of these tested transformation estimation methods are summarized.
DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation
Authors: Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, Steven Lovegrove
Date: 2019-01-16 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D geometry, 3D Reconstruction, 3D Shape Representation

Computer graphics, 3D computer vision and robotics communities have produced
multiple approaches to representing 3D geometry for rendering and
reconstruction. These provide trade-offs across fidelity, efficiency and
compression capabilities. In this work, we introduce DeepSDF, a learned
continuous Signed Distance Function (SDF) representation of a class of shapes
that enables high quality shape representation, interpolation and completion
from partial and noisy 3D input data. DeepSDF, like its classical counterpart,
represents a shape's surface by a continuous volumetric field: the magnitude of
a point in the field represents the distance to the surface boundary and the
sign indicates whether the region is inside (-) or outside (+) of the shape,
hence our representation implicitly encodes a shape's boundary as the
zero-level-set of the learned function while explicitly representing the
classification of space as being part of the shapes interior or not. While
classical SDF's both in analytical or discretized voxel form typically
represent the surface of a single shape, DeepSDF can represent an entire class
of shapes. Furthermore, we show state-of-the-art performance for learned 3D
shape representation and completion while reducing the model size by an order
of magnitude compared with previous work.
ReNeg and Backseat Driver: Learning from Demonstration with Continuous Human Feedback
Authors: Jacob Beck, Zoe Papakipos, Michael Littman
Date: 2019-01-16 00:00:00
Proceeding: ICLR 2019 5
Tasks: continuous-control, Continuous Control

In autonomous vehicle (AV) control, allowing mistakes can be quite dangerous
and costly in the real world. For this reason we investigate methods of
training an AV without allowing the agent to explore and instead having a human
explorer collect the data. Supervised learning has been explored for AV
control, but it encounters the issue of the covariate shift. That is, training
data collected from an optimal demonstration consists only of the states
induced by the optimal control policy, but at runtime, the trained agent may
encounter a vastly different state distribution with little relevant training
data. To mitigate this issue, we have our human explorer make sub-optimal
decisions. In order to have our agent not replicate these sub-optimal
decisions, supervised learning requires that we either erase these actions, or
replace these action with the correct action. Erasing is wasteful and replacing
is difficult, since it is not easy to know the correct action without driving.
We propose an alternate framework that includes continuous scalar feedback for
each action, marking which actions we should replicate, which we should avoid,
and how sure we are. Our framework learns continuous control from sub-optimal
demonstration and evaluative feedback collected before training. We find that a
human demonstrator can explore sub-optimal states in a safe manner, while still
getting enough gradation to benefit learning. The collection method for data
and feedback we call "Backseat Driver." We call the more general learning
framework ReNeg, since it learns a regression from states to actions given
negative as well as positive examples. We empirically validate several models
in the ReNeg framework, testing on lane-following with limited data. We find
that the best solution is a generalization of mean-squared error and
outperforms supervised learning on the positive examples alone.
Investigating Antigram Behaviour using Distributional Semantics
Authors: Saptarshi Sengupta
Date: 2019-01-15 00:00:00
Tasks: Semantic Similarity, Semantic Textual Similarity

The field of computational linguistics constantly presents new challenges and topics for research. Whether it be analyzing word usage changes over time or identifying relationships between pairs of seemingly unrelated words. To this point, we identify Anagrams and Antigrams as words possessing such unique properties. The presented work is an exploration into generating anagrams from a given word and determining whether there exists antigram (semantically opposite anagrams) relationships between the pairs of generated anagrams using GloVe embeddings. We propose a rudimentary, yet interpretable, rule-based algorithm for detecting antigrams. On a small dataset of just 12 antigrams, our approach yielded an accuracy of 39\% which shows that there is much work left to be done in this space.
Spectrogram Feature Losses for Music Source Separation
Authors: Abhimanyu Sahai, Romann Weber, Brian McWilliams
Date: 2019-01-15 00:00:00
Tasks: Deep Learning, Music Source Separation

In this paper we study deep learning-based music source separation, and explore using an alternative loss to the standard spectrogram pixel-level L2 loss for model training. Our main contribution is in demonstrating that adding a high-level feature loss term, extracted from the spectrograms using a VGG net, can improve separation quality vis-a-vis a pure pixel-level loss. We show this improvement in the context of the MMDenseNet, a State-of-the-Art deep learning model for this task, for the extraction of drums and vocal sounds from songs in the musdb18 database, covering a broad range of western music genres. We believe that this finding can be generalized and applied to broader machine learning-based systems in the audio domain.
Bonseyes AI Pipeline -- bringing AI to you. End-to-end integration of data, algorithms and deployment tools
Authors: Miguel de Prado, Jing Su, Rabia Saeed, Lorenzo Keller, Noelia Vallez, Andrew Anderson, David Gregg, Luca Benini, Tim Llewellynn, Nabil Ouerhani, Rozenn Dahyot and, Nuria Pazos
Date: 2019-01-15 00:00:00
Tasks: Automatic Speech Recognition (ASR), image-classification, Image Classification, Keyword Spotting, object-detection, Object Detection, Speech Recognition

Next generation of embedded Information and Communication Technology (ICT) systems are collaborative systems able to perform autonomous tasks. The remarkable expansion of the embedded ICT market, together with the rise and breakthroughs of Artificial Intelligence (AI), have put the focus on the Edge as it stands as one of the keys for the next technological revolution: the seamless integration of AI in our daily life. However, training and deployment of custom AI solutions on embedded devices require a fine-grained integration of data, algorithms, and tools to achieve high accuracy. Such integration requires a high level of expertise that becomes a real bottleneck for small and medium enterprises wanting to deploy AI solutions on the Edge which, ultimately, slows down the adoption of AI on daily-life applications. In this work, we present a modular AI pipeline as an integrating framework to bring data, algorithms, and deployment tools together. By removing the integration barriers and lowering the required expertise, we can interconnect the different stages of tools and provide a modular end-to-end development of AI products for embedded devices. Our AI pipeline consists of four modular main steps: i) data ingestion, ii) model training, iii) deployment optimization and, iv) the IoT hub integration. To show the effectiveness of our pipeline, we provide examples of different AI applications during each of the steps. Besides, we integrate our deployment framework, LPDNN, into the AI pipeline and present its lightweight architecture and deployment capabilities for embedded devices. Finally, we demonstrate the results of the AI pipeline by showing the deployment of several AI applications such as keyword spotting, image classification and object detection on a set of well-known embedded platforms, where LPDNN consistently outperforms all other popular deployment frameworks.
On complexity of branching droplets in electrical field
Authors: Mohammad Mahdi Dehshibi, Jitka Cejkova, Dominik Svara, Andrew Adamatzky
Date: 2019-01-15 00:00:00

Decanol droplets in a thin layer of sodium decanoate with sodium chloride
exhibit bifurcation branching growth due to interplay between osmotic pressure,
diffusion and surface tension. We aimed to evaluate if morphology of the
branching droplets changes when the droplets are subject to electrical
potential difference. We analysed graph-theoretic structure of the droplets and
applied several complexity measures. We found that, in overall, the current
increases complexity of the branching droplets in terms of number of connected
components and nodes in their graph presentations, morphological complexity and
compressibility.
Answering Comparative Questions: Better than Ten-Blue-Links?
Authors: Matthias Schildwächter, Alexander Bondarenko, Julian Zenker, Matthias Hagen, Chris Biemann, Alexander Panchenko
Date: 2019-01-15 00:00:00

We present CAM (comparative argumentative machine), a novel open-domain IR
system to argumentatively compare objects with respect to information extracted
from the Common Crawl. In a user study, the participants obtained 15% more
accurate answers using CAM compared to a "traditional" keyword-based search and
were 20% faster in finding the answer to comparative questions.
Analysis and algorithms for $\ell_p$-based semi-supervised learning on graphs
Authors: Mauricio Flores, Jeff Calder, Gilad Lerman
Date: 2019-01-15 00:00:00
Tasks: General Classification

This paper addresses theory and applications of $\ell_p$-based Laplacian regularization in semi-supervised learning. The graph $p$-Laplacian for $p>2$ has been proposed recently as a replacement for the standard ($p=2$) graph Laplacian in semi-supervised learning problems with very few labels, where Laplacian learning is degenerate. In the first part of the paper we prove new discrete to continuum convergence results for $p$-Laplace problems on $k$-nearest neighbor ($k$-NN) graphs, which are more commonly used in practice than random geometric graphs. Our analysis shows that, on $k$-NN graphs, the $p$-Laplacian retains information about the data distribution as $p\to \infty$ and Lipschitz learning ($p=\infty$) is sensitive to the data distribution. This situation can be contrasted with random geometric graphs, where the $p$-Laplacian forgets the data distribution as $p\to \infty$. We also present a general framework for proving discrete to continuum convergence results in graph-based learning that only requires pointwise consistency and monotonicity. In the second part of the paper, we develop fast algorithms for solving the variational and game-theoretic $p$-Laplace equations on weighted graphs for $p>2$. We present several efficient and scalable algorithms for both formulations, and present numerical results on synthetic data indicating their convergence properties. Finally, we conduct extensive numerical experiments on the MNIST, FashionMNIST and EMNIST datasets that illustrate the effectiveness of the $p$-Laplacian formulation for semi-supervised learning with few labels. In particular, we find that Lipschitz learning ($p=\infty$) performs well with very few labels on $k$-NN graphs, which experimentally validates our theoretical findings that Lipschitz learning retains information about the data distribution (the unlabeled data) on $k$-NN graphs.
The Bayesian Prophet: A Low-Regret Framework for Online Decision Making
Authors: Alberto Vera, Siddhartha Banerjee
Date: 2019-01-15 00:00:00
Tasks: Decision Making

We develop a new framework for designing online policies given access to an oracle providing statistical information about an offline benchmark. Having access to such prediction oracles enables simple and natural Bayesian selection policies, and raises the question as to how these policies perform in different settings. Our work makes two important contributions towards this question: First, we develop a general technique we call *compensated coupling* which can be used to derive bounds on the expected regret (i.e., additive loss with respect to a benchmark) for any online policy and offline benchmark. Second, using this technique, we show that a natural greedy policy, which we call *the Bayes Selector*, has constant expected regret (i.e., independent of the number of arrivals and resource levels) for a large class of problems we refer to as Online Allocation with finite types, which includes widely-studied Online Packing and Online Matching problems. Our results generalize and simplify several existing results for Online Packing and Online Matching, and suggest a promising pathway for obtaining oracle-driven policies for other online decision-making settings.
Light-weighted Saliency Detection with Distinctively Lower Memory Cost and Model Size
Authors: Shanghua Xiao
Date: 2019-01-15 00:00:00
Tasks: Saliency Detection

Deep neural networks (DNNs) based saliency detection approaches have succeed
in recent years, and improved the performance by a great margin via
increasingly sophisticated network architecture. Despite the performance
improvement, the computational cost is excessively high for such low level
visual task. In this work, we propose a light-weighted saliency detection
approach with distinctively lower runtime memory cost and model size. We
evaluated the performance of our approach on multiple benchmark datasets, and
achieved competitive results comparing with state-of-the-art methods on
multiple metrics. We also evaluated the computational cost of our approach with
multiple measurements. The runtime memory cost of our approach is 42 to 99
times fewer comparing with the previous DNNs based methods. The model size of
our approach is 63 to 129 times smaller, and takes less than 1 Megabytes
storage space with out any deep compression technique.
Real-world Underwater Enhancement: Challenges, Benchmarks, and Solutions
Authors: Risheng Liu, Xin Fan, Ming Zhu, Minjun Hou, Zhongxuan Luo
Date: 2019-01-15 00:00:00
Tasks: Image Enhancement, object-detection, Object Detection

Underwater image enhancement is such an important low-level vision task with
many applications that numerous algorithms have been proposed in recent years.
These algorithms developed upon various assumptions demonstrate successes from
various aspects using different data sets and different metrics. In this work,
we setup an undersea image capturing system, and construct a large-scale
Real-world Underwater Image Enhancement (RUIE) data set divided into three
subsets. The three subsets target at three challenging aspects for enhancement,
i.e., image visibility quality, color casts, and higher-level
detection/classification, respectively. We conduct extensive and systematic
experiments on RUIE to evaluate the effectiveness and limitations of various
algorithms to enhance visibility and correct color casts on images with
hierarchical categories of degradation. Moreover, underwater image enhancement
in practice usually serves as a preprocessing step for mid-level and high-level
vision tasks. We thus exploit the object detection performance on enhanced
images as a brand new task-specific evaluation criterion. The findings from
these evaluations not only confirm what is commonly believed, but also suggest
promising solutions and new directions for visibility enhancement, color
correction, and object detection on real-world underwater images.
MAD-GAN: Multivariate Anomaly Detection for Time Series Data with Generative Adversarial Networks
Authors: Dan Li, Dacheng Chen, Lei Shi, Baihong Jin, Jonathan Goh, See-Kiong Ng
Date: 2019-01-15 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Time Series, Time Series Analysis

The prevalence of networked sensors and actuators in many real-world systems
such as smart buildings, factories, power plants, and data centers generate
substantial amounts of multivariate time series data for these systems. The
rich sensor data can be continuously monitored for intrusion events through
anomaly detection. However, conventional threshold-based anomaly detection
methods are inadequate due to the dynamic complexities of these systems, while
supervised machine learning methods are unable to exploit the large amounts of
data due to the lack of labeled data. On the other hand, current unsupervised
machine learning approaches have not fully exploited the spatial-temporal
correlation and other dependencies amongst the multiple variables
(sensors/actuators) in the system for detecting anomalies. In this work, we
propose an unsupervised multivariate anomaly detection method based on
Generative Adversarial Networks (GANs). Instead of treating each data stream
independently, our proposed MAD-GAN framework considers the entire variable set
concurrently to capture the latent interactions amongst the variables. We also
fully exploit both the generator and discriminator produced by the GAN, using a
novel anomaly score called DR-score to detect anomalies by discrimination and
reconstruction. We have tested our proposed MAD-GAN using two recent datasets
collected from real-world CPS: the Secure Water Treatment (SWaT) and the Water
Distribution (WADI) datasets. Our experimental results showed that the proposed
MAD-GAN is effective in reporting anomalies caused by various cyber-intrusions
compared in these complex real-world systems.
Large-Scale Joint Topic, Sentiment & User Preference Analysis for Online Reviews
Authors: Xinli Yu, Zheng Chen, Wei-Shih Yang, Xiaohua Hu, Erjia Yan
Date: 2019-01-14 00:00:00
Tasks: Variational Inference

This paper presents a non-trivial reconstruction of a previous joint
topic-sentiment-preference review model TSPRA with stick-breaking
representation under the framework of variational inference (VI) and stochastic
variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves
topics, word sentiments and user preferences altogether and has been shown to
achieve good performance, but for large data set it can only learn from a
relatively small sample. We develop the variational models vTSPRA and svTSPRA
to improve the time use, and our new approach is capable of processing millions
of reviews. We rebuild the generative process, improve the rating regression,
solve and present the coordinate-ascent updates of variational parameters, and
show the time complexity of each iteration is theoretically linear to the
corpus size, and the experiments on Amazon data sets show it converges faster
than TSPRA and attains better results given the same amount of time. In
addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor
oscillations of sentiment and preference overtime. Some interesting
fluctuations are captured and possible explanations are provided. The results
give strong visual evidence that user preference is better treated as an
independent factor from sentiment.