pwc-archive/papers-with-abstracts huggingface dataset:
An Interpretable Machine Vision Approach to Human Activity Recognition using Photoplethysmograph Sensor Data
Authors: Eoin Brophy, José Juan Dominguez Veiga, Zhengwei Wang, Alan F. Smeaton, Tomas E. Ward
Date: 2018-12-03 00:00:00
Tasks: Activity Recognition, Human Activity Recognition, Playing the Game of 2048, Time Series Analysis

The current gold standard for human activity recognition (HAR) is based on
the use of cameras. However, the poor scalability of camera systems renders
them impractical in pursuit of the goal of wider adoption of HAR in mobile
computing contexts. Consequently, researchers instead rely on wearable sensors
and in particular inertial sensors. A particularly prevalent wearable is the
smart watch which due to its integrated inertial and optical sensing
capabilities holds great potential for realising better HAR in a non-obtrusive
way. This paper seeks to simplify the wearable approach to HAR through
determining if the wrist-mounted optical sensor alone typically found in a
smartwatch or similar device can be used as a useful source of data for
activity recognition. The approach has the potential to eliminate the need for
the inertial sensing element which would in turn reduce the cost of and
complexity of smartwatches and fitness trackers. This could potentially
commoditise the hardware requirements for HAR while retaining the functionality
of both heart rate monitoring and activity capture all from a single optical
sensor. Our approach relies on the adoption of machine vision for activity
recognition based on suitably scaled plots of the optical signals. We take this
approach so as to produce classifications that are easily explainable and
interpretable by non-technical users. More specifically, images of
photoplethysmography signal time series are used to retrain the penultimate
layer of a convolutional neural network which has initially been trained on the
ImageNet database. We then use the 2048 dimensional features from the
penultimate layer as input to a support vector machine. Results from the
experiment yielded an average classification accuracy of 92.3%. This result
outperforms that of an optical and inertial sensor combined (78%) and
illustrates the capability of HAR systems using...
Knowledge Distillation with Feature Maps for Image Classification
Authors: Wei-Chun Chen, Chia-Che Chang, Chien-Yu Lu, Che-Rung Lee
Date: 2018-12-03 00:00:00
Tasks: Classification, General Classification, Generative Adversarial Network, image-classification, Image Classification, Knowledge Distillation

The model reduction problem that eases the computation costs and latency of
complex deep learning architectures has received an increasing number of
investigations owing to its importance in model deployment. One promising
method is knowledge distillation (KD), which creates a fast-to-execute student
model to mimic a large teacher network. In this paper, we propose a method,
called KDFM (Knowledge Distillation with Feature Maps), which improves the
effectiveness of KD by learning the feature maps from the teacher network. Two
major techniques used in KDFM are shared classifier and generative adversarial
network. Experimental results show that KDFM can use a four layers CNN to mimic
DenseNet-40 and use MobileNet to mimic DenseNet-100. Both student networks have
less than 1\% accuracy loss comparing to their teacher models for CIFAR-100
datasets. The student networks are 2-6 times faster than their teacher models
for inference, and the model size of MobileNet is less than half of
DenseNet-100's.
Proceedings of the fourth "international Traveling Workshop on Interactions between low-complexity data models and Sensing Techniques" (iTWIST'18)
Authors: Sandrine Anthoine, Yannick Boursier, Laurent Jacques
Date: 2018-12-03 00:00:00
Tasks: Philosophy

The iTWIST workshop series aim at fostering collaboration between
international scientific teams for developing new theories, applications and
generalizations of low-complexity models. These events emphasize dissemination
of ideas through both specific oral and poster presentations, as well as free
discussions. For this fourth edition, iTWIST'18 gathered in CIRM, Marseille,
France, 74 international participants and featured 7 invited talks, 16 oral
presentations, and 21 posters.
  From iTWIST'18, the scientific committee has decided that the workshop
proceedings will adopt the episcience.org philosophy, combined with arXiv.org:
in a nutshell, "the proceedings are equivalent to an overlay page, built above
arXiv.org; they add value to these archives by attaching a scientific caution
to the validated papers."
  This means that all papers listed in the HTML page of this arxiv publication
(see the menu on the right) have been thoroughly evaluated and approved by two
independent reviewers, and authors have revised their work according to the
comments provided by these reviewers.
Deep Hierarchical Machine: a Flexible Divide-and-Conquer Architecture
Authors: Shichao Li, Xin Yang, Tim Cheng
Date: 2018-12-03 00:00:00
Tasks: Face Alignment, General Classification, image-classification, Image Classification, regression, Representation Learning

We propose Deep Hierarchical Machine (DHM), a model inspired from the
divide-and-conquer strategy while emphasizing representation learning ability
and flexibility. A stochastic routing framework as used by recent deep neural
decision/regression forests is incorporated, but we remove the need to evaluate
unnecessary computation paths by utilizing a different topology and introducing
a probabilistic pruning technique. We also show a specified version of DHM
(DSHM) for efficiency, which inherits the sparse feature extraction process as
in traditional decision tree with pixel-difference feature. To achieve sparse
feature extraction, we propose to utilize sparse convolution operation in DSHM
and show one possibility of introducing sparse convolution kernels by using
local binary convolution layer. DHM can be applied to both classification and
regression problems, and we validate it on standard image classification and
face alignment tasks to show its advantages over past architectures.
Unsupervised Deep Slow Feature Analysis for Change Detection in Multi-Temporal Remote Sensing Images
Authors: Bo Du, Lixiang Ru, Chen Wu, Liangpei Zhang
Date: 2018-12-03 00:00:00
Tasks: Change Detection

Change detection has been a hotspot in remote sensing technology for a long time. With the increasing availability of multi-temporal remote sensing images, numerous change detection algorithms have been proposed. Among these methods, image transformation methods with feature extraction and mapping could effectively highlight the changed information and thus has better change detection performance. However, changes of multi-temporal images are usually complex, existing methods are not effective enough. In recent years, deep network has shown its brilliant performance in many fields including feature extraction and projection. Therefore, in this paper, based on deep network and slow feature analysis (SFA) theory, we proposed a new change detection algorithm for multi-temporal remotes sensing images called Deep Slow Feature Analysis (DSFA). In DSFA model, two symmetric deep networks are utilized for projecting the input data of bi-temporal imagery. Then, the SFA module is deployed to suppress the unchanged components and highlight the changed components of the transformed features. The CVA pre-detection is employed to find unchanged pixels with high confidence as training samples. Finally, the change intensity is calculated with chi-square distance and the changes are determined by threshold algorithms. The experiments are performed on two real-world datasets and a public hyperspectral dataset. The visual comparison and quantitative evaluation have both shown that DSFA could outperform the other state-of-the-art algorithms, including other SFA-based and deep learning methods.
Protection of an information system by artificial intelligence: a three-phase approach based on behaviour analysis to detect a hostile scenario
Authors: Jean-Philippe Fauvelle, Alexandre Dey, Sylvain Navers
Date: 2018-12-03 00:00:00
Tasks: Intrusion Detection, Management

The analysis of the behaviour of individuals and entities (UEBA) is an area
of artificial intelligence that detects hostile actions (e.g. attacks, fraud,
influence, poisoning) due to the unusual nature of observed events, by affixing
to a signature-based operation. A UEBA process usually involves two phases,
learning and inference. Intrusion detection systems (IDS) available still
suffer from bias, including over-simplification of problems, underexploitation
of the AI potential, insufficient consideration of the temporality of events,
and perfectible management of the memory cycle of behaviours. In addition,
while an alert generated by a signature-based IDS can refer to the signature on
which the detection is based, the IDS in the UEBA domain produce results, often
associated with a score, whose explainable character is less obvious. Our
unsupervised approach is to enrich this process by adding a third phase to
correlate events (incongruities, weak signals) that are presumed to be linked
together, with the benefit of a reduction of false positives and negatives. We
also seek to avoid a so-called "boiled frog" bias inherent in continuous
learning. Our first results are interesting and have an explainable character,
both on synthetic and real data.
Spatial-temporal Fusion Convolutional Neural Network for Simulated Driving Behavior Recognition
Authors: Yaocong Hu, MingQi Lu, Xiaobo Lu
Date: 2018-12-03 00:00:00
Tasks: Management, Optical Flow Estimation

Abnormal driving behaviour is one of the leading cause of terrible traffic
accidents endangering human life. Therefore, study on driving behaviour
surveillance has become essential to traffic security and public management. In
this paper, we conduct this promising research and employ a two stream CNN
framework for video-based driving behaviour recognition, in which spatial
stream CNN captures appearance information from still frames, whilst temporal
stream CNN captures motion information with pre-computed optical flow
displacement between a few adjacent video frames. We investigate different
spatial-temporal fusion strategies to combine the intra frame static clues and
inter frame dynamic clues for final behaviour recognition. So as to validate
the effectiveness of the designed spatial-temporal deep learning based model,
we create a simulated driving behaviour dataset, containing 1237 videos with 6
different driving behavior for recognition. Experiment result shows that our
proposed method obtains noticeable performance improvements compared to the
existing methods.
Examining Deep Learning Architectures for Crime Classification and Prediction
Authors: Panagiotis Stalidis, Theodoros Semertzidis, Petros Daras
Date: 2018-12-03 00:00:00
Tasks: Classification, Crime Prediction, Deep Learning, General Classification, Time Series, Time Series Analysis

In this paper, a detailed study on crime classification and prediction using
deep learning architectures is presented. We examine the effectiveness of deep
learning algorithms on this domain and provide recommendations for designing
and training deep learning systems for predicting crime areas, using open data
from police reports. Having as training data time-series of crime types per
location, a comparative study of 10 state-of-the-art methods against 3
different deep learning configurations is conducted. In our experiments with
five publicly available datasets, we demonstrate that the deep learning-based
methods consistently outperform the existing best-performing methods. Moreover,
we evaluate the effectiveness of different parameters in the deep learning
architectures and give insights for configuring them in order to achieve
improved performance in crime classification and finally crime prediction.
Resource Constrained Deep Reinforcement Learning
Authors: Abhinav Bhatia, Pradeep Varakantham, Akshat Kumar
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, Management, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

In urban environments, supply resources have to be constantly matched to the
"right" locations (where customer demand is present) so as to improve quality
of life. For instance, ambulances have to be matched to base stations regularly
so as to reduce response time for emergency incidents in EMS (Emergency
Management Systems); vehicles (cars, bikes, scooters etc.) have to be matched
to docking stations so as to reduce lost demand in shared mobility systems.
Such problem domains are challenging owing to the demand uncertainty,
combinatorial action spaces (due to allocation) and constraints on allocation
of resources (e.g., total resources, minimum and maximum number of resources at
locations and regions).
  Existing systems typically employ myopic and greedy optimization approaches
to optimize allocation of supply resources to locations. Such approaches
typically are unable to handle surges or variances in demand patterns well.
Recent research has demonstrated the ability of Deep RL methods in adapting
well to highly uncertain environments. However, existing Deep RL methods are
unable to handle combinatorial action spaces and constraints on allocation of
resources. To that end, we have developed three approaches on top of the well
known actor critic approach, DDPG (Deep Deterministic Policy Gradient) that are
able to handle constraints on resource allocation. More importantly, we
demonstrate that they are able to outperform leading approaches on simulators
validated on semi-real and real data sets.
Deep Learning Approach for Predicting 30 Day Readmissions after Coronary Artery Bypass Graft Surgery
Authors: Ramesh B. Manyam, Yanqing Zhang, William B. Keeling, Jose Binongo, Michael Kayatta, Seth Carter
Date: 2018-12-03 00:00:00
Tasks: BIG-bench Machine Learning, Survival Analysis

Hospital Readmissions within 30 days after discharge following Coronary
Artery Bypass Graft (CABG) Surgery are substantial contributors to healthcare
costs. Many predictive models were developed to identify risk factors for
readmissions. However, majority of the existing models use statistical analysis
techniques with data available at discharge. We propose an ensembled model to
predict CABG readmissions using pre-discharge perioperative data and machine
learning survival analysis techniques. Firstly, we applied fifty one potential
readmission risk variables to Cox Proportional Hazard (CPH) survival regression
univariate analysis. Fourteen of them turned out to be significant (with p
value < 0.05), contributing to readmissions. Subsequently, we applied these 14
predictors to multivariate CPH model and Deep Learning Neural Network (NN)
representation of the CPH model, DeepSurv. We validated this new ensembled
model with 453 isolated adult CABG cases. Nine of the fourteen perioperative
risk variables were identified as the most significant with Hazard Ratios (HR)
of greater than 1.0. The concordance index metrics for CPH, DeepSurv, and
ensembled models were then evaluated with training and validation datasets. Our
ensembled model yielded promising results in terms of c-statistics, as we
raised the the number of iterations and data set sizes. 30 day all-cause
readmissions among isolated CABG patients can be predicted more effectively
with perioperative pre-discharge data, using machine learning survival analysis
techniques. Prediction accuracy levels could be improved further with deep
learning algorithms.
Rademacher Complexity and Generalization Performance of Multi-category Margin Classifiers
Authors: Khadija Musayeva, Fabien Lauer, Yann Guermeur
Date: 2018-12-03 00:00:00

One of the main open problems in the theory of multi-category margin
classification is the form of the optimal dependency of a guaranteed risk on
the number C of categories, the sample size m and the margin parameter gamma.
From a practical point of view, the theoretical analysis of generalization
performance contributes to the development of new learning algorithms. In this
paper, we focus only on the theoretical aspect of the question posed. More
precisely, under minimal learnability assumptions, we derive a new risk bound
for multi-category margin classifiers. We improve the dependency on C over the
state of the art when the margin loss function considered satisfies the
Lipschitz condition. We start with the basic supremum inequality that involves
a Rademacher complexity as a capacity measure. This capacity measure is then
linked to the metric entropy through the chaining method. In this context, our
improvement is based on the introduction of a new combinatorial metric entropy
bound.
Towards Visual Feature Translation
Authors: Jie Hu, Rongrong Ji, Hong Liu, Shengchuan Zhang, Cheng Deng, Qi Tian
Date: 2018-12-03 00:00:00
Proceeding: CVPR 2019 6
Tasks: Translation

Most existing visual search systems are deployed based upon fixed kinds of
visual features, which prohibits the feature reusing across different systems
or when upgrading systems with a new type of feature. Such a setting is
obviously inflexible and time/memory consuming, which is indeed mendable if
visual features can be "translated" across systems. In this paper, we make the
first attempt towards visual feature translation to break through the barrier
of using features across different visual search systems. To this end, we
propose a Hybrid Auto-Encoder (HAE) to translate visual features, which learns
a mapping by minimizing the translation and reconstruction errors. Based upon
HAE, an Undirected Affinity Measurement (UAM) is further designed to quantify
the affinity among different types of visual features. Extensive experiments
have been conducted on several public datasets with sixteen different types of
widely-used features in visual search systems. Quantitative results show the
encouraging possibilities of feature translation. For the first time, the
affinity among widely-used features like SIFT and DELF is reported.
Practical Window Setting Optimization for Medical Image Deep Learning
Authors: Hyunkwang Lee, Myeongchan Kim, Synho Do
Date: 2018-12-03 00:00:00
Tasks: Computed Tomography (CT), Deep Learning, Diagnostic

The recent advancements in deep learning have allowed for numerous
applications in computed tomography (CT), with potential to improve diagnostic
accuracy, speed of interpretation, and clinical efficiency. However, the deep
learning community has to date neglected window display settings - a key
feature of clinical CT interpretation and opportunity for additional
optimization. Here we propose a window setting optimization (WSO) module that
is fully trainable with convolutional neural networks (CNNs) to find optimal
window settings for clinical performance. Our approach was inspired by the
method commonly used by practicing radiologists to interpret CT images by
adjusting window settings to increase the visualization of certain pathologies.
Our approach provides optimal window ranges to enhance the conspicuity of
abnormalities, and was used to enable performance enhancement for intracranial
hemorrhage and urinary stone detection. On each task, the WSO model
outperformed models trained over the full range of Hounsfield unit values in CT
images, as well as images windowed with pre-defined settings. The WSO module
can be readily applied to any analysis of CT images, and can be further
generalized to tasks on other medical imaging modalities.
Visual Foresight: Model-Based Deep Reinforcement Learning for Vision-Based Robotic Control
Authors: Frederik Ebert, Chelsea Finn, Sudeep Dasari, Annie Xie, Alex Lee, Sergey Levine
Date: 2018-12-03 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Deep reinforcement learning (RL) algorithms can learn complex robotic skills
from raw sensory inputs, but have yet to achieve the kind of broad
generalization and applicability demonstrated by deep learning methods in
supervised domains. We present a deep RL method that is practical for
real-world robotics tasks, such as robotic manipulation, and generalizes
effectively to never-before-seen tasks and objects. In these settings, ground
truth reward signals are typically unavailable, and we therefore propose a
self-supervised model-based approach, where a predictive model learns to
directly predict the future from raw sensory readings, such as camera images.
At test time, we explore three distinct goal specification methods: designated
pixels, where a user specifies desired object manipulation tasks by selecting
particular pixels in an image and corresponding goal positions, goal images,
where the desired goal state is specified with an image, and image classifiers,
which define spaces of goal states. Our deep predictive models are trained
using data collected autonomously and continuously by a robot interacting with
hundreds of objects, without human supervision. We demonstrate that visual MPC
can generalize to never-before-seen objects---both rigid and deformable---and
solve a range of user-defined object manipulation tasks using the same model.
Split learning for health: Distributed deep learning without sharing raw patient data
Authors: Praneeth Vepakomma, Otkrist Gupta, Tristan Swedish, Ramesh Raskar
Date: 2018-12-03 00:00:00
Tasks: Deep Learning, Federated Learning

Can health entities collaboratively train deep learning models without
sharing sensitive raw data? This paper proposes several configurations of a
distributed deep learning method called SplitNN to facilitate such
collaborations. SplitNN does not share raw data or model details with
collaborating institutions. The proposed configurations of splitNN cater to
practical settings of i) entities holding different modalities of patient data,
ii) centralized and local health entities collaborating on multiple tasks and
iii) learning without sharing labels. We compare performance and resource
efficiency trade-offs of splitNN and other distributed deep learning methods
like federated learning, large batch synchronous stochastic gradient descent
and show highly encouraging results for splitNN.
Signal Reconstruction from Modulo Observations
Authors: Viraj Shah, Chinmay Hegde
Date: 2018-12-03 00:00:00
Tasks: Retrieval

We consider the problem of reconstructing a signal from under-determined modulo observations (or measurements). This observation model is inspired by a (relatively) less well-known imaging mechanism called modulo imaging, which can be used to extend the dynamic range of imaging systems; variations of this model have also been studied under the category of phase unwrapping. Signal reconstruction in the under-determined regime with modulo observations is a challenging ill-posed problem, and existing reconstruction methods cannot be used directly. In this paper, we propose a novel approach to solving the inverse problem limited to two modulo periods, inspired by recent advances in algorithms for phase retrieval under sparsity constraints. We show that given a sufficient number of measurements, our algorithm perfectly recovers the underlying signal and provides improved performance over other existing algorithms. We also provide experiments validating our approach on both synthetic and real data to depict its superior performance.