pwc-archive/papers-with-abstracts huggingface dataset:
Building a Reproducible Machine Learning Pipeline
Authors: Peter Sugimura, Florian Hartl
Date: 2018-10-09 00:00:00
Tasks: BIG-bench Machine Learning

Reproducibility of modeling is a problem that exists for any machine learning
practitioner, whether in industry or academia. The consequences of an
irreproducible model can include significant financial costs, lost time, and
even loss of personal reputation (if results prove unable to be replicated).
This paper will first discuss the problems we have encountered while building a
variety of machine learning models, and subsequently describe the framework we
built to tackle the problem of model reproducibility. The framework is
comprised of four main components (data, feature, scoring, and evaluation
layers), which are themselves comprised of well defined transformations. This
enables us to not only exactly replicate a model, but also to reuse the
transformations across different models. As a result, the platform has
dramatically increased the speed of both offline and online experimentation
while also ensuring model reproducibility.
Improvement of K Mean Clustering Algorithm Based on Density
Authors: Su Chang, Xu Zhenzong, Gao Xuan
Date: 2018-10-09 00:00:00
Tasks: Clustering

The purpose of this paper is to improve the traditional K-means algorithm. In
the traditional K mean clustering algorithm, the initial clustering centers are
generated randomly in the data set. It is easy to fall into the local minimum
solution when the initial cluster centers are randomly generated. The initial
clustering center selected by K-means clustering algorithm which based on
density is more representative. The experimental results show that the improved
K clustering algorithm can eliminate the dependence on the initial cluster, and
the accuracy of clustering is improved.
Interpreting Winograd Schemas Via the SP Theory of Intelligence and Its Realisation in the SP Computer Model
Authors: J. Gerard Wolff
Date: 2018-10-09 00:00:00

In 'Winograd Schema' (WS) sentences like "The city councilmen refused the
demonstrators a permit because they feared violence" and "The city councilmen
refused the demonstrators a permit because they advocated revolution", it is
easy for adults to understand what "they" refers to but can be difficult for AI
systems. This paper describes how the SP System -- outlined in an appendix --
may solve this kind of problem of interpretation. The central idea is that a
knowledge of discontinuous associations amongst linguistic features, and an
ability to recognise such patterns of associations, provides a robust means of
determining what a pronoun like "they" refers to. For any AI system to solve
this kind of problem, it needs appropriate knowledge of relevant syntax and
semantics which, ideally, it should learn for itself. Although the SP System
has some strengths in unsupervised learning, its capabilities in this area are
not yet good enough to learn the kind of knowledge needed to interpret WS
examples, so it must be supplied with such knowledge at the outset. However,
its existing strengths in unsupervised learning suggest that it has potential
to learn the kind of knowledge needed for the interpretation of WS examples. In
particular, it has potential to learn the kind of discontinuous association of
linguistic features mentioned earlier.
Is your Statement Purposeless? Predicting Computer Science Graduation Admission Acceptance based on Statement Of Purpose
Authors: Diptesh Kanojia, Nikhil Wani, Pushpak Bhattacharyya
Date: 2018-10-09 00:00:00
Proceeding: WS 2017 12

We present a quantitative, data-driven machine learning approach to mitigate
the problem of unpredictability of Computer Science Graduate School Admissions.
In this paper, we discuss the possibility of a system which may help
prospective applicants evaluate their Statement of Purpose (SOP) based on our
system output. We, then, identify feature sets which can be used to train a
predictive model. We train a model over fifty manually verified SOPs for which
it uses an SVM classifier and achieves the highest accuracy of 92% with 10-fold
cross-validation. We also perform experiments to establish that Word Embedding
based features and Document Similarity-based features outperform other
identified feature combinations. We plan to deploy our application as a web
service and release it as a FOSS service.
Investigating Enactive Learning for Autonomous Intelligent Agents
Authors: Rafik Hadfi
Date: 2018-10-09 00:00:00
Tasks: Reinforcement Learning, Reinforcement Learning (RL)

The enactive approach to cognition is typically proposed as a viable
alternative to traditional cognitive science. Enactive cognition displaces the
explanatory focus from the internal representations of the agent to the direct
sensorimotor interaction with its environment. In this paper, we investigate
enactive learning through means of artificial agent simulations. We compare the
performances of the enactive agent to an agent operating on classical
reinforcement learning in foraging tasks within maze environments. The
characteristics of the agents are analysed in terms of the accessibility of the
environmental states, goals, and exploration/exploitation tradeoffs. We confirm
that the enactive agent can successfully interact with its environment and
learn to avoid unfavourable interactions using intrinsically defined goals. The
performance of the enactive agent is shown to be limited by the number of
affordable actions.
Optimized Gated Deep Learning Architectures for Sensor Fusion
Authors: Myung Seok Shim, Peng Li
Date: 2018-10-08 00:00:00
Proceeding: ICLR 2019 5
Tasks: Activity Recognition, Autonomous Driving, Decision Making, Deep Learning, Human Activity Recognition, Sensor Fusion

Sensor fusion is a key technology that integrates various sensory inputs to
allow for robust decision making in many applications such as autonomous
driving and robot control. Deep neural networks have been adopted for sensor
fusion in a body of recent studies. Among these, the so-called netgated
architecture was proposed, which has demonstrated improved performances over
the conventional convolutional neural networks (CNN). In this paper, we address
several limitations of the baseline negated architecture by proposing two
further optimized architectures: a coarser-grained gated architecture employing
(feature) group-level fusion weights and a two-stage gated architectures
leveraging both the group-level and feature level fusion weights. Using driving
mode prediction and human activity recognition datasets, we demonstrate the
significant performance improvements brought by the proposed gated
architectures and also their robustness in the presence of sensor noise and
failures.
WiPIN: Operation-free Passive Person Identification Using Wi-Fi Signals
Authors: Fei Wang, Jinsong Han, Feng Lin, Kui Ren
Date: 2018-10-06 00:00:00
Tasks: Person Identification

Wi-Fi signals-based person identification attracts increasing attention in the booming Internet-of-Things era mainly due to its pervasiveness and passiveness. Most previous work applies gaits extracted from WiFi distortions caused by the person walking to achieve the identification. However, to extract useful gait, a person must walk along a pre-defined path for several meters, which requires user high collaboration and increases identification time overhead, thus limiting use scenarios. Moreover, gait based work has severe shortcoming in identification performance, especially when the user volume is large. In order to eliminate the above limitations, in this paper, we present an operation-free person identification system, namely WiPIN, that requires least user collaboration and achieves good performance. WiPIN is based on an entirely new insight that Wi-Fi signals would carry person body information when propagating through the body, which is potentially discriminated for person identification. Then we demonstrate the feasibility on commodity off-the-shelf Wi-Fi devices by well-designed signal pre-processing, feature extraction, and identity matching algorithms. Results show that WiPIN achieves 92% identification accuracy over 30 users, high robustness to various experimental settings, and low identifying time overhead, i.e., less than 300ms.
Deep learning: Extrapolation tool for ab initio nuclear theory
Authors: Gianina Alina Negoita, James P. Vary, Glenn R. Luecke, Pieter Maris, Andrey M. Shirokov, Ik Jae Shin, Youngman Kim, Esmond G. Ng, Chao Yang, Matthew Lockner, Gurpur M. Prabhu
Date: 2018-10-06 00:00:00
Tasks: Deep Learning

Ab initio approaches in nuclear theory, such as the no-core shell model (NCSM), have been developed for approximately solving finite nuclei with realistic strong interactions. The NCSM and other approaches require an extrapolation of the results obtained in a finite basis space to the infinite basis space limit and assessment of the uncertainty of those extrapolations. Each observable requires a separate extrapolation and most observables have no proven extrapolation method. We propose a feed-forward artificial neural network (ANN) method as an extrapolation tool to obtain the ground state energy and the ground state point-proton root-mean-square (rms) radius along with their extrapolation uncertainties. The designed ANNs are sufficient to produce results for these two very different observables in $^6$Li from the ab initio NCSM results in small basis spaces that satisfy the following theoretical physics condition: independence of basis space parameters in the limit of extremely large matrices. Comparisons of the ANN results with other extrapolation methods are also provided.
Computationally Efficient Deep Neural Network for Computed Tomography Image Reconstruction
Authors: Dufan Wu, Kyungsang Kim, Quanzheng Li
Date: 2018-10-05 00:00:00
Tasks: Computed Tomography (CT), GPU, Image Reconstruction

Deep-neural-network-based image reconstruction has demonstrated promising performance in medical imaging for under-sampled and low-dose scenarios. However, it requires large amount of memory and extensive time for the training. It is especially challenging to train the reconstruction networks for three-dimensional computed tomography (CT) because of the high resolution of CT images. The purpose of this work is to reduce the memory and time consumption of the training of the reconstruction networks for CT to make it practical for current hardware, while maintaining the quality of the reconstructed images. We unrolled the proximal gradient descent algorithm for iterative image reconstruction to finite iterations and replaced the terms related to the penalty function with trainable convolutional neural networks (CNN). The network was trained greedily iteration by iteration in the image-domain on patches, which requires reasonable amount of memory and time on mainstream graphics processing unit (GPU). To overcome the local-minimum problem caused by greedy learning, we used deep UNet as the CNN and incorporated separable quadratic surrogate with ordered subsets for data fidelity, so that the solution could escape from easy local minimums and achieve better image quality. The proposed method achieved comparable image quality with state-of-the-art neural network for CT image reconstruction on 2D sparse-view and limited-angle problems on the low-dose CT challenge dataset.
A Deep Learning Approach to the Inversion of Borehole Resistivity Measurements
Authors: M. Shahriari, D. Pardo, A. Picón, A. Galdrán, J. Del Ser, C. Torres-Verdín
Date: 2018-10-05 00:00:00

We use borehole resistivity measurements to map the electrical properties of
the subsurface and to increase the productivity of a reservoir. When used for
geosteering purposes, it becomes essential to invert them in real time. In this
work, we explore the possibility of using Deep Neural Network (DNN) to perform
a rapid inversion of borehole resistivity measurements. Herein, we build a DNN
that approximates the following inverse problem: given a set of borehole
resistivity measurements, the DNN is designed to deliver a physically
meaningful and data-consistent piecewise one-dimensional layered model of the
surrounding subsurface. Once the DNN is built, we can perform the actual
inversion of the field measurements in real time. We illustrate the performance
of DNN of logging-while-drilling measurements acquired on high-angle wells via
synthetic data.
Deep Convolutional Neural Networks for Noise Detection in ECGs
Authors: Jennifer N. John, Conner Galloway, Alexander Valys
Date: 2018-10-05 00:00:00

Mobile electrocardiogram (ECG) recording technologies represent a promising
tool to fight the ongoing epidemic of cardiovascular diseases, which are
responsible for more deaths globally than any other cause. While the ability to
monitor one's heart activity at any time in any place is a crucial advantage of
such technologies, it is also the cause of a drawback: signal noise due to
environmental factors can render the ECGs illegible. In this work, we develop
convolutional neural networks (CNNs) to automatically label ECGs for noise,
training them on a novel noise-annotated dataset. By reducing distraction from
noisy intervals of signals, such networks have the potential to increase the
accuracy of models for the detection of atrial fibrillation, long QT syndrome,
and other cardiovascular conditions. Comparing several architectures, we find
that a 16-layer CNN adapted from the VGG16 network which generates one
prediction per second on a 10-second input performs exceptionally well on this
task, with an AUC of 0.977.
Current Trends and Future Research Directions for Interactive Music
Authors: Mauricio Toro
Date: 2018-10-05 00:00:00

In this review, it is explained and compared different software and
formalisms used in music interaction: sequencers, computer-assisted
improvisation, meta- instruments, score-following, asynchronous dataflow
languages, synchronous dataflow languages, process calculi, temporal
constraints and interactive scores. Formal approaches have the advantage of
providing rigorous semantics of the behavior of the model and proving
correctness during execution. The main disadvantage of formal approaches is
lack of commercial tools.
Survival prediction using ensemble tumor segmentation and transfer learning
Authors: Mariano Cabezas, Sergi Valverde, Sandra González-Villà, Albert Clérigues, Mostafa Salem, Kaisar Kushibar, Jose Bernal, Arnau Oliver, Xavier Lladó
Date: 2018-10-04 00:00:00
Tasks: Survival Prediction, Transfer Learning, Tumor Segmentation

Segmenting tumors and their subregions is a challenging task as demonstrated
by the annual BraTS challenge. Moreover, predicting the survival of the patient
using mainly imaging features, while being a desirable outcome to evaluate the
treatment of the patient, it is also a difficult task. In this paper, we
present a cascaded pipeline to segment the tumor and its subregions and then we
use these results and other clinical features together with image features
coming from a pretrained VGG-16 network to predict the survival of the patient.
Preliminary results with the training and validation dataset show a promising
start in terms of segmentation, while the prediction values could be improved
with further testing on the feature extraction part of the network.
Image-to-Video Person Re-Identification by Reusing Cross-modal Embeddings
Authors: Zhongwei Xie, Lin Li, Xian Zhong, Luo Zhong
Date: 2018-10-04 00:00:00
Tasks: Image Captioning, Image-To-Video Person Re-Identification, Person Re-Identification, Video-Based Person Re-Identification, Video Captioning

Image-to-video person re-identification identifies a target person by a probe
image from quantities of pedestrian videos captured by non-overlapping cameras.
Despite the great progress achieved,it's still challenging to match in the
multimodal scenario,i.e. between image and video. Currently,state-of-the-art
approaches mainly focus on the task-specific data,neglecting the extra
information on the different but related tasks. In this paper,we propose an
end-to-end neural network framework for image-to-video person reidentification
by leveraging cross-modal embeddings learned from extra information.Concretely
speaking,cross-modal embeddings from image captioning and video captioning
models are reused to help learned features be projected into a coordinated
space,where similarity can be directly computed. Besides,training steps from
fixed model reuse approach are integrated into our framework,which can
incorporate beneficial information and eventually make the target networks
independent of existing models. Apart from that,our proposed framework resorts
to CNNs and LSTMs for extracting visual and spatiotemporal features,and
combines the strengths of identification and verification model to improve the
discriminative ability of the learned feature. The experimental results
demonstrate the effectiveness of our framework on narrowing down the gap
between heterogeneous data and obtaining observable improvement in
image-to-video person re-identification.
GPU based Parallel Optimization for Real Time Panoramic Video Stitching
Authors: Chengyao Du, Jingling Yuan, Jiansheng Dong, Lin Li, Mincheng Chen, Tao Li
Date: 2018-10-04 00:00:00
Tasks: CPU, GPU, Image Stitching

Panoramic video is a sort of video recorded at the same point of view to
record the full scene. With the development of video surveillance and the
requirement for 3D converged video surveillance in smart cities, CPU and GPU
are required to possess strong processing abilities to make panoramic video.
The traditional panoramic products depend on post processing, which results in
high power consumption, low stability and unsatisfying performance in real
time. In order to solve these problems,we propose a real-time panoramic video
stitching framework.The framework we propose mainly consists of three
algorithms, LORB image feature extraction algorithm, feature point matching
algorithm based on LSH and GPU parallel video stitching algorithm based on
CUDA.The experiment results show that the algorithm mentioned can improve the
performance in the stages of feature extraction of images stitching and
matching, the running speed of which is 11 times than that of the traditional
ORB algorithm and 639 times than that of the traditional SIFT algorithm. Based
on analyzing the GPU resources occupancy rate of each resolution image
stitching, we further propose a stream parallel strategy to maximize the
utilization of GPU resources. Compared with the L-ORB algorithm, the efficiency
of this strategy is improved by 1.6-2.5 times, and it can make full use of GPU
resources. The performance of the system accomplished in the paper is 29.2
times than that of the former embedded one, while the power dissipation is
reduced to 10W.
On the Evaluation and Validation of Off-the-shelf Statistical Shape Modeling Tools: A Clinical Application
Authors: Anupama Goparaju, Ibolya Csecs, Alan Morris, Evgueni Kholmovski, Nassir Marrouche, Ross Whitaker, Shireen Elhabian
Date: 2018-10-03 00:00:00
Tasks: Anatomy

Statistical shape modeling (SSM) has proven useful in many areas of biology
and medicine as a new generation of morphometric approaches for the
quantitative analysis of anatomical shapes. Recently, the increased
availability of high-resolution in vivo images of anatomy has led to the
development and distribution of open-source computational tools to model
anatomical shapes and their variability within populations with unprecedented
detail and statistical power. Nonetheless, there is little work on the
evaluation and validation of such tools as related to clinical applications
that rely on morphometric quantifications for treatment planning. To address
this lack of validation, we systematically assess the outcome of widely used
off-the-shelf SSM tools, namely ShapeWorks, SPHARM-PDM, and Deformetrica, in
the context of designing closure devices for left atrium appendage (LAA) in
atrial fibrillation (AF) patients to prevent stroke, where an incomplete LAA
closure may be worse than no closure. This study is motivated by the potential
role of SSM in the geometric design of closure devices, which could be informed
by population-level statistics, and patient-specific device selection, which is
driven by anatomical measurements that could be automated by relating
patient-level anatomy to population-level morphometrics. Hence, understanding
the consequences of different SSM tools for the final analysis is critical for
the careful choice of the tool to be deployed in real clinical scenarios.
Results demonstrate that estimated measurements from ShapeWorks model are more
consistent compared to models from Deformetrica and SPHARM-PDM. Furthermore,
ShapeWorks and Deformetrica shape models capture clinically relevant
population-level variability compared to SPHARM-PDM models.
SAM-GCNN: A Gated Convolutional Neural Network with Segment-Level Attention Mechanism for Home Activity Monitoring
Authors: Yu-Han Shen, Ke-Xin He, Wei-Qiang Zhang
Date: 2018-10-03 00:00:00
Tasks: Home Activity Monitoring

In this paper, we propose a method for home activity monitoring. We
demonstrate our model on dataset of Detection and Classification of Acoustic
Scenes and Events (DCASE) 2018 Challenge Task 5. This task aims to classify
multi-channel audios into one of the provided pre-defined classes. All of these
classes are daily activities performed in a home environment. To tackle this
task, we propose a gated convolutional neural network with segment-level
attention mechanism (SAM-GCNN). The proposed framework is a convolutional model
with two auxiliary modules: a gated convolutional neural network and a
segment-level attention mechanism. Furthermore, we adopted model ensemble to
enhance the capability of generalization of our model. We evaluated our work on
the development dataset of DCASE 2018 Task 5 and achieved competitive
performance, with a macro-averaged F-1 score increasing from 83.76% to 89.33%,
compared with the convolutional baseline system.
DeepImageSpam: Deep Learning based Image Spam Detection
Authors: Amara Dinesh Kumar, Vinayakumar R, Soman Kp
Date: 2018-10-03 00:00:00
Tasks: BIG-bench Machine Learning, Deep Learning, General Classification, Spam detection

Hackers and spammers are employing innovative and novel techniques to deceive
novice and even knowledgeable internet users. Image spam is one of such
technique where the spammer varies and changes some portion of the image such
that it is indistinguishable from the original image fooling the users. This
paper proposes a deep learning based approach for image spam detection using
the convolutional neural networks which uses a dataset with 810 natural images
and 928 spam images for classification achieving an accuracy of 91.7%
outperforming the existing image processing and machine learning techniques
A Brief Survey on Autonomous Vehicle Possible Attacks, Exploits and Vulnerabilities
Authors: Amara Dinesh Kumar, Koti Naga Renu Chebrolu, Vinayakumar R, Soman Kp
Date: 2018-10-03 00:00:00
Tasks: Autonomous Vehicles

Advanced driver assistance systems are advancing at a rapid pace and all
major companies started investing in developing the autonomous vehicles. But
the security and reliability is still uncertain and debatable. Imagine that a
vehicle is compromised by the attackers and then what they can do. An attacker
can control brake, accelerate and even steering which can lead to catastrophic
consequences. This paper gives a very short and brief overview of most of the
possible attacks on autonomous vehicle software and hardware and their
potential implications.