pwc-archive/papers-with-abstracts huggingface dataset:
Multi-class Active Learning: A Hybrid Informative and Representative Criterion Inspired Approach
Authors: Xi Fang, Zengmao Wang, Xinyao Tang, Chen Wu
Date: 2018-03-06 00:00:00
Tasks: Active Learning, Informativeness

Labeling each instance in a large dataset is extremely labor- and time-
consuming . One way to alleviate this problem is active learning, which aims to
which discover the most valuable instances for labeling to construct a powerful
classifier. Considering both informativeness and representativeness provides a
promising way to design a practical active learning. However, most existing
active learning methods select instances favoring either informativeness or
representativeness. Meanwhile, many are designed based on the binary class, so
that they may present suboptimal solutions on the datasets with multiple
classes. In this paper, a hybrid informative and representative criterion based
multi-class active learning approach is proposed. We combine the informative
informativeness and representativeness into one formula, which can be solved
under a unified framework. The informativeness is measured by the margin
minimum while the representative information is measured by the maximum mean
discrepancy. By minimizing the upper bound for the true risk, we generalize the
empirical risk minimization principle to the active learning setting.
Simultaneously, our proposed method makes full use of the label information,
and the proposed active learning is designed based on multiple classes. So the
proposed method is not suitable to the binary class but also the multiple
classes. We conduct our experiments on twelve benchmark UCI data sets, and the
experimental results demonstrate that the proposed method performs better than
some state-of-the-art methods.
Fully Convolutional Grasp Detection Network with Oriented Anchor Box
Authors: Xinwen Zhou, Xuguang Lan, Hanbo Zhang, Zhiqiang Tian, Yang Zhang, Nanning Zheng
Date: 2018-03-06 00:00:00

In this paper, we present a real-time approach to predict multiple grasping
poses for a parallel-plate robotic gripper using RGB images. A model with
oriented anchor box mechanism is proposed and a new matching strategy is used
during the training process. An end-to-end fully convolutional neural network
is employed in our work. The network consists of two parts: the feature
extractor and multi-grasp predictor. The feature extractor is a deep
convolutional neural network. The multi-grasp predictor regresses grasp
rectangles from predefined oriented rectangles, called oriented anchor boxes,
and classifies the rectangles into graspable and ungraspable. On the standard
Cornell Grasp Dataset, our model achieves an accuracy of 97.74% and 96.61% on
image-wise split and object-wise split respectively, and outperforms the latest
state-of-the-art approach by 1.74% on image-wise split and 0.51% on object-wise
split.
Code Review Comments: Language Matters
Authors: Vasiliki Efstathiou, Diomidis Spinellis
Date: 2018-03-06 00:00:00

Recent research provides evidence that effective communication in
collaborative software development has significant impact on the software
development lifecycle. Although related qualitative and quantitative studies
point out textual characteristics of well-formed messages, the underlying
semantics of the intertwined linguistic structures still remain largely
misinterpreted or ignored. Especially, regarding quality of code reviews the
importance of thorough feedback, and explicit rationale is often mentioned but
rarely linked with related linguistic features. As a first step towards
addressing this shortcoming, we propose grounding these studies on theories of
linguistics. We particularly focus on linguistic structures of coherent speech
and explain how they can be exploited in practice. We reflect on related
approaches and examine through a preliminary study on four open source
projects, possible links between existing findings and the directions we
suggest for detecting textual features of useful code reviews.
On Breast Cancer Detection: An Application of Machine Learning Algorithms on the Wisconsin Diagnostic Dataset
Authors: Abien Fred Agarap
Date: 2017-11-20 00:00:00
Tasks: All, BIG-bench Machine Learning, Breast Cancer Detection, Diagnostic, General Classification, regression, Specificity

This paper presents a comparison of six machine learning (ML) algorithms:
GRU-SVM (Agarap, 2017), Linear Regression, Multilayer Perceptron (MLP), Nearest
Neighbor (NN) search, Softmax Regression, and Support Vector Machine (SVM) on
the Wisconsin Diagnostic Breast Cancer (WDBC) dataset (Wolberg, Street, &
Mangasarian, 1992) by measuring their classification test accuracy and their
sensitivity and specificity values. The said dataset consists of features which
were computed from digitized images of FNA tests on a breast mass (Wolberg,
Street, & Mangasarian, 1992). For the implementation of the ML algorithms, the
dataset was partitioned in the following fashion: 70% for training phase, and
30% for the testing phase. The hyper-parameters used for all the classifiers
were manually assigned. Results show that all the presented ML algorithms
performed well (all exceeded 90% test accuracy) on the classification task. The
MLP algorithm stands out among the implemented algorithms with a test accuracy
of ~99.04%.
Differentially Private Distributed Learning for Language Modeling Tasks
Authors: Vadim Popov, Mikhail Kudinov, Irina Piontkovskaya, Petr Vytovtov, Alex Nevidomsky
Date: 2017-12-20 00:00:00
Tasks: General Knowledge, Language Modeling, Language Modelling

One of the big challenges in machine learning applications is that training
data can be different from the real-world data faced by the algorithm. In
language modeling, users' language (e.g. in private messaging) could change in
a year and be completely different from what we observe in publicly available
data. At the same time, public data can be used for obtaining general knowledge
(i.e. general model of English). We study approaches to distributed fine-tuning
of a general model on user private data with the additional requirements of
maintaining the quality on the general data and minimization of communication
costs. We propose a novel technique that significantly improves prediction
quality on users' language compared to a general model and outperforms gradient
compression methods in terms of communication efficiency. The proposed
procedure is fast and leads to an almost 70% perplexity reduction and 8.7
percentage point improvement in keystroke saving rate on informal English
texts. We also show that the range of tasks our approach is applicable to is
not limited by language modeling only. Finally, we propose an experimental
framework for evaluating differential privacy of distributed training of
language models and show that our approach has good privacy guarantees.
Low-rank Optimization with Convex Constraints
Authors: Christian Grussler, Anders Rantzer, Pontus Giselsson
Date: 2016-06-06 00:00:00

The problem of low-rank approximation with convex constraints, which appears
in data analysis, system identification, model order reduction, low-order
controller design and low-complexity modelling is considered. Given a matrix,
the objective is to find a low-rank approximation that meets rank and convex
constraints, while minimizing the distance to the matrix in the squared
Frobenius norm. In many situations, this non-convex problem is convexified by
nuclear norm regularization. However, we will see that the approximations
obtained by this method may be far from optimal. In this paper, we propose an
alternative convex relaxation that uses the convex envelope of the squared
Frobenius norm and the rank constraint. With this approach, easily verifiable
conditions are obtained under which the solutions to the convex relaxation and
the original non-convex problem coincide. An SDP representation of the convex
envelope is derived, which allows us to apply this approach to several known
problems. Our example on optimal low-rank Hankel approximation/model reduction
illustrates that the proposed convex relaxation performs consistently better
than nuclear norm regularization and may outperform balanced truncation.
Conceptualization of Object Compositions Using Persistent Homology
Authors: Christian A. Mueller, Andreas Birk
Date: 2018-03-06 00:00:00
Tasks: Object

A topological shape analysis is proposed and utilized to learn concepts that
reflect shape commonalities. Our approach is two-fold: i) a spatial topology
analysis of point cloud segment constellations within objects. Therein
constellations are decomposed and described in an hierarchical manner - from
single segments to segment groups until a single group reflects an entire
object. ii) a topology analysis of the description space in which segment
decompositions are exposed in. Inspired by Persistent Homology, hidden groups
of shape commonalities are revealed from object segment decompositions.
Experiments show that extracted persistent groups of commonalities can
represent semantically meaningful shape concepts. We also show the
generalization capability of the proposed approach considering samples of
external datasets.
Co-occurrence of the Benford-like and Zipf Laws Arising from the Texts Representing Human and Artificial Languages
Authors: Evgeny Shulzinger, Irina Legchenkova, Edward Bormashenko
Date: 2018-03-06 00:00:00

We demonstrate that large texts, representing human (English, Russian,
Ukrainian) and artificial (C++, Java) languages, display quantitative patterns
characterized by the Benford-like and Zipf laws. The frequency of a word
following the Zipf law is inversely proportional to its rank, whereas the total
numbers of a certain word appearing in the text generate the uneven
Benford-like distribution of leading numbers. Excluding the most popular words
essentially improves the correlation of actual textual data with the Zipfian
distribution, whereas the Benford distribution of leading numbers (arising from
the overall amount of a certain word) is insensitive to the same elimination
procedure. The calculated values of the moduli of slopes of double
logarithmical plots for artificial languages (C++, Java) are markedly larger
than those for human ones.
A Non-Technical Survey on Deep Convolutional Neural Network Architectures
Authors: Felix Altenberger, Claus Lenz
Date: 2018-03-06 00:00:00
Tasks: image-classification, Image Classification, Natural Language Understanding, Object, object-detection, Object Detection, Object Localization, Object Recognition, Survey

Artificial neural networks have recently shown great results in many
disciplines and a variety of applications, including natural language
understanding, speech processing, games and image data generation. One
particular application in which the strong performance of artificial neural
networks was demonstrated is the recognition of objects in images, where deep
convolutional neural networks are commonly applied. In this survey, we give a
comprehensive introduction to this topic (object recognition with deep
convolutional neural networks), with a strong focus on the evolution of network
architectures. Therefore, we aim to compress the most important concepts in
this field in a simple and non-technical manner to allow for future researchers
to have a quick general understanding.
  This work is structured as follows:
  1. We will explain the basic ideas of (convolutional) neural networks and
deep learning and examine their usage for three object recognition tasks: image
classification, object localization and object detection.
  2. We give a review on the evolution of deep convolutional neural networks by
providing an extensive overview of the most important network architectures
presented in chronological order of their appearances.
MIRIAM: A Multimodal Chat-Based Interface for Autonomous Systems
Authors: Helen Hastie, Francisco J. Chiyah Garcia, David A. Robb, Pedro Patron, Atanas Laskov
Date: 2018-03-06 00:00:00
Tasks: Autonomous Vehicles

We present MIRIAM (Multimodal Intelligent inteRactIon for Autonomous
systeMs), a multimodal interface to support situation awareness of autonomous
vehicles through chat-based interaction. The user is able to chat about the
vehicle's plan, objectives, previous activities and mission progress. The
system is mixed initiative in that it pro-actively sends messages about key
events, such as fault warnings. We will demonstrate MIRIAM using SeeByte's
SeeTrack command and control interface and Neptune autonomy simulator.
Nonlocality-Reinforced Convolutional Neural Networks for Image Denoising
Authors: Cristóvão Cruz, Alessandro Foi, Vladimir Katkovnik, Karen Egiazarian
Date: 2018-03-06 00:00:00
Tasks: Denoising, Image Denoising

We introduce a paradigm for nonlocal sparsity reinforced deep convolutional
neural network denoising. It is a combination of a local multiscale denoising
by a convolutional neural network (CNN) based denoiser and a nonlocal denoising
based on a nonlocal filter (NLF) exploiting the mutual similarities between
groups of patches. CNN models are leveraged with noise levels that
progressively decrease at every iteration of our framework, while their output
is regularized by a nonlocal prior implicit within the NLF. Unlike complicated
neural networks that embed the nonlocality prior within the layers of the
network, our framework is modular, it uses standard pre-trained CNNs together
with standard nonlocal filters. An instance of the proposed framework, called
NN3D, is evaluated over large grayscale image datasets showing state-of-the-art
performance.
HexaConv
Authors: Emiel Hoogeboom, Jorn W. T. Peters, Taco S. Cohen, Max Welling
Date: 2018-03-06 00:00:00
Proceeding: ICLR 2018 1
Tasks: Aerial Scene Classification, Scene Classification

The effectiveness of Convolutional Neural Networks stems in large part from
their ability to exploit the translation invariance that is inherent in many
learning problems. Recently, it was shown that CNNs can exploit other
invariances, such as rotation invariance, by using group convolutions instead
of planar convolutions. However, for reasons of performance and ease of
implementation, it has been necessary to limit the group convolution to
transformations that can be applied to the filters without interpolation. Thus,
for images with square pixels, only integer translations, rotations by
multiples of 90 degrees, and reflections are admissible.
  Whereas the square tiling provides a 4-fold rotational symmetry, a hexagonal
tiling of the plane has a 6-fold rotational symmetry. In this paper we show how
one can efficiently implement planar convolution and group convolution over
hexagonal lattices, by re-using existing highly optimized convolution routines.
We find that, due to the reduced anisotropy of hexagonal filters, planar
HexaConv provides better accuracy than planar convolution with square filters,
given a fixed parameter budget. Furthermore, we find that the increased degree
of symmetry of the hexagonal grid increases the effectiveness of group
convolutions, by allowing for more parameter sharing. We show that our method
significantly outperforms conventional CNNs on the AID aerial scene
classification dataset, even outperforming ImageNet pre-trained models.
The ORCA Hub: Explainable Offshore Robotics through Intelligent Interfaces
Authors: Helen Hastie, Katrin Lohan, Mike Chantler, David A. Robb, Subramanian Ramamoorthy, Ron Petrick, Sethu Vijayakumar, David Lane
Date: 2018-03-06 00:00:00

We present the UK Robotics and Artificial Intelligence Hub for Offshore
Robotics for Certification of Assets (ORCA Hub), a 3.5 year EPSRC funded,
multi-site project. The ORCA Hub vision is to use teams of robots and
autonomous intelligent systems (AIS) to work on offshore energy platforms to
enable cheaper, safer and more efficient working practices. The ORCA Hub will
research, integrate, validate and deploy remote AIS solutions that can operate
with existing and future offshore energy assets and sensors, interacting safely
in autonomous or semi-autonomous modes in complex and cluttered environments,
co-operating with remote operators. The goal is that through the use of such
robotic systems offshore, the need for personnel will decrease. To enable this
to happen, the remote operator will need a high level of situation awareness
and key to this is the transparency of what the autonomous systems are doing
and why. This increased transparency will facilitate a trusting relationship,
which is particularly key in high-stakes, hazardous situations.
A Hybrid Method for Traffic Flow Forecasting Using Multimodal Deep Learning
Authors: Shengdong Du, Tianrui Li, Xun Gong, Shi-Jinn Horng
Date: 2018-03-06 00:00:00
Tasks: Deep Learning, Multimodal Deep Learning

Traffic flow forecasting has been regarded as a key problem of intelligent
transport systems. In this work, we propose a hybrid multimodal deep learning
method for short-term traffic flow forecasting, which can jointly and
adaptively learn the spatial-temporal correlation features and long temporal
interdependence of multi-modality traffic data by an attention auxiliary
multimodal deep learning architecture. According to the highly nonlinear
characteristics of multi-modality traffic data, the base module of our method
consists of one-dimensional Convolutional Neural Networks (1D CNN) and Gated
Recurrent Units (GRU) with the attention mechanism. The former is to capture
the local trend features and the latter is to capture the long temporal
dependencies. Then, we design a hybrid multimodal deep learning framework
(HMDLF) for fusing share representation features of different modality traffic
data by multiple CNN-GRU-Attention modules. The experimental results indicate
that the proposed multimodal deep learning model is capable of dealing with
complex nonlinear urban traffic flow forecasting with satisfying accuracy and
effectiveness.
Where is my Device? - Detecting the Smart Device's Wearing Location in the Context of Active Safety for Vulnerable Road Users
Authors: Maarten Bieshaar
Date: 2018-03-06 00:00:00
Tasks: General Classification

This article describes an approach to detect the wearing location of smart
devices worn by pedestrians and cyclists. The detection, which is based solely
on the sensors of the smart devices, is important context-information which can
be used to parametrize subsequent algorithms, e.g. for dead reckoning or
intention detection to improve the safety of vulnerable road users. The wearing
location recognition can in terms of Organic Computing (OC) be seen as a step
towards self-awareness and self-adaptation. For the wearing location detection
a two-stage process is presented. It is subdivided into moving detection
followed by the wearing location classification. Finally, the approach is
evaluated on a real world dataset consisting of pedestrians and cyclists.
Cooperative Tracking of Cyclists Based on Smart Devices and Infrastructure
Authors: Günther Reitberger, Stefan Zernetsch, Maarten Bieshaar, Bernhard Sick, Konrad Doll, Erich Fuchs
Date: 2018-03-06 00:00:00

In future traffic scenarios, vehicles and other traffic participants will be
interconnected and equipped with various types of sensors, allowing for
cooperation based on data or information exchange. This article presents an
approach to cooperative tracking of cyclists using smart devices and
infrastructure-based sensors. A smart device is carried by the cyclists and an
intersection is equipped with a wide angle stereo camera system. Two tracking
models are presented and compared. The first model is based on the stereo
camera system detections only, whereas the second model cooperatively combines
the camera based detections with velocity and yaw rate data provided by the
smart device. Our aim is to overcome limitations of tracking approaches based
on single data sources. We show in numerical evaluations on scenes where
cyclists are starting or turning right that the cooperation leads to an
improvement in both the ability to keep track of a cyclist and the accuracy of
the track particularly when it comes to occlusions in the visual system. We,
therefore, contribute to the safety of vulnerable road users in future traffic.
Do deep nets really need weight decay and dropout?
Authors: Alex Hernández-García, Peter König
Date: 2018-02-20 00:00:00
Tasks: Data Augmentation, Object Recognition

The impressive success of modern deep neural networks on computer vision
tasks has been achieved through models of very large capacity compared to the
number of available training examples. This overparameterization is often said
to be controlled with the help of different regularization techniques, mainly
weight decay and dropout. However, since these techniques reduce the effective
capacity of the model, typically even deeper and wider architectures are
required to compensate for the reduced capacity. Therefore, there seems to be a
waste of capacity in this practice. In this paper we build upon recent research
that suggests that explicit regularization may not be as important as widely
believed and carry out an ablation study that concludes that weight decay and
dropout may not be necessary for object recognition if enough data augmentation
is introduced.
Explain Yourself: A Natural Language Interface for Scrutable Autonomous Robots
Authors: Francisco J. Chiyah Garcia, David A. Robb, Xingkun Liu, Atanas Laskov, Pedro Patron, Helen Hastie
Date: 2018-03-06 00:00:00

Autonomous systems in remote locations have a high degree of autonomy and
there is a need to explain what they are doing and why in order to increase
transparency and maintain trust. Here, we describe a natural language chat
interface that enables vehicle behaviour to be queried by the user. We obtain
an interpretable model of autonomy through having an expert 'speak out-loud'
and provide explanations during a mission. This approach is agnostic to the
type of autonomy model and as expert and operator are from the same user-group,
we predict that these explanations will align well with the operator's mental
model, increase transparency and assist with operator training.
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
Authors: Alessandro Aimar, Hesham Mostafa, Enrico Calabrese, Antonio Rios-Navarro, Ricardo Tapiador-Morales, Iulia-Alexandra Lungu, Moritz B. Milde, Federico Corradi, Alejandro Linares-Barranco, Shih-Chii Liu, Tobi Delbruck
Date: 2017-06-05 00:00:00

Convolutional neural networks (CNNs) have become the dominant neural network
architecture for solving many state-of-the-art (SOA) visual processing tasks.
Even though Graphical Processing Units (GPUs) are most often used in training
and deploying CNNs, their power efficiency is less than 10 GOp/s/W for
single-frame runtime inference. We propose a flexible and efficient CNN
accelerator architecture called NullHop that implements SOA CNNs useful for
low-power and low-latency application scenarios. NullHop exploits the sparsity
of neuron activations in CNNs to accelerate the computation and reduce memory
requirements. The flexible architecture allows high utilization of available
computing resources across kernel sizes ranging from 1x1 to 7x7. NullHop can
process up to 128 input and 128 output feature maps per layer in a single pass.
We implemented the proposed architecture on a Xilinx Zynq FPGA platform and
present results showing how our implementation reduces external memory
transfers and compute time in five different CNNs ranging from small ones up to
the widely known large VGG16 and VGG19 CNNs. Post-synthesis simulations using
Mentor Modelsim in a 28nm process with a clock frequency of 500 MHz show that
the VGG19 network achieves over 450 GOp/s. By exploiting sparsity, NullHop
achieves an efficiency of 368%, maintains over 98% utilization of the MAC
units, and achieves a power efficiency of over 3TOp/s/W in a core area of
6.3mm$^2$. As further proof of NullHop's usability, we interfaced its FPGA
implementation with a neuromorphic event camera for real time interactive
demonstrations.