pwc-archive/papers-with-abstracts huggingface dataset:
Domain Adaptive Segmentation in Volume Electron Microscopy Imaging
Authors: Joris Roels, Julian Hennies, Yvan Saeys, Wilfried Philips, Anna Kreshuk
Date: 2018-10-23 00:00:00
Tasks: Classification, Decoder, Domain Adaptation, General Classification, Segmentation

In the last years, automated segmentation has become a necessary tool for
volume electron microscopy (EM) imaging. So far, the best performing techniques
have been largely based on fully supervised encoder-decoder CNNs, requiring a
substantial amount of annotated images. Domain Adaptation (DA) aims to
alleviate the annotation burden by 'adapting' the networks trained on existing
groundtruth data (source domain) to work on a different (target) domain with as
little additional annotation as possible. Most DA research is focused on the
classification task, whereas volume EM segmentation remains rather unexplored.
In this work, we extend recently proposed classification DA techniques to an
encoder-decoder layout and propose a novel method that adds a reconstruction
decoder to the classical encoder-decoder segmentation in order to align source
and target encoder features. The method has been validated on the task of
segmenting mitochondria in EM volumes. We have performed DA from brain EM
images to HeLa cells and from isotropic FIB/SEM volumes to anisotropic TEM
volumes. In all cases, the proposed method has outperformed the extended
classification DA techniques and the finetuning baseline. An implementation of
our work can be found on
https://github.com/JorisRoels/domain-adaptive-segmentation.
OCAPIS: R package for Ordinal Classification And Preprocessing In Scala
Authors: M. Cristina Heredia-Gómez, Salvador García, Pedro Antonio Gutiérrez, Francisco Herrera
Date: 2018-10-23 00:00:00
Tasks: Classification, General Classification, Ordinal Classification

Ordinal Data are those where a natural order exist between the labels. The
classification and pre-processing of this type of data is attracting more and
more interest in the area of machine learning, due to its presence in many
common problems. Traditionally, ordinal classification problems have been
approached as nominal problems. However, that implies not taking into account
their natural order constraints. In this paper, an innovative R package named
ocapis (Ordinal Classification and Preprocessing In Scala) is introduced.
Implemented mainly in Scala and available through Github, this library includes
four learners and two pre-processing algorithms for ordinal and monotonic data.
Main features of the package and examples of installation and use are explained
throughout this manuscript.
Design Challenges of Multi-UAV Systems in Cyber-Physical Applications: A Comprehensive Survey, and Future Directions
Authors: Reza Shakeri, Mohammed Ali Al-Garadi, Ahmed Badawy, Amr Mohamed, Tamer Khattab, Abdulla Al-Ali, Khaled A. Harras, Mohsen Guizani
Date: 2018-10-23 00:00:00

Unmanned Aerial Vehicles (UAVs) have recently rapidly grown to facilitate a
wide range of innovative applications that can fundamentally change the way
cyber-physical systems (CPSs) are designed. CPSs are a modern generation of
systems with synergic cooperation between computational and physical potentials
that can interact with humans through several new mechanisms. The main
advantages of using UAVs in CPS application is their exceptional features,
including their mobility, dynamism, effortless deployment, adaptive altitude,
agility, adjustability, and effective appraisal of real-world functions anytime
and anywhere. Furthermore, from the technology perspective, UAVs are predicted
to be a vital element of the development of advanced CPSs. Therefore, in this
survey, we aim to pinpoint the most fundamental and important design challenges
of multi-UAV systems for CPS applications. We highlight key and versatile
aspects that span the coverage and tracking of targets and infrastructure
objects, energy-efficient navigation, and image analysis using machine learning
for fine-grained CPS applications. Key prototypes and testbeds are also
investigated to show how these practical technologies can facilitate CPS
applications. We present and propose state-of-the-art algorithms to address
design challenges with both quantitative and qualitative methods and map these
challenges with important CPS applications to draw insightful conclusions on
the challenges of each application. Finally, we summarize potential new
directions and ideas that could shape future research in these areas.
CEREALS - Cost-Effective REgion-based Active Learning for Semantic Segmentation
Authors: Radek Mackowiak, Philip Lenz, Omair Ghori, Ferran Diego, Oliver Lange, Carsten Rother
Date: 2018-10-23 00:00:00
Tasks: Active Learning, Image Segmentation, Segmentation, Semantic Segmentation

State of the art methods for semantic image segmentation are trained in a
supervised fashion using a large corpus of fully labeled training images.
However, gathering such a corpus is expensive, due to human annotation effort,
in contrast to gathering unlabeled data. We propose an active learning-based
strategy, called CEREALS, in which a human only has to hand-label a few,
automatically selected, regions within an unlabeled image corpus. This
minimizes human annotation effort while maximizing the performance of a
semantic image segmentation method. The automatic selection procedure is
achieved by: a) using a suitable information measure combined with an estimate
about human annotation effort, which is inferred from a learned cost model, and
b) exploiting the spatial coherency of an image. The performance of CEREALS is
demonstrated on Cityscapes, where we are able to reduce the annotation effort
to 17%, while keeping 95% of the mean Intersection over Union (mIoU) of a model
that was trained with the fully annotated training set of Cityscapes.
Color naming guided intrinsic image decomposition
Authors: Yuanliu Liu, Zejian yuan
Date: 2018-10-23 00:00:00
Tasks: Color Constancy, Intrinsic Image Decomposition

Intrinsic image decomposition is a severely under-constrained problem. User
interactions can help to reduce the ambiguity of the decomposition
considerably. The traditional way of user interaction is to draw scribbles that
indicate regions with constant reflectance or shading. However the effect
scopes of the scribbles are quite limited, so dozens of scribbles are often
needed to rectify the whole decomposition, which is time consuming. In this
paper we propose an efficient way of user interaction that users need only to
annotate the color composition of the image. Color composition reveals the
global distribution of reflectance, so it can help to adapt the whole
decomposition directly. We build a generative model of the process that the
albedo of the material produces both the reflectance through imaging and the
color labels by color naming. Our model fuses effectively the physical
properties of image formation and the top-down information from human color
perception. Experimental results show that color naming can improve the
performance of intrinsic image decomposition, especially in cleaning the
shadows left in reflectance and solving the color constancy problem.
Ain't Nobody Got Time For Coding: Structure-Aware Program Synthesis From Natural Language
Authors: Jakub Bednarek, Karol Piaskowski, Krzysztof Krawiec
Date: 2018-10-23 00:00:00
Proceeding: ICLR 2019 5
Tasks: Decoder, Program Synthesis, Sentence

Program synthesis from natural language (NL) is practical for humans and,
once technically feasible, would significantly facilitate software development
and revolutionize end-user programming. We present SAPS, an end-to-end neural
network capable of mapping relatively complex, multi-sentence NL specifications
to snippets of executable code. The proposed architecture relies exclusively on
neural components, and is trained on abstract syntax trees, combined with a
pretrained word embedding and a bi-directional multi-layer LSTM for processing
of word sequences. The decoder features a doubly-recurrent LSTM, for which we
propose novel signal propagation schemes and soft attention mechanism. When
applied to a large dataset of problems proposed in a previous study, SAPS
performs on par with or better than the method proposed there, producing
correct programs in over 92% of cases. In contrast to other methods, it does
not require post-processing of the resulting programs, and uses a
fixed-dimensional latent representation as the only interface between the NL
analyzer and the source code generator.
Finding Appropriate Traffic Regulations via Graph Convolutional Networks
Authors: Tomoharu Iwata, Takuma Otsuka, Hitoshi Shimizu, Hiroshi Sawada, Futoshi Naya, Naonori Ueda
Date: 2018-10-23 00:00:00

Appropriate traffic regulations, e.g. planned road closure, are important in
congested events. Crowd simulators have been used to find appropriate
regulations by simulating multiple scenarios with different regulations.
However, this approach requires multiple simulation runs, which are
time-consuming. In this paper, we propose a method to learn a function that
outputs regulation effects given the current traffic situation as inputs. If
the function is learned using the training data of many simulation runs in
advance, we can obtain an appropriate regulation efficiently by bypassing
simulations for the current situation. We use the graph convolutional networks
for modeling the function, which enable us to find regulations even for unseen
areas. With the proposed method, we construct a graph for each area, where a
node represents a road, and an edge represents the road connection. By running
crowd simulations with various regulations on various areas, we generate
traffic situations and regulation effects. The graph convolutional networks are
trained to output the regulation effects given the graph with the traffic
situation information as inputs. With experiments using real-world road
networks and a crowd simulator, we demonstrate that the proposed method can
find a road to close that reduces the average time needed to reach the
destination.
Consistency-aware Shading Orders Selective Fusion for Intrinsic Image Decomposition
Authors: Yuanliu Liu, Ang Li, Zejian yuan, Badong Chen, Nanning Zheng
Date: 2018-10-23 00:00:00
Tasks: Intrinsic Image Decomposition

We address the problem of decomposing a single image into reflectance and
shading. The difficulty comes from the fact that the components of image---the
surface albedo, the direct illumination, and the ambient illumination---are
coupled heavily in observed image. We propose to infer the shading by ordering
pixels by their relative brightness, without knowing the absolute values of the
image components beforehand. The pairwise shading orders are estimated in two
ways: brightness order and low-order fittings of local shading field. The
brightness order is a non-local measure, which can be applied to any pair of
pixels including those whose reflectance and shading are both different. The
low-order fittings are used for pixel pairs within local regions of smooth
shading. Together, they can capture both global order structure and local
variations of the shading. We propose a Consistency-aware Selective Fusion
(CSF) to integrate the pairwise orders into a globally consistent order. The
iterative selection process solves the conflicts between the pairwise orders
obtained by different estimation methods. Inconsistent or unreliable pairwise
orders will be automatically excluded from the fusion to avoid polluting the
global order. Experiments on the MIT Intrinsic Image dataset show that the
proposed model is effective at recovering the shading including deep shadows.
Our model also works well on natural images from the IIW dataset, the UIUC
Shadow dataset and the NYU-Depth dataset, where the colors of direct lights and
ambient lights are quite different.
Semi-supervised acoustic model training for speech with code-switching
Authors: Emre Yilmaz, Mitchell McLaren, Henk van den Heuvel, David A. van Leeuwen
Date: 2018-10-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Retrieval, speaker-diarization, Speaker Diarization, Speaker Recognition, speech-recognition, Speech Recognition

In the FAME! project, we aim to develop an automatic speech recognition (ASR)
system for Frisian-Dutch code-switching (CS) speech extracted from the archives
of a local broadcaster with the ultimate goal of building a spoken document
retrieval system. Unlike Dutch, Frisian is a low-resourced language with a very
limited amount of manually annotated speech data. In this paper, we describe
several automatic annotation approaches to enable using of a large amount of
raw bilingual broadcast data for acoustic model training in a semi-supervised
setting. Previously, it has been shown that the best-performing ASR system is
obtained by two-stage multilingual deep neural network (DNN) training using 11
hours of manually annotated CS speech (reference) data together with speech
data from other high-resourced languages. We compare the quality of
transcriptions provided by this bilingual ASR system with several other
approaches that use a language recognition system for assigning language labels
to raw speech segments at the front-end and using monolingual ASR resources for
transcription. We further investigate automatic annotation of the speakers
appearing in the raw broadcast data by first labeling with (pseudo) speaker
tags using a speaker diarization system and then linking to the known speakers
appearing in the reference data using a speaker recognition system. These
speaker labels are essential for speaker-adaptive training in the proposed
setting. We train acoustic models using the manually and automatically
annotated data and run recognition experiments on the development and test data
of the FAME! speech corpus to quantify the quality of the automatic
annotations. The ASR and CS detection results demonstrate the potential of
using automatic language and speaker tagging in semi-supervised bilingual
acoustic model training.
Challenges of Convex Quadratic Bi-objective Benchmark Problems
Authors: Tobias Glasmachers
Date: 2018-10-23 00:00:00

Convex quadratic objective functions are an important base case in
state-of-the-art benchmark collections for single-objective optimization on
continuous domains. Although often considered rather simple, they represent the
highly relevant challenges of non-separability and ill-conditioning. In the
multi-objective case, quadratic benchmark problems are under-represented. In
this paper we analyze the specific challenges that can be posed by quadratic
functions in the bi-objective case. Our construction yields a full factorial
design of 54 different problem classes. We perform experiments with
well-established algorithms to demonstrate the insights that can be supported
by this function class. We find huge performance differences, which can be
clearly attributed to two root causes: non-separability and alignment of the
Pareto set with the coordinate system.
Unsupervised Features Extraction for Binary Similarity Using Graph Embedding Neural Networks
Authors: Roberto Baldoni, Giuseppe Antonio Di Luna, Luca Massarelli, Fabio Petroni, Leonardo Querzoni
Date: 2018-10-23 00:00:00
Tasks: Graph Embedding, Malware Analysis, Semantic Similarity, Semantic Textual Similarity, Vulnerability Detection

In this paper we consider the binary similarity problem that consists in
determining if two binary functions are similar only considering their compiled
form. This problem is know to be crucial in several application scenarios, such
as copyright disputes, malware analysis, vulnerability detection, etc. The
current state-of-the-art solutions in this field work by creating an embedding
model that maps binary functions into vectors in $\mathbb{R}^{n}$. Such
embedding model captures syntactic and semantic similarity between binaries,
i.e., similar binary functions are mapped to points that are close in the
vector space. This strategy has many advantages, one of them is the possibility
to precompute embeddings of several binary functions, and then compare them
with simple geometric operations (e.g., dot product). In [32] functions are
first transformed in Annotated Control Flow Graphs (ACFGs) constituted by
manually engineered features and then graphs are embedded into vectors using a
deep neural network architecture. In this paper we propose and test several
ways to compute annotated control flow graphs that use unsupervised approaches
for feature learning, without incurring a human bias. Our methods are inspired
after techniques used in the natural language processing community (e.g., we
use word2vec to encode assembly instructions). We show that our approach is
indeed successful, and it leads to better performance than previous
state-of-the-art solutions. Furthermore, we report on a qualitative analysis of
functions embeddings. We found interesting cases in which embeddings are
clustered according to the semantic of the original binary function.
Action-Agnostic Human Pose Forecasting
Authors: Hsu-kuang Chiu, Ehsan Adeli, Borui Wang, De-An Huang, Juan Carlos Niebles
Date: 2018-10-23 00:00:00
Tasks: Human Dynamics, Human Pose Forecasting

Predicting and forecasting human dynamics is a very interesting but
challenging task with several prospective applications in robotics,
health-care, etc. Recently, several methods have been developed for human pose
forecasting; however, they often introduce a number of limitations in their
settings. For instance, previous work either focused only on short-term or
long-term predictions, while sacrificing one or the other. Furthermore, they
included the activity labels as part of the training process, and require them
at testing time. These limitations confine the usage of pose forecasting models
for real-world applications, as often there are no activity-related annotations
for testing scenarios. In this paper, we propose a new action-agnostic method
for short- and long-term human pose forecasting. To this end, we propose a new
recurrent neural network for modeling the hierarchical and multi-scale
characteristics of the human dynamics, denoted by triangular-prism RNN
(TP-RNN). Our model captures the latent hierarchical structure embedded in
temporal human pose sequences by encoding the temporal dependencies with
different time-scales. For evaluation, we run an extensive set of experiments
on Human 3.6M and Penn Action datasets and show that our method outperforms
baseline and state-of-the-art methods quantitatively and qualitatively. Codes
are available at https://github.com/eddyhkchiu/pose_forecast_wacv/
Online learning with feedback graphs and switching costs
Authors: Anshuka Rangi, Massimo Franceschetti
Date: 2018-10-23 00:00:00
Tasks: Multi-Armed Bandits

We study online learning when partial feedback information is provided following every action of the learning process, and the learner incurs switching costs for changing his actions. In this setting, the feedback information system can be represented by a graph, and previous works studied the expected regret of the learner in the case of a clique (Expert setup), or disconnected single loops (Multi-Armed Bandits (MAB)). This work provides a lower bound on the expected regret in the Partial Information (PI) setting, namely for general feedback graphs --excluding the clique. Additionally, it shows that all algorithms that are optimal without switching costs are necessarily sub-optimal in the presence of switching costs, which motivates the need to design new algorithms. We propose two new algorithms: Threshold Based EXP3 and EXP3. SC. For the two special cases of symmetric PI setting and MAB, the expected regret of both of these algorithms is order optimal in the duration of the learning process. Additionally, Threshold Based EXP3 is order optimal in the switching cost, whereas EXP3. SC is not. Finally, empirical evaluations show that Threshold Based EXP3 outperforms the previously proposed order-optimal algorithms EXP3 SET in the presence of switching costs, and Batch EXP3 in the MAB setting with switching costs.
Large scale visual place recognition with sub-linear storage growth
Authors: Huu Le, Michael Milford
Date: 2018-10-23 00:00:00
Tasks: Chunking, feature selection, Visual Place Recognition

Robotic and animal mapping systems share many of the same objectives and
challenges, but differ in one key aspect: where much of the research in robotic
mapping has focused on solving the data association problem, the grid cell
neurons underlying maps in the mammalian brain appear to intentionally break
data association by encoding many locations with a single grid cell neuron. One
potential benefit of this intentional aliasing is both sub-linear map storage
and computational requirements growth with environment size, which we
demonstrated in a previous proof-of-concept study that detected and encoded
mutually complementary co-prime pattern frequencies in the visual map data. In
this research, we solve several of the key theoretical and practical
limitations of that prototype model and achieve significantly better sub-linear
storage growth, a factor reduction in storage requirements per map location,
scalability to large datasets on standard compute equipment and improved
robustness to environments with visually challenging appearance change. These
improvements are achieved through several innovations including a flexible
user-driven choice mechanism for the periodic patterns underlying the new
encoding method, a parallelized chunking technique that splits the map into
sub-sections processed in parallel and a novel feature selection approach that
selects only the image information most relevant to the encoded temporal
patterns. We evaluate our techniques on two large benchmark datasets with the
comparison to the previous state-of-the-art system, as well as providing a
detailed analysis of system performance with respect to parameters such as
required precision performance and the number of cyclic patterns encoded.
Face Recognition from Sequential Sparse 3D Data via Deep Registration
Authors: Yang Tan, Hongxin Lin, Zelin Xiao, Shengyong Ding, Hongyang Chao
Date: 2018-10-23 00:00:00
Tasks: Face Recognition

Previous works have shown that face recognition with high accurate 3D data is
more reliable and insensitive to pose and illumination variations. Recently,
low-cost and portable 3D acquisition techniques like ToF(Time of Flight) and
DoE based structured light systems enable us to access 3D data easily, e.g.,
via a mobile phone. However, such devices only provide sparse(limited speckles
in structured light system) and noisy 3D data which can not support face
recognition directly. In this paper, we aim at achieving high-performance face
recognition for devices equipped with such modules which is very meaningful in
practice as such devices will be very popular. We propose a framework to
perform face recognition by fusing a sequence of low-quality 3D data. As 3D
data are sparse and noisy which can not be well handled by conventional methods
like the ICP algorithm, we design a PointNet-like Deep Registration
Network(DRNet) which works with ordered 3D point coordinates while preserving
the ability of mining local structures via convolution. Meanwhile we develop a
novel loss function to optimize our DRNet based on the quaternion expression
which obviously outperforms other widely used functions. For face recognition,
we design a deep convolutional network which takes the fused 3D depth-map as
input based on AMSoftmax model. Experiments show that our DRNet can achieve
rotation error 0.95{\deg} and translation error 0.28mm for registration. The
face recognition on fused data also achieves rank-1 accuracy 99.2% , FAR-0.001
97.5% on Bosphorus dataset which is comparable with state-of-the-art
high-quality data based recognition performance.