pwc-archive/papers-with-abstracts huggingface dataset:
Optimization Design of Decentralized Control for Complex Decentralized Systems
Authors: Ying Huang, Jiyang Dai, Chen Peng
Date: 2018-09-03 00:00:00

A new method is developed to deal with the problem that a complex
decentralized control system needs to keep centralized control performance. The
systematic procedure emphasizes quickly finding the decentralized
subcontrollers that matching the closed-loop performance and robustness
characteristics of the centralized controller, which is featured by the fact
that GA is used to optimize the design of centralized H-infinity controller
K(s) and decentralized engine subcontroller KT(s), and that only one interface
variable needs to satisfy decentralized control system requirement according to
the proposed selection principle. The optimization design is motivated by the
implementation issues where it is desirable to reduce the time in trial and
error process and accurately find the best decentralized subcontrollers. The
method is applied to decentralized control system design for a short takeoff
and landing fighter. By comparing the simulation results of the decentralized
control system with those of the centralized control system, the target of the
decentralized control attains the performance and robustness of centralized
control is validated.
Adversarial Attack Type I: Cheat Classifiers by Significant Changes
Authors: Sanli Tang, Xiaolin Huang, Mingjian Chen, Chengjin Sun, Jie Yang
Date: 2018-09-03 00:00:00
Tasks: Adversarial Attack, Vocal Bursts Type Prediction

Despite the great success of deep neural networks, the adversarial attack can cheat some well-trained classifiers by small permutations. In this paper, we propose another type of adversarial attack that can cheat classifiers by significant changes. For example, we can significantly change a face but well-trained neural networks still recognize the adversarial and the original example as the same person. Statistically, the existing adversarial attack increases Type II error and the proposed one aims at Type I error, which are hence named as Type II and Type I adversarial attack, respectively. The two types of attack are equally important but are essentially different, which are intuitively explained and numerically evaluated. To implement the proposed attack, a supervised variation autoencoder is designed and then the classifier is attacked by updating the latent variables using gradient information. {Besides, with pre-trained generative models, Type I attack on latent spaces is investigated as well.} Experimental results show that our method is practical and effective to generate Type I adversarial examples on large-scale image datasets. Most of these generated examples can pass detectors designed for defending Type II attack and the strengthening strategy is only efficient with a specific type attack, both implying that the underlying reasons for Type I and Type II attack are different.
IoU is not submodular
Authors: Tanguy Kerdoncuff, Rémi Emonet
Date: 2018-09-03 00:00:00
Tasks: BIG-bench Machine Learning, Segmentation, Semantic Segmentation

This short article aims at demonstrate that the Intersection over Union (or
Jaccard index) is not a submodular function. This mistake has been made in an
article which is cited and used as a foundation in another article. The
Intersection of Union is widely used in machine learning as a cost function
especially for imbalance data and semantic segmentation.
Affordance Extraction and Inference based on Semantic Role Labeling
Authors: Daniel Loureiro, Alípio Mário Jorge
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 11
Tasks: Common Sense Reasoning, Semantic Role Labeling, Word Embeddings, Word Similarity

Common-sense reasoning is becoming increasingly important for the advancement
of Natural Language Processing. While word embeddings have been very
successful, they cannot explain which aspects of 'coffee' and 'tea' make them
similar, or how they could be related to 'shop'. In this paper, we propose an
explicit word representation that builds upon the Distributional Hypothesis to
represent meaning from semantic roles, and allow inference of relations from
their meshing, as supported by the affordance-based Indexical Hypothesis. We
find that our model improves the state-of-the-art on unsupervised word
similarity tasks while allowing for direct inference of new relations from the
same vector space.
Optical Flow Super-Resolution Based on Image Guidence Using Convolutional Neural Network
Authors: Liping Zhang, Zongqing Lu, Qingmin Liao
Date: 2018-09-03 00:00:00
Tasks: Image Super-Resolution, Optical Flow Estimation, Super-Resolution

The convolutional neural network model for optical flow estimation usually
outputs a low-resolution(LR) optical flow field. To obtain the corresponding
full image resolution,interpolation and variational approach are the most
common options, which do not effectively improve the results. With the
motivation of various convolutional neural network(CNN) structures succeeded in
single image super-resolution(SISR) task, an end-to-end convolutional neural
network is proposed to reconstruct the high resolution(HR) optical flow field
from initial LR optical flow with the guidence of the first frame used in
optical flow estimation. Our optical flow super-resolution(OFSR) problem
differs from the general SISR problem in two main aspects. Firstly, the optical
flow includes less texture information than image so that the SISR CNN
structures can't be directly used in our OFSR problem. Secondly, the initial LR
optical flow data contains estimation error, while the LR image data for SISR
is generally a bicubic downsampled, blurred, and noisy version of HR ground
truth. We evaluate the proposed approach on two different optical flow
estimation mehods and show that it can not only obtain the full image
resolution, but generate more accurate optical flow field (Accuracy improve 15%
on FlyingChairs, 13% on MPI Sintel) with sharper edges than the estimation
result of original method.
Data-to-Text Generation with Content Selection and Planning
Authors: Ratish Puduppully, Li Dong, Mirella Lapata
Date: 2018-09-03 00:00:00
Tasks: Data-to-Text Generation, Descriptive, Text Generation

Recent advances in data-to-text generation have led to the use of large-scale
datasets and neural network models which are trained end-to-end, without
explicitly modeling what to say and in what order. In this work, we present a
neural network architecture which incorporates content selection and planning
without sacrificing end-to-end training. We decompose the generation task into
two stages. Given a corpus of data records (paired with descriptive documents),
we first generate a content plan highlighting which information should be
mentioned and in which order and then generate the document while taking the
content plan into account. Automatic and human-based evaluation experiments
show that our model outperforms strong baselines improving the state-of-the-art
on the recently released RotoWire dataset.
PathGAN: Visual Scanpath Prediction with Generative Adversarial Networks
Authors: Marc Assens, Xavier Giro-i-Nieto, Kevin McGuinness, Noel E. O'Connor
Date: 2018-09-03 00:00:00
Tasks: Prediction, Scanpath prediction

We introduce PathGAN, a deep neural network for visual scanpath prediction
trained on adversarial examples. A visual scanpath is defined as the sequence
of fixation points over an image defined by a human observer with its gaze.
PathGAN is composed of two parts, the generator and the discriminator. Both
parts extract features from images using off-the-shelf networks, and train
recurrent layers to generate or discriminate scanpaths accordingly. In scanpath
prediction, the stochastic nature of the data makes it very difficult to
generate realistic predictions using supervised learning strategies, but we
adopt adversarial training as a suitable alternative. Our experiments prove how
PathGAN improves the state of the art of visual scanpath prediction on the iSUN
and Salient360! datasets. Source code and models are available at
https://imatge-upc.github.io/pathgan/
ViewpointS: towards a Collective Brain
Authors: Philippe Lemoisson, Stefano A. Cerri
Date: 2018-09-03 00:00:00
Tasks: Recommendation Systems

Tracing knowledge acquisition and linking learning events to interaction
between peers is a major challenge of our times. We have conceived, designed
and evaluated a new paradigm for constructing and using collective knowledge by
Web interactions that we called ViewpointS. By exploiting the similarity with
Edelman's Theory of Neuronal Group Selection (TNGS), we conjecture that it may
be metaphorically considered a Collective Brain, especially effective in the
case of trans-disciplinary representations. Far from being without doubts, in
the paper we present the reasons (and the limits) of our proposal that aims to
become a useful integrating tool for future quantitative explorations of
individual as well as collective learning at different degrees of granu-larity.
We are therefore challenging each of the current approaches: the logical one in
the semantic Web, the statistical one in mining and deep learning, the social
one in recommender systems based on authority and trust; not in each of their
own preferred field of operation, rather in their integration weaknesses far
from the holistic and dynamic behavior of the human brain.
End-to-End Argument Mining for Discussion Threads Based on Parallel Constrained Pointer Architecture
Authors: Gaku Morio, Katsuhide Fujita
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 11
Tasks: Argument Mining, Sentence

Argument Mining (AM) is a relatively recent discipline, which concentrates on
extracting claims or premises from discourses, and inferring their structures.
However, many existing works do not consider micro-level AM studies on
discussion threads sufficiently. In this paper, we tackle AM for discussion
threads. Our main contributions are follows: (1) A novel combination scheme
focusing on micro-level inner- and inter- post schemes for a discussion thread.
(2) Annotation of large-scale civic discussion threads with the scheme. (3)
Parallel constrained pointer architecture (PCPA), a novel end-to-end technique
to discriminate sentence types, inner-post relations, and inter-post
interactions simultaneously. The experimental results demonstrate that our
proposed model shows better accuracy in terms of relations extraction, in
comparison to existing state-of-the-art models.
Semantic Information G Theory and Logical Bayesian Inference for Machine Learning
Authors: Chenguang Lu
Date: 2018-09-03 00:00:00
Tasks: Bayesian Inference, BIG-bench Machine Learning

An important problem with machine learning is that when label number n>2, it is very difficult to construct and optimize a group of learning functions, and we wish that optimized learning functions are still useful when prior distribution P(x) (where x is an instance) is changed. To resolve this problem, the semantic information G theory, Logical Bayesian Inference (LBI), and a group of Channel Matching (CM) algorithms together form a systematic solution. A semantic channel in the G theory consists of a group of truth functions or membership functions. In comparison with likelihood functions, Bayesian posteriors, and Logistic functions used by popular methods, membership functions can be more conveniently used as learning functions without the above problem. In Logical Bayesian Inference (LBI), every label's learning is independent. For Multilabel learning, we can directly obtain a group of optimized membership functions from a big enough sample with labels, without preparing different samples for different labels. A group of Channel Matching (CM) algorithms is developed for machine learning. For the Maximum Mutual Information (MMI) classification of three classes with Gaussian distributions on a two-dimensional feature space, 2-3 iterations can make mutual information between three classes and three labels surpass 99% of the MMI for most initial partitions. For mixture models, the Expectation-Maximization (EM) algorithm is improved and becomes the CM-EM algorithm, which can outperform the EM algorithm when mixture ratios are imbalanced, or local convergence exists. The CM iteration algorithm needs to combine neural networks for MMI classifications on high-dimensional feature spaces. LBI needs further studies for the unification of statistics and logic.
Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence
Authors: Jogendra Nath Kundu, Rahul M. V., Aditya Ganeshan, R. Venkatesh Babu
Date: 2018-09-03 00:00:00
Tasks: Pose Estimation, Viewpoint Estimation

Understanding the geometry and pose of objects in 2D images is a fundamental
necessity for a wide range of real world applications. Driven by deep neural
networks, recent methods have brought significant improvements to object pose
estimation. However, they suffer due to scarcity of keypoint/pose-annotated
real images and hence can not exploit the object's 3D structural information
effectively. In this work, we propose a data-efficient method which utilizes
the geometric regularity of intraclass objects for pose estimation. First, we
learn pose-invariant local descriptors of object parts from simple 2D RGB
images. These descriptors, along with keypoints obtained from renders of a
fixed 3D template model are then used to generate keypoint correspondence maps
for a given monocular real image. Finally, a pose estimation network predicts
3D pose of the object using these correspondence maps. This pipeline is further
extended to a multi-view approach, which assimilates keypoint information from
correspondence sets generated from multiple views of the 3D template model.
Fusion of multi-view information significantly improves geometric comprehension
of the system which in turn enhances the pose estimation performance.
Furthermore, use of correspondence framework responsible for the learning of
pose invariant keypoint descriptor also allows us to effectively alleviate the
data-scarcity problem. This enables our method to achieve state-of-the-art
performance on multiple real-image viewpoint estimation datasets, such as
Pascal3D+ and ObjectNet3D. To encourage reproducible research, we have released
the codes for our proposed approach.
Emergence of Communication in an Interactive World with Consistent Speakers
Authors: Ben Bogin, Mor Geva, Jonathan Berant
Date: 2018-09-03 00:00:00

Training agents to communicate with one another given task-based supervision
only has attracted considerable attention recently, due to the growing interest
in developing models for human-agent interaction. Prior work on the topic
focused on simple environments, where training using policy gradient was
feasible despite the non-stationarity of the agents during training. In this
paper, we present a more challenging environment for testing the emergence of
communication from raw pixels, where training using policy gradient fails. We
propose a new model and training algorithm, that utilizes the structure of a
learned representation space to produce more consistent speakers at the initial
phases of training, which stabilizes learning. We empirically show that our
algorithm substantially improves performance compared to policy gradient. We
also propose a new alignment-based metric for measuring context-independence in
emerged communication and find our method increases context-independence
compared to policy gradient and other competitive baselines.
Learning Vision-based Cohesive Flight in Drone Swarms
Authors: Fabian Schilling, Julien Lecoeur, Fabrizio Schiano, Dario Floreano
Date: 2018-09-03 00:00:00
Tasks: Collision Avoidance

This paper presents a data-driven approach to learning vision-based
collective behavior from a simple flocking algorithm. We simulate a swarm of
quadrotor drones and formulate the controller as a regression problem in which
we generate 3D velocity commands directly from raw camera images. The dataset
is created by simultaneously acquiring omnidirectional images and computing the
corresponding control command from the flocking algorithm. We show that a
convolutional neural network trained on the visual inputs of the drone can
learn not only robust collision avoidance but also coherence of the flock in a
sample-efficient manner. The neural controller effectively learns to localize
other agents in the visual input, which we show by visualizing the regions with
the most influence on the motion of an agent. This weakly supervised saliency
map can be computed efficiently and may be used as a prior for subsequent
detection and relative localization of other agents. We remove the dependence
on sharing positions among flock members by taking only local visual
information into account for control. Our work can therefore be seen as the
first step towards a fully decentralized, vision-based flock without the need
for communication or visual markers to aid detection of other agents.
Machine learning for predicting thermal power consumption of the Mars Express Spacecraft
Authors: Matej Petković, Redouane Boumghar, Martin Breskvar, Sašo Džeroski, Dragi Kocev, Jurica Levatić, Luke Lucas, Aljaž Osojnik, Bernard Ženko, Nikola Simidjievski
Date: 2018-09-03 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering

The thermal subsystem of the Mars Express (MEX) spacecraft keeps the on-board
equipment within its pre-defined operating temperatures range. To plan and
optimize the scientific operations of MEX, its operators need to estimate in
advance, as accurately as possible, the power consumption of the thermal
subsystem. The remaining power can then be allocated for scientific purposes.
We present a machine learning pipeline for efficiently constructing accurate
predictive models for predicting the power of the thermal subsystem on board
MEX. In particular, we employ state-of-the-art feature engineering approaches
for transforming raw telemetry data, in turn used for constructing accurate
models with different state-of-the-art machine learning methods. We show that
the proposed pipeline considerably improve our previous (competition-winning)
work in terms of time efficiency and predictive performance. Moreover, while
achieving superior predictive performance, the constructed models also provide
important insight into the spacecraft's behavior, allowing for further analyses
and optimal planning of MEX's operation.
Multilingual Clustering of Streaming News
Authors: Sebastião Miranda, Artūrs Znotiņš, Shay B. Cohen, Guntis Barzdins
Date: 2018-09-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Articles, Clustering

Clustering news across languages enables efficient media monitoring by
aggregating articles from multilingual sources into coherent stories. Doing so
in an online setting allows scalable processing of massive news streams. To
this end, we describe a novel method for clustering an incoming stream of
multilingual documents into monolingual and crosslingual story clusters. Unlike
typical clustering approaches that consider a small and known number of labels,
we tackle the problem of discovering an ever growing number of cluster labels
in an online fashion, using real news datasets in multiple languages. Our
method is simple to implement, computationally efficient and produces
state-of-the-art results on datasets in German, English and Spanish.
Crowdsourcing Semantic Label Propagation in Relation Classification
Authors: Anca Dumitrache, Lora Aroyo, Chris Welty
Date: 2018-09-03 00:00:00
Proceeding: WS 2018 11
Tasks: Classification, General Classification, Relation, Relation Classification, Relation Extraction, Sentence

Distant supervision is a popular method for performing relation extraction
from text that is known to produce noisy labels. Most progress in relation
extraction and classification has been made with crowdsourced corrections to
distant-supervised labels, and there is evidence that indicates still more
would be better. In this paper, we explore the problem of propagating human
annotation signals gathered for open-domain relation classification through the
CrowdTruth methodology for crowdsourcing, that captures ambiguity in
annotations by measuring inter-annotator disagreement. Our approach propagates
annotations to sentences that are similar in a low dimensional embedding space,
expanding the number of labels by two orders of magnitude. Our experiments show
significant improvement in a sentence-level multi-class relation classifier.
Adaptive Semi-supervised Learning for Cross-domain Sentiment Classification
Authors: Ruidan He, Wee Sun Lee, Hwee Tou Ng, Daniel Dahlmeier
Date: 2018-09-03 00:00:00
Proceeding: EMNLP 2018 10
Tasks: Classification, General Classification, Sentiment Analysis, Sentiment Classification

We consider the cross-domain sentiment classification problem, where a
sentiment classifier is to be learned from a source domain and to be
generalized to a target domain. Our approach explicitly minimizes the distance
between the source and the target instances in an embedded feature space. With
the difference between source and target minimized, we then exploit additional
information from the target domain by consolidating the idea of semi-supervised
learning, for which, we jointly employ two regularizations -- entropy
minimization and self-ensemble bootstrapping -- to incorporate the unlabeled
target data for classifier refinement. Our experimental results demonstrate
that the proposed approach can better leverage unlabeled data from the target
domain and achieve substantial improvements over baseline methods in various
experimental settings.
Embedding a $θ$-invariant code into a complete one
Authors: Jean Néraud, Carla Selmi
Date: 2018-01-16 00:00:00

Let A be a finite or countable alphabet and let $\theta$ be a literal
(anti-)automorphism onto A * (by definition, such a correspondence is
determinated by a permutation of the alphabet). This paper deals with sets
which are invariant under $\theta$ ($\theta$-invariant for short) that is,
languages L such that $\theta$ (L) is a subset of L.We establish an extension
of the famous defect theorem. With regards to the so-called notion of
completeness, we provide a series of examples of finite complete
$\theta$-invariant codes. Moreover, we establish a formula which allows to
embed any non-complete $\theta$-invariant code into a complete one. As a
consequence, in the family of the so-called thin $\theta$--invariant codes,
maximality and completeness are two equivalent notions.
Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning
Authors: Hugo Caselles-Dupré, Louis Annabi, Oksana Hagen, Michael Garcia-Ortiz, David Filliat
Date: 2018-09-03 00:00:00
Tasks: Lifelong learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Flatland is a simple, lightweight environment for fast prototyping and
testing of reinforcement learning agents. It is of lower complexity compared to
similar 3D platforms (e.g. DeepMind Lab or VizDoom), but emulates physical
properties of the real world, such as continuity, multi-modal
partially-observable states with first-person view and coherent physics. We
propose to use it as an intermediary benchmark for problems related to Lifelong
Learning. Flatland is highly customizable and offers a wide range of task
difficulty to extensively evaluate the properties of artificial agents. We
experiment with three reinforcement learning baseline agents and show that they
can rapidly solve a navigation task in Flatland. A video of an agent acting in
Flatland is available here: https://youtu.be/I5y6Y2ZypdA.