pwc-archive/papers-with-abstracts huggingface dataset:
Identifying Dogmatism in Social Media: Signals and Models
Authors: Ethan Fast, Eric Horvitz
Date: 2016-09-01 00:00:00
Proceeding: EMNLP 2016 11

We explore linguistic and behavioral features of dogmatism in social media
and construct statistical models that can identify dogmatic comments. Our model
is based on a corpus of Reddit posts, collected across a diverse set of
conversational topics and annotated via paid crowdsourcing. We operationalize
key aspects of dogmatism described by existing psychology theories (such as
over-confidence), finding they have predictive power. We also find evidence for
new signals of dogmatism, such as the tendency of dogmatic posts to refrain
from signaling cognitive processes. When we use our predictive model to analyze
millions of other Reddit posts, we find evidence that suggests dogmatism is a
deeper personality trait, present for dogmatic users across many different
domains, and that users who engage on dogmatic comments tend to show increases
in dogmatic posts themselves.
Deep Learning Human Mind for Automated Visual Classification
Authors: Concetto Spampinato, Simone Palazzo, Isaak Kavasidis, Daniela Giordano, Mubarak Shah, Nasim Souly
Date: 2016-09-01 00:00:00
Proceeding: CVPR 2017 7
Tasks: Classification, Deep Learning, EEG, Electroencephalogram (EEG), General Classification, Object, Object Categorization

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories. Afterwards, we train a Convolutional Neural Network (CNN)-based regressor to project images onto the learned manifold, thus effectively allowing machines to employ human brain-based features for automated visual classification. We use a 32-channel EEG to record brain activity of seven subjects while looking at images of 40 ImageNet object classes. The proposed RNN based approach for discriminating object classes using brain signals reaches an average accuracy of about 40%, which outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain-driven approach obtains competitive performance, comparable to those achieved by powerful CNN models, both on ImageNet and CalTech 101, thus demonstrating its classification and generalization capabilities. This gives us a real hope that, indeed, human mind can be read and transferred to machines.
Unethical Research: How to Create a Malevolent Artificial Intelligence
Authors: Federico Pistono, Roman V. Yampolskiy
Date: 2016-05-10 00:00:00

Cybersecurity research involves publishing papers about malicious exploits as
much as publishing information on how to design tools to protect
cyber-infrastructure. It is this information exchange between ethical hackers
and security experts, which results in a well-balanced cyber-ecosystem. In the
blooming domain of AI Safety Engineering, hundreds of papers have been
published on different proposals geared at the creation of a safe machine, yet
nothing, to our knowledge, has been published on how to design a malevolent
machine. Availability of such information would be of great value particularly
to computer scientists, mathematicians, and others who have an interest in AI
safety, and who are attempting to avoid the spontaneous emergence or the
deliberate creation of a dangerous AI, which can negatively affect human
activities and in the worst case cause the complete obliteration of the human
species. This paper provides some general guidelines for the creation of a
Malevolent Artificial Intelligence (MAI).
Complete Dictionary Recovery over the Sphere II: Recovery by Riemannian Trust-region Method
Authors: Ju Sun, Qing Qu, John Wright
Date: 2015-11-15 00:00:00
Tasks: Dictionary Learning

We consider the problem of recovering a complete (i.e., square and
invertible) matrix $\mathbf A_0$, from $\mathbf Y \in \mathbb{R}^{n \times p}$
with $\mathbf Y = \mathbf A_0 \mathbf X_0$, provided $\mathbf X_0$ is
sufficiently sparse. This recovery problem is central to theoretical
understanding of dictionary learning, which seeks a sparse representation for a
collection of input signals and finds numerous applications in modern signal
processing and machine learning. We give the first efficient algorithm that
provably recovers $\mathbf A_0$ when $\mathbf X_0$ has $O(n)$ nonzeros per
column, under suitable probability model for $\mathbf X_0$.
  Our algorithmic pipeline centers around solving a certain nonconvex
optimization problem with a spherical constraint, and hence is naturally
phrased in the language of manifold optimization. In a companion paper
(arXiv:1511.03607), we have showed that with high probability our nonconvex
formulation has no "spurious" local minimizers and around any saddle point the
objective function has a negative directional curvature. In this paper, we take
advantage of the particular geometric structure, and describe a Riemannian
trust region algorithm that provably converges to a local minimizer with from
arbitrary initializations. Such minimizers give excellent approximations to
rows of $\mathbf X_0$. The rows are then recovered by linear programming
rounding and deflation.
Complete Dictionary Recovery over the Sphere I: Overview and the Geometric Picture
Authors: Ju Sun, Qing Qu, John Wright
Date: 2015-11-11 00:00:00
Tasks: Dictionary Learning

We consider the problem of recovering a complete (i.e., square and
invertible) matrix $\mathbf A_0$, from $\mathbf Y \in \mathbb{R}^{n \times p}$
with $\mathbf Y = \mathbf A_0 \mathbf X_0$, provided $\mathbf X_0$ is
sufficiently sparse. This recovery problem is central to theoretical
understanding of dictionary learning, which seeks a sparse representation for a
collection of input signals and finds numerous applications in modern signal
processing and machine learning. We give the first efficient algorithm that
provably recovers $\mathbf A_0$ when $\mathbf X_0$ has $O(n)$ nonzeros per
column, under suitable probability model for $\mathbf X_0$. In contrast, prior
results based on efficient algorithms either only guarantee recovery when
$\mathbf X_0$ has $O(\sqrt{n})$ zeros per column, or require multiple rounds of
SDP relaxation to work when $\mathbf X_0$ has $O(n^{1-\delta})$ nonzeros per
column (for any constant $\delta \in (0, 1)$). }
  Our algorithmic pipeline centers around solving a certain nonconvex
optimization problem with a spherical constraint. In this paper, we provide a
geometric characterization of the objective landscape. In particular, we show
that the problem is highly structured: with high probability, (1) there are no
"spurious" local minimizers; and (2) around all saddle points the objective has
a negative directional curvature. This distinctive structure makes the problem
amenable to efficient optimization algorithms. In a companion paper
(arXiv:1511.04777), we design a second-order trust-region algorithm over the
sphere that provably converges to a local minimizer from arbitrary
initializations, despite the presence of saddle points.
Partially Observable Markov Decision Process for Recommender Systems
Authors: Zhongqi Lu, Qiang Yang
Date: 2016-08-28 00:00:00
Tasks: Recommendation Systems

We report the "Recurrent Deterioration" (RD) phenomenon observed in online
recommender systems. The RD phenomenon is reflected by the trend of performance
degradation when the recommendation model is always trained based on users'
feedbacks of the previous recommendations. There are several reasons for the
recommender systems to encounter the RD phenomenon, including the lack of
negative training data and the evolution of users' interests, etc. Motivated to
tackle the problems causing the RD phenomenon, we propose the POMDP-Rec
framework, which is a neural-optimized Partially Observable Markov Decision
Process algorithm for recommender systems. We show that the POMDP-Rec framework
effectively uses the accumulated historical data from real-world recommender
systems and automatically achieves comparable results with those models
fine-tuned exhaustively by domain exports on public datasets.
Semantic Image Based Geolocation Given a Map
Authors: Arsalan Mousavian, Jana Kosecka
Date: 2016-09-01 00:00:00
Tasks: geo-localization, Visual Place Recognition

The problem visual place recognition is commonly used strategy for
localization. Most successful appearance based methods typically rely on a
large database of views endowed with local or global image descriptors and
strive to retrieve the views of the same location. The quality of the results
is often affected by the density of the reference views and the robustness of
the image representation with respect to viewpoint variations, clutter and
seasonal changes. In this work we present an approach for geo-locating a novel
view and determining camera location and orientation using a map and a sparse
set of geo-tagged reference views. We propose a novel technique for detection
and identification of building facades from geo-tagged reference view using the
map and geometry of the building facades. We compute the likelihood of camera
location and orientation of the query images using the detected landmark
(building) identities from reference views, 2D map of the environment, and
geometry of building facades. We evaluate our approach for building
identification and geo-localization on a new challenging outdoors urban dataset
exhibiting large variations in appearance and viewpoint.
Gland Segmentation in Colon Histology Images: The GlaS Challenge Contest
Authors: Korsuk Sirinukunwattana, Josien P. W. Pluim, Hao Chen, Xiaojuan Qi, Pheng-Ann Heng, Yun Bo Guo, Li Yang Wang, Bogdan J. Matuszewski, Elia Bruni, Urko Sanchez, Anton Böhm, Olaf Ronneberger, Bassem Ben Cheikh, Daniel Racoceanu, Philipp Kainz, Michael Pfeiffer, Martin Urschler, David R. J. Snead, Nasir M. Rajpoot
Date: 2016-03-01 00:00:00
Tasks: Prognosis

Colorectal adenocarcinoma originating in intestinal glandular structures is
the most common form of colon cancer. In clinical practice, the morphology of
intestinal glands, including architectural appearance and glandular formation,
is used by pathologists to inform prognosis and plan the treatment of
individual patients. However, achieving good inter-observer as well as
intra-observer reproducibility of cancer grading is still a major challenge in
modern pathology. An automated approach which quantifies the morphology of
glands is a solution to the problem. This paper provides an overview to the
Gland Segmentation in Colon Histology Images Challenge Contest (GlaS) held at
MICCAI'2015. Details of the challenge, including organization, dataset and
evaluation criteria, are presented, along with the method descriptions and
evaluation results from the top performing methods.
Segmentation Free Object Discovery in Video
Authors: Giovanni Cuffaro, Federico Becattini, Claudio Baecchi, Lorenzo Seidenari, Alberto del Bimbo
Date: 2016-09-01 00:00:00
Tasks: Object, Object Discovery, Segmentation

In this paper we present a simple yet effective approach to extend without
supervision any object proposal from static images to videos. Unlike previous
methods, these spatio-temporal proposals, to which we refer as tracks, are
generated relying on little or no visual content by only exploiting bounding
boxes spatial correlations through time. The tracks that we obtain are likely
to represent objects and are a general-purpose tool to represent meaningful
video content for a wide variety of tasks. For unannotated videos, tracks can
be used to discover content without any supervision. As further contribution we
also propose a novel and dataset-independent method to evaluate a generic
object proposal based on the entropy of a classifier output response. We
experiment on two competitive datasets, namely YouTube Objects and ILSVRC-2015
VID.
Employing traditional machine learning algorithms for big data streams analysis: the case of object trajectory prediction
Authors: Angelos Valsamis, Konstantinos Tserpes, Dimitrios Zissis, Dimosthenis Anagnostopoulos, Theodora Varvarigou
Date: 2016-09-01 00:00:00
Tasks: BIG-bench Machine Learning, Trajectory Prediction

In this paper, we model the trajectory of sea vessels and provide a service
that predicts in near-real time the position of any given vessel in 4', 10',
20' and 40' time intervals. We explore the necessary tradeoffs between
accuracy, performance and resource utilization are explored given the large
volume and update rates of input data. We start with building models based on
well-established machine learning algorithms using static datasets and
multi-scan training approaches and identify the best candidate to be used in
implementing a single-pass predictive approach, under real-time constraints.
The results are measured in terms of accuracy and performance and are compared
against the baseline kinematic equations. Results show that it is possible to
efficiently model the trajectory of multiple vessels using a single model,
which is trained and evaluated using an adequately large, static dataset, thus
achieving a significant gain in terms of resource usage while not compromising
accuracy.
Spatio-temporal video autoencoder with differentiable memory
Authors: Viorica Patraucean, Ankur Handa, Roberto Cipolla
Date: 2015-11-19 00:00:00
Tasks: Decoder, Motion Estimation, Optical Flow Estimation, Semantic Segmentation, Weakly supervised Semantic Segmentation, Weakly-Supervised Semantic Segmentation

We describe a new spatio-temporal video autoencoder, based on a classic
spatial image autoencoder and a novel nested temporal autoencoder. The temporal
encoder is represented by a differentiable visual memory composed of
convolutional long short-term memory (LSTM) cells that integrate changes over
time. Here we target motion changes and use as temporal decoder a robust
optical flow prediction module together with an image sampler serving as
built-in feedback loop. The architecture is end-to-end differentiable. At each
time step, the system receives as input a video frame, predicts the optical
flow based on the current observation and the LSTM memory state as a dense
transformation map, and applies it to the current frame to generate the next
frame. By minimising the reconstruction error between the predicted next frame
and the corresponding ground truth next frame, we train the whole system to
extract features useful for motion estimation without any supervision effort.
We present one direct application of the proposed framework in
weakly-supervised semantic segmentation of videos through label propagation
using optical flow.
Dataset and Neural Recurrent Sequence Labeling Model for Open-Domain Factoid Question Answering
Authors: Peng Li, Wei Li, Zhengyan He, Xuguang Wang, Ying Cao, Jie zhou, Wei Xu
Date: 2016-07-21 00:00:00
Tasks: Answer Generation, Question Answering

While question answering (QA) with neural network, i.e. neural QA, has
achieved promising results in recent years, lacking of large scale real-word QA
dataset is still a challenge for developing and evaluating neural QA system. To
alleviate this problem, we propose a large scale human annotated real-world QA
dataset WebQA with more than 42k questions and 556k evidences. As existing
neural QA methods resolve QA either as sequence generation or
classification/ranking problem, they face challenges of expensive softmax
computation, unseen answers handling or separate candidate answer generation
component. In this work, we cast neural QA as a sequence labeling problem and
propose an end-to-end sequence labeling model, which overcomes all the above
challenges. Experimental results on WebQA show that our model outperforms the
baselines significantly with an F1 score of 74.69% with word-based input, and
the performance drops only 3.72 F1 points with more challenging character-based
input.
Transferring Object-Scene Convolutional Neural Networks for Event Recognition in Still Images
Authors: Limin Wang, Zhe Wang, Yu Qiao, Luc van Gool
Date: 2016-09-01 00:00:00
Tasks: Multi-Task Learning

Event recognition in still images is an intriguing problem and has potential
for real applications. This paper addresses the problem of event recognition by
proposing a convolutional neural network that exploits knowledge of objects and
scenes for event classification (OS2E-CNN). Intuitively, it stands to reason
that there exists a correlation among the concepts of objects, scenes, and
events. We empirically demonstrate that the recognition of objects and scenes
substantially contributes to the recognition of events. Meanwhile, we propose
an iterative selection method to identify a subset of object and scene classes,
which help to more efficiently and effectively transfer their deep
representations to event recognition. Specifically, we develop three types of
transferring techniques: (1) initialization-based transferring, (2)
knowledge-based transferring, and (3) data-based transferring. These newly
designed transferring techniques exploit multi-task learning frameworks to
incorporate extra knowledge from other networks and additional datasets into
the training procedure of event CNNs. These multi-task learning frameworks turn
out to be effective in reducing the effect of over-fitting and improving the
generalization ability of the learned CNNs. With OS2E-CNN, we design a
multi-ratio and multi-scale cropping strategy, and propose an end-to-end event
recognition pipeline. We perform experiments on three event recognition
benchmarks: the ChaLearn Cultural Event Recognition dataset, the Web Image
Dataset for Event Recognition (WIDER), and the UIUC Sports Event dataset. The
experimental results show that our proposed algorithm successfully adapts
object and scene representations towards the event dataset and that it achieves
the current state-of-the-art performance on these challenging datasets.
Single photon in hierarchical architecture for physical reinforcement learning: Photon intelligence
Authors: Makoto Naruse, Martin Berthel, Aurélien Drezet, Serge Huant, Hirokazu Hori, Song-Ju Kim
Date: 2016-09-01 00:00:00
Tasks: Decision Making, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Understanding and using natural processes for intelligent functionalities,
referred to as natural intelligence, has recently attracted interest from a
variety of fields, including post-silicon computing for artificial intelligence
and decision making in the behavioural sciences. In a past study, we
successfully used the wave-particle duality of single photons to solve the
two-armed bandit problem, which constitutes the foundation of reinforcement
learning and decision making. In this study, we propose and confirm a
hierarchical architecture for single-photon-based reinforcement learning and
decision making that verifies the scalability of the principle. Specifically,
the four-armed bandit problem is solved given zero prior knowledge in a
two-layer hierarchical architecture, where polarization is autonomously adapted
in order to effect adequate decision making using single-photon measurements.
In the hierarchical structure, the notion of layer-dependent decisions emerges.
The optimal solutions in the coarse layer and in the fine layer, however,
conflict with each other in some contradictive problems. We show that while
what we call a tournament strategy resolves such contradictions, the
probabilistic nature of single photons allows for the direct location of the
optimal solution even for contradictive problems, hence manifesting the
exploration ability of single photons. This study provides insights into photon
intelligence in hierarchical architectures for future artificial intelligence
as well as the potential of natural processes for intelligent functionalities.
From Community Detection to Community Deception
Authors: Valeria Fionda, Giuseppe Pirrò
Date: 2016-09-01 00:00:00
Tasks: Community Detection

The community deception problem is about how to hide a target community C
from community detection algorithms. The need for deception emerges whenever a
group of entities (e.g., activists, police enforcements) want to cooperate
while concealing their existence as a community. In this paper we introduce and
formalize the community deception problem. To solve this problem, we describe
algorithms that carefully rewire the connections of C's members. We
experimentally show how several existing community detection algorithms can be
deceived, and quantify the level of deception by introducing a deception score.
We believe that our study is intriguing since, while showing how deception can
be realized it raises awareness for the design of novel detection algorithms
robust to deception techniques.
Syntax Evolution: Problems and Recursion
Authors: Ramón Casares
Date: 2015-08-12 00:00:00

To investigate the evolution of syntax, we need to ascertain the evolutionary r\^ole of syntax and, before that, the very nature of syntax. Here, we will assume that syntax is computing. And then, since we are computationally Turing complete, we meet an evolutionary anomaly, the anomaly of sytax: we are syntactically too competent for syntax. Assuming that problem solving is computing, and realizing that the evolutionary advantage of Turing completeness is full problem solving and not syntactic proficiency, we explain the anomaly of syntax by postulating that syntax and problem solving co-evolved in humans towards Turing completeness. Examining the requirements that full problem solving impose on language, we find firstly that semantics is not sufficient and that syntax is necessary to represent problems. Our final conclusion is that full problem solving requires a functional semantics on an infinite tree-structured syntax. Besides these results, the introduction of Turing completeness and problem solving to explain the evolution of syntax should help us to fit the evolution of language within the evolution of cognition, giving us some new clues to understand the elusive relation between language and thinking.
The Bayesian SLOPE
Authors: Amir Sepehri
Date: 2016-08-31 00:00:00
Tasks: regression, Variable Selection

The SLOPE estimates regression coefficients by minimizing a regularized
residual sum of squares using a sorted-$\ell_1$-norm penalty. The SLOPE
combines testing and estimation in regression problems. It exhibits suitable
variable selection and prediction properties, as well as minimax optimality.
This paper introduces the Bayesian SLOPE procedure for linear regression. The
classical SLOPE estimate is the posterior mode in the normal regression problem
with an appropriate prior on the coefficients. The Bayesian SLOPE considers the
full Bayesian model and has the advantage of offering credible sets and
standard error estimates for the parameters. Moreover, the hierarchical
Bayesian framework allows for full Bayesian and empirical Bayes treatment of
the penalty coefficients; whereas it is not clear how to choose these
coefficients when using the SLOPE on a general design matrix. A direct
characterization of the posterior is provided which suggests a Gibbs sampler
that does not involve latent variables. An efficient hybrid Gibbs sampler for
the Bayesian SLOPE is introduced. Point estimation using the posterior mean is
highlighted, which automatically facilitates the Bayesian prediction of future
observations. These are demonstrated on real and synthetic data.