pwc-archive/papers-with-abstracts huggingface dataset:
Spatio-temporal Video Parsing for Abnormality Detection
Authors: Borislav Antić, Björn Ommer
Date: 2015-02-22 00:00:00
Tasks: Anomaly Detection

Abnormality detection in video poses particular challenges due to the
infinite size of the class of all irregular objects and behaviors. Thus no (or
by far not enough) abnormal training samples are available and we need to find
abnormalities in test data without actually knowing what they are.
Nevertheless, the prevailing concept of the field is to directly search for
individual abnormal local patches or image regions independent of another. To
address this problem, we propose a method for joint detection of abnormalities
in videos by spatio-temporal video parsing. The goal of video parsing is to
find a set of indispensable normal spatio-temporal object hypotheses that
jointly explain all the foreground of a video, while, at the same time, being
supported by normal training samples. Consequently, we avoid a direct detection
of abnormalities and discover them indirectly as those hypotheses which are
needed for covering the foreground without finding an explanation for
themselves by normal samples. Abnormalities are localized by MAP inference in a
graphical model and we solve it efficiently by formulating it as a convex
optimization problem. We experimentally evaluate our approach on several
challenging benchmark sets, improving over the state-of-the-art on all standard
benchmarks both in terms of abnormality classification and localization.
Video Text Localization with an emphasis on Edge Features
Authors: B. H. Shekar, Smitha M. L.
Date: 2015-02-22 00:00:00
Tasks: Retrieval, Text Detection

The text detection and localization plays a major role in video analysis and
understanding. The scene text embedded in video consist of high-level semantics
and hence contributes significantly to visual content analysis and retrieval.
This paper proposes a novel method to robustly localize the texts in natural
scene images and videos based on sobel edge emphasizing approach. The input
image is preprocessed and edge emphasis is done to detect the text clusters.
Further, a set of rules have been devised using morphological operators for
false positive elimination and connected component analysis is performed to
detect the text regions and hence text localization is performed. The
experimental results obtained on publicly available standard datasets
illustrate that the proposed method can detect and localize the texts of
various sizes, fonts and colors.
Bi-Level Image Thresholding obtained by means of Kaniadakis Entropy
Authors: Amelia Carolina Sparavigna
Date: 2015-02-16 00:00:00

In this paper we are proposing the use of Kaniadakis entropy in the bi-level
thresholding of images, in the framework of a maximum entropy principle. We
discuss the role of its entropic index in determining the threshold and in
driving an "image transition", that is, an abrupt transition in the appearance
of the corresponding bi-level image. Some examples are proposed to illustrate
the method and for comparing it to the approach which is using the Tsallis
entropy.
Nearly optimal classification for semimetrics
Authors: Lee-Ad Gottlieb, Aryeh Kontorovich
Date: 2015-02-22 00:00:00
Tasks: Classification, General Classification

We initiate the rigorous study of classification in semimetric spaces, which
are point sets with a distance function that is non-negative and symmetric, but
need not satisfy the triangle inequality. For metric spaces, the doubling
dimension essentially characterizes both the runtime and sample complexity of
classification algorithms --- yet we show that this is not the case for
semimetrics. Instead, we define the {\em density dimension} and discover that
it plays a central role in the statistical and algorithmic feasibility of
learning in semimetric spaces. We present nearly optimal sample compression
algorithms and use these to obtain generalization guarantees, including fast
rates. The latter hold for general sample compression schemes and may be of
independent interest.
SDCA without Duality
Authors: Shai Shalev-Shwartz
Date: 2015-02-22 00:00:00

Stochastic Dual Coordinate Ascent is a popular method for solving regularized
loss minimization for the case of convex losses. In this paper we show how a
variant of SDCA can be applied for non-convex losses. We prove linear
convergence rate even if individual loss functions are non-convex as long as
the expected loss is convex.
Clustering multi-way data: a novel algebraic approach
Authors: Eric Kernfeld, Shuchin Aeron, Misha Kilmer
Date: 2014-12-22 00:00:00
Tasks: Clustering, Image Clustering

In this paper, we develop a method for unsupervised clustering of two-way
(matrix) data by combining two recent innovations from different fields: the
Sparse Subspace Clustering (SSC) algorithm [10], which groups points coming
from a union of subspaces into their respective subspaces, and the t-product
[18], which was introduced to provide a matrix-like multiplication for third
order tensors. Our algorithm is analogous to SSC in that an "affinity" between
different data points is built using a sparse self-representation of the data.
Unlike SSC, we employ the t-product in the self-representation. This allows us
more flexibility in modeling; infact, SSC is a special case of our method. When
using the t-product, three-way arrays are treated as matrices whose elements
(scalars) are n-tuples or tubes. Convolutions take the place of scalar
multiplication. This framework allows us to embed the 2-D data into a
vector-space-like structure called a free module over a commutative ring. These
free modules retain many properties of complex inner-product spaces, and we
leverage that to provide theoretical guarantees on our algorithm. We show that
compared to vector-space counterparts, SSmC achieves higher accuracy and better
able to cluster data with less preprocessing in some image clustering problems.
In particular we show the performance of the proposed method on Weizmann face
database, the Extended Yale B Face database and the MNIST handwritten digits
database.
Using NLP to measure democracy
Authors: Thiago Marzagão
Date: 2015-02-22 00:00:00
Tasks: Articles, regression

This paper uses natural language processing to create the first machine-coded
democracy index, which I call Automated Democracy Scores (ADS). The ADS are
based on 42 million news articles from 6,043 different sources and cover all
independent countries in the 1993-2012 period. Unlike the democracy indices we
have today the ADS are replicable and have standard errors small enough to
actually distinguish between cases.
  The ADS are produced with supervised learning. Three approaches are tried: a)
a combination of Latent Semantic Analysis and tree-based regression methods; b)
a combination of Latent Dirichlet Allocation and tree-based regression methods;
and c) the Wordscores algorithm. The Wordscores algorithm outperforms the
alternatives, so it is the one on which the ADS are based.
  There is a web application where anyone can change the training set and see
how the results change: democracy-scores.org
Learning Generative Models with Visual Attention
Authors: Yichuan Tang, Nitish Srivastava, Ruslan Salakhutdinov
Date: 2013-12-20 00:00:00
Proceeding: NeurIPS 2014 12

Attention has long been proposed by psychologists as important for
effectively dealing with the enormous sensory stimulus available in the
neocortex. Inspired by the visual attention models in computational
neuroscience and the need of object-centric data for generative models, we
describe for generative learning framework using attentional mechanisms.
Attentional mechanisms can propagate signals from region of interest in a scene
to an aligned canonical representation, where generative modeling takes place.
By ignoring background clutter, generative models can concentrate their
resources on the object of interest. Our model is a proper graphical model
where the 2D Similarity transformation is a part of the top-down process. A
ConvNet is employed to provide good initializations during posterior inference
which is based on Hamiltonian Monte Carlo. Upon learning images of faces, our
model can robustly attend to face regions of novel test subjects. More
importantly, our model can learn generative models of new faces from a novel
dataset of large images where the face locations are not known.
Detection of Planted Solutions for Flat Satisfiability Problems
Authors: Quentin Berthet, Jordan S. Ellenberg
Date: 2015-02-21 00:00:00
Tasks: Two-sample testing

We study the detection problem of finding planted solutions in random
instances of flat satisfiability problems, a generalization of boolean
satisfiability formulas. We describe the properties of random instances of flat
satisfiability, as well of the optimal rates of detection of the associated
hypothesis testing problem. We also study the performance of an algorithmically
efficient testing procedure. We introduce a modification of our model, the
light planting of solutions, and show that it is as hard as the problem of
learning parity with noise. This hints strongly at the difficulty of detecting
planted flat satisfiability for a wide class of tests.
Universal Memory Architectures for Autonomous Machines
Authors: Dan P. Guralnik, Daniel E. Koditschek
Date: 2015-02-21 00:00:00

We propose a self-organizing memory architecture for perceptual experience,
capable of supporting autonomous learning and goal-directed problem solving in
the absence of any prior information about the agent's environment. The
architecture is simple enough to ensure (1) a quadratic bound (in the number of
available sensors) on space requirements, and (2) a quadratic bound on the
time-complexity of the update-execute cycle. At the same time, it is
sufficiently complex to provide the agent with an internal representation which
is (3) minimal among all representations of its class which account for every
sensory equivalence class subject to the agent's belief state; (4) capable, in
principle, of recovering the homotopy type of the system's state space; (5)
learnable with arbitrary precision through a random application of the
available actions. The provable properties of an effectively trained memory
structure exploit a duality between weak poc sets -- a symbolic (discrete)
representation of subset nesting relations -- and non-positively curved cubical
complexes, whose rich convexity theory underlies the planning cycle of the
proposed architecture.
Sensitivity Analysis for Computationally Expensive Models using Optimization and Objective-oriented Surrogate Approximations
Authors: Yi-Lun Wang, Christine A. Shoemaker
Date: 2014-10-27 00:00:00
Tasks: Experimental Design, Sensitivity

In this paper, we focus on developing efficient sensitivity analysis methods
for a computationally expensive objective function $f(x)$ in the case that the
minimization of it has just been performed. Here "computationally expensive"
means that each of its evaluation takes significant amount of time, and
therefore our main goal to use a small number of function evaluations of $f(x)$
to further infer the sensitivity information of these different parameters.
Correspondingly, we consider the optimization procedure as an adaptive
experimental design and re-use its available function evaluations as the
initial design points to establish a surrogate model $s(x)$ (or called response
surface). The sensitivity analysis is performed on $s(x)$, which is an lieu of
$f(x)$. Furthermore, we propose a new local multivariate sensitivity measure,
for example, around the optimal solution, for high dimensional problems. Then a
corresponding "objective-oriented experimental design" is proposed in order to
make the generated surrogate $s(x)$ better suitable for the accurate
calculation of the proposed specific local sensitivity quantities. In addition,
we demonstrate the better performance of the Gaussian radial basis function
interpolator over Kriging in our cases, which are of relatively high
dimensionality and few experimental design points. Numerical experiments
demonstrate that the optimization procedure and the "objective-oriented
experimental design" behavior much better than the classical Latin Hypercube
Design. In addition, the performance of Kriging is not as good as Gaussian RBF,
especially in the case of high dimensional problems.
Unified vector space mapping for knowledge representation systems
Authors: Dmytro Filatov, Taras Filatov
Date: 2015-02-21 00:00:00
Tasks: Decoder, Information Retrieval, Retrieval

One of the most significant problems which inhibits further developments in
the areas of Knowledge Representation and Artificial Intelligence is a problem
of semantic alignment or knowledge mapping. The progress in its solution will
be greatly beneficial for further advances of information retrieval, ontology
alignment, relevance calculation, text mining, natural language processing etc.
In the paper the concept of multidimensional global knowledge map, elaborated
through unsupervised extraction of dependencies from large documents corpus, is
proposed. In addition, the problem of direct Human - Knowledge Representation
System interface is addressed and a concept of adaptive decoder proposed for
the purpose of interaction with previously described unified mapping model. In
combination these two approaches are suggested as basis for a development of a
new generation of knowledge representation systems.
Deep Learning using Linear Support Vector Machines
Authors: Yichuan Tang
Date: 2013-06-02 00:00:00
Tasks: Deep Learning, General Classification, image-classification, Image Classification, Representation Learning, Speech Recognition

Recently, fully-connected and convolutional neural networks have been trained
to achieve state-of-the-art performance on a wide variety of tasks such as
speech recognition, image classification, natural language processing, and
bioinformatics. For classification tasks, most of these "deep learning" models
employ the softmax activation function for prediction and minimize
cross-entropy loss. In this paper, we demonstrate a small but consistent
advantage of replacing the softmax layer with a linear support vector machine.
Learning minimizes a margin-based loss instead of the cross-entropy loss. While
there have been various combinations of neural nets and SVMs in prior art, our
results using L2-SVMs show that by simply replacing softmax with linear SVMs
gives significant gains on popular deep learning datasets MNIST, CIFAR-10, and
the ICML 2013 Representation Learning Workshop's face expression recognition
challenge.
Design of a Framework to Facilitate Decisions Using Information Fusion
Authors: Tamer M. Abo Neama, Ismail A. Ismail, Tarek S. Sobh, M. Zaki
Date: 2015-02-17 00:00:00
Tasks: Decision Making

Information fusion is an advanced research area which can assist decision
makers in enhancing their decisions. This paper aims at designing a new
multi-layer framework that can support the process of performing decisions from
the obtained beliefs using information fusion. Since it is not an easy task to
cross the gap between computed beliefs of certain hypothesis and decisions, the
proposed framework consists of the following layers in order to provide a
suitable architecture (ordered bottom up): 1. A layer for combination of basic
belief assignments using an information fusion approach. Such approach exploits
Dezert-Smarandache Theory, DSmT, and proportional conflict redistribution to
provide more realistic final beliefs. 2. A layer for computation of pignistic
probability of the underlying propositions from the corresponding final
beliefs. 3. A layer for performing probabilistic reasoning using a Bayesian
network that can obtain the probable reason of a proposition from its pignistic
probability. 4. Ranking the system decisions is ultimately used to support
decision making. A case study has been accomplished at various operational
conditions in order to prove the concept, in addition it pointed out that: 1.
The use of DSmT for information fusion yields not only more realistic beliefs
but also reliable pignistic probabilities for the underlying propositions. 2.
Exploiting the pignistic probability for the integration of the information
fusion with the Bayesian network provides probabilistic inference and enable
decision making on the basis of both belief based probabilities for the
underlying propositions and Bayesian based probabilities for the corresponding
reasons. A comparative study of the proposed framework with respect to other
information fusion systems confirms its superiority to support decision making.
Reinforcement Learning in a Neurally Controlled Robot Using Dopamine Modulated STDP
Authors: Richard Evans
Date: 2015-02-21 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Recent work has shown that dopamine-modulated STDP can solve many of the
issues associated with reinforcement learning, such as the distal reward
problem. Spiking neural networks provide a useful technique in implementing
reinforcement learning in an embodied context as they can deal with continuous
parameter spaces and as such are better at generalizing the correct behaviour
to perform in a given context.
  In this project we implement a version of DA-modulated STDP in an embodied
robot on a food foraging task. Through simulated dopaminergic neurons we show
how the robot is able to learn a sequence of behaviours in order to achieve a
food reward. In tests the robot was able to learn food-attraction behaviour,
and subsequently unlearn this behaviour when the environment changed, in all 50
trials. Moreover we show that the robot is able to operate in an environment
whereby the optimal behaviour changes rapidly and so the agent must constantly
relearn. In a more complex environment, consisting of food-containers, the
robot was able to learn food-container attraction in 95% of trials, despite the
large temporal distance between the correct behaviour and the reward. This is
achieved by shifting the dopamine response from the primary stimulus (food) to
the secondary stimulus (food-container).
  Our work provides insights into the reasons behind some observed biological
phenomena, such as the bursting behaviour observed in dopaminergic neurons. As
well as demonstrating how spiking neural network controlled robots are able to
solve a range of reinforcement learning tasks.
Study of a Robust Algorithm Applied in the Optimal Position Tuning for the Camera Lens in Automated Visual Inspection Systems
Authors: Radu Arsinte
Date: 2015-02-21 00:00:00
Tasks: Position

This paper present the mathematical fundaments and experimental study of an
algorithm used to find the optimal position for the camera lens to obtain a
maximum of details. This information can be further applied to a appropriate
system to automatically correct this position. The algorithm is based on the
evaluation of a so called resolution function who calculates the maximum of
gradient in a certain zone of the image. The paper also presents alternative
forms of the function, results of measurements and set up a set of practical
rules for the right application of the algorithm.
Intra-and-Inter-Constraint-based Video Enhancement based on Piecewise Tone Mapping
Authors: Yuanzhe Chen, Weiyao Lin, Chongyang Zhang, Zhenzhong Chen, Ning Xu, Jun Xie
Date: 2015-02-21 00:00:00
Tasks: Tone Mapping, Video Enhancement

Video enhancement plays an important role in various video applications. In
this paper, we propose a new intra-and-inter-constraint-based video enhancement
approach aiming to 1) achieve high intra-frame quality of the entire picture
where multiple region-of-interests (ROIs) can be adaptively and simultaneously
enhanced, and 2) guarantee the inter-frame quality consistencies among video
frames. We first analyze features from different ROIs and create a piecewise
tone mapping curve for the entire frame such that the intra-frame quality of a
frame can be enhanced. We further introduce new inter-frame constraints to
improve the temporal quality consistency. Experimental results show that the
proposed algorithm obviously outperforms the state-of-the-art algorithms.
A Heat-Map-based Algorithm for Recognizing Group Activities in Videos
Authors: Weiyao Lin, Hang Chu, Jianxin Wu, Bin Sheng, Zhenzhong Chen
Date: 2015-02-21 00:00:00
Tasks: Activity Recognition, Group Activity Recognition

In this paper, a new heat-map-based (HMB) algorithm is proposed for group
activity recognition. The proposed algorithm first models human trajectories as
series of "heat sources" and then applies a thermal diffusion process to create
a heat map (HM) for representing the group activities. Based on this heat map,
a new key-point based (KPB) method is used for handling the alignments among
heat maps with different scales and rotations. And a surface-fitting (SF)
method is also proposed for recognizing group activities. Our proposed HM
feature can efficiently embed the temporal motion information of the group
activities while the proposed KPB and SF methods can effectively utilize the
characteristics of the heat map for activity recognition. Experimental results
demonstrate the effectiveness of our proposed algorithms.
A new network-based algorithm for human activity recognition in video
Authors: Weiyao Lin, Yuanzhe Chen, Jianxin Wu, Hanli Wang, Bin Sheng, Hongxiang Li
Date: 2015-02-21 00:00:00
Tasks: Action Detection, Activity Detection, Activity Recognition, Activity Recognition In Videos, Group Activity Recognition, Human Activity Recognition

In this paper, a new network-transmission-based (NTB) algorithm is proposed
for human activity recognition in videos. The proposed NTB algorithm models the
entire scene as an error-free network. In this network, each node corresponds
to a patch of the scene and each edge represents the activity correlation
between the corresponding patches. Based on this network, we further model
people in the scene as packages while human activities can be modeled as the
process of package transmission in the network. By analyzing these specific
"package transmission" processes, various activities can be effectively
detected. The implementation of our NTB algorithm into abnormal activity
detection and group activity recognition are described in detail in the paper.
Experimental results demonstrate the effectiveness of our proposed algorithm.
MILJS : Brand New JavaScript Libraries for Matrix Calculation and Machine Learning
Authors: Ken Miura, Tetsuaki Mano, Atsushi Kanehira, Yuichiro Tsuchiya, Tatsuya Harada
Date: 2015-02-21 00:00:00
Tasks: BIG-bench Machine Learning

MILJS is a collection of state-of-the-art, platform-independent, scalable,
fast JavaScript libraries for matrix calculation and machine learning. Our core
library offering a matrix calculation is called Sushi, which exhibits far
better performance than any other leading machine learning libraries written in
JavaScript. Especially, our matrix multiplication is 177 times faster than the
fastest JavaScript benchmark. Based on Sushi, a machine learning library called
Tempura is provided, which supports various algorithms widely used in machine
learning research. We also provide Soba as a visualization library. The
implementations of our libraries are clearly written, properly documented and
thus can are easy to get started with, as long as there is a web browser. These
libraries are available from http://mil-tokyo.github.io/ under the MIT license.