pwc-archive/papers-with-abstracts huggingface dataset:
Feature base fusion for splicing forgery detection based on neuro fuzzy
Authors: Habib Ghaffari Hadigheh, Ghazali bin sulong
Date: 2017-01-29 00:00:00
Tasks: Clustering, Image Forensics

Most of researches on image forensics have been mainly focused on detection
of artifacts introduced by a single processing tool. They lead in the
development of many specialized algorithms looking for one or more particular
footprints under specific settings. Naturally, the performance of such
algorithms are not perfect, and accordingly the provided output might be noisy,
inaccurate and only partially correct. Furthermore, a forged image in practical
scenarios is often the result of utilizing several tools available by
image-processing software systems. Therefore, reliable tamper detection
requires developing more poweful tools to deal with various tempering
scenarios. Fusion of forgery detection tools based on Fuzzy Inference System
has been used before for addressing this problem. Adjusting the membership
functions and defining proper fuzzy rules for attaining to better results are
time-consuming processes. This can be accounted as main disadvantage of fuzzy
inference systems. In this paper, a Neuro-Fuzzy inference system for fusion of
forgery detection tools is developed. The neural network characteristic of
these systems provides appropriate tool for automatically adjusting the
membership functions. Moreover, initial fuzzy inference system is generated
based on fuzzy clustering techniques. The proposed framework is implemented and
validated on a benchmark image splicing data set in which three forgery
detection tools are fused based on adaptive Neuro-Fuzzy inference system. The
outcome of the proposed method reveals that applying Neuro Fuzzy inference
systems could be a better approach for fusion of forgery detection tools.
Scalable Influence Maximization for Multiple Products in Continuous-Time Diffusion Networks
Authors: Nan Du, YIngyu Liang, Maria-Florina Balcan, Manuel Gomez-Rodriguez, Hongyuan Zha, Le Song
Date: 2016-12-08 00:00:00
Tasks: Marketing

A typical viral marketing model identifies influential users in a social
network to maximize a single product adoption assuming unlimited user
attention, campaign budgets, and time. In reality, multiple products need
campaigns, users have limited attention, convincing users incurs costs, and
advertisers have limited budgets and expect the adoptions to be maximized soon.
Facing these user, monetary, and timing constraints, we formulate the problem
as a submodular maximization task in a continuous-time diffusion model under
the intersection of a matroid and multiple knapsack constraints. We propose a
randomized algorithm estimating the user influence in a network
($|\mathcal{V}|$ nodes, $|\mathcal{E}|$ edges) to an accuracy of $\epsilon$
with $n=\mathcal{O}(1/\epsilon^2)$ randomizations and
$\tilde{\mathcal{O}}(n|\mathcal{E}|+n|\mathcal{V}|)$ computations. By
exploiting the influence estimation algorithm as a subroutine, we develop an
adaptive threshold greedy algorithm achieving an approximation factor $k_a/(2+2
k)$ of the optimal when $k_a$ out of the $k$ knapsack constraints are active.
Extensive experiments on networks of millions of nodes demonstrate that the
proposed algorithms achieve the state-of-the-art in terms of effectiveness and
scalability.
Rhythm Transcription of Polyphonic Piano Music Based on Merged-Output HMM for Multiple Voices
Authors: Eita Nakamura, Kazuyoshi Yoshii, Shigeki Sagayama
Date: 2017-01-29 00:00:00
Tasks: Rhythm, valid

In a recent conference paper, we have reported a rhythm transcription method
based on a merged-output hidden Markov model (HMM) that explicitly describes
the multiple-voice structure of polyphonic music. This model solves a major
problem of conventional methods that could not properly describe the nature of
multiple voices as in polyrhythmic scores or in the phenomenon of loose
synchrony between voices. In this paper we present a complete description of
the proposed model and develop an inference technique, which is valid for any
merged-output HMMs for which output probabilities depend on past events. We
also examine the influence of the architecture and parameters of the method in
terms of accuracies of rhythm transcription and voice separation and perform
comparative evaluations with six other algorithms. Using MIDI recordings of
classical piano pieces, we found that the proposed model outperformed other
methods by more than 12 points in the accuracy for polyrhythmic performances
and performed almost as good as the best one for non-polyrhythmic performances.
This reveals the state-of-the-art methods of rhythm transcription for the first
time in the literature. Publicly available source codes are also provided for
future comparisons.
Pooling Facial Segments to Face: The Shallow and Deep Ends
Authors: Upal Mahbub, Sayantan Sarkar, Rama Chellappa
Date: 2017-01-29 00:00:00
Tasks: Data Augmentation, Face Detection

Generic face detection algorithms do not perform very well in the mobile
domain due to significant presence of occluded and partially visible faces. One
promising technique to handle the challenge of partial faces is to design face
detectors based on facial segments. In this paper two such face detectors
namely, SegFace and DeepSegFace, are proposed that detect the presence of a
face given arbitrary combinations of certain face segments. Both methods use
proposals from facial segments as input that are found using weak boosted
classifiers. SegFace is a shallow and fast algorithm using traditional
features, tailored for situations where real time constraints must be
satisfied. On the other hand, DeepSegFace is a more powerful algorithm based on
a deep convolutional neutral network (DCNN) architecture. DeepSegFace offers
certain advantages over other DCNN-based face detectors as it requires
relatively little amount of data to train by utilizing a novel data
augmentation scheme and is very robust to occlusion by design. Extensive
experiments show the superiority of the proposed methods, specially
DeepSegFace, over other state-of-the-art face detectors in terms of
precision-recall and ROC curve on two mobile face datasets.
Extracting Bilingual Persian Italian Lexicon from Comparable Corpora Using Different Types of Seed Dictionaries
Authors: Ebrahim Ansari, M. H. Sadreddini, Lucio Grandinetti, Mahsa Radinmehr, Ziba Khosravan, Mehdi Sheikhalishahi
Date: 2017-01-29 00:00:00

Bilingual dictionaries are very important in various fields of natural language processing. In recent years, research on extracting new bilingual lexicons from non-parallel (comparable) corpora have been proposed. Almost all use a small existing dictionary or other resources to make an initial list called the "seed dictionary". In this paper, we discuss the use of different types of dictionaries as the initial starting list for creating a bilingual Persian-Italian lexicon from a comparable corpus. Our experiments apply state-of-the-art techniques on three different seed dictionaries; an existing dictionary, a dictionary created with pivot-based schema, and a dictionary extracted from a small Persian-Italian parallel text. The interesting challenge of our approach is to find a way to combine different dictionaries together in order to produce a better and more accurate lexicon. In order to combine seed dictionaries, we propose two different combination models and examine the effect of our novel combination models on various comparable corpora that have differing degrees of comparability. We conclude with a proposal for a new weighting system to improve the extracted lexicon. The experimental results produced by our implementation show the efficiency of our proposed models.
Using English as Pivot to Extract Persian-Italian Parallel Sentences from Non-Parallel Corpora
Authors: Ebrahim Ansari, M. H. Sadreddini, Mostafa Sheikhalishahi, Richard Wallace, Fatemeh Alimardani
Date: 2017-01-29 00:00:00
Tasks: Machine Translation, Sentence, Sentence Similarity, Translation

The effectiveness of a statistical machine translation system (SMT) is very
dependent upon the amount of parallel corpus used in the training phase. For
low-resource language pairs there are not enough parallel corpora to build an
accurate SMT. In this paper, a novel approach is presented to extract bilingual
Persian-Italian parallel sentences from a non-parallel (comparable) corpus. In
this study, English is used as the pivot language to compute the matching
scores between source and target sentences and candidate selection phase.
Additionally, a new monolingual sentence similarity metric, Normalized Google
Distance (NGD) is proposed to improve the matching process. Moreover, some
extensions of the baseline system are applied to improve the quality of
extracted sentences measured with BLEU. Experimental results show that using
the new pivot based extraction can increase the quality of bilingual corpus
significantly and consequently improves the performance of the Persian-Italian
SMT system.
On a spatial-temporal decomposition of the optical flow
Authors: Aniello Raffale Patrone, Otmar Scherzer
Date: 2015-05-13 00:00:00
Tasks: Motion Detection, Optical Flow Estimation

In this paper we present a decomposition algorithm for computation of the
spatial-temporal optical flow of a dynamic image sequence. We consider several
applications, such as the extraction of temporal motion features and motion
detection in dynamic sequences under varying illumination conditions, such as
they appear for instance in psychological flickering experiments. For the
numerical implementation we are solving an integro-differential equation by a
fixed point iteration. For comparison purposes we use a standard time dependent
optical flow algorithm, which in contrast to our method, constitutes in solving
a spatial-temporal differential equation.
The Opacity of Backbones
Authors: Lane A. Hemaspaandra, David E. Narváez
Date: 2016-06-11 00:00:00

This paper approaches, using structural complexity theory, the question of
whether there is a chasm between knowing an object exists and getting one's
hands on the object or its properties. In particular, we study the
nontransparency of so-called backbones. A backbone of a boolean formula $F$ is
a collection $S$ of its variables for which there is a unique partial
assignment $a_S$ such that $F[a_S]$ is satisfiable [MZK+99,WGS03]. We show
that, under the widely believed assumption that integer factoring is hard,
there exist sets of boolean formulas that have obvious, nontrivial backbones
yet finding the values, $a_S$, of those backbones is intractable. We also show
that, under the same assumption, there exist sets of boolean formulas that
obviously have large backbones yet producing such a backbone $S$ is
intractable. Furthermore, we show that if integer factoring is not merely
worst-case hard but is frequently hard, as is widely believed, then the
frequency of hardness in our two results is not too much less than that
frequency. These results hold more generally, namely, in the settings where,
respectively, one's assumption is that P $\neq$ NP $\cap$ coNP or that some
problem in NP $\cap$ coNP is frequently hard.
Deep Recurrent Neural Network for Protein Function Prediction from Sequence
Authors: Xueliang Liu
Date: 2017-01-28 00:00:00
Tasks: BIG-bench Machine Learning, Feature Engineering, Protein Function Prediction

As high-throughput biological sequencing becomes faster and cheaper, the need
to extract useful information from sequencing becomes ever more paramount,
often limited by low-throughput experimental characterizations. For proteins,
accurate prediction of their functions directly from their primary amino-acid
sequences has been a long standing challenge. Here, machine learning using
artificial recurrent neural networks (RNN) was applied towards classification
of protein function directly from primary sequence without sequence alignment,
heuristic scoring or feature engineering. The RNN models containing
long-short-term-memory (LSTM) units trained on public, annotated datasets from
UniProt achieved high performance for in-class prediction of four important
protein functions tested, particularly compared to other machine learning
algorithms using sequence-derived protein features. RNN models were used also
for out-of-class predictions of phylogenetically distinct protein families with
similar functions, including proteins of the CRISPR-associated nuclease,
ferritin-like iron storage and cytochrome P450 families. Applying the trained
RNN models on the partially unannotated UniRef100 database predicted not only
candidates validated by existing annotations but also currently unannotated
sequences. Some RNN predictions for the ferritin-like iron sequestering
function were experimentally validated, even though their sequences differ
significantly from known, characterized proteins and from each other and cannot
be easily predicted using popular bioinformatics methods. As sequencing and
experimental characterization data increases rapidly, the machine-learning
approach based on RNN could be useful for discovery and prediction of
homologues for a wide range of protein functions.
Efficient Rank Aggregation via Lehmer Codes
Authors: Pan Li, Arya Mazumdar, Olgica Milenkovic
Date: 2017-01-28 00:00:00

We propose a novel rank aggregation method based on converting permutations
into their corresponding Lehmer codes or other subdiagonal images. Lehmer
codes, also known as inversion vectors, are vector representations of
permutations in which each coordinate can take values not restricted by the
values of other coordinates. This transformation allows for decoupling of the
coordinates and for performing aggregation via simple scalar median or mode
computations. We present simulation results illustrating the performance of
this completely parallelizable approach and analytically prove that both the
mode and median aggregation procedure recover the correct centroid aggregate
with small sample complexity when the permutations are drawn according to the
well-known Mallows models. The proposed Lehmer code approach may also be used
on partial rankings, with similar performance guarantees.
Practical Reasoning with Norms for Autonomous Software Agents (Full Edition)
Authors: Zohreh Shams, Marina De Vos, Julian Padget, Wamberto W. Vasconcelos
Date: 2017-01-28 00:00:00

Autonomous software agents operating in dynamic environments need to
constantly reason about actions in pursuit of their goals, while taking into
consideration norms which might be imposed on those actions. Normative
practical reasoning supports agents making decisions about what is best for
them to (not) do in a given situation. What makes practical reasoning
challenging is the interplay between goals that agents are pursuing and the
norms that the agents are trying to uphold. We offer a formalisation to allow
agents to plan for multiple goals and norms in the presence of durative actions
that can be executed concurrently. We compare plans based on decision-theoretic
notions (i.e. utility) such that the utility gain of goals and utility loss of
norm violations are the basis for this comparison. The set of optimal plans
consists of plans that maximise the overall utility, each of which can be
chosen by the agent to execute. We provide an implementation of our proposal in
Answer Set Programming, thus allowing us to state the original problem in terms
of a logic program that can be queried for solutions with specific properties.
The implementation is proven to be sound and complete.
Multiclass MinMax Rank Aggregation
Authors: Pan Li, Olgica Milenkovic
Date: 2017-01-28 00:00:00

We introduce a new family of minmax rank aggregation problems under two
distance measures, the Kendall {\tau} and the Spearman footrule. As the
problems are NP-hard, we proceed to describe a number of constant-approximation
algorithms for solving them. We conclude with illustrative applications of the
aggregation methods on the Mallows model and genomic data.
Pure Rough Mereology and Counting
Authors: A. Mani
Date: 2017-01-28 00:00:00

The study of mereology (parts and wholes) in the context of formal approaches
to vagueness can be approached in a number of ways. In the context of rough
sets, mereological concepts with a set-theoretic or valuation based ontology
acquire complex and diverse behavior. In this research a general rough set
framework called granular operator spaces is extended and the nature of
parthood in it is explored from a minimally intrusive point of view. This is
used to develop counting strategies that help in classifying the framework. The
developed methodologies would be useful for drawing involved conclusions about
the nature of data (and validity of assumptions about it) from antichains
derived from context. The problem addressed is also about whether counting
procedures help in confirming that the approximations involved in formation of
data are indeed rough approximations?
Deep Local Video Feature for Action Recognition
Authors: Zhenzhong Lan, Yi Zhu, Alexander G. Hauptmann
Date: 2017-01-25 00:00:00
Tasks: Action Recognition, GPU, Temporal Action Localization

We investigate the problem of representing an entire video using CNN features
for human action recognition. Currently, limited by GPU memory, we have not
been able to feed a whole video into CNN/RNNs for end-to-end learning. A common
practice is to use sampled frames as inputs and video labels as supervision.
One major problem of this popular approach is that the local samples may not
contain the information indicated by global labels. To deal with this problem,
we propose to treat the deep networks trained on local inputs as local feature
extractors. After extracting local features, we aggregate them into global
features and train another mapping function on the same training data to map
the global features into global labels. We study a set of problems regarding
this new type of local features such as how to aggregate them into global
features. Experimental results on HMDB51 and UCF101 datasets show that, for
these new local features, a simple maximum pooling on the sparsely sampled
features lead to significant performance improvement.
Treelogy: A Novel Tree Classifier Utilizing Deep and Hand-crafted Representations
Authors: İlke Çuğu, Eren Şener, Çağrı Erciyes, Burak Balci, Emre Akın, Itır Önal, Ahmet Oğuz Akyüz
Date: 2017-01-28 00:00:00
Tasks: Classification, General Classification

We propose a novel tree classification system called Treelogy, that fuses
deep representations with hand-crafted features obtained from leaf images to
perform leaf-based plant classification. Key to this system are segmentation of
the leaf from an untextured background, using convolutional neural networks
(CNNs) for learning deep representations, extracting hand-crafted features with
a number of image processing techniques, training a linear SVM with feature
vectors, merging SVM and CNN results, and identifying the species from a
dataset of 57 trees. Our classification results show that fusion of deep
representations with hand-crafted features leads to the highest accuracy. The
proposed algorithm is embedded in a smart-phone application, which is publicly
available. Furthermore, our novel dataset comprised of 5408 leaf images is also
made public for use of other researchers.
Face Detection using Deep Learning: An Improved Faster RCNN Approach
Authors: Xudong Sun, Pengcheng Wu, Steven C. H. Hoi
Date: 2017-01-28 00:00:00
Tasks: Deep Learning, Face Detection

In this report, we present a new face detection scheme using deep learning
and achieve the state-of-the-art detection performance on the well-known FDDB
face detetion benchmark evaluation. In particular, we improve the
state-of-the-art faster RCNN framework by combining a number of strategies,
including feature concatenation, hard negative mining, multi-scale training,
model pretraining, and proper calibration of key parameters. As a consequence,
the proposed scheme obtained the state-of-the-art face detection performance,
making it the best model in terms of ROC curves among all the published methods
on the FDDB benchmark.
Detection, Segmentation and Recognition of Face and its Features Using Neural Network
Authors: Smriti Tikoo, Nitin Malik
Date: 2017-01-28 00:00:00
Tasks: Face Detection

Face detection and recognition has been prevalent with research scholars and
diverse approaches have been incorporated till date to serve purpose. The
rampant advent of biometric analysis systems, which may be full body scanners,
or iris detection and recognition systems and the finger print recognition
systems, and surveillance systems deployed for safety and security purposes
have contributed to inclination towards same. Advances has been made with
frontal view, lateral view of the face or using facial expressions such as
anger, happiness and gloominess, still images and video image to be used for
detection and recognition. This led to newer methods for face detection and
recognition to be introduced in achieving accurate results and economically
feasible and extremely secure. Techniques such as Principal Component analysis
(PCA), Independent component analysis (ICA), Linear Discriminant Analysis
(LDA), have been the predominant ones to be used. But with improvements needed
in the previous approaches Neural Networks based recognition was like boon to
the industry. It not only enhanced the recognition but also the efficiency of
the process. Choosing Backpropagation as the learning method was clearly out of
its efficiency to recognize nonlinear faces with an acceptance ratio of more
than 90% and execution time of only few seconds.
Detection of Face using Viola Jones and Recognition using Back Propagation Neural Network
Authors: Smriti Tikoo, Nitin Malik
Date: 2017-01-28 00:00:00

Detection and recognition of the facial images of people is an intricate
problem which has garnered much attention during recent years due to its ever
increasing applications in numerous fields. It continues to pose a challenge in
finding a robust solution to it. Its scope extends to catering the security,
commercial and law enforcement applications. Research for moreover a decade on
this subject has brought about remarkable development with the modus operandi
like human computer interaction, biometric analysis and content based coding of
images, videos and surveillance. A trivial task for brain but cumbersome to be
imitated artificially. The commonalities in faces does pose a problem on
various grounds but features such as skin color, gender differentiate a person
from the other. In this paper the facial detection has been carried out using
Viola-Jones algorithm and recognition of face has been done using Back
Propagation Neural Network (BPNN).
Entropic Causality and Greedy Minimum Entropy Coupling
Authors: Murat Kocaoglu, Alexandros G. Dimakis, Sriram Vishwanath, Babak Hassibi
Date: 2017-01-28 00:00:00

We study the problem of identifying the causal relationship between two
discrete random variables from observational data. We recently proposed a novel
framework called entropic causality that works in a very general functional
model but makes the assumption that the unobserved exogenous variable has small
entropy in the true causal direction.
  This framework requires the solution of a minimum entropy coupling problem:
Given marginal distributions of m discrete random variables, each on n states,
find the joint distribution with minimum entropy, that respects the given
marginals. This corresponds to minimizing a concave function of nm variables
over a convex polytope defined by nm linear constraints, called a
transportation polytope. Unfortunately, it was recently shown that this minimum
entropy coupling problem is NP-hard, even for 2 variables with n states. Even
representing points (joint distributions) over this space can require
exponential complexity (in n, m) if done naively.
  In our recent work we introduced an efficient greedy algorithm to find an
approximate solution for this problem. In this paper we analyze this algorithm
and establish two results: that our algorithm always finds a local minimum and
also is within an additive approximation error from the unknown global optimum.