pwc-archive/papers-with-abstracts huggingface dataset:
SimpleQuestions Nearly Solved: A New Upperbound and Baseline Approach
Authors: Michael Petrochuk, Luke Zettlemoyer
Date: 2018-04-24 00:00:00
Proceeding: EMNLP 2018 10

The SimpleQuestions dataset is one of the most commonly used benchmarks for
studying single-relation factoid questions. In this paper, we present new
evidence that this benchmark can be nearly solved by standard methods. First we
show that ambiguity in the data bounds performance on this benchmark at 83.4%;
there are often multiple answers that cannot be disambiguated from the
linguistic signal alone. Second we introduce a baseline that sets a new
state-of-the-art performance level at 78.1% accuracy, despite using standard
methods. Finally, we report an empirical analysis showing that the upperbound
is loose; roughly a third of the remaining errors are also not resolvable from
the linguistic signal. Together, these results suggest that the SimpleQuestions
dataset is nearly solved.
Collective Entity Disambiguation with Structured Gradient Tree Boosting
Authors: Yi Yang, Ozan .Irsoy, Kazi Shefaet Rahman
Date: 2018-02-28 00:00:00
Proceeding: NAACL 2018 6
Tasks: Entity Disambiguation

We present a gradient-tree-boosting-based structured learning model for
jointly disambiguating named entities in a document. Gradient tree boosting is
a widely used machine learning algorithm that underlies many top-performing
natural language processing systems. Surprisingly, most works limit the use of
gradient tree boosting as a tool for regular classification or regression
problems, despite the structured nature of language. To the best of our
knowledge, our work is the first one that employs the structured gradient tree
boosting (SGTB) algorithm for collective entity disambiguation. By defining
global features over previous disambiguation decisions and jointly modeling
them with local features, our system is able to produce globally optimized
entity assignments for mentions in a document. Exact inference is prohibitively
expensive for our globally normalized model. To solve this problem, we propose
Bidirectional Beam Search with Gold path (BiBSG), an approximate inference
algorithm that is a variant of the standard beam search algorithm. BiBSG makes
use of global information from both past and future to perform better local
search. Experiments on standard benchmark datasets show that SGTB significantly
improves upon published results. Specifically, SGTB outperforms the previous
state-of-the-art neural system by near 1\% absolute accuracy on the popular
AIDA-CoNLL dataset.
Block-Structure Based Time-Series Models For Graph Sequences
Authors: Mehrnaz Amjadi, Theja Tulabandhula
Date: 2018-04-24 00:00:00
Tasks: Community Detection, Time Series, Time Series Analysis

Although the computational and statistical trade-off for modeling single
graphs, for instance, using block models is relatively well understood,
extending such results to sequences of graphs has proven to be difficult. In
this work, we take a step in this direction by proposing two models for graph
sequences that capture: (a) link persistence between nodes across time, and (b)
community persistence of each node across time. In the first model, we assume
that the latent community of each node does not change over time, and in the
second model we relax this assumption suitably. For both of these proposed
models, we provide statistically and computationally efficient inference
algorithms, whose unique feature is that they leverage community detection
methods that work on single graphs. We also provide experimental results
validating the suitability of our models and methods on synthetic and real
instances.
Existence versus Exploitation: The Opacity of Backbones and Backdoors Under a Weak Assumption
Authors: Lane A. Hemaspaandra, David E. Narváez
Date: 2017-06-14 00:00:00

Backdoors and backbones of Boolean formulas are hidden structural properties.
A natural goal, already in part realized, is that solver algorithms seek to
obtain substantially better performance by exploiting these structures.
  However, the present paper is not intended to improve the performance of SAT
solvers, but rather is a cautionary paper. In particular, the theme of this
paper is that there is a potential chasm between the existence of such
structures in the Boolean formula and being able to effectively exploit them.
This does not mean that these structures are not useful to solvers. It does
mean that one must be very careful not to assume that it is computationally
easy to go from the existence of a structure to being able to get one's hands
on it and/or being able to exploit the structure.
  For example, in this paper we show that, under the assumption that P $\neq$
NP, there are easily recognizable families of Boolean formulas with strong
backdoors that are easy to find, yet for which it is hard (in fact,
NP-complete) to determine whether the formulas are satisfiable. We also show
that, also under the assumption P $\neq$ NP, there are easily recognizable sets
of Boolean formulas for which it is hard (in fact, NP-complete) to determine
whether they have a large backbone.
Solving Inverse Computational Imaging Problems using Deep Pixel-level Prior
Authors: Akshat Dave, Anil Kumar Vadathya, Ramana Subramanyam, Rahul Baburajan, Kaushik Mitra
Date: 2018-02-27 00:00:00

Signal reconstruction is a challenging aspect of computational imaging as it
often involves solving ill-posed inverse problems. Recently, deep feed-forward
neural networks have led to state-of-the-art results in solving various inverse
imaging problems. However, being task specific, these networks have to be
learned for each inverse problem. On the other hand, a more flexible approach
would be to learn a deep generative model once and then use it as a signal
prior for solving various inverse problems. We show that among the various
state of the art deep generative models, autoregressive models are especially
suitable for our purpose for the following reasons. First, they explicitly
model the pixel level dependencies and hence are capable of reconstructing
low-level details such as texture patterns and edges better. Second, they
provide an explicit expression for the image prior which can then be used for
MAP based inference along with the forward model. Third, they can model long
range dependencies in images which make them ideal for handling global
multiplexing as encountered in various compressive imaging systems. We
demonstrate the efficacy of our proposed approach in solving three
computational imaging problems: Single Pixel Camera (SPC), LiSens and FlatCam.
For both real and simulated cases, we obtain better reconstructions than the
state-of-the-art methods in terms of perceptual and quantitative metrics.
Asymmetric Clusters and Outliers: Mixtures of Multivariate Contaminated Shifted Asymmetric Laplace Distributions
Authors: Katherine Morris, Antonio Punzo, Paul D. McNicholas, Ryan P. Browne
Date: 2014-02-26 00:00:00
Tasks: parameter estimation

Mixtures of multivariate contaminated shifted asymmetric Laplace
distributions are developed for handling asymmetric clusters in the presence of
outliers (also referred to as bad points herein). In addition to the parameters
of the related non-contaminated mixture, for each (asymmetric) cluster, our
model has one parameter controlling the proportion of outliers and one
specifying the degree of contamination. Crucially, these parameters do not have
to be specified a priori, adding a flexibility to our approach that is absent
from other approaches such as trimming. Moreover, each observation is given a
posterior probability of belonging to a particular cluster, and of being an
outlier or not; advantageously, this allows for the automatic detection of
outliers. An expectation-conditional maximization algorithm is outlined for
parameter estimation and various implementation issues are discussed. The
behaviour of the proposed model is investigated, and compared with
well-established finite mixtures, on artificial and real data.
Assessing Language Proficiency from Eye Movements in Reading
Authors: Yevgeni Berzak, Boris Katz, Roger Levy
Date: 2018-04-19 00:00:00
Proceeding: NAACL 2018 6

We present a novel approach for determining learners' second language
proficiency which utilizes behavioral traces of eye movements during reading.
Our approach provides stand-alone eyetracking based English proficiency scores
which reflect the extent to which the learner's gaze patterns in reading are
similar to those of native English speakers. We show that our scores correlate
strongly with standardized English proficiency tests. We also demonstrate that
gaze information can be used to accurately predict the outcomes of such tests.
Our approach yields the strongest performance when the test taker is presented
with a suite of sentences for which we have eyetracking data from other
readers. However, it remains effective even using eyetracking with sentences
for which eye movement data have not been previously collected. By deriving
proficiency as an automatic byproduct of eye movements during ordinary reading,
our approach offers a potentially valuable new tool for second language
proficiency assessment. More broadly, our results open the door to future
methods for inferring reader characteristics from the behavioral traces of
reading.
Face Recognition: Primates in the Wild
Authors: Debayan Deb, Susan Wiper, Alexandra Russo, Sixue Gong, Yichun Shi, Cori Tymoszek, Anil Jain
Date: 2018-04-24 00:00:00
Tasks: All, Face Recognition

We present a new method of primate face recognition, and evaluate this method
on several endangered primates, including golden monkeys, lemurs, and
chimpanzees. The three datasets contain a total of 11,637 images of 280
individual primates from 14 species. Primate face recognition performance is
evaluated using two existing state-of-the-art open-source systems, (i) FaceNet
and (ii) SphereFace, (iii) a lemur face recognition system from literature, and
(iv) our new convolutional neural network (CNN) architecture called PrimNet.
Three recognition scenarios are considered: verification (1:1 comparison), and
both open-set and closed-set identification (1:N search). We demonstrate that
PrimNet outperforms all of the other systems in all three scenarios for all
primate species tested. Finally, we implement an Android application of this
recognition system to assist primate researchers and conservationists in the
wild for individual recognition of primates.
Towards an Unsupervised Entrainment Distance in Conversational Speech using Deep Neural Networks
Authors: Md Nasir, Brian Baucom, Shrikanth Narayanan, Panayiotis Georgiou
Date: 2018-04-23 00:00:00

Entrainment is a known adaptation mechanism that causes interaction
participants to adapt or synchronize their acoustic characteristics.
Understanding how interlocutors tend to adapt to each other's speaking style
through entrainment involves measuring a range of acoustic features and
comparing those via multiple signal comparison methods. In this work, we
present a turn-level distance measure obtained in an unsupervised manner using
a Deep Neural Network (DNN) model, which we call Neural Entrainment Distance
(NED). This metric establishes a framework that learns an embedding from the
population-wide entrainment in an unlabeled training corpus. We use the
framework for a set of acoustic features and validate the measure
experimentally by showing its efficacy in distinguishing real conversations
from fake ones created by randomly shuffling speaker turns. Moreover, we show
real world evidence of the validity of the proposed measure. We find that high
value of NED is associated with high ratings of emotional bond in suicide
assessment interviews, which is consistent with prior studies.
Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers
Authors: Ishai Rosenberg, Asaf Shabtai, Yuval Elovici, Lior Rokach
Date: 2018-04-23 00:00:00

In this paper, we present a generic, query-efficient black-box attack against API call-based machine learning malware classifiers. We generate adversarial examples by modifying the malware's API call sequences and non-sequential features (printable strings), and these adversarial examples will be misclassified by the target malware classifier without affecting the malware's functionality. In contrast to previous studies, our attack minimizes the number of malware classifier queries required. In addition, in our attack, the attacker must only know the class predicted by the malware classifier; attacker knowledge of the malware classifier's confidence score is optional. We evaluate the attack effectiveness when attacks are performed against a variety of malware classifier architectures, including recurrent neural network (RNN) variants, deep neural networks, support vector machines, and gradient boosted decision trees. Our attack success rate is around 98% when the classifier's confidence score is known and 64% when just the classifier's predicted class is known. We implement four state-of-the-art query-efficient attacks and show that our attack requires fewer queries and less knowledge about the attacked model's architecture than other existing query-efficient attacks, making it practical for attacking cloud-based malware classifiers at a minimal cost.
Neural-Brane: Neural Bayesian Personalized Ranking for Attributed Network Embedding
Authors: Vachik S. Dave, Baichuan Zhang, Pin-Yu Chen, Mohammad Al Hasan
Date: 2018-04-23 00:00:00
Tasks: Clustering, Community Detection, General Classification, Link Prediction, Network Embedding, Node Classification

Network embedding methodologies, which learn a distributed vector
representation for each vertex in a network, have attracted considerable
interest in recent years. Existing works have demonstrated that vertex
representation learned through an embedding method provides superior
performance in many real-world applications, such as node classification, link
prediction, and community detection. However, most of the existing methods for
network embedding only utilize topological information of a vertex, ignoring a
rich set of nodal attributes (such as, user profiles of an online social
network, or textual contents of a citation network), which is abundant in all
real-life networks. A joint network embedding that takes into account both
attributional and relational information entails a complete network information
and could further enrich the learned vector representations. In this work, we
present Neural-Brane, a novel Neural Bayesian Personalized Ranking based
Attributed Network Embedding. For a given network, Neural-Brane extracts latent
feature representation of its vertices using a designed neural network model
that unifies network topological information and nodal attributes; Besides, it
utilizes Bayesian personalized ranking objective, which exploits the proximity
ordering between a similar node-pair and a dissimilar node-pair. We evaluate
the quality of vertex embedding produced by Neural-Brane by solving the node
classification and clustering tasks on four real-world datasets. Experimental
results demonstrate the superiority of our proposed method over the
state-of-the-art existing methods.
A Call for Clarity in Reporting BLEU Scores
Authors: Matt Post
Date: 2018-04-23 00:00:00
Proceeding: WS 2018 10
Tasks: Machine Translation, Translation

The field of machine translation faces an under-recognized problem because of
inconsistency in the reporting of scores from its dominant metric. Although
people refer to "the" BLEU score, BLEU is in fact a parameterized metric whose
values can vary wildly with changes to these parameters. These parameters are
often not reported or are hard to find, and consequently, BLEU scores between
papers cannot be directly compared. I quantify this variation, finding
differences as high as 1.8 between commonly used configurations. The main
culprit is different tokenization and normalization schemes applied to the
reference. Pointing to the success of the parsing community, I suggest machine
translation researchers settle upon the BLEU scheme used by the annual
Conference on Machine Translation (WMT), which does not allow for user-supplied
reference processing, and provide a new tool, SacreBLEU, to facilitate this.
Analyzing and Characterizing User Intent in Information-seeking Conversations
Authors: Chen Qu, Liu Yang, W. Bruce Croft, Johanne R. Trippas, Yongfeng Zhang, Minghui Qiu
Date: 2018-04-23 00:00:00
Tasks: Conversational Search, Question Answering

Understanding and characterizing how people interact in information-seeking
conversations is crucial in developing conversational search systems. In this
paper, we introduce a new dataset designed for this purpose and use it to
analyze information-seeking conversations by user intent distribution,
co-occurrence, and flow patterns. The MSDialog dataset is a labeled dialog
dataset of question answering (QA) interactions between information seekers and
providers from an online forum on Microsoft products. The dataset contains more
than 2,000 multi-turn QA dialogs with 10,000 utterances that are annotated with
user intent on the utterance level. Annotations were done using crowdsourcing.
With MSDialog, we find some highly recurring patterns in user intent during an
information-seeking process. They could be useful for designing conversational
search systems. We will make our dataset freely available to encourage
exploration of information-seeking conversation models.
Siamese Generative Adversarial Privatizer for Biometric Data
Authors: Witold Oleszkiewicz, Peter Kairouz, Karol Piczak, Ram Rajagopal, Tomasz Trzcinski
Date: 2018-04-23 00:00:00
Tasks: Emotion Recognition

State-of-the-art machine learning algorithms can be fooled by carefully
crafted adversarial examples. As such, adversarial examples present a concrete
problem in AI safety. In this work we turn the tables and ask the following
question: can we harness the power of adversarial examples to prevent malicious
adversaries from learning identifying information from data while allowing
non-malicious entities to benefit from the utility of the same data? For
instance, can we use adversarial examples to anonymize biometric dataset of
faces while retaining usefulness of this data for other purposes, such as
emotion recognition? To address this question, we propose a simple yet
effective method, called Siamese Generative Adversarial Privatizer (SGAP), that
exploits the properties of a Siamese neural network to find discriminative
features that convey identifying information. When coupled with a generative
model, our approach is able to correctly locate and disguise identifying
information, while minimally reducing the utility of the privatized dataset.
Extensive evaluation on a biometric dataset of fingerprints and cartoon faces
confirms usefulness of our simple yet effective method.
Detecting Syntactic Features of Translated Chinese
Authors: Hai Hu, Wen Li, Sandra Kübler
Date: 2018-04-23 00:00:00
Proceeding: WS 2018 6
Tasks: Translation

We present a machine learning approach to distinguish texts translated to
Chinese (by humans) from texts originally written in Chinese, with a focus on a
wide range of syntactic features. Using Support Vector Machines (SVMs) as
classifier on a genre-balanced corpus in translation studies of Chinese, we
find that constituent parse trees and dependency triples as features without
lexical information perform very well on the task, with an F-measure above 90%,
close to the results of lexical n-gram features, without the risk of learning
topic information rather than translation features. Thus, we claim syntactic
features alone can accurately distinguish translated from original Chinese.
Translated Chinese exhibits an increased use of determiners, subject position
pronouns, NP + 'de' as NP modifiers, multiple NPs or VPs conjoined by a Chinese
specific punctuation, among other structures. We also interpret the syntactic
features with reference to previous translation studies in Chinese,
particularly the usage of pronouns.
A machine learning model for identifying cyclic alternating patterns in the sleeping brain
Authors: Aditya Chindhade, Abhijeet Alshi, Aakash Bhatia, Kedar Dabhadkar, Pranav Sivadas Menon
Date: 2018-04-23 00:00:00
Tasks: BIG-bench Machine Learning, EEG, Electroencephalogram (EEG), Feature Engineering

Electroencephalography (EEG) is a method to record the electrical signals in
the brain. Recognizing the EEG patterns in the sleeping brain gives insights
into the understanding of sleeping disorders. The dataset under consideration
contains EEG data points associated with various physiological conditions. This
study attempts to generalize the detection of particular patterns associated
with the Non-Rapid Eye Movement (NREM) sleep cycle of the brain using a machine
learning model. The proposed model uses additional feature engineering to
incorporate sequential information for training a classifier to predict the
occurrence of Cyclic Alternating Pattern (CAP) sequences in the sleep cycle,
which are often associated with sleep disorders.
Can Eye Movement Data Be Used As Ground Truth For Word Embeddings Evaluation?
Authors: Amir Bakarov
Date: 2018-04-23 00:00:00
Tasks: Embeddings Evaluation, Sentence, Word Embeddings, Word Similarity

In recent years a certain success in the task of modeling lexical semantics
was obtained with distributional semantic models. Nevertheless, the scientific
community is still unaware what is the most reliable evaluation method for
these models. Some researchers argue that the only possible gold standard could
be obtained from neuro-cognitive resources that store information about human
cognition. One of such resources is eye movement data on silent reading. The
goal of this work is to test the hypothesis of whether such data could be used
to evaluate distributional semantic models on different languages. We propose
experiments with English and Russian eye movement datasets (Provo Corpus, GECO
and Russian Sentence Corpus), word vectors (Skip-Gram models trained on
national corpora and Web corpora) and word similarity datasets of Russian and
English assessed by humans in order to find the existence of correlation
between embeddings and eye movement data and test the hypothesis that this
correlation is language independent. As a result, we found that the validity of
the hypothesis being tested could be questioned.
Discovery of Driving Patterns by Trajectory Segmentation
Authors: Sobhan Moosavi, Arnab Nandi, Rajiv Ramnath
Date: 2018-04-23 00:00:00
Tasks: Management, Segmentation

Telematics data is becoming increasingly available due to the ubiquity of devices that collect data during drives, for different purposes, such as usage based insurance (UBI), fleet management, navigation of connected vehicles, etc. Consequently, a variety of data-analytic applications have become feasible that extract valuable insights from the data. In this paper, we address the especially challenging problem of discovering behavior-based driving patterns from only externally observable phenomena (e.g. vehicle's speed). We present a trajectory segmentation approach capable of discovering driving patterns as separate segments, based on the behavior of drivers. This segmentation approach includes a novel transformation of trajectories along with a dynamic programming approach for segmentation. We apply the segmentation approach on a real-word, rich dataset of personal car trajectories provided by a major insurance company based in Columbus, Ohio. Analysis and preliminary results show the applicability of approach for finding significant driving patterns.