pwc-archive/papers-with-abstracts huggingface dataset:
A Comparative Analysis of Android Malware
Authors: Neeraj Chavan, Fabio Di Troia, Mark Stamp
Date: 2019-01-21 00:00:00
Tasks: Binary Classification, Classification, Feature Engineering, General Classification

In this paper, we present a comparative analysis of benign and malicious
Android applications, based on static features. In particular, we focus our
attention on the permissions requested by an application. We consider both
binary classification of malware versus benign, as well as the multiclass
problem, where we classify malware samples into their respective families. Our
experiments are based on substantial malware datasets and we employ a wide
variety of machine learning techniques, including decision trees and random
forests, support vector machines, logistic model trees, AdaBoost, and
artificial neural networks. We find that permissions are a strong feature and
that by careful feature engineering, we can significantly reduce the number of
features needed for highly accurate detection and classification.
Using Deep Neural Network for Android Malware Detection
Authors: Abdelmonim Naway, Yuancheng LI
Date: 2019-01-16 00:00:00
Tasks: Android Malware Detection, Malware Detection

The pervasiveness of the Android operating system, with the availability of
applications almost for everything, is readily accessible in the official
Google play store or a dozen alternative third-party markets. Additionally, the
vital role of smartphones in modern life leads to store significant information
on devices, not only personal information but also corporate information, which
attract malware developers to develop applications that can infiltrate user's
devices to steal information and perform harmful tasks. This accompanied with
the limitation of currently defenses techniques such as ineffective screening
in Google play store, weak or no screening in third-party markets. Antiviruses
software that still relies on a signature-based database that is effective only
in identifying known malware. To contrive with malicious applications that are
increased in volume and sophistication, we propose an Android malware detection
system that applies deep learning technique to face the threats of Android
malware. Extensive experiments on a real-world dataset contain benign and
malicious applications uncovered that the proposed system reaches an accuracy
of 95.31%.
Defending via strategic ML selection
Authors: Eitan Farchi, Onn Shehory, Guy Barash
Date: 2019-01-16 00:00:00

The results of a learning process depend on the input data. There are cases
in which an adversary can strategically tamper with the input data to affect
the outcome of the learning process. While some datasets are difficult to
attack, many others are susceptible to manipulation. A resourceful attacker can
tamper with large portions of the dataset and affect them. An attacker can
additionally strategically focus on a preferred subset of the attributes in the
dataset to maximize the effectiveness of the attack and minimize the resources
allocated to data manipulation. In light of this vulnerability, we introduce a
solution according to which the defender implements an array of learners, and
their activation is performed strategically. The defender computes the (game
theoretic) strategy space and accordingly applies a dominant strategy where
possible, and a Nash-stable strategy otherwise. In this paper we provide the
details of this approach. We analyze Nash equilibrium in such a strategic
learning environment, and demonstrate our solution by specific examples.
Neural Speed Reading with Structural-Jump-LSTM
Authors: Christian Hansen, Casper Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Date: 2019-03-20 00:00:00
Proceeding: ICLR 2019 5
Tasks: Sentence

Recurrent neural networks (RNNs) can model natural language by sequentially
'reading' input tokens and outputting a distributed representation of each
token. Due to the sequential nature of RNNs, inference time is linearly
dependent on the input length, and all inputs are read regardless of their
importance. Efforts to speed up this inference, known as 'neural speed
reading', either ignore or skim over part of the input. We present
Structural-Jump-LSTM: the first neural speed reading model to both skip and
jump text during inference. The model consists of a standard LSTM and two
agents: one capable of skipping single words when reading, and one capable of
exploiting punctuation structure (sub-sentence separators (,:), sentence end
symbols (.!?), or end of text markers) to jump ahead after reading a word. A
comprehensive experimental evaluation of our model against all five
state-of-the-art neural reading models shows that Structural-Jump-LSTM achieves
the best overall floating point operations (FLOP) reduction (hence is faster),
while keeping the same accuracy or even improving it compared to a vanilla LSTM
that reads the whole text.
A Survey of Code-switched Speech and Language Processing
Authors: Sunayana Sitaram, Khyathi Raghavi Chandu, Sai Krishna Rallabandi, Alan W. black
Date: 2019-03-25 00:00:00
Tasks: Survey

Code-switching, the alternation of languages within a conversation or utterance, is a common communicative phenomenon that occurs in multilingual communities across the world. This survey reviews computational approaches for code-switched Speech and Natural Language Processing. We motivate why processing code-switched text and speech is essential for building intelligent agents and systems that interact with users in multilingual communities. As code-switching data and resources are scarce, we list what is available in various code-switched language pairs with the language processing tasks they can be used for. We review code-switching research in various Speech and NLP applications, including language processing tools and end-to-end systems. We conclude with future directions and open problems in the field.
Neural Abstractive Text Summarization and Fake News Detection
Authors: Soheil Esmaeilzadeh, Gao Xian Peh, Angela Xu
Date: 2019-03-24 00:00:00
Tasks: Abstractive Text Summarization, Articles, Classification, Decoder, Fake News Detection, General Classification, Text Summarization

In this work, we study abstractive text summarization by exploring different models such as LSTM-encoder-decoder with attention, pointer-generator networks, coverage mechanisms, and transformers. Upon extensive and careful hyperparameter tuning we compare the proposed architectures against each other for the abstractive text summarization task. Finally, as an extension of our work, we apply our text summarization model as a feature extractor for a fake news detection task where the news articles prior to classification will be summarized and the results are compared against the classification using only the original news text. keywords: LSTM, encoder-deconder, abstractive text summarization, pointer-generator, coverage mechanism, transformers, fake news detection
A Convolutional Neural Network for Language-Agnostic Source Code Summarization
Authors: Jessica Moore, Ben Gelman, David Slater
Date: 2019-03-29 00:00:00
Tasks: Code Summarization, Decoder, Descriptive, Source Code Summarization

Descriptive comments play a crucial role in the software engineering process.
They decrease development time, enable better bug detection, and facilitate the
reuse of previously written code. However, comments are commonly the last of a
software developer's priorities and are thus either insufficient or missing
entirely. Automatic source code summarization may therefore have the ability to
significantly improve the software development process. We introduce a novel
encoder-decoder model that summarizes source code, effectively writing a
comment to describe the code's functionality. We make two primary innovations
beyond current source code summarization models. First, our encoder is fully
language-agnostic and requires no complex input preprocessing. Second, our
decoder has an open vocabulary, enabling it to predict any word, even ones not
seen in training. We demonstrate results comparable to state-of-the-art methods
on a single-language data set and provide the first results on a data set
consisting of multiple programming languages.
An Atomistic Machine Learning Package for Surface Science and Catalysis
Authors: Martin Hangaard Hansen, José A. Garrido Torres, Paul C. Jennings, ZiYun Wang, Jacob R. Boes, Osman G. Mamun, Thomas Bligaard
Date: 2019-04-01 00:00:00
Tasks: Active Learning, BIG-bench Machine Learning

We present work flows and a software module for machine learning model
building in surface science and heterogeneous catalysis. This includes
fingerprinting atomic structures from 3D structure and/or connectivity
information, it includes descriptor selection methods and benchmarks, and it
includes active learning frameworks for atomic structure optimization,
acceleration of screening studies and for exploration of the structure space of
nano particles, which are all atomic structure problems relevant for surface
science and heterogeneous catalysis. Our overall goal is to provide a
repository to ease machine learning model building for catalysis, to advance
the models beyond the chemical intuition of the user and to increase autonomy
for exploration of chemical space.
Distributed Metropolis Sampler with Optimal Parallelism
Authors: Weiming Feng, Thomas P. Hayes, Yitong Yin
Date: 2019-04-01 00:00:00

The Metropolis-Hastings algorithm is a fundamental Markov chain Monte Carlo (MCMC) method for sampling and inference. With the advent of Big Data, distributed and parallel variants of MCMC methods are attracting increased attention. In this paper, we give a distributed algorithm that can correctly simulate sequential single-site Metropolis chains without any bias in a fully asynchronous message-passing model. Furthermore, if a natural Lipschitz condition is satisfied by the Metropolis filters, our algorithm can simulate $N$-step Metropolis chains within $O(N/n+\log n)$ rounds of asynchronous communications, where $n$ is the number of variables. For sequential single-site dynamics, whose mixing requires $\Omega(n\log n)$ steps, this achieves an optimal linear speedup. For several well-studied important graphical models, including proper graph coloring, hardcore model, and Ising model, our condition for linear speedup is weaker than the respective uniqueness (mixing) conditions. The novel idea in our algorithm is to resolve updates in advance: the local Metropolis filters can often be executed correctly before the full information about neighboring spins is available. This achieves optimal parallelism without introducing any bias.
Sentiment analysis with genetically evolved Gaussian kernels
Authors: Ibai Roman, Alexander Mendiburu, Roberto Santana, Jose A. Lozano
Date: 2019-04-01 00:00:00
Tasks: Gaussian Processes, Sentiment Analysis

Sentiment analysis consists of evaluating opinions or statements from the analysis of text. Among the methods used to estimate the degree in which a text expresses a given sentiment, are those based on Gaussian Processes. However, traditional Gaussian Processes methods use a predefined kernel with hyperparameters that can be tuned but whose structure can not be adapted. In this paper, we propose the application of Genetic Programming for evolving Gaussian Process kernels that are more precise for sentiment analysis. We use use a very flexible representation of kernels combined with a multi-objective approach that simultaneously considers two quality metrics and the computational time spent by the kernels. Our results show that the algorithm can outperform Gaussian Processes with traditional kernels for some of the sentiment analysis tasks considered.
Structured Minimally Supervised Learning for Neural Relation Extraction
Authors: Fan Bai, Alan Ritter
Date: 2019-03-29 00:00:00
Proceeding: NAACL 2019 6
Tasks: Relation, Relation Extraction, Sentence

We present an approach to minimally supervised relation extraction that combines the benefits of learned representations and structured learning, and accurately predicts sentence-level relation mentions given only proposition-level supervision from a KB. By explicitly reasoning about missing data during learning, our approach enables large-scale training of 1D convolutional neural networks while mitigating the issue of label noise inherent in distant supervision. Our approach achieves state-of-the-art results on minimally supervised sentential relation extraction, outperforming a number of baselines, including a competitive approach that uses the attention layer of a purely neural model.
Linguistic generalization and compositionality in modern artificial neural networks
Authors: Marco Baroni
Date: 2019-03-30 00:00:00
Tasks: Systematic Generalization

In the last decade, deep artificial neural networks have achieved astounding performance in many natural language processing tasks. Given the high productivity of language, these models must possess effective generalization abilities. It is widely assumed that humans handle linguistic productivity by means of algebraic compositional rules: Are deep networks similarly compositional? After reviewing the main innovations characterizing current deep language processing networks, I discuss a set of studies suggesting that deep networks are capable of subtle grammar-dependent generalizations, but also that they do not rely on systematic compositional rules. I argue that the intriguing behaviour of these devices (still awaiting a full understanding) should be of interest to linguists and cognitive scientists, as it offers a new perspective on possible computational strategies to deal with linguistic productivity beyond rule-based compositionality, and it might lead to new insights into the less systematic generalization patterns that also appear in natural language.
Modeling Drug-Disease Relations with Linguistic and Knowledge Graph Constraints
Authors: Bruno Godefroy, Christopher Potts
Date: 2019-03-31 00:00:00
Tasks: Knowledge Graphs

FDA drug labels are rich sources of information about drugs and drug-disease
relations, but their complexity makes them challenging texts to analyze in
isolation. To overcome this, we situate these labels in two health knowledge
graphs: one built from precise structured information about drugs and diseases,
and another built entirely from a database of clinical narrative texts using
simple heuristic methods. We show that Probabilistic Soft Logic models defined
over these graphs are superior to text-only and relation-only variants, and
that the clinical narratives graph delivers exceptional results with little
manual effort. Finally, we release a new dataset of drug labels with
annotations for five distinct drug-disease relations.
SART - Similarity, Analogies, and Relatedness for Tatar Language: New Benchmark Datasets for Word Embeddings Evaluation
Authors: Albina Khusainova, Adil Khan, Adín Ramírez Rivera
Date: 2019-03-31 00:00:00
Tasks: Embeddings Evaluation, Language Modeling, Language Modelling, Word Embeddings

There is a huge imbalance between languages currently spoken and
corresponding resources to study them. Most of the attention naturally goes to
the "big" languages: those which have the largest presence in terms of media
and number of speakers. Other less represented languages sometimes do not even
have a good quality corpus to study them. In this paper, we tackle this
imbalance by presenting a new set of evaluation resources for Tatar, a language
of the Turkic language family which is mainly spoken in Tatarstan Republic,
Russia.
  We present three datasets: Similarity and Relatedness datasets that consist
of human scored word pairs and can be used to evaluate semantic models; and
Analogies dataset that comprises analogy questions and allows to explore
semantic, syntactic, and morphological aspects of language modeling. All three
datasets build upon existing datasets for the English language and follow the
same structure. However, they are not mere translations. They take into account
specifics of the Tatar language and expand beyond the original datasets. We
evaluate state-of-the-art word embedding models for two languages using our
proposed datasets for Tatar and the original datasets for English and report
our findings on performance comparison.
Using Similarity Measures to Select Pretraining Data for NER
Authors: Xiang Dai, Sarvnaz Karimi, Ben Hachey, Cecile Paris
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Word vectors and Language Models (LMs) pretrained on a large amount of unlabelled data can dramatically improve various Natural Language Processing (NLP) tasks. However, the measure and impact of similarity between pretraining data and target task data are left to intuition. We propose three cost-effective measures to quantify different aspects of similarity between source pretraining and target task data. We demonstrate that these measures are good predictors of the usefulness of pretrained models for Named Entity Recognition (NER) over 30 data pairs. Results also suggest that pretrained LMs are more effective and more predictable than pretrained word vectors, but pretrained word vectors are better when pretraining data is dissimilar.
Discontinuous Constituency Parsing with a Stack-Free Transition System and a Dynamic Oracle
Authors: Maximin Coavoux, Shay B. Cohen
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Constituency Parsing, Sentence

We introduce a novel transition system for discontinuous constituency
parsing. Instead of storing subtrees in a stack --i.e. a data structure with
linear-time sequential access-- the proposed system uses a set of parsing
items, with constant-time random access. This change makes it possible to
construct any discontinuous constituency tree in exactly $4n - 2$ transitions
for a sentence of length $n$. At each parsing step, the parser considers every
item in the set to be combined with a focus item and to construct a new
constituent in a bottom-up fashion. The parsing strategy is based on the
assumption that most syntactic structures can be parsed incrementally and that
the set --the memory of the parser-- remains reasonably small on average.
Moreover, we introduce a provably correct dynamic oracle for the new transition
system, and present the first experiments in discontinuous constituency parsing
using a dynamic oracle. Our parser obtains state-of-the-art results on three
English and German discontinuous treebanks.
Multimodal Machine Translation with Embedding Prediction
Authors: Tosho Hirasawa, Hayahide Yamagishi, Yukio Matsumura, Mamoru Komachi
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Machine Translation, Multimodal Machine Translation, NMT, Prediction, Translation, Word Embeddings, Word Translation

Multimodal machine translation is an attractive application of neural machine
translation (NMT). It helps computers to deeply understand visual objects and
their relations with natural languages. However, multimodal NMT systems suffer
from a shortage of available training data, resulting in poor performance for
translating rare words. In NMT, pretrained word embeddings have been shown to
improve NMT of low-resource domains, and a search-based approach is proposed to
address the rare word problem. In this study, we effectively combine these two
approaches in the context of multimodal NMT and explore how we can take full
advantage of pretrained word embeddings to better translate rare words. We
report overall performance improvements of 1.24 METEOR and 2.49 BLEU and
achieve an improvement of 7.67 F-score for rare word translation.
Recognizing Musical Entities in User-generated Content
Authors: Lorenzo Porcaro, Horacio Saggion
Date: 2019-04-01 00:00:00
Tasks: Articles, General Classification, Genre classification, Information Retrieval, Music Information Retrieval, Music Recommendation, Retrieval

Recognizing Musical Entities is important for Music Information Retrieval
(MIR) since it can improve the performance of several tasks such as music
recommendation, genre classification or artist similarity. However, most entity
recognition systems in the music domain have concentrated on formal texts (e.g.
artists' biographies, encyclopedic articles, etc.), ignoring rich and noisy
user-generated content. In this work, we present a novel method to recognize
musical entities in Twitter content generated by users following a classical
music radio channel. Our approach takes advantage of both formal radio schedule
and users' tweets to improve entity recognition. We instantiate several machine
learning algorithms to perform entity recognition combining task-specific and
corpus-based features. We also show how to improve recognition results by
jointly considering formal and user-generated content
Syntactic Interchangeability in Word Embedding Models
Authors: Daniel Hershcovich, Assaf Toledo, Alon Halfon, Noam Slonim
Date: 2019-04-01 00:00:00
Proceeding: WS 2019 6
Tasks: POS, valid, Word Similarity

Nearest neighbors in word embedding models are commonly observed to be
semantically similar, but the relations between them can vary greatly. We
investigate the extent to which word embedding models preserve syntactic
interchangeability, as reflected by distances between word vectors, and the
effect of hyper-parameters---context window size in particular. We use part of
speech (POS) as a proxy for syntactic interchangeability, as generally
speaking, words with the same POS are syntactically valid in the same contexts.
We also investigate the relationship between interchangeability and similarity
as judged by commonly-used word similarity benchmarks, and correlate the result
with the performance of word embedding models on these benchmarks. Our results
will inform future research and applications in the selection of word embedding
model, suggesting a principle for an appropriate selection of the context
window size parameter depending on the use-case.
Ranking and Selecting Multi-Hop Knowledge Paths to Better Predict Human Needs
Authors: Debjit Paul, Anette Frank
Date: 2019-04-01 00:00:00
Proceeding: NAACL 2019 6
Tasks: Common Sense Reasoning

To make machines better understand sentiments, research needs to move from
polarity identification to understanding the reasons that underlie the
expression of sentiment. Categorizing the goals or needs of humans is one way
to explain the expression of sentiment in text. Humans are good at
understanding situations described in natural language and can easily connect
them to the character's psychological needs using commonsense knowledge. We
present a novel method to extract, rank, filter and select multi-hop relation
paths from a commonsense knowledge resource to interpret the expression of
sentiment in terms of their underlying human needs. We efficiently integrate
the acquired knowledge paths in a neural model that interfaces context
representations with knowledge using a gated attention mechanism. We assess the
model's performance on a recently published dataset for categorizing human
needs. Selectively integrating knowledge paths boosts performance and
establishes a new state-of-the-art. Our model offers interpretability through
the learned attention map over commonsense knowledge paths. Human evaluation
highlights the relevance of the encoded knowledge.