pwc-archive/papers-with-abstracts huggingface dataset:
One-shot Voice Conversion by Separating Speaker and Content Representations with Instance Normalization
Authors: Ju-chieh Chou, Cheng-chieh Yeh, Hung-Yi Lee
Date: 2019-04-10 00:00:00
Tasks: Voice Conversion

Recently, voice conversion (VC) without parallel data has been successfully adapted to multi-target scenario in which a single model is trained to convert the input voice to many different speakers. However, such model suffers from the limitation that it can only convert the voice to the speakers in the training data, which narrows down the applicable scenario of VC. In this paper, we proposed a novel one-shot VC approach which is able to perform VC by only an example utterance from source and target speaker respectively, and the source and target speaker do not even need to be seen during training. This is achieved by disentangling speaker and content representations with instance normalization (IN). Objective and subjective evaluation shows that our model is able to generate the voice similar to target speaker. In addition to the performance measurement, we also demonstrate that this model is able to learn meaningful speaker representations without any supervision.
Compressed sensing reconstruction using Expectation Propagation
Authors: Alfredo Braunstein, Anna Paola Muntoni, Andrea Pagnani, Mirko Pieropan
Date: 2019-04-10 00:00:00
Tasks: Bayesian Inference, compressed sensing

Many interesting problems in fields ranging from telecommunications to computational biology can be formalized in terms of large underdetermined systems of linear equations with additional constraints or regularizers. One of the most studied ones, the Compressed Sensing problem (CS), consists in finding the solution with the smallest number of non-zero components of a given system of linear equations $\boldsymbol y = \mathbf{F} \boldsymbol{w}$ for known measurement vector $\boldsymbol{y}$ and sensing matrix $\mathbf{F}$. Here, we will address the compressed sensing problem within a Bayesian inference framework where the sparsity constraint is remapped into a singular prior distribution (called Spike-and-Slab or Bernoulli-Gauss). Solution to the problem is attempted through the computation of marginal distributions via Expectation Propagation (EP), an iterative computational scheme originally developed in Statistical Physics. We will show that this strategy is comparatively more accurate than the alternatives in solving instances of CS generated from statistically correlated measurement matrices. For computational strategies based on the Bayesian framework such as variants of Belief Propagation, this is to be expected, as they implicitly rely on the hypothesis of statistical independence among the entries of the sensing matrix. Perhaps surprisingly, the method outperforms uniformly also all the other state-of-the-art methods in our tests.
Diagnosis of Celiac Disease and Environmental Enteropathy on Biopsy Images Using Color Balancing on Convolutional Neural Networks
Authors: Kamran Kowsari, Rasoul Sali, Marium N. Khan, William Adorno, S. Asad Ali, Sean R. Moore, Beatrice C. Amadi, Paul Kelly, Sana Syed, Donald E. Brown
Date: 2019-04-10 00:00:00
Tasks: Nutrition

Celiac Disease (CD) and Environmental Enteropathy (EE) are common causes of malnutrition and adversely impact normal childhood development. CD is an autoimmune disorder that is prevalent worldwide and is caused by an increased sensitivity to gluten. Gluten exposure destructs the small intestinal epithelial barrier, resulting in nutrient mal-absorption and childhood under-nutrition. EE also results in barrier dysfunction but is thought to be caused by an increased vulnerability to infections. EE has been implicated as the predominant cause of under-nutrition, oral vaccine failure, and impaired cognitive development in low-and-middle-income countries. Both conditions require a tissue biopsy for diagnosis, and a major challenge of interpreting clinical biopsy images to differentiate between these gastrointestinal diseases is striking histopathologic overlap between them. In the current study, we propose a convolutional neural network (CNN) to classify duodenal biopsy images from subjects with CD, EE, and healthy controls. We evaluated the performance of our proposed model using a large cohort containing 1000 biopsy images. Our evaluations show that the proposed model achieves an area under ROC of 0.99, 1.00, and 0.97 for CD, EE, and healthy controls, respectively. These results demonstrate the discriminative power of the proposed model in duodenal biopsies classification.
The Convergence of Iterative Delegations in Liquid Democracy in a Social Network
Authors: Bruno Escoffier, Hugo Gilbert, Adèle Pass-Lanneau
Date: 2019-04-10 00:00:00
Tasks: Decision Making

Liquid democracy is a collective decision making paradigm which lies between direct and representative democracy. One of its main features is that voters can delegate their votes in a transitive manner such that: A delegates to B and B delegates to C leads to A indirectly delegates to C. These delegations can be effectively empowered by implementing liquid democracy in a social network, so that voters can delegate their votes to any of their neighbors in the network. However, it is uncertain that such a delegation process will lead to a stable state where all voters are satisfied with the people representing them. We study the stability (w.r.t. voters preferences) of the delegation process in liquid democracy and model it as a game in which the players are the voters and the strategies are their possible delegations. We answer several questions on the equilibria of this process in any social network or in social networks that correspond to restricted types of graphs. We show that a Nash-equilibrium may not exist, and that it is even NP-complete to decide whether one exists or not. This holds even if the social network is a complete graph or a bounded degree graph. We further show that this existence problem is W[1]-hard w.r.t. the treewidth of the social network. Besides these hardness results, we demonstrate that an equilibrium always exists whatever the preferences of the voters iff the social network is a tree. We design a dynamic programming procedure to determine some desirable equilibria (e.g., minimizing the dissatisfaction of the voters) in polynomial time for tree social networks. Lastly, we study the convergence of delegation dynamics. Unfortunately, when an equilibrium exists, we show that a best response dynamics may not converge, even if the social network is a path or a complete graph.
Safer Deep RL with Shallow MCTS: A Case Study in Pommerman
Authors: Bilal Kartal, Pablo Hernandez-Leal, Chao GAO, Matthew E. Taylor
Date: 2019-04-10 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Safe Reinforcement Learning

Safe reinforcement learning has many variants and it is still an open
research problem. Here, we focus on how to use action guidance by means of a
non-expert demonstrator to avoid catastrophic events in a domain with sparse,
delayed, and deceptive rewards: the recently-proposed multi-agent benchmark of
Pommerman. This domain is very challenging for reinforcement learning (RL) ---
past work has shown that model-free RL algorithms fail to achieve significant
learning. In this paper, we shed light into the reasons behind this failure by
exemplifying and analyzing the high rate of catastrophic events (i.e.,
suicides) that happen under random exploration in this domain. While model-free
random exploration is typically futile, we propose a new framework where even a
non-expert simulated demonstrator, e.g., planning algorithms such as Monte
Carlo tree search with small number of rollouts, can be integrated to
asynchronous distributed deep reinforcement learning methods. Compared to
vanilla deep RL algorithms, our proposed methods both learn faster and converge
to better policies on a two-player mini version of the Pommerman game.
RawNet: Fast End-to-End Neural Vocoder
Authors: Yunchao He, Yujun Wang
Date: 2019-04-10 00:00:00
Tasks: Speech Synthesis

Neural network-based vocoders have recently demonstrated the powerful ability to synthesize high-quality speech. These models usually generate samples by conditioning on spectral features, such as Mel-spectrogram and fundamental frequency, which is crucial to speech synthesis. However, the feature extraction procession tends to depend heavily on human knowledge resulting in a less expressive description of the origin audio. In this work, we proposed RawNet, a complete end-to-end neural vocoder following the auto-encoder structure for speaker-dependent and -independent speech synthesis. It automatically learns to extract features and recover audio using neural networks, which include a coder network to capture a higher representation of the input audio and an autoregressive voder network to restore the audio in a sample-by-sample manner. The coder and voder are jointly trained directly on the raw waveform without any human-designed features. The experimental results show that RawNet achieves a better speech quality using a simplified model architecture and obtains a faster speech generation speed at the inference stage.
Corpora Generation for Grammatical Error Correction
Authors: Jared Lichtarge, Chris Alberti, Shankar Kumar, Noam Shazeer, Niki Parmar, Simon Tong
Date: 2019-04-10 00:00:00
Proceeding: NAACL 2019 6
Tasks: Grammatical Error Correction, Machine Translation, Translation

Grammatical Error Correction (GEC) has been recently modeled using the
sequence-to-sequence framework. However, unlike sequence transduction problems
such as machine translation, GEC suffers from the lack of plentiful parallel
data. We describe two approaches for generating large parallel datasets for GEC
using publicly available Wikipedia data. The first method extracts
source-target pairs from Wikipedia edit histories with minimal filtration
heuristics, while the second method introduces noise into Wikipedia sentences
via round-trip translation through bridge languages. Both strategies yield
similar sized parallel corpora containing around 4B tokens. We employ an
iterative decoding strategy that is tailored to the loosely supervised nature
of our constructed corpora. We demonstrate that neural GEC models trained using
either type of corpora give similar performance. Fine-tuning these models on
the Lang-8 corpus and ensembling allows us to surpass the state of the art on
both the CoNLL-2014 benchmark and the JFLEG task. We provide systematic
analysis that compares the two approaches to data generation and highlights the
effectiveness of ensembling.
Event-based Access to Historical Italian War Memoirs
Authors: Marco Rovera, Federico Nanni, Simone Paolo Ponzetto
Date: 2019-04-08 00:00:00

The progressive digitization of historical archives provides new, often domain specific, textual resources that report on facts and events which have happened in the past; among these, memoirs are a very common type of primary source. In this paper, we present an approach for extracting information from Italian historical war memoirs and turning it into structured knowledge. This is based on the semantic notions of events, participants and roles. We evaluate quantitatively each of the key-steps of our approach and provide a graph-based representation of the extracted knowledge, which allows to move between a Close and a Distant Reading of the collection.
Weakly Supervised Grammatical Error Correction using Iterative Decoding
Authors: Jared Lichtarge, Christopher Alberti, Shankar Kumar, Noam Shazeer, Niki Parmar
Date: 2018-10-31 00:00:00
Tasks: Grammatical Error Correction

We describe an approach to Grammatical Error Correction (GEC) that is
effective at making use of models trained on large amounts of weakly supervised
bitext. We train the Transformer sequence-to-sequence model on 4B tokens of
Wikipedia revisions and employ an iterative decoding strategy that is tailored
to the loosely-supervised nature of the Wikipedia training corpus. Finetuning
on the Lang-8 corpus and ensembling yields an F0.5 of 58.3 on the CoNLL'14
benchmark and a GLEU of 62.4 on JFLEG. The combination of weakly supervised
training and iterative decoding obtains an F0.5 of 48.2 on CoNLL'14 even
without using any labeled GEC data.
SPEAK YOUR MIND! Towards Imagined Speech Recognition With Hierarchical Deep Learning
Authors: Pramit Saha, Muhammad Abdul-Mageed, Sidney Fels
Date: 2019-04-08 00:00:00
Tasks: Brain Computer Interface, General Classification, speech-recognition, Speech Recognition, Speech Synthesis

Speech-related Brain Computer Interface (BCI) technologies provide effective
vocal communication strategies for controlling devices through speech commands
interpreted from brain signals. In order to infer imagined speech from active
thoughts, we propose a novel hierarchical deep learning BCI system for
subject-independent classification of 11 speech tokens including phonemes and
words. Our novel approach exploits predicted articulatory information of six
phonological categories (e.g., nasal, bilabial) as an intermediate step for
classifying the phonemes and words, thereby finding discriminative signal
responsible for natural speech synthesis. The proposed network is composed of
hierarchical combination of spatial and temporal CNN cascaded with a deep
autoencoder. Our best models on the KARA database achieve an average accuracy
of 83.42% across the six different binary phonological classification tasks,
and 53.36% for the individual token identification task, significantly
outperforming our baselines. Ultimately, our work suggests the possible
existence of a brain imagery footprint for the underlying articulatory movement
related to different sounds that can be used to aid imagined speech decoding.
Machine Learning in IoT Security: Current Solutions and Future Challenges
Authors: Fatima Hussain, Rasheed Hussain, Syed Ali Hassan, Ekram Hossain
Date: 2019-03-14 00:00:00
Tasks: BIG-bench Machine Learning

The future Internet of Things (IoT) will have a deep economical, commercial
and social impact on our lives. The participating nodes in IoT networks are
usually resource-constrained, which makes them luring targets for cyber
attacks. In this regard, extensive efforts have been made to address the
security and privacy issues in IoT networks primarily through traditional
cryptographic approaches. However, the unique characteristics of IoT nodes
render the existing solutions insufficient to encompass the entire security
spectrum of the IoT networks. This is, at least in part, because of the
resource constraints, heterogeneity, massive real-time data generated by the
IoT devices, and the extensively dynamic behavior of the networks. Therefore,
Machine Learning (ML) and Deep Learning (DL) techniques, which are able to
provide embedded intelligence in the IoT devices and networks, are leveraged to
cope with different security problems. In this paper, we systematically review
the security requirements, attack vectors, and the current security solutions
for the IoT networks. We then shed light on the gaps in these security
solutions that call for ML and DL approaches. We also discuss in detail the
existing ML and DL solutions for addressing different security problems in IoT
networks. At last, based on the detailed investigation of the existing
solutions in the literature, we discuss the future research directions for ML-
and DL-based IoT security.
TG-PSM: Tunable Greedy Packet Sequence Morphing Based on Trace Clustering
Authors: Farzam Fanitabasi
Date: 2019-04-01 00:00:00
Tasks: Clustering

Common privacy enhancing technologies fail to effectively hide certain
statistical aspects of encrypted traffic, namely individual packets length,
packets direction and, packets timing. Recent researches have shown that using
such attributes, an adversary is able to extract various information from the
encrypted traffic such as the visited website and used protocol. Such attacks
are called traffic analysis. Proposed countermeasures attempt to change the
distribution of such features. however, either they fail to effectively reduce
attacker's accuracy or do so while enforcing high bandwidth overhead and timing
delay. In this paper, through the use of a predefined set of clustered traces
of websites and a greedy packet morphing algorithm, we introduce a website
fingerprinting countermeasure called TG-PSM. Firstly, this method clusters
websites based on their behavior in different phases of loading. Secondly, it
finds a suitable target site for any visiting website based on user indicated
importance degree; thus providing dynamic tunability. Thirdly, this method
morphs the given website to the target website using a greedy algorithm
considering the distance and the resulted overhead. Our evaluations show that
TG-PSM outperforms previous countermeasures regarding attacker accuracy
reduction and enforced bandwidth, e.g., reducing bandwidth overhead over 40%
while maintaining attacker's accuracy.
Malware Evasion Attack and Defense
Authors: Yonghong Huang, Utkarsh Verma, Celeste Fralick, Gabriel Infante-Lopezy, Brajesh Kumarz, Carl Woodward
Date: 2019-04-07 00:00:00
Tasks: BIG-bench Machine Learning, Malware Detection

Machine learning (ML) classifiers are vulnerable to adversarial examples. An
adversarial example is an input sample which is slightly modified to induce
misclassification in an ML classifier. In this work, we investigate white-box
and grey-box evasion attacks to an ML-based malware detector and conduct
performance evaluations in a real-world setting. We compare the defense
approaches in mitigating the attacks. We propose a framework for deploying
grey-box and black-box attacks to malware detection systems.
Deep Back-Projection Networks for Single Image Super-resolution
Authors: Muhammad Haris, Greg Shakhnarovich, Norimichi Ukita
Date: 2019-04-04 00:00:00
Tasks: Image Super-Resolution, Super-Resolution

Previous feed-forward architectures of recently proposed deep super-resolution networks learn the features of low-resolution inputs and the non-linear mapping from those to a high-resolution output. However, this approach does not fully address the mutual dependencies of low- and high-resolution images. We propose Deep Back-Projection Networks (DBPN), the winner of two image super-resolution challenges (NTIRE2018 and PIRM2018), that exploit iterative up- and down-sampling layers. These layers are formed as a unit providing an error feedback mechanism for projection errors. We construct mutually-connected up- and down-sampling units each of which represents different types of low- and high-resolution components. We also show that extending this idea to demonstrate a new insight towards more efficient network design substantially, such as parameter sharing on the projection module and transition layer on projection step. The experimental results yield superior results and in particular establishing new state-of-the-art results across multiple data sets, especially for large scaling factors such as 8x.
Improving SIEM for Critical SCADA Water Infrastructures Using Machine Learning
Authors: Hanan Hindy, David Brosset, Ethan Bayne, Amar Seeam, Xavier Bellekens
Date: 2019-03-06 00:00:00
Tasks: Anomaly Detection, BIG-bench Machine Learning, Cyber Attack Detection

Network Control Systems (NAC) have been used in many industrial processes.
They aim to reduce the human factor burden and efficiently handle the complex
process and communication of those systems. Supervisory control and data
acquisition (SCADA) systems are used in industrial, infrastructure and facility
processes (e.g. manufacturing, fabrication, oil and water pipelines, building
ventilation, etc.) Like other Internet of Things (IoT) implementations, SCADA
systems are vulnerable to cyber-attacks, therefore, a robust anomaly detection
is a major requirement. However, having an accurate anomaly detection system is
not an easy task, due to the difficulty to differentiate between cyber-attacks
and system internal failures (e.g. hardware failures). In this paper, we
present a model that detects anomaly events in a water system controlled by
SCADA. Six Machine Learning techniques have been used in building and
evaluating the model. The model classifies different anomaly events including
hardware failures (e.g. sensor failures), sabotage and cyber-attacks (e.g. DoS
and Spoofing). Unlike other detection systems, our proposed work focuses on
notifying the operator when an anomaly occurs with a probability of the event
occurring. This additional information helps in accelerating the mitigation
process. The model is trained and tested using a real-world dataset.
Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems
Authors: Hadi Abdullah, Washington Garcia, Christian Peeters, Patrick Traynor, Kevin R. B. Butler, Joseph Wilson
Date: 2019-03-18 00:00:00
Tasks: Audio Signal Processing, BIG-bench Machine Learning, Speaker Recognition

Voice Processing Systems (VPSes), now widely deployed, have been made
significantly more accurate through the application of recent advances in
machine learning. However, adversarial machine learning has similarly advanced
and has been used to demonstrate that VPSes are vulnerable to the injection of
hidden commands - audio obscured by noise that is correctly recognized by a VPS
but not by human beings. Such attacks, though, are often highly dependent on
white-box knowledge of a specific machine learning model and limited to
specific microphones and speakers, making their use across different acoustic
hardware platforms (and thus their practicality) limited. In this paper, we
break these dependencies and make hidden command attacks more practical through
model-agnostic (blackbox) attacks, which exploit knowledge of the signal
processing algorithms commonly used by VPSes to generate the data fed into
machine learning systems. Specifically, we exploit the fact that multiple
source audio samples have similar feature vectors when transformed by acoustic
feature extraction algorithms (e.g., FFTs). We develop four classes of
perturbations that create unintelligible audio and test them against 12 machine
learning models, including 7 proprietary models (e.g., Google Speech API, Bing
Speech API, IBM Speech API, Azure Speaker API, etc), and demonstrate successful
attacks against all targets. Moreover, we successfully use our maliciously
generated audio samples in multiple hardware configurations, demonstrating
effectiveness across both models and real systems. In so doing, we demonstrate
that domain-specific knowledge of audio signal processing represents a
practical means of generating successful hidden voice command attacks.
Exploring Author Gender in Book Rating and Recommendation
Authors: Michael D. Ekstrand, Daniel Kluver
Date: 2018-08-22 00:00:00
Tasks: Collaborative Filtering

Collaborative filtering algorithms find useful patterns in rating and consumption data and exploit these patterns to guide users to good items. Many of the patterns in rating datasets reflect important real-world differences between the various users and items in the data; other patterns may be irrelevant or possibly undesirable for social or ethical reasons, particularly if they reflect undesired discrimination, such as discrimination in publishing or purchasing against authors who are women or ethnic minorities. In this work, we examine the response of collaborative filtering recommender algorithms to the distribution of their input data with respect to a dimension of social concern, namely content creator gender. Using publicly-available book ratings data, we measure the distribution of the genders of the authors of books in user rating profiles and recommendation lists produced from this data. We find that common collaborative filtering algorithms differ in the gender distribution of their recommendation lists, and in the relationship of that output distribution to user profile distribution.