pwc-archive/papers-with-abstracts huggingface dataset:
Fine-Grained Named Entity Recognition using ELMo and Wikidata
Authors: Cihan Dogan, Aimore Dutra, Adam Gara, Alfredo Gemma, Lei Shi, Michael Sigamani, Ella Walters
Date: 2019-04-23 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER)

Fine-grained Named Entity Recognition is a task whereby we detect and
classify entity mentions to a large set of types. These types can span diverse
domains such as finance, healthcare, and politics. We observe that when the
type set spans several domains the accuracy of the entity detection becomes a
limitation for supervised learning models. The primary reason being the lack of
datasets where entity boundaries are properly annotated, whilst covering a
large spectrum of entity types. Furthermore, many named entity systems suffer
when considering the categorization of fine grained entity types. Our work
attempts to address these issues, in part, by combining state-of-the-art deep
learning models (ELMo) with an expansive knowledge base (Wikidata). Using our
framework, we cross-validate our model on the 112 fine-grained entity types
based on the hierarchy given from the Wiki(gold) dataset.
Natural Language Interactions in Autonomous Vehicles: Intent Detection and Slot Filling from Passenger Utterances
Authors: Eda Okur, Shachi H. Kumar, Saurav Sahay, Asli Arslan Esme, Lama Nachman
Date: 2019-04-23 00:00:00
Tasks: Automatic Speech Recognition, Automatic Speech Recognition (ASR), Autonomous Vehicles, Intent Detection, slot-filling, Slot Filling, speech-recognition, Speech Recognition, Speech-to-Text

Understanding passenger intents and extracting relevant slots are important
building blocks towards developing contextual dialogue systems for natural
interactions in autonomous vehicles (AV). In this work, we explored AMIE
(Automated-vehicle Multi-modal In-cabin Experience), the in-cabin agent
responsible for handling certain passenger-vehicle interactions. When the
passengers give instructions to AMIE, the agent should parse such commands
properly and trigger the appropriate functionality of the AV system. In our
current explorations, we focused on AMIE scenarios describing usages around
setting or changing the destination and route, updating driving behavior or
speed, finishing the trip and other use-cases to support various natural
commands. We collected a multi-modal in-cabin dataset with multi-turn dialogues
between the passengers and AMIE using a Wizard-of-Oz scheme via a realistic
scavenger hunt game activity. After exploring various recent Recurrent Neural
Networks (RNN) based techniques, we introduced our own hierarchical joint
models to recognize passenger intents along with relevant slots associated with
the action to be performed in AV scenarios. Our experimental results
outperformed certain competitive baselines and achieved overall F1 scores of
0.91 for utterance-level intent detection and 0.96 for slot filling tasks. In
addition, we conducted initial speech-to-text explorations by comparing
intent/slot models trained and tested on human transcriptions versus noisy
Automatic Speech Recognition (ASR) outputs. Finally, we compared the results
with single passenger rides versus the rides with multiple passengers.
Comparing Samples from the $\mathcal{G}^0$ Distribution using a Geodesic Distance
Authors: Alejandro C. Frery, Juliana Gambini
Date: 2019-04-23 00:00:00

The $\mathcal{G}^0$ distribution is widely used for monopolarized SAR image
modeling because it can characterize regions with different degree of texture
accurately. It is indexed by three parameters: the number of looks (which can
be estimated for the whole image), a scale parameter and a texture parameter.
This paper presents a new proposal for comparing samples from the
$\mathcal{G}^0$ distribution using a Geodesic Distance (GD) as a measure of
dissimilarity between models. The objective is quantifying the difference
between pairs of samples from SAR data using both local parameters (scale and
texture) of the $\mathcal{G}^0$ distribution. We propose three tests based on
the GD which combine the tests presented in~\cite{GeodesicDistanceGI0JSTARS},
and we estimate their probability distributions using permutation methods.
Balanced Crossover Operators in Genetic Algorithms
Authors: Luca Manzoni, Luca Mariot, Eva Tuba
Date: 2019-04-23 00:00:00
Tasks: Combinatorial Optimization

In several combinatorial optimization problems arising in cryptography and design theory, the admissible solutions must often satisfy a balancedness constraint, such as being represented by bitstrings with a fixed number of ones. For this reason, several works in the literature tackling these optimization problems with Genetic Algorithms (GA) introduced new balanced crossover operators which ensure that the offspring has the same balancedness characteristics of the parents. However, the use of such operators has never been thoroughly motivated, except for some generic considerations about search space reduction. In this paper, we undertake a rigorous statistical investigation on the effect of balanced and unbalanced crossover operators against three optimization problems from the area of cryptography and coding theory: nonlinear balanced Boolean functions, binary Orthogonal Arrays (OA) and bent functions. In particular, we consider three different balanced crossover operators (each with two variants: "left-to-right" and "shuffled"), two of which have never been published before, and compare their performances with classic one-point crossover. We are able to confirm that the balanced crossover operators performs better than all three balanced crossover operators. Furthermore, in two out of three crossovers, the "left-to-right" version performs better than the "shuffled" version.
Chunkflow: Distributed Hybrid Cloud Processing of Large 3D Images by Convolutional Nets
Authors: Jingpeng Wu, William M. Silversmith, Kisuk Lee, H. Sebastian Seung
Date: 2019-04-23 00:00:00

It is now common to process volumetric biomedical images using 3D Convolutional Networks (ConvNets). This can be challenging for the teravoxel and even petavoxel images that are being acquired today by light or electron microscopy. Here we introduce chunkflow, a software framework for distributing ConvNet processing over local and cloud GPUs and CPUs. The image volume is divided into overlapping chunks, each chunk is processed by a ConvNet, and the results are blended together to yield the output image. The frontend submits ConvNet tasks to a cloud queue. The tasks are executed by local and cloud GPUs and CPUs. Thanks to the fault-tolerant architecture of Chunkflow, cost can be greatly reduced by utilizing cheap unstable cloud instances. Chunkflow currently supports PyTorch for GPUs and PZnet for CPUs. To illustrate its usage, a large 3D brain image from serial section electron microscopy was processed by a 3D ConvNet with a U-Net style architecture. Chunkflow provides some chunk operations for general use, and the operations can be composed flexibly in a command line interface.
3D Dynamic Point Cloud Inpainting via Temporal Consistency on Graphs
Authors: Zeqing Fu, Wei Hu, Zongming Guo
Date: 2019-04-23 00:00:00

With the development of 3D laser scanning techniques and depth sensors, 3D dynamic point clouds have attracted increasing attention as a representation of 3D objects in motion, enabling various applications such as 3D immersive tele-presence, gaming and navigation. However, dynamic point clouds usually exhibit holes of missing data, mainly due to the fast motion, the limitation of acquisition and complicated structure. Leveraging on graph signal processing tools, we represent irregular point clouds on graphs and propose a novel inpainting method exploiting both intra-frame self-similarity and inter-frame consistency in 3D dynamic point clouds. Specifically, for each missing region in every frame of the point cloud sequence, we search for its self-similar regions in the current frame and corresponding ones in adjacent frames as references. Then we formulate dynamic point cloud inpainting as an optimization problem based on the two types of references, which is regularized by a graph-signal smoothness prior. Experimental results show the proposed approach outperforms three competing methods significantly, both in objective and subjective quality.
Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
Authors: Linsen Dong, Guanyu Gao, Xinyi Zhang, Liang-Yu Chen, Yonggang Wen
Date: 2019-04-23 00:00:00
Tasks: Autonomous Driving, Model-based Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Model-Based Reinforcement Learning (MBRL) is one category of Reinforcement Learning (RL) algorithms which can improve sampling efficiency by modeling and approximating system dynamics. It has been widely adopted in the research of robotics, autonomous driving, etc. Despite its popularity, there still lacks some sophisticated and reusable open-source frameworks to facilitate MBRL research and experiments. To fill this gap, we develop a flexible and modularized framework, Baconian, which allows researchers to easily implement a MBRL testbed by customizing or building upon our provided modules and algorithms. Our framework can free users from re-implementing popular MBRL algorithms from scratch thus greatly save users' efforts on MBRL experiments.
End-to-End Spoken Language Translation
Authors: Michelle Guo, Albert Haque, Prateek Verma
Date: 2019-04-23 00:00:00
Tasks: Sentence, Spoken Language Understanding, Translation

In this paper, we address the task of spoken language understanding. We
present a method for translating spoken sentences from one language into spoken
sentences in another language. Given spectrogram-spectrogram pairs, our model
can be trained completely from scratch to translate unseen sentences. Our
method consists of a pyramidal-bidirectional recurrent network combined with a
convolutional network to output sentence-level spectrograms in the target
language. Empirically, our model achieves competitive performance with
state-of-the-art methods on multiple languages and can generalize to unseen
speakers.
Data Cleaning for Accurate, Fair, and Robust Models: A Big Data - AI Integration Approach
Authors: Ki Hyun Tae, Yuji Roh, Young Hun Oh, Hyunsu Kim, Steven Euijong Whang
Date: 2019-04-22 00:00:00
Tasks: BIG-bench Machine Learning, Fairness, Management

The wide use of machine learning is fundamentally changing the software
development paradigm (a.k.a. Software 2.0) where data becomes a first-class
citizen, on par with code. As machine learning is used in sensitive
applications, it becomes imperative that the trained model is accurate, fair,
and robust to attacks. While many techniques have been proposed to improve the
model training process (in-processing approach) or the trained model itself
(post-processing), we argue that the most effective method is to clean the root
cause of error: the data the model is trained on (pre-processing).
Historically, there are at least three research communities that have been
separately studying this problem: data management, machine learning (model
fairness), and security. Although a significant amount of research has been
done by each community, ultimately the same datasets must be preprocessed, and
there is little understanding how the techniques relate to each other and can
possibly be integrated. We contend that it is time to extend the notion of data
cleaning for modern machine learning needs. We identify dependencies among the
data preprocessing techniques and propose MLClean, a unified data cleaning
framework that integrates the techniques and helps train accurate and fair
models. This work is part of a broader trend of Big data -- Artificial
Intelligence (AI) integration.
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions
Authors: Li Chen
Date: 2019-04-03 00:00:00
Tasks: BIG-bench Machine Learning, Classification, General Classification, image-classification, Image Classification, Malware Classification, Malware Detection, Transfer Learning

My research lies in the intersection of security and machine learning. This
overview summarizes one component of my research: combining computer vision
with malware exploit detection for enhanced security solutions. I will present
the perspectives of efficacy, reliability and resiliency to formulate threat
detection as computer vision problems and develop state-of-the-art image-based
malware classification. Representing malware binary as images provides a direct
visualization of data samples, reduces the efforts for feature extraction, and
consumes the whole binary for holistic structural analysis. Employing transfer
learning of deep neural networks effective for large scale image classification
to malware classification demonstrates superior classification efficacy
compared with classical machine learning algorithms. To enhance reliability of
these vision-based malware detectors, interpretation frameworks can be
constructed on the malware visual representations and useful for extracting
faithful explanation, so that security practitioners have confidence in the
model before deployment. In cyber-security applications, we should always
assume that a malware writer constantly modifies code to bypass detection.
Addressing the resiliency of the malware detectors is equivalently important as
efficacy and reliability. Via understanding the attack surfaces of machine
learning models used for malware detection, we can greatly improve the
robustness of the algorithms to combat malware adversaries in the wild. Finally
I will discuss future research directions worth pursuing in this research
community.
Local Frequency Interpretation and Non-Local Self-Similarity on Graph for Point Cloud Inpainting
Authors: Zeqing Fu, Wei Hu, Zongming Guo
Date: 2018-09-28 00:00:00

As 3D scanning devices and depth sensors mature, point clouds have attracted
increasing attention as a format for 3D object representation, with
applications in various fields such as tele-presence, navigation and heritage
reconstruction. However, point clouds usually exhibit holes of missing data,
mainly due to the limitation of acquisition techniques and complicated
structure. Further, point clouds are defined on irregular non-Euclidean
domains, which is challenging to address especially with conventional signal
processing tools. Hence, leveraging on recent advances in graph signal
processing, we propose an efficient point cloud inpainting method, exploiting
both the local smoothness and the non-local self-similarity in point clouds.
Specifically, we first propose a frequency interpretation in graph nodal
domain, based on which we introduce the local graph-signal smoothness prior in
order to describe the local smoothness of point clouds. Secondly, we explore
the characteristics of non-local self-similarity, by globally searching for the
most similar area to the missing region. The similarity metric between two
areas is defined based on the direct component and the anisotropic graph total
variation of normals in each area. Finally, we formulate the hole-filling step
as an optimization problem based on the selected most similar area and
regularized by the graph-signal smoothness prior. Besides, we propose
voxelization and automatic hole detection methods for the point cloud prior to
inpainting. Experimental results show that the proposed approach outperforms
four competing methods significantly, both in objective and subjective quality.
Setup of a Recurrent Neural Network as a Body Model for Solving Inverse and Forward Kinematics as well as Dynamics for a Redundant Manipulator
Authors: Malte Schilling
Date: 2019-04-12 00:00:00

An internal model of the own body can be assumed a fundamental and
evolutionary-early representation as it is present throughout the animal
kingdom. Such functional models are, on the one hand, required in motor
control, for example solving the inverse kinematic or dynamic task in
goal-directed movements or a forward task in ballistic movements. On the other
hand, such models are recruited in cognitive tasks as are planning ahead or
observation of actions of a conspecific. Here, we present a functional internal
body model that is based on the Mean of Multiple Computations principle. For
the first time such a model is completely realized in a recurrent neural
network as necessary normalization steps are integrated into the neural model
itself. Secondly, a dynamic extension is applied to the model. It is shown how
the neural network solves a series of inverse tasks. Furthermore, emerging
representation in transformational layers are analyzed that show a form of
prototypical population-coding as found in place or direction cells.
Quantum-Inspired Computing: Can it be a Microscopic Computing Model of the Brain?
Authors: Yasunao Katayama
Date: 2019-04-11 00:00:00
Tasks: Quantum Machine Learning

Quantum computing and the workings of the brain have many aspects in common and have been attracting increasing attention in academia and industry. The computation in both is parallel and non-discrete. Though the underlying physical dynamics (e.g., equation of motion) may be deterministic, the observed or interpreted outcomes are often probabilistic. Consequently, various investigations have been undertaken to understand and reproduce the brain on the basis of quantum physics and computing. However, there have been arguments on whether the brain can and have to take advantage of quantum phenomena that need to survive in the macroscopic space-time region at room temperature. This paper presents a unique microscopic computational model for the brain based on an ansatz that the brain computes in a manner similar to quantum computing, but with classical waves. Log-scale encoding of information in the context of computing with waves is shown to play a critical role in bridging the computing models with classical and quantum waves. Our quantum-inspired computing model opens up a possibility of unifying the computing framework of artificial intelligence and quantum computing beyond quantum machine learning approaches.
Alterations in Structural Correlation Networks with Prior Concussion in Collision-Sport Athletes
Authors: Muhammad Usman Sadiq, Diana Svaldi, Trey Shenk, Evan Breedlove, Victoria Poole, Greg Tamer, Kausar Abbas, Thomas Talavage
Date: 2019-04-18 00:00:00

Several studies have used structural correlation networks, derived from
anatomical covariance of brain regions, to analyze neurologic changes
associated with multiple sclerosis, schizophrenia and breast cancer [1][2].
Graph-theoretical analyses of human brain structural networks have consistently
shown the characteristic of small-worldness that reflects a network with both
high segregation and high integration. A large neuroimaging literature on
football players, with and without history of concussion, has shown both
functional and anatomical changes. Here we use graph-based topological
properties of anatomical correlation networks to study the effect of prior
concussion in collision-sport athletes. 40 high school collision-sport athletes
(23 male football, 17 female soccer; CSA) without self-reported history of
concussion (HOC-), 18 athletes (13 male football, 5 female soccer) with
self-reported history of concussion (HOC+) and 24 healthy controls (19 male, 5
female; CN) participated in imaging sessions before the beginning of a
competition season. The extracted residual volumes for each group were used for
building the correlation networks and their small-worldness, , is calculated.
The small-worldness of CSA without prior history of concussion, , is
significantly greater than that of controls, . CSA with prior history have
significantly higher (vs. 95% confidence interval) small-worldness compared to
HOC+, over a range of network densities. The longer path lengths in HOC+ group
could indicate disrupted neuronal integration relative to healthy controls.
Forecasting Weakly Correlated Time Series in Tasks of Electronic Commerce
Authors: Lyudmyla Kirichenko, Tamara Radivilova, Illya Zinkevich
Date: 2019-04-16 00:00:00
Tasks: Time Series, Time Series Analysis

Forecasting of weakly correlated time series of conversion rate by methods of
exponential smoothing, neural network and decision tree on the example of
conversion percent series for an electronic store is considered in the paper.
The advantages and disadvantages of each method are considered.
Boosting insights in insurance tariff plans with tree-based machine learning methods
Authors: Roel Henckaerts, Marie-Pier Côté, Katrien Antonio, Roel Verbelen
Date: 2019-04-12 00:00:00
Tasks: BIG-bench Machine Learning

Pricing actuaries typically operate within the framework of generalized linear models (GLMs). With the upswing of data analytics, our study puts focus on machine learning methods to develop full tariff plans built from both the frequency and severity of claims. We adapt the loss functions used in the algorithms such that the specific characteristics of insurance data are carefully incorporated: highly unbalanced count data with excess zeros and varying exposure on the frequency side combined with scarce, but potentially long-tailed data on the severity side. A key requirement is the need for transparent and interpretable pricing models which are easily explainable to all stakeholders. We therefore focus on machine learning with decision trees: starting from simple regression trees, we work towards more advanced ensembles such as random forests and boosted trees. We show how to choose the optimal tuning parameters for these models in an elaborate cross-validation scheme, we present visualization tools to obtain insights from the resulting models and the economic value of these new modeling approaches is evaluated. Boosted trees outperform the classical GLMs, allowing the insurer to form profitable portfolios and to guard against potential adverse risk selection.
Automated assessment of knowledge hierarchy evolution: comparing directed acyclic graphs
Authors: Guruprasad Nayak, Sourav Dutta, Deepak Ajwani, Patrick Nicholson, Alessandra Sala
Date: 2019-06-01 00:00:00
Proceeding: Information Retrieval Journal 2019 6
Tasks: Knowledge Graph Completion, Knowledge Graphs, Question Answering

Automated construction of knowledge hierarchies from huge data corpora is gaining increasing attention in recent years, in order to tackle the infeasibility of manually extracting and semantically linking millions of concepts. As a knowledge hierarchy evolves with these automated techniques, there is a need for measures to assess its temporal evolution, quantifying the similarities between different versions and identifying the relative growth of different subgraphs in the knowledge hierarchy. In this paper, we focus on measures that leverage structural properties of the knowledge hierarchy graph to assess the temporal changes. We propose a principled and scalable similarity measure, based on Katz similarity between concept nodes, for comparing different versions of a knowledge hierarchy, modeled as a generic directed acyclic graph. We present theoretical analysis to depict that the proposed measure accurately captures the salient properties of taxonomic hierarchies, assesses changes in the ordering of nodes, along with the logical subsumption of relationships among concepts. We also present a linear time variant of the measure, and show that our measures, unlike previous approaches, are tunable to cater to diverse application needs. We further show that our measure provides interpretability, thereby identifying the key structural and logical difference in the hierarchies. Experiments on a real DBpedia and biological knowledge hierarchy showcase that our measures accurately capture structural similarity, while providing enhanced scalability and tunability. Also, we demonstrate that the temporal evolution of different subgraphs in this knowledge hierarchy, as captured purely by our structural measure, corresponds well with the known disruptions in the related subject areas.