pwc-archive/papers-with-abstracts huggingface dataset:
Predicting the Programming Language of Questions and Snippets of StackOverflow Using Natural Language Processing
Authors: Kamel Alreshedy, Dhanush Dharmaretnam, Daniel M. German, Venkatesh Srinivasan, T. Aaron Gulliver
Date: 2018-09-21 00:00:00
Tasks: BIG-bench Machine Learning, TAG

Stack Overflow is the most popular Q&A website among software developers. As
a platform for knowledge sharing and acquisition, the questions posted in Stack
Overflow usually contain a code snippet. Stack Overflow relies on users to
properly tag the programming language of a question and it simply assumes that
the programming language of the snippets inside a question is the same as the
tag of the question itself. In this paper, we propose a classifier to predict
the programming language of questions posted in Stack Overflow using Natural
Language Processing (NLP) and Machine Learning (ML). The classifier achieves an
accuracy of 91.1% in predicting the 24 most popular programming languages by
combining features from the title, body and the code snippets of the question.
We also propose a classifier that only uses the title and body of the question
and has an accuracy of 81.1%. Finally, we propose a classifier of code snippets
only that achieves an accuracy of 77.7%. These results show that deploying
Machine Learning techniques on the combination of text and the code snippets of
a question provides the best performance. These results demonstrate also that
it is possible to identify the programming language of a snippet of few lines
of source code. We visualize the feature space of two programming languages
Java and SQL in order to identify some special properties of information inside
the questions in Stack Overflow corresponding to these languages.
Refining Coarse-grained Spatial Data using Auxiliary Spatial Data Sets with Various Granularities
Authors: Yusuke Tanaka, Tomoharu Iwata, Toshiyuki Tanaka, Takeshi Kurashima, Maya Okawa, Hiroyuki Toda
Date: 2018-09-21 00:00:00
Tasks: Gaussian Processes

We propose a probabilistic model for refining coarse-grained spatial data by utilizing auxiliary spatial data sets. Existing methods require that the spatial granularities of the auxiliary data sets are the same as the desired granularity of target data. The proposed model can effectively make use of auxiliary data sets with various granularities by hierarchically incorporating Gaussian processes. With the proposed model, a distribution for each auxiliary data set on the continuous space is modeled using a Gaussian process, where the representation of uncertainty considers the levels of granularity. The fine-grained target data are modeled by another Gaussian process that considers both the spatial correlation and the auxiliary data sets with their uncertainty. We integrate the Gaussian process with a spatial aggregation process that transforms the fine-grained target data into the coarse-grained target data, by which we can infer the fine-grained target Gaussian process from the coarse-grained data. Our model is designed such that the inference of model parameters based on the exact marginal likelihood is possible, in which the variables of fine-grained target and auxiliary data are analytically integrated out. Our experiments on real-world spatial data sets demonstrate the effectiveness of the proposed model.
CollaboNet: collaboration of deep neural networks for biomedical named entity recognition
Authors: Wonjin Yoon, Chan Ho So, Jinhyuk Lee, Jaewoo Kang
Date: 2018-09-21 00:00:00
Tasks: named-entity-recognition, Named Entity Recognition, Named Entity Recognition (NER), NER, Relation Extraction

Background: Finding biomedical named entities is one of the most essential tasks in biomedical text mining. Recently, deep learning-based approaches have been applied to biomedical named entity recognition (BioNER) and showed promising results. However, as deep learning approaches need an abundant amount of training data, a lack of data can hinder performance. BioNER datasets are scarce resources and each dataset covers only a small subset of entity types. Furthermore, many bio entities are polysemous, which is one of the major obstacles in named entity recognition. Results: To address the lack of data and the entity type misclassification problem, we propose CollaboNet which utilizes a combination of multiple NER models. In CollaboNet, models trained on a different dataset are connected to each other so that a target model obtains information from other collaborator models to reduce false positives. Every model is an expert on their target entity type and takes turns serving as a target and a collaborator model during training time. The experimental results show that CollaboNet can be used to greatly reduce the number of false positives and misclassified entities including polysemous words. CollaboNet achieved state-of-the-art performance in terms of precision, recall and F1 score. Conclusions: We demonstrated the benefits of combining multiple models for BioNER. Our model has successfully reduced the number of misclassified entities and improved the performance by leveraging multiple datasets annotated for different entity types. Given the state-of-the-art performance of our model, we believe that CollaboNet can improve the accuracy of downstream biomedical text mining applications such as bio-entity relation extraction.
SCC: Automatic Classification of Code Snippets
Authors: Kamel Alreshedy, Dhanush Dharmaretnam, Daniel M. German, Venkatesh Srinivasan, T. Aaron Gulliver
Date: 2018-09-21 00:00:00
Tasks: Classification, Code Classification, General Classification

Determining the programming language of a source code file has been
considered in the research community; it has been shown that Machine Learning
(ML) and Natural Language Processing (NLP) algorithms can be effective in
identifying the programming language of source code files. However, determining
the programming language of a code snippet or a few lines of source code is
still a challenging task. Online forums such as Stack Overflow and code
repositories such as GitHub contain a large number of code snippets. In this
paper, we describe Source Code Classification (SCC), a classifier that can
identify the programming language of code snippets written in 21 different
programming languages. A Multinomial Naive Bayes (MNB) classifier is employed
which is trained using Stack Overflow posts. It is shown to achieve an accuracy
of 75% which is higher than that with Programming Languages Identification (PLI
a proprietary online classifier of snippets) whose accuracy is only 55.5%. The
average score for precision, recall and the F1 score with the proposed tool are
0.76, 0.75 and 0.75, respectively. In addition, it can distinguish between code
snippets from a family of programming languages such as C, C++ and C#, and can
also identify the programming language version such as C# 3.0, C# 4.0 and C#
5.0.
LIDAR-Camera Fusion for Road Detection Using Fully Convolutional Neural Networks
Authors: Luca Caltagirone, Mauro Bellone, Lennart Svensson, Mattias Wahde
Date: 2018-09-21 00:00:00

In this work, a deep learning approach has been developed to carry out road
detection by fusing LIDAR point clouds and camera images. An unstructured and
sparse point cloud is first projected onto the camera image plane and then
upsampled to obtain a set of dense 2D images encoding spatial information.
Several fully convolutional neural networks (FCNs) are then trained to carry
out road detection, either by using data from a single sensor, or by using
three fusion strategies: early, late, and the newly proposed cross fusion.
Whereas in the former two fusion approaches, the integration of multimodal
information is carried out at a predefined depth level, the cross fusion FCN is
designed to directly learn from data where to integrate information; this is
accomplished by using trainable cross connections between the LIDAR and the
camera processing branches.
  To further highlight the benefits of using a multimodal system for road
detection, a data set consisting of visually challenging scenes was extracted
from driving sequences of the KITTI raw data set. It was then demonstrated
that, as expected, a purely camera-based FCN severely underperforms on this
data set. A multimodal system, on the other hand, is still able to provide high
accuracy. Finally, the proposed cross fusion FCN was evaluated on the KITTI
road benchmark where it achieved excellent performance, with a MaxF score of
96.03%, ranking it among the top-performing approaches.
Adversarial 3D Human Pose Estimation via Multimodal Depth Supervision
Authors: Kun Zhou, Jinmiao Cai, Yao Li, Yulong Shi, Xiaoguang Han, Nianjuan Jiang, Kui Jia, Jiangbo Lu
Date: 2018-09-21 00:00:00
Tasks: 3D Human Pose Estimation, Pose Estimation

In this paper, a novel deep-learning based framework is proposed to infer 3D
human poses from a single image. Specifically, a two-phase approach is
developed. We firstly utilize a generator with two branches for the extraction
of explicit and implicit depth information respectively. During the training
process, an adversarial scheme is also employed to further improve the
performance. The implicit and explicit depth information with the estimated 2D
joints generated by a widely used estimator, in the second step, are together
fed into a deep 3D pose regressor for the final pose generation. Our method
achieves MPJPE of 58.68mm on the ECCV2018 3D Human Pose Estimation Challenge.
Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes
Authors: Peng-Shuai Wang, Chun-Yu Sun, Yang Liu, Xin Tong
Date: 2018-09-21 00:00:00
Tasks: Decoder

We present an Adaptive Octree-based Convolutional Neural Network (Adaptive
O-CNN) for efficient 3D shape encoding and decoding. Different from
volumetric-based or octree-based CNN methods that represent a 3D shape with
voxels in the same resolution, our method represents a 3D shape adaptively with
octants at different levels and models the 3D shape within each octant with a
planar patch. Based on this adaptive patch-based representation, we propose an
Adaptive O-CNN encoder and decoder for encoding and decoding 3D shapes. The
Adaptive O-CNN encoder takes the planar patch normal and displacement as input
and performs 3D convolutions only at the octants at each level, while the
Adaptive O-CNN decoder infers the shape occupancy and subdivision status of
octants at each level and estimates the best plane normal and displacement for
each leaf octant. As a general framework for 3D shape analysis and generation,
the Adaptive O-CNN not only reduces the memory and computational cost, but also
offers better shape generation capability than the existing 3D-CNN approaches.
We validate Adaptive O-CNN in terms of efficiency and effectiveness on
different shape analysis and generation tasks, including shape classification,
3D autoencoding, shape prediction from a single image, and shape completion for
noisy and incomplete point clouds.
Automatic Rule Learning for Autonomous Driving Using Semantic Memory
Authors: Dmitriy Korchev, Aruna Jammalamadaka, Rajan Bhattacharyya
Date: 2018-09-21 00:00:00
Tasks: Autonomous Driving

This paper presents a novel approach for automatic rule learning applicable
to an autonomous driving system using real driving data.
Large-Scale Video Classification with Feature Space Augmentation coupled with Learned Label Relations and Ensembling
Authors: Choongyeun Cho, Benjamin Antin, Sanchit Arora, Shwan Ashrafi, Peilin Duan, Dang The Huynh, Lee James, Hang Tuan Nguyen, Mojtaba Solgi, Cuong Van Than
Date: 2018-09-21 00:00:00
Tasks: General Classification, Video Classification, Video Understanding

This paper presents the Axon AI's solution to the 2nd YouTube-8M Video
Understanding Challenge, achieving the final global average precision (GAP) of
88.733% on the private test set (ranked 3rd among 394 teams, not considering
the model size constraint), and 87.287% using a model that meets size
requirement. Two sets of 7 individual models belonging to 3 different families
were trained separately. Then, the inference results on a training data were
aggregated from these multiple models and fed to train a compact model that
meets the model size requirement. In order to further improve performance we
explored and employed data over/sub-sampling in feature space, an additional
regularization term during training exploiting label relationship, and learned
weights for ensembling different individual models.
Solving Large Extensive-Form Games with Strategy Constraints
Authors: Trevor Davis, Kevin Waugh, Michael Bowling
Date: 2018-09-20 00:00:00
Tasks: counterfactual, Form

Extensive-form games are a common model for multiagent interactions with
imperfect information. In two-player zero-sum games, the typical solution
concept is a Nash equilibrium over the unconstrained strategy set for each
player. In many situations, however, we would like to constrain the set of
possible strategies. For example, constraints are a natural way to model
limited resources, risk mitigation, safety, consistency with past observations
of behavior, or other secondary objectives for an agent. In small games,
optimal strategies under linear constraints can be found by solving a linear
program; however, state-of-the-art algorithms for solving large games cannot
handle general constraints. In this work we introduce a generalized form of
Counterfactual Regret Minimization that provably finds optimal strategies under
any feasible set of convex constraints. We demonstrate the effectiveness of our
algorithm for finding strategies that mitigate risk in security games, and for
opponent modeling in poker games when given only partial observations of
private information.
Predicting the Argumenthood of English Prepositional Phrases
Authors: Najoung Kim, Kyle Rawlins, Benjamin Van Durme, Paul Smolensky
Date: 2018-09-20 00:00:00
Tasks: Diagnostic, Prediction, Semantic Role Labeling, Sentence, Word Embeddings

Distinguishing between arguments and adjuncts of a verb is a longstanding,
nontrivial problem. In natural language processing, argumenthood information is
important in tasks such as semantic role labeling (SRL) and prepositional
phrase (PP) attachment disambiguation. In theoretical linguistics, many
diagnostic tests for argumenthood exist but they often yield conflicting and
potentially gradient results. This is especially the case for syntactically
oblique items such as PPs. We propose two PP argumenthood prediction tasks
branching from these two motivations: (1) binary argument-adjunct
classification of PPs in VerbNet, and (2) gradient argumenthood prediction
using human judgments as gold standard, and report results from prediction
models that use pretrained word embeddings and other linguistically informed
features. Our best results on each task are (1) $acc.=0.955$, $F_1=0.954$
(ELMo+BiLSTM) and (2) Pearson's $r=0.624$ (word2vec+MLP). Furthermore, we
demonstrate the utility of argumenthood prediction in improving sentence
representations via performance gains on SRL when a sentence encoder is
pretrained with our tasks.
Probabilistic Logic Programming with Beta-Distributed Random Variables
Authors: Federico Cerutti, Lance Kaplan, Angelika Kimmig, Murat Sensoy
Date: 2018-09-20 00:00:00
Tasks: Decision Making, Decision Making Under Uncertainty

We enable aProbLog---a probabilistic logical programming approach---to reason
in presence of uncertain probabilities represented as Beta-distributed random
variables. We achieve the same performance of state-of-the-art algorithms for
highly specified and engineered domains, while simultaneously we maintain the
flexibility offered by aProbLog in handling complex relational domains. Our
motivation is that faithfully capturing the distribution of probabilities is
necessary to compute an expected utility for effective decision making under
uncertainty: unfortunately, these probability distributions can be highly
uncertain due to sparse data. To understand and accurately manipulate such
probability distributions we need a well-defined theoretical framework that is
provided by the Beta distribution, which specifies a distribution of
probabilities representing all the possible values of a probability when the
exact value is unknown.
Uncertainty Aware AI ML: Why and How
Authors: Lance Kaplan, Federico Cerutti, Murat Sensoy, Alun Preece, Paul Sullivan
Date: 2018-09-20 00:00:00
Tasks: BIG-bench Machine Learning

This paper argues the need for research to realize uncertainty-aware
artificial intelligence and machine learning (AI\&ML) systems for decision
support by describing a number of motivating scenarios. Furthermore, the paper
defines uncertainty-awareness and lays out the challenges along with surveying
some promising research directions. A theoretical demonstration illustrates how
two emerging uncertainty-aware ML and AI technologies could be integrated and
be of value for a route planning operation.
Learning Quickly to Plan Quickly Using Modular Meta-Learning
Authors: Rohan Chitnis, Leslie Pack Kaelbling, Tomás Lozano-Pérez
Date: 2018-09-20 00:00:00
Tasks: Meta-Learning

Multi-object manipulation problems in continuous state and action spaces can
be solved by planners that search over sampled values for the continuous
parameters of operators. The efficiency of these planners depends critically on
the effectiveness of the samplers used, but effective sampling in turn depends
on details of the robot, environment, and task. Our strategy is to learn
functions called "specializers" that generate values for continuous operator
parameters, given a state description and values for the discrete parameters.
Rather than trying to learn a single specializer for each operator from large
amounts of data on a single task, we take a modular meta-learning approach. We
train on multiple tasks and learn a variety of specializers that, on a new
task, can be quickly adapted using relatively little data -- thus, our system
"learns quickly to plan quickly" using these specializers. We validate our
approach experimentally in simulated 3D pick-and-place tasks with continuous
state and action spaces. Visit http://tinyurl.com/chitnis-icra-19 for a
supplementary video.
On Folding and Twisting (and whatknot): towards a characterization of workspaces in syntax
Authors: Diego Gabriel Krivochen
Date: 2018-09-20 00:00:00

Syntactic theory has traditionally adopted a constructivist approach, in
which a set of atomic elements are manipulated by combinatory operations to
yield derived, complex elements. Syntactic structure is thus seen as the result
or discrete recursive combinatorics over lexical items which get assembled into
phrases, which are themselves combined to form sentences. This view is common
to European and American structuralism (e.g., Benveniste, 1971; Hockett, 1958)
and different incarnations of generative grammar, transformational and
non-transformational (Chomsky, 1956, 1995; and Kaplan & Bresnan, 1982; Gazdar,
1982). Since at least Uriagereka (2002), there has been some attention paid to
the fact that syntactic operations must apply somewhere, particularly when
copying and movement operations are considered. Contemporary syntactic theory
has thus somewhat acknowledged the importance of formalizing aspects of the
spaces in which elements are manipulated, but it is still a vastly
underexplored area. In this paper we explore the consequences of
conceptualizing syntax as a set of topological operations applying over spaces
rather than over discrete elements. We argue that there are empirical
advantages in such a view for the treatment of long-distance dependencies and
cross-derivational dependencies: constraints on possible configurations emerge
from the dynamics of the system.
LaSOT: A High-quality Benchmark for Large-scale Single Object Tracking
Authors: Heng Fan, Liting Lin, Fan Yang, Peng Chu, Ge Deng, Sijia Yu, Hexin Bai, Yong Xu, Chunyuan Liao, Haibin Ling
Date: 2018-09-20 00:00:00
Proceeding: CVPR 2019 6
Tasks: Object Tracking, Vocal Bursts Intensity Prediction

In this paper, we present LaSOT, a high-quality benchmark for Large-scale
Single Object Tracking. LaSOT consists of 1,400 sequences with more than 3.5M
frames in total. Each frame in these sequences is carefully and manually
annotated with a bounding box, making LaSOT the largest, to the best of our
knowledge, densely annotated tracking benchmark. The average video length of
LaSOT is more than 2,500 frames, and each sequence comprises various challenges
deriving from the wild where target objects may disappear and re-appear again
in the view. By releasing LaSOT, we expect to provide the community with a
large-scale dedicated benchmark with high quality for both the training of deep
trackers and the veritable evaluation of tracking algorithms. Moreover,
considering the close connections of visual appearance and natural language, we
enrich LaSOT by providing additional language specification, aiming at
encouraging the exploration of natural linguistic feature for tracking. A
thorough experimental evaluation of 35 tracking algorithms on LaSOT is
presented with detailed analysis, and the results demonstrate that there is
still a big room for improvements.
Bias Amplification in Artificial Intelligence Systems
Authors: Kirsten Lloyd
Date: 2018-09-20 00:00:00

As Artificial Intelligence (AI) technologies proliferate, concern has
centered around the long-term dangers of job loss or threats of machines
causing harm to humans. All of this concern, however, detracts from the more
pertinent and already existing threats posed by AI today: its ability to
amplify bias found in training datasets, and swiftly impact marginalized
populations at scale. Government and public sector institutions have a
responsibility to citizens to establish a dialogue with technology developers
and release thoughtful policy around data standards to ensure diverse
representation in datasets to prevent bias amplification and ensure that AI
systems are built with inclusion in mind.
LSTM-based Whisper Detection
Authors: Zeynab Raeesy, Kellen Gillespie, Zhenpei Yang, Chengyuan Ma, Thomas Drugman, Jiacheng Gu, Roland Maas, Ariya Rastrow, Björn Hoffmeister
Date: 2018-09-20 00:00:00
Tasks: Benchmarking

This article presents a whisper speech detector in the far-field domain. The proposed system consists of a long-short term memory (LSTM) neural network trained on log-filterbank energy (LFBE) acoustic features. This model is trained and evaluated on recordings of human interactions with voice-controlled, far-field devices in whisper and normal phonation modes. We compare multiple inference approaches for utterance-level classification by examining trajectories of the LSTM posteriors. In addition, we engineer a set of features based on the signal characteristics inherent to whisper speech, and evaluate their effectiveness in further separating whisper from normal speech. A benchmarking of these features using multilayer perceptrons (MLP) and LSTMs suggests that the proposed features, in combination with LFBE features, can help us further improve our classifiers. We prove that, with enough data, the LSTM model is indeed as capable of learning whisper characteristics from LFBE features alone compared to a simpler MLP model that uses both LFBE and features engineered for separating whisper and normal speech. In addition, we prove that the LSTM classifiers accuracy can be further improved with the incorporation of the proposed engineered features.