pwc-archive/papers-with-abstracts huggingface dataset:
Giving Attention to the Unexpected: Using Prosody Innovations in Disfluency Detection
Authors: Vicky Zayats, Mari Ostendorf
Date: 2019-04-08 00:00:00
Proceeding: NAACL 2019 6

Disfluencies in spontaneous speech are known to be associated with prosodic
disruptions. However, most algorithms for disfluency detection use only word
transcripts. Integrating prosodic cues has proved difficult because of the many
sources of variability affecting the acoustic correlates. This paper introduces
a new approach to extracting acoustic-prosodic cues using text-based
distributional prediction of acoustic cues to derive vector z-score features
(innovations). We explore both early and late fusion techniques for integrating
text and prosody, showing gains over a high-accuracy text-only model.
Hierarchical Temporal Convolutional Networks for Dynamic Recommender Systems
Authors: Jiaxuan You, Yichen Wang, Aditya Pal, Pong Eksombatchai, Chuck Rosenberg, Jure Leskovec
Date: 2019-04-08 00:00:00
Tasks: GPU, Recommendation Systems, Session-Based Recommendations

Recommender systems that can learn from cross-session data to dynamically
predict the next item a user will choose are crucial for online platforms.
However, existing approaches often use out-of-the-box sequence models which are
limited by speed and memory consumption, are often infeasible for production
environments, and usually do not incorporate cross-session information, which
is crucial for effective recommendations. Here we propose Hierarchical Temporal
Convolutional Networks (HierTCN), a hierarchical deep learning architecture
that makes dynamic recommendations based on users' sequential multi-session
interactions with items. HierTCN is designed for web-scale systems with
billions of items and hundreds of millions of users. It consists of two levels
of models: The high-level model uses Recurrent Neural Networks (RNN) to
aggregate users' evolving long-term interests across different sessions, while
the low-level model is implemented with Temporal Convolutional Networks (TCN),
utilizing both the long-term interests and the short-term interactions within
sessions to predict the next interaction. We conduct extensive experiments on a
public XING dataset and a large-scale Pinterest dataset that contains 6 million
users with 1.6 billion interactions. We show that HierTCN is 2.5x faster than
RNN-based models and uses 90% less data memory compared to TCN-based models. We
further develop an effective data caching scheme and a queue-based mini-batch
generator, enabling our model to be trained within 24 hours on a single GPU.
Our model consistently outperforms state-of-the-art dynamic recommendation
methods, with up to 18% improvement in recall and 10% in mean reciprocal rank.
Learning Attribute Patterns in High-Dimensional Structured Latent Attribute Models
Authors: Yuqi Gu, Gongjun Xu
Date: 2019-04-08 00:00:00
Tasks: Attribute, Vocal Bursts Intensity Prediction

Structured latent attribute models (SLAMs) are a special family of discrete latent variable models widely used in social and biological sciences. This paper considers the problem of learning significant attribute patterns from a SLAM with potentially high-dimensional configurations of the latent attributes. We address the theoretical identifiability issue, propose a penalized likelihood method for the selection of the attribute patterns, and further establish the selection consistency in such an overfitted SLAM with diverging number of latent patterns. The good performance of the proposed methodology is illustrated by simulation studies and two real datasets in educational assessment.
Lecturer Performance System Using Neural Network with Particle Swarm Optimization
Authors: Tarik A. Rashid, Hawraz A. Ahmad
Date: 2019-04-08 00:00:00

The field of analyzing performance is very important and sensitive in
particular when it is related to the performance of lecturers in academic
institutions. Locating the weak points of lecturers through a system that
provides an early warning to notify or reward the lecturers with warned or
punished notices will help them to improve their weaknesses, leads to a better
quality in the institutions. The current system has major issues in the higher
education at Salahaddin University-Erbil (SUE) in Kurdistan-Iraq. These issues
are: first, the assessment of lecturers' activities is conducted traditionally
via the Quality Assurance Teams at different departments and colleges at the
university, second, the outcomes in some cases of lecturers' performance
provoke a low level of acceptance among lectures, as these cases are reflected
and viewed by some academic communities as unfair cases, and finally, the
current system is not accurate and vigorous. In this paper, Particle Swarm
Optimization Neural Network is used to assess performance of lecturers in more
fruitful way and also to enhance the accuracy of recognition system. Different
real and novel data sets are collected from SUE. The prepared datasets
preprocessed and important features are then fed as input source to the
training and testing phases. Particle Swarm Optimization is used to find the
best weights and biases in the training phase of the neural network. The best
accuracy rate obtained in the test phase is 98.28 %.
Controlling Steering Angle for Cooperative Self-driving Vehicles utilizing CNN and LSTM-based Deep Networks
Authors: Rodolfo Valiente, Mahdi Zaman, Sedat Ozer, Yaser P. Fallah
Date: 2019-04-08 00:00:00
Tasks: Autonomous Vehicles

A fundamental challenge in autonomous vehicles is adjusting the steering angle at different road conditions. Recent state-of-the-art solutions addressing this challenge include deep learning techniques as they provide end-to-end solution to predict steering angles directly from the raw input images with higher accuracy. Most of these works ignore the temporal dependencies between the image frames. In this paper, we tackle the problem of utilizing multiple sets of images shared between two autonomous vehicles to improve the accuracy of controlling the steering angle by considering the temporal dependencies between the image frames. This problem has not been studied in the literature widely. We present and study a new deep architecture to predict the steering angle automatically by using Long-Short-Term-Memory (LSTM) in our deep architecture. Our deep architecture is an end-to-end network that utilizes CNN, LSTM and fully connected (FC) layers and it uses both present and futures images (shared by a vehicle ahead via Vehicle-to-Vehicle (V2V) communication) as input to control the steering angle. Our model demonstrates the lowest error when compared to the other existing approaches in the literature.
Improved Embeddings with Easy Positive Triplet Mining
Authors: Hong Xuan, Abby Stylianou, Robert Pless
Date: 2019-04-08 00:00:00
Tasks: Image Retrieval, Metric Learning, Retrieval, Triplet

Deep metric learning seeks to define an embedding where semantically similar images are embedded to nearby locations, and semantically dissimilar images are embedded to distant locations. Substantial work has focused on loss functions and strategies to learn these embeddings by pushing images from the same class as close together in the embedding space as possible. In this paper, we propose an alternative, loosened embedding strategy that requires the embedding function only map each training image to the most similar examples from the same class, an approach we call "Easy Positive" mining. We provide a collection of experiments and visualizations that highlight that this Easy Positive mining leads to embeddings that are more flexible and generalize better to new unseen data. This simple mining strategy yields recall performance that exceeds state of the art approaches (including those with complicated loss functions and ensemble methods) on image retrieval datasets including CUB, Stanford Online Products, In-Shop Clothes and Hotels-50K.
CODAH: An Adversarially Authored Question-Answer Dataset for Common Sense
Authors: Michael Chen, Mike D'Arcy, Alisa Liu, Jared Fernandez, Doug Downey
Date: 2019-04-08 00:00:00
Tasks: Common Sense Reasoning, Question Answering, Sentence, Sentence Completion

Commonsense reasoning is a critical AI capability, but it is difficult to construct challenging datasets that test common sense. Recent neural question answering systems, based on large pre-trained models of language, have already achieved near-human-level performance on commonsense knowledge benchmarks. These systems do not possess human-level common sense, but are able to exploit limitations of the datasets to achieve human-level scores. We introduce the CODAH dataset, an adversarially-constructed evaluation dataset for testing common sense. CODAH forms a challenging extension to the recently-proposed SWAG dataset, which tests commonsense knowledge using sentence-completion questions that describe situations observed in video. To produce a more difficult dataset, we introduce a novel procedure for question acquisition in which workers author questions designed to target weaknesses of state-of-the-art neural question answering systems. Workers are rewarded for submissions that models fail to answer correctly both before and after fine-tuning (in cross-validation). We create 2.8k questions via this procedure and evaluate the performance of multiple state-of-the-art question answering systems on our dataset. We observe a significant gap between human performance, which is 95.3%, and the performance of the best baseline accuracy of 67.5% by the BERT-Large model.
Audio Classification of Bit-Representation Waveform
Authors: Masaki Okawa, Takuya Saito, Naoki Sawada, Hiromitsu Nishizaki
Date: 2019-04-08 00:00:00
Tasks: Audio Classification, Classification, Event Detection, General Classification, Genre classification, Music Classification, Music Genre Classification

This study investigated the waveform representation for audio signal classification. Recently, many studies on audio waveform classification such as acoustic event detection and music genre classification have been published. Most studies on audio waveform classification have proposed the use of a deep learning (neural network) framework. Generally, a frequency analysis method such as Fourier transform is applied to extract the frequency or spectral information from the input audio waveform before inputting the raw audio waveform into the neural network. In contrast to these previous studies, in this paper, we propose a novel waveform representation method, in which audio waveforms are represented as a bit sequence, for audio classification. In our experiment, we compare the proposed bit representation waveform, which is directly given to a neural network, to other representations of audio waveforms such as a raw audio waveform and a power spectrum with two classification tasks: one is an acoustic event classification task and the other is a sound/music classification task. The experimental results showed that the bit representation waveform achieved the best classification performance for both the tasks.
A Robust Visual System for Small Target Motion Detection Against Cluttered Moving Backgrounds
Authors: Hongxin Wang, Jigen Peng, Xuqiang Zheng, Shigang Yue
Date: 2019-04-08 00:00:00
Tasks: Motion Detection

Monitoring small objects against cluttered moving backgrounds is a huge
challenge to future robotic vision systems. As a source of inspiration, insects
are quite apt at searching for mates and tracking prey -- which always appear
as small dim speckles in the visual field. The exquisite sensitivity of insects
for small target motion, as revealed recently, is coming from a class of
specific neurons called small target motion detectors (STMDs). Although a few
STMD-based models have been proposed, these existing models only use motion
information for small target detection and cannot discriminate small targets
from small-target-like background features (named as fake features). To address
this problem, this paper proposes a novel visual system model (STMD+) for small
target motion detection, which is composed of four subsystems -- ommatidia,
motion pathway, contrast pathway and mushroom body. Compared to existing
STMD-based models, the additional contrast pathway extracts directional
contrast from luminance signals to eliminate false positive background motion.
The directional contrast and the extracted motion information by the motion
pathway are integrated in the mushroom body for small target discrimination.
Extensive experiments showed the significant and consistent improvements of the
proposed visual system model over existing STMD-based models against fake
features.
Optimizing Majority Voting Based Systems Under a Resource Constraint for Multiclass Problems
Authors: Attila Tiba, Andras Hajdu, Gyorgy Terdik, Henrietta Toman
Date: 2019-04-08 00:00:00
Tasks: General Classification

Ensemble-based approaches are very effective in various fields in raising the
accuracy of its individual members, when some voting rule is applied for
aggregating the individual decisions. In this paper, we investigate how to find
and characterize the ensembles having the highest accuracy if the total cost of
the ensemble members is bounded. This question leads to Knapsack problem with
non-linear and non-separable objective function in binary and multiclass
classification if the majority voting is chosen for the aggregation. As the
conventional solving methods cannot be applied for this task, a novel
stochastic approach was introduced in the binary case where the energy function
is discussed as the joint probability function of the member accuracy. We show
some theoretical results with respect to the expected ensemble accuracy and its
variance in the multiclass classification problem which can help us to solve
the Knapsack problem.
Deep Learning the EEG Manifold for Phonological Categorization from Active Thoughts
Authors: Pramit Saha, Muhammad Abdul-Mageed, Sidney Fels
Date: 2019-04-08 00:00:00
Tasks: Binary Classification, Deep Learning, EEG, Electroencephalogram (EEG), General Classification, Speech Synthesis

Speech-related Brain Computer Interfaces (BCI) aim primarily at finding an
alternative vocal communication pathway for people with speaking disabilities.
As a step towards full decoding of imagined speech from active thoughts, we
present a BCI system for subject-independent classification of phonological
categories exploiting a novel deep learning based hierarchical feature
extraction scheme. To better capture the complex representation of
high-dimensional electroencephalography (EEG) data, we compute the joint
variability of EEG electrodes into a channel cross-covariance matrix. We then
extract the spatio-temporal information encoded within the matrix using a mixed
deep neural network strategy. Our model framework is composed of a
convolutional neural network (CNN), a long-short term network (LSTM), and a
deep autoencoder. We train the individual networks hierarchically, feeding
their combined outputs in a final gradient boosting classification step. Our
best models achieve an average accuracy of 77.9% across five different binary
classification tasks, providing a significant 22.5% improvement over previous
methods. As we also show visually, our work demonstrates that the speech
imagery EEG possesses significant discriminative information about the intended
articulatory movements responsible for natural speech synthesis.
Heterogeneous Memory Enhanced Multimodal Attention Model for Video Question Answering
Authors: Chenyou Fan, Xiaofan Zhang, Shu Zhang, Wensheng Wang, Chi Zhang, Heng Huang
Date: 2019-04-08 00:00:00
Proceeding: CVPR 2019 6
Tasks: Question Answering, Video Question Answering, Visual Question Answering (VQA)

In this paper, we propose a novel end-to-end trainable Video Question
Answering (VideoQA) framework with three major components: 1) a new
heterogeneous memory which can effectively learn global context information
from appearance and motion features; 2) a redesigned question memory which
helps understand the complex semantics of question and highlights queried
subjects; and 3) a new multimodal fusion layer which performs multi-step
reasoning by attending to relevant visual and textual hints with self-updated
attention. Our VideoQA model firstly generates the global context-aware visual
and textual features respectively by interacting current inputs with memory
contents. After that, it makes the attentional fusion of the multimodal visual
and textual representations to infer the correct answer. Multiple cycles of
reasoning can be made to iteratively refine attention weights of the multimodal
data and improve the final representation of the QA pair. Experimental results
demonstrate our approach achieves state-of-the-art performance on four VideoQA
benchmark datasets.
Relational Reasoning Network (RRN) for Anatomical Landmarking
Authors: Neslisah Torosdagli, Syed Anwar, Payal Verma, Denise K Liberton, Janice S. Lee, Wade W. Han, Ulas Bagci
Date: 2019-04-08 00:00:00
Tasks: Imputation, Informativeness, Relational Reasoning, Segmentation

Purpose: We perform anatomical landmarking for craniomaxillofacial (CMF) bones without explicitly segmenting them. Towards this, we propose a new simple yet efficient deep network architecture, called \textit{relational reasoning network (RRN)}, to accurately learn the local and the global relations among the landmarks in CMF bones; specifically, mandible, maxilla, and nasal bones. Approach: The proposed RRN works in an end-to-end manner, utilizing learned relations of the landmarks based on dense-block units. For a given few landmarks as input, RRN treats the landmarking process similar to a data imputation problem where predicted landmarks are considered missing. Results: We applied RRN to cone beam computed tomography scans obtained from 250 patients. With a 4-fold cross validation technique, we obtained an average root mean squared error of less than 2 mm per landmark. Our proposed RRN has revealed unique relationships among the landmarks that help us in inferring several \textit{reasoning} about informativeness of the landmark points. The proposed system identifies the missing landmark locations accurately even when severe pathology or deformation are present in the bones. Conclusions: Accurately identifying anatomical landmarks is a crucial step in deformation analysis and surgical planning for CMF surgeries. Achieving this goal without the need for explicit bone segmentation addresses a major limitation of segmentation based approaches, where segmentation failure (as often the case in bones with severe pathology or deformation) could easily lead to incorrect landmarking. To the best of our knowledge, this is the first of its kind algorithm finding anatomical relations of the objects using deep learning.
Hierarchical Deep Feature Learning For Decoding Imagined Speech From EEG
Authors: Pramit Saha, Sidney Fels
Date: 2019-04-08 00:00:00
Tasks: EEG, Electroencephalogram (EEG), General Classification

We propose a mixed deep neural network strategy, incorporating parallel
combination of Convolutional (CNN) and Recurrent Neural Networks (RNN),
cascaded with deep autoencoders and fully connected layers towards automatic
identification of imagined speech from EEG. Instead of utilizing raw EEG
channel data, we compute the joint variability of the channels in the form of a
covariance matrix that provide spatio-temporal representations of EEG. The
networks are trained hierarchically and the extracted features are passed onto
the next network hierarchy until the final classification. Using a publicly
available EEG based speech imagery database we demonstrate around 23.45%
improvement of accuracy over the baseline method. Our approach demonstrates the
promise of a mixed DNN approach for complex spatial-temporal classification
problems.
L2AE-D: Learning to Aggregate Embeddings for Few-shot Learning with Meta-level Dropout
Authors: Heda Song, Mercedes Torres Torres, Ender Özcan, Isaac Triguero
Date: 2019-04-08 00:00:00
Tasks: Few-Shot Learning, Meta-Learning

Few-shot learning focuses on learning a new visual concept with very limited
labelled examples. A successful approach to tackle this problem is to compare
the similarity between examples in a learned metric space based on
convolutional neural networks. However, existing methods typically suffer from
meta-level overfitting due to the limited amount of training tasks and do not
normally consider the importance of the convolutional features of different
examples within the same channel. To address these limitations, we make the
following two contributions: (a) We propose a novel meta-learning approach for
aggregating useful convolutional features and suppressing noisy ones based on a
channel-wise attention mechanism to improve class representations. The proposed
model does not require fine-tuning and can be trained in an end-to-end manner.
The main novelty lies in incorporating a shared weight generation module that
learns to assign different weights to the feature maps of different examples
within the same channel. (b) We also introduce a simple meta-level dropout
technique that reduces meta-level overfitting in several few-shot learning
approaches. In our experiments, we find that this simple technique
significantly improves the performance of the proposed method as well as
various state-of-the-art meta-learning algorithms. Applying our method to
few-shot image recognition using Omniglot and miniImageNet datasets shows that
it is capable of delivering a state-of-the-art classification performance.
Quantifying the presence of graffiti in urban environments
Authors: Eric K. Tokuda, Claudio T. Silva, Roberto M. Cesar-Jr
Date: 2019-04-08 00:00:00

Graffiti is a common phenomenon in urban scenarios. Differently from urban
art, graffiti tagging is a vandalism act and many local governments are putting
great effort to combat it. The graffiti map of a region can be a very useful
resource because it may allow one to potentially combat vandalism in locations
with high level of graffiti and also to cleanup saturated regions to discourage
future acts. There is currently no automatic way of obtaining a graffiti map of
a region and it is obtained by manual inspection by the police or by popular
participation. In this sense, we describe an ongoing work where we propose an
automatic way of obtaining a graffiti map of a neighbourhood. It consists of
the systematic collection of street view images followed by the identification
of graffiti tags in the collected dataset and finally, in the calculation of
the proposed graffiti level of that location. We validate the proposed method
by evaluating the geographical distribution of graffiti in a city known to have
high concentration of graffiti -- Sao Paulo, Brazil.