pwc-archive/papers-with-abstracts huggingface dataset:
Future Automation Engineering using Structural Graph Convolutional Neural Networks
Authors: Jiang Wan, Blake S. Pollard, Sujit Rokka Chhetri, Palash Goyal, Mohammad Abdullah Al Faruque, Arquimedes Canedo
Date: 2018-08-24 00:00:00
Tasks: Clustering, General Classification, Graph Learning, Knowledge Graphs

The digitalization of automation engineering generates large quantities of
engineering data that is interlinked in knowledge graphs. Classifying and
clustering subgraphs according to their functionality is useful to discover
functionally equivalent engineering artifacts that exhibit different graph
structures. This paper presents a new graph learning algorithm designed to
classify engineering data artifacts -- represented in the form of graphs --
according to their structure and neighborhood features. Our Structural Graph
Convolutional Neural Network (SGCNN) is capable of learning graphs and
subgraphs with a novel graph invariant convolution kernel and
downsampling/pooling algorithm. On a realistic engineering-related dataset, we
show that SGCNN is capable of achieving ~91% classification accuracy.
Automatic Foreground Extraction from Imperfect Backgrounds using Multi-Agent Consensus Equilibrium
Authors: Xiran Wang, Jason Juang, Stanley H. Chan
Date: 2018-08-24 00:00:00
Tasks: Denoising, Image Denoising, Image Matting, Semantic Segmentation, Video Segmentation, Video Semantic Segmentation

Extracting accurate foreground objects from a scene is an essential step for many video applications. Traditional background subtraction algorithms can generate coarse estimates, but generating high quality masks requires professional softwares with significant human interventions, e.g., providing trimaps or labeling key frames. We propose an automatic foreground extraction method in applications where a static but imperfect background is available. Examples include filming and surveillance where the background can be captured before the objects enter the scene or after they leave the scene. Our proposed method is very robust and produces significantly better estimates than state-of-the-art background subtraction, video segmentation and alpha matting methods. The key innovation of our method is a novel information fusion technique. The fusion framework allows us to integrate the individual strengths of alpha matting, background subtraction and image denoising to produce an overall better estimate. Such integration is particularly important when handling complex scenes with imperfect background. We show how the framework is developed, and how the individual components are built. Extensive experiments and ablation studies are conducted to evaluate the proposed method.
GoT-WAVE: Temporal network alignment using graphlet-orbit transitions
Authors: David Aparício, Pedro Ribeiro, Tijana Milenković, Fernando Silva
Date: 2018-08-24 00:00:00

Global pairwise network alignment (GPNA) aims to find a one-to-one node
mapping between two networks that identifies conserved network regions. GPNA
algorithms optimize node conservation (NC) and edge conservation (EC). NC
quantifies topological similarity between nodes. Graphlet-based degree vectors
(GDVs) are a state-of-the-art topological NC measure. Dynamic GDVs (DGDVs) were
used as a dynamic NC measure within the first-ever algorithms for GPNA of
temporal networks: DynaMAGNA++ and DynaWAVE. The latter is superior for larger
networks. We recently developed a different graphlet-based measure of temporal
node similarity, graphlet-orbit transitions (GoTs). Here, we use GoTs instead
of DGDVs as a new dynamic NC measure within DynaWAVE, resulting in a new
approach, GoT-WAVE.
  On synthetic networks, GoT-WAVE improves DynaWAVE's accuracy by 25% and speed
by 64%. On real networks, when optimizing only dynamic NC, each method is
superior ~50% of the time. While DynaWAVE benefits more from also optimizing
dynamic EC, only GoT-WAVE can support directed edges. Hence, GoT-WAVE is a
promising new temporal GPNA algorithm, which efficiently optimizes dynamic NC.
Future work on better incorporating dynamic EC may yield further improvements.
MVOR: A Multi-view RGB-D Operating Room Dataset for 2D and 3D Human Pose Estimation
Authors: Vinkle Srivastav, Thibaut Issenhuth, Abdolrahim Kadkhodamohammadi, Michel de Mathelin, Afshin Gangi, Nicolas Padoy
Date: 2018-08-24 00:00:00
Tasks: 3D Human Pose Estimation, 3D Pose Estimation, Camera Calibration, Human Detection, Pose Estimation

Person detection and pose estimation is a key requirement to develop intelligent context-aware assistance systems. To foster the development of human pose estimation methods and their applications in the Operating Room (OR), we release the Multi-View Operating Room (MVOR) dataset, the first public dataset recorded during real clinical interventions. It consists of 732 synchronized multi-view frames recorded by three RGB-D cameras in a hybrid OR. It also includes the visual challenges present in such environments, such as occlusions and clutter. We provide camera calibration parameters, color and depth frames, human bounding boxes, and 2D/3D pose annotations. In this paper, we present the dataset, its annotations, as well as baseline results from several recent person detection and 2D/3D pose estimation methods. Since we need to blur some parts of the images to hide identity and nudity in the released dataset, we also present a comparative study of how the baselines have been impacted by the blurring. Results show a large margin for improvement and suggest that the MVOR dataset can be useful to compare the performance of the different methods.
STDP Learning of Image Patches with Convolutional Spiking Neural Networks
Authors: Daniel J. Saunders, Hava T. Siegelmann, Robert Kozma, Miklós Ruszinkó
Date: 2018-08-24 00:00:00
Tasks: BIG-bench Machine Learning

Spiking neural networks are motivated from principles of neural systems and
may possess unexplored advantages in the context of machine learning. A class
of \textit{convolutional spiking neural networks} is introduced, trained to
detect image features with an unsupervised, competitive learning mechanism.
Image features can be shared within subpopulations of neurons, or each may
evolve independently to capture different features in different regions of
input space. We analyze the time and memory requirements of learning with and
operating such networks. The MNIST dataset is used as an experimental testbed,
and comparisons are made between the performance and convergence speed of a
baseline spiking neural network.
An Empirical Study of Rich Subgroup Fairness for Machine Learning
Authors: Michael Kearns, Seth Neel, Aaron Roth, Zhiwei Steven Wu
Date: 2018-08-24 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Kearns et al. [2018] recently proposed a notion of rich subgroup fairness
intended to bridge the gap between statistical and individual notions of
fairness. Rich subgroup fairness picks a statistical fairness constraint (say,
equalizing false positive rates across protected groups), but then asks that
this constraint hold over an exponentially or infinitely large collection of
subgroups defined by a class of functions with bounded VC dimension. They give
an algorithm guaranteed to learn subject to this constraint, under the
condition that it has access to oracles for perfectly learning absent a
fairness constraint. In this paper, we undertake an extensive empirical
evaluation of the algorithm of Kearns et al. On four real datasets for which
fairness is a concern, we investigate the basic convergence of the algorithm
when instantiated with fast heuristics in place of learning oracles, measure
the tradeoffs between fairness and accuracy, and compare this approach with the
recent algorithm of Agarwal et al. [2018], which implements weaker and more
traditional marginal fairness constraints defined by individual protected
attributes. We find that in general, the Kearns et al. algorithm converges
quickly, large gains in fairness can be obtained with mild costs to accuracy,
and that optimizing accuracy subject only to marginal fairness leads to
classifiers with substantial subgroup unfairness. We also provide a number of
analyses and visualizations of the dynamics and behavior of the Kearns et al.
algorithm. Overall we find this algorithm to be effective on real data, and
rich subgroup fairness to be a viable notion in practice.
Different but Equal: Comparing User Collaboration with Digital Personal Assistants vs. Teams of Expert Agents
Authors: Claudio S. Pinhanez, Heloisa Candello, Mauro C. Pichiliani, Marisa Vasconcelos, Melina Guerra, Maíra G. de Bayser, Paulo Cavalin
Date: 2018-08-24 00:00:00
Tasks: Chatbot

This work compares user collaboration with conversational personal assistants
vs. teams of expert chatbots. Two studies were performed to investigate whether
each approach affects accomplishment of tasks and collaboration costs.
Participants interacted with two equivalent financial advice chatbot systems,
one composed of a single conversational adviser and the other based on a team
of four experts chatbots. Results indicated that users had different forms of
experiences but were equally able to achieve their goals. Contrary to the
expected, there were evidences that in the teamwork situation that users were
more able to predict agent behavior better and did not have an overhead to
maintain common ground, indicating similar collaboration costs. The results
point towards the feasibility of either of the two approaches for user
collaboration with conversational agents.
From Random to Supervised: A Novel Dropout Mechanism Integrated with Global Information
Authors: Hengru Xu, Shen Li, Renfen Hu, Si Li, Sheng Gao
Date: 2018-08-24 00:00:00
Proceeding: CONLL 2018 10
Tasks: Classification, General Classification, Sentiment Analysis, text-classification, Text Classification, Topic Classification

Dropout is used to avoid overfitting by randomly dropping units from the
neural networks during training. Inspired by dropout, this paper presents
GI-Dropout, a novel dropout method integrating with global information to
improve neural networks for text classification. Unlike the traditional dropout
method in which the units are dropped randomly according to the same
probability, we aim to use explicit instructions based on global information of
the dataset to guide the training process. With GI-Dropout, the model is
supposed to pay more attention to inapparent features or patterns. Experiments
demonstrate the effectiveness of the dropout with global information on seven
text classification tasks, including sentiment analysis and topic
classification.
Measuring LDA Topic Stability from Clusters of Replicated Runs
Authors: Mika Mäntylä, Maëlick Claes, Umar Farooq
Date: 2018-08-24 00:00:00
Tasks: Clustering

Background: Unstructured and textual data is increasing rapidly and Latent
Dirichlet Allocation (LDA) topic modeling is a popular data analysis methods
for it. Past work suggests that instability of LDA topics may lead to
systematic errors. Aim: We propose a method that relies on replicated LDA runs,
clustering, and providing a stability metric for the topics. Method: We
generate k LDA topics and replicate this process n times resulting in n*k
topics. Then we use K-medioids to cluster the n*k topics to k clusters. The k
clusters now represent the original LDA topics and we present them like normal
LDA topics showing the ten most probable words. For the clusters, we try
multiple stability metrics, out of which we recommend Rank-Biased Overlap,
showing the stability of the topics inside the clusters. Results: We provide an
initial validation where our method is used for 270,000 Mozilla Firefox commit
messages with k=20 and n=20. We show how our topic stability metrics are
related to the contents of the topics. Conclusions: Advances in text mining
enable us to analyze large masses of text in software engineering but
non-deterministic algorithms, such as LDA, may lead to unreplicable
conclusions. Our approach makes LDA stability transparent and is also
complementary rather than alternative to many prior works that focus on LDA
parameter tuning.
Memory Time Span in LSTMs for Multi-Speaker Source Separation
Authors: Jeroen Zegers, Hugo Van hamme
Date: 2018-08-24 00:00:00
Tasks: Multi-Speaker Source Separation

With deep learning approaches becoming state-of-the-art in many speech (as
well as non-speech) related machine learning tasks, efforts are being taken to
delve into the neural networks which are often considered as a black box. In
this paper it is analyzed how recurrent neural network (RNNs) cope with
temporal dependencies by determining the relevant memory time span in a long
short-term memory (LSTM) cell. This is done by leaking the state variable with
a controlled lifetime and evaluating the task performance. This technique can
be used for any task to estimate the time span the LSTM exploits in that
specific scenario. The focus in this paper is on the task of separating
speakers from overlapping speech. We discern two effects: A long term effect,
probably due to speaker characterization and a short term effect, probably
exploiting phone-size formant tracks.
Multi-scenario deep learning for multi-speaker source separation
Authors: Jeroen Zegers, Hugo Van hamme
Date: 2018-08-24 00:00:00
Tasks: Deep Learning, Multi-Speaker Source Separation

Research in deep learning for multi-speaker source separation has received a
boost in the last years. However, most studies are restricted to mixtures of a
specific number of speakers, called a specific scenario. While some works
included experiments for different scenarios, research towards combining data
of different scenarios or creating a single model for multiple scenarios have
been very rare. In this work it is shown that data of a specific scenario is
relevant for solving another scenario. Furthermore, it is concluded that a
single model, trained on different scenarios is capable of matching performance
of scenario specific models.
Atherosclerotic carotid plaques on panoramic imaging: an automatic detection using deep learning with small dataset
Authors: Lazar Kats, Marilena Vered, Ayelet Zlotogorski-Hurvitz, Itai Harpaz
Date: 2018-08-24 00:00:00
Tasks: Data Augmentation, Specificity

Stroke is the second most frequent cause of death worldwide with a
considerable economic burden on the health systems. In about 15% of strokes,
atherosclerotic carotid plaques (ACPs) constitute the main etiological factor.
Early detection of ACPs may have a key-role for preventing strokes by managing
the patient a-priory to the occurrence of the damage. ACPs can be detected on
panoramic images. As these are one of the most common images performed for
routine dental practice, they can be used as a source of available data for
computerized methods of automatic detection in order to significantly increase
timely diagnosis of ACPs. Recently, there has been a definite breakthrough in
the field of analysis of medical images due to the use of deep learning based
on neural networks. These methods, however have been barely used in dentistry.
In this study we used the Faster Region-based Convolutional Network (Faster
R-CNN) for deep learning. We aimed to assess the operation of the algorithm on
a small database of 65 panoramic images. Due to a small amount of available
training data, we had to use data augmentation by changing the brightness and
randomly flipping and rotating cropped regions of interest in multiple angles.
Receiver Operating Characteristic (ROC) analysis was performed to calculate the
accuracy of detection. ACP was detected with a sensitivity of 75%, specificity
of 80% and an accuracy of 83%. The ROC analysis showed a significant Area Under
Curve (AUC) difference from 0.5. Our novelty lies in that we have showed the
efficiency of the Faster R-CNN algorithm in detecting ACPs on routine panoramic
images based on a small database. There is a need to further improve the
application of the algorithm to the level of introducing this methodology in
routine dental practice in order to enable us to prevent stroke events.
Under the Hood: Using Diagnostic Classifiers to Investigate and Improve how Language Models Track Agreement Information
Authors: Mario Giulianelli, Jacqueline Harding, Florian Mohnert, Dieuwke Hupkes, Willem Zuidema
Date: 2018-08-24 00:00:00
Proceeding: WS 2018 11
Tasks: Diagnostic, Language Modeling, Language Modelling

How do neural language models keep track of number agreement between subject and verb? We show that `diagnostic classifiers', trained to predict number from the internal states of a language model, provide a detailed understanding of how, when, and where this information is represented. Moreover, they give us insight into when and where number information is corrupted in cases where the language model ends up making agreement errors. To demonstrate the causal role played by the representations we find, we then use agreement information to influence the course of the LSTM during the processing of difficult sentences. Results from such an intervention reveal a large increase in the language model's accuracy. Together, these results show that diagnostic classifiers give us an unrivalled detailed look into the representation of linguistic information in neural models, and demonstrate that this knowledge can be used to improve their performance.
Self-Paced Multi-Task Clustering
Authors: Yazhou Ren, Xiaofan Que, Dezhong Yao, Zenglin Xu
Date: 2018-08-24 00:00:00
Tasks: Clustering

Multi-task clustering (MTC) has attracted a lot of research attentions in
machine learning due to its ability in utilizing the relationship among
different tasks. Despite the success of traditional MTC models, they are either
easy to stuck into local optima, or sensitive to outliers and noisy data. To
alleviate these problems, we propose a novel self-paced multi-task clustering
(SPMTC) paradigm. In detail, SPMTC progressively selects data examples to train
a series of MTC models with increasing complexity, thus highly decreases the
risk of trapping into poor local optima. Furthermore, to reduce the negative
influence of outliers and noisy data, we design a soft version of SPMTC to
further improve the clustering performance. The corresponding SPMTC framework
can be easily solved by an alternating optimization method. The proposed model
is guaranteed to converge and experiments on real data sets have demonstrated
its promising results compared with state-of-the-art multi-task clustering
methods.
Towards Machine Learning-Based Optimal HAS
Authors: Christian Sieber, Korbinian Hagn, Christian Moldovan, Tobias Hoßfeld, Wolfgang Kellerer
Date: 2018-08-24 00:00:00
Tasks: BIG-bench Machine Learning, Reinforcement Learning

Mobile video consumption is increasing and sophisticated video quality
adaptation strategies are required to deal with mobile throughput fluctuations.
These adaptation strategies have to keep the switching frequency low, the
average quality high and prevent stalling occurrences to ensure customer
satisfaction. This paper proposes a novel methodology for the design of machine
learning-based adaptation logics named HASBRAIN. Furthermore, the performance
of a trained neural network against two algorithms from the literature is
evaluated. We first use a modified existing optimization formulation to
calculate optimal adaptation paths with a minimum number of quality switches
for a wide range of videos and for challenging mobile throughput patterns.
Afterwards we use the resulting optimal adaptation paths to train and compare
different machine learning models. The evaluation shows that an artificial
neural network-based model can reach a high average quality with a low number
of switches in the mobile scenario. The proposed methodology is general enough
to be extended for further designs of machine learning-based algorithms and the
provided model can be deployed in on-demand streaming scenarios or be further
refined using reward-based mechanisms such as reinforcement learning. All
tools, models and datasets created during the work are provided as open-source
software.
Role Semantics for Better Models of Implicit Discourse Relations
Authors: Michael Roth
Date: 2018-08-24 00:00:00
Proceeding: WS 2017 1

Predicting the structure of a discourse is challenging because relations
between discourse segments are often implicit and thus hard to distinguish
computationally. I extend previous work to classify implicit discourse
relations by introducing a novel set of features on the level of semantic
roles. My results demonstrate that such features are helpful, yielding results
competitive with other feature-rich approaches on the PDTB. My main
contribution is an analysis of improvements that can be traced back to
role-based features, providing insights into why and when role semantics is
helpful.
Decision fusion with multiple spatial supports by conditional random fields
Authors: Devis Tuia, Michele Volpi, Gabriele Moser
Date: 2018-08-24 00:00:00
Tasks: General Classification

Classification of remotely sensed images into land cover or land use is
highly dependent on geographical information at least at two levels. First,
land cover classes are observed in a spatially smooth domain separated by sharp
region boundaries. Second, land classes and observation scale are also tightly
intertwined: they tend to be consistent within areas of homogeneous appearance,
or regions, in the sense that all pixels within a roof should be classified as
roof, independently on the spatial support used for the classification. In this
paper, we follow these two observations and encode them as priors in an energy
minimization framework based on conditional random fields (CRFs), where
classification results obtained at pixel and region levels are
probabilistically fused. The aim is to enforce the final maps to be consistent
not only in their own spatial supports (pixel and region) but also across
supports, i.e., by getting the predictions on the pixel lattice and on the set
of regions to agree. To this end, we define an energy function with three
terms: 1) a data term for the individual elements in each support
(support-specific nodes); 2) spatial regularization terms in a neighborhood for
each of the supports (support-specific edges); and 3) a regularization term
between individual pixels and the region containing each of them (intersupports
edges). We utilize these priors in a unified energy minimization problem that
can be optimized by standard solvers. The proposed 2LCRF model consists of a
CRF defined over a bipartite graph, i.e., two interconnected layers within a
single graph accounting for interlattice connections.
A Jointly Learned Context-Aware Place of Interest Embedding for Trip Recommendations
Authors: Jiayuan He, Jianzhong Qi, Kotagiri Ramamohanarao
Date: 2018-08-24 00:00:00

Trip recommendation is an important location-based service that helps relieve
users from the time and efforts for trip planning. It aims to recommend a
sequence of places of interest (POIs) for a user to visit that maximizes the
user's satisfaction. When adding a POI to a recommended trip, it is essential
to understand the context of the recommendation, including the POI popularity,
other POIs co-occurring in the trip, and the preferences of the user. These
contextual factors are learned separately in existing studies, while in
reality, they impact jointly on a user's choice of a POI to visit. In this
study, we propose a POI embedding model to jointly learn the impact of these
contextual factors. We call the learned POI embedding a context-aware POI
embedding. To showcase the effectiveness of this embedding, we apply it to
generate trip recommendations given a user and a time budget. We propose two
trip recommendation algorithms based on our context-aware POI embedding. The
first algorithm finds the exact optimal trip by transforming and solving the
trip recommendation problem as an integer linear programming problem. To
achieve a high computation efficiency, the second algorithm finds a
heuristically optimal trip based on adaptive large neighborhood search. We
perform extensive experiments on real datasets. The results show that our
proposed algorithms consistently outperform state-of-the-art algorithms in trip
recommendation quality, with an advantage of up to 43% in F1-score.