pwc-archive/papers-with-abstracts huggingface dataset:
Discovering Common Change-Point Patterns in Functional Connectivity Across Subjects
Authors: Mengyu Dai, Zhengwu Zhang, Anuj Srivastava
Date: 2019-04-26 00:00:00
Tasks: Functional Connectivity, Time Series, Time Series Analysis

This paper studies change-points in human brain functional connectivity (FC)
and seeks patterns that are common across multiple subjects under identical
external stimulus. FC relates to the similarity of fMRI responses across
different brain regions when the brain is simply resting or performing a task.
While the dynamic nature of FC is well accepted, this paper develops a formal
statistical test for finding {\it change-points} in times series associated
with FC. It represents short-term connectivity by a symmetric positive-definite
matrix, and uses a Riemannian metric on this space to develop a graphical
method for detecting change-points in a time series of such matrices. It also
provides a graphical representation of estimated FC for stationary subintervals
in between the detected change-points. Furthermore, it uses a temporal
alignment of the test statistic, viewed as a real-valued function over time, to
remove inter-subject variability and to discover common change-point patterns
across subjects. This method is illustrated using data from Human Connectome
Project (HCP) database for multiple subjects and tasks.
Recurrent Embedding Aggregation Network for Video Face Recognition
Authors: Sixue Gong, Yichun Shi, Anil K. Jain
Date: 2019-04-26 00:00:00
Tasks: Face Recognition

Recurrent networks have been successful in analyzing temporal data and have been widely used for video analysis. However, for video face recognition, where the base CNNs trained on large-scale data already provide discriminative features, using Long Short-Term Memory (LSTM), a popular recurrent network, for feature learning could lead to overfitting and degrade the performance instead. We propose a Recurrent Embedding Aggregation Network (REAN) for set to set face recognition. Compared with LSTM, REAN is robust against overfitting because it only learns how to aggregate the pre-trained embeddings rather than learning representations from scratch. Compared with quality-aware aggregation methods, REAN can take advantage of the context information to circumvent the noise introduced by redundant video frames. Empirical results on three public domain video face recognition datasets, IJB-S, YTF, and PaSC show that the proposed REAN significantly outperforms naive CNN-LSTM structure and quality-aware aggregation methods.
A Distributed Method for Fitting Laplacian Regularized Stratified Models
Authors: Jonathan Tuck, Shane Barratt, Stephen Boyd
Date: 2019-04-26 00:00:00

Stratified models are models that depend in an arbitrary way on a set of selected categorical features, and depend linearly on the other features. In a basic and traditional formulation a separate model is fit for each value of the categorical feature, using only the data that has the specific categorical value. To this formulation we add Laplacian regularization, which encourages the model parameters for neighboring categorical values to be similar. Laplacian regularization allows us to specify one or more weighted graphs on the stratification feature values. For example, stratifying over the days of the week, we can specify that the Sunday model parameter should be close to the Saturday and Monday model parameters. The regularization improves the performance of the model over the traditional stratified model, since the model for each value of the categorical `borrows strength' from its neighbors. In particular, it produces a model even for categorical values that did not appear in the training data set. We propose an efficient distributed method for fitting stratified models, based on the alternating direction method of multipliers (ADMM). When the fitting loss functions are convex, the stratified model fitting problem is convex, and our method computes the global minimizer of the loss plus regularization; in other cases it computes a local minimizer. The method is very efficient, and naturally scales to large data sets or numbers of stratified feature values. We illustrate our method with a variety of examples.
RevealNet: Seeing Behind Objects in RGB-D Scans
Authors: Ji Hou, Angela Dai, Matthias Nießner
Date: 2019-04-26 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Reconstruction, 3D Semantic Instance Segmentation, Instance Segmentation, Object, Semantic Segmentation

During 3D reconstruction, it is often the case that people cannot scan each individual object from all views, resulting in missing geometry in the captured scan. This missing geometry can be fundamentally limiting for many applications, e.g., a robot needs to know the unseen geometry to perform a precise grasp on an object. Thus, we introduce the task of semantic instance completion: from an incomplete RGB-D scan of a scene, we aim to detect the individual object instances and infer their complete object geometry. This will open up new possibilities for interactions with objects in a scene, for instance for virtual or robotic agents. We tackle this problem by introducing RevealNet, a new data-driven approach that jointly detects object instances and predicts their complete geometry. This enables a semantically meaningful decomposition of a scanned scene into individual, complete 3D objects, including hidden and unobserved object parts. RevealNet is an end-to-end 3D neural network architecture that leverages joint color and geometry feature learning. The fully-convolutional nature of our 3D network enables efficient inference of semantic instance completion for 3D scans at scale of large indoor environments in a single forward pass. We show that predicting complete object geometry improves both 3D detection and instance segmentation performance. We evaluate on both real and synthetic scan benchmark data for the new task, where we outperform state-of-the-art approaches by over 15 in mAP@0.5 on ScanNet, and over 18 in mAP@0.5 on SUNCG.
Relevant features for Gender Classification in NIR Periocular Images
Authors: Ignacio Viedma, Juan Tapia, Andres Iturriaga, Christoph Busch
Date: 2019-04-26 00:00:00
Tasks: Classification, Gender Classification, General Classification

Most gender classifications methods from NIR images have used iris
information. Recent work has explored the use of the whole periocular iris
region which has surprisingly achieve better results. This suggests the most
relevant information for gender classification is not located in the iris as
expected. In this work, we analyze and demonstrate the location of the most
relevant features that describe gender in periocular NIR images and evaluate
its influence its classification. Experiments show that the periocular region
contains more gender information than the iris region. We extracted several
features (intensity, texture, and shape) and classified them according to its
relevance using the XgBoost algorithm. Support Vector Machine and nine ensemble
classifiers were used for testing gender accuracy when using the most relevant
features. The best classification results were obtained when 4,000 features
located on the periocular region were used (89.22\%). Additional experiments
with the full periocular iris images versus the iris-Occluded images were
performed. The gender classification rates obtained were 84.35\% and 85.75\%
respectively. We also contribute to the state of the art with a new database
(UNAB-Gender). From results, we suggest focussing only on the surrounding area
of the iris. This allows us to realize a faster classification of gender from
NIR periocular images.
Knowing When to Stop: Evaluation and Verification of Conformity to Output-size Specifications
Authors: Chenglong Wang, Rudy Bunel, Krishnamurthy Dvijotham, Po-Sen Huang, Edward Grefenstette, Pushmeet Kohli
Date: 2019-04-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: Image Captioning, Machine Translation, Translation

Models such as Sequence-to-Sequence and Image-to-Sequence are widely used in
real world applications. While the ability of these neural architectures to
produce variable-length outputs makes them extremely effective for problems
like Machine Translation and Image Captioning, it also leaves them vulnerable
to failures of the form where the model produces outputs of undesirable length.
This behavior can have severe consequences such as usage of increased
computation and induce faults in downstream modules that expect outputs of a
certain length. Motivated by the need to have a better understanding of the
failures of these models, this paper proposes and studies the novel output-size
modulation problem and makes two key technical contributions. First, to
evaluate model robustness, we develop an easy-to-compute differentiable proxy
objective that can be used with gradient-based algorithms to find
output-lengthening inputs. Second and more importantly, we develop a
verification approach that can formally verify whether a network always
produces outputs within a certain length. Experimental results on Machine
Translation and Image Captioning show that our output-lengthening approach can
produce outputs that are 50 times longer than the input, while our verification
approach can, given a model and input domain, prove that the output length is
below a certain size.
Distributed Classification of Urban Congestion Using VANET
Authors: Al Mallah Ranwa, Farooq Bilal, Quintero Alejandro
Date: 2019-04-26 00:00:00
Tasks: Classification, General Classification

Vehicular Ad-hoc NETworks (VANET) can efficiently detect traffic congestion, but detection is not enough because congestion can be further classified as recurrent and non-recurrent congestion (NRC). In particular, NRC in an urban network is mainly caused by incidents, workzones, special events and adverse weather. We propose a framework for the real-time distributed classification of congestion into its components on a heterogeneous urban road network using VANET. We present models built on an understanding of the spatial and temporal causality measures and trained on synthetic data extended from a real case study of Cologne. Our performance evaluation shows a predictive accuracy of 87.63\% for the deterministic Classification Tree (CT), 88.83\% for the Naive Bayesian classifier (NB), 89.51\% for Random Forest (RF) and 89.17\% for the boosting technique. This framework can assist transportation agencies in reducing urban congestion by developing effective congestion mitigation strategies knowing the root causes of congestion.
Capturing human categorization of natural images at scale by combining deep networks and cognitive models
Authors: Ruairidh M. Battleday, Joshua C. Peterson, Thomas L. Griffiths
Date: 2019-04-26 00:00:00

Human categorization is one of the most important and successful targets of
cognitive modeling in psychology, yet decades of development and assessment of
competing models have been contingent on small sets of simple, artificial
experimental stimuli. Here we extend this modeling paradigm to the domain of
natural images, revealing the crucial role that stimulus representation plays
in categorization and its implications for conclusions about how people form
categories. Applying psychological models of categorization to natural images
required two significant advances. First, we conducted the first large-scale
experimental study of human categorization, involving over 500,000 human
categorization judgments of 10,000 natural images from ten non-overlapping
object categories. Second, we addressed the traditional bottleneck of
representing high-dimensional images in cognitive models by exploring the best
of current supervised and unsupervised deep and shallow machine learning
methods. We find that selecting sufficiently expressive, data-driven
representations is crucial to capturing human categorization, and using these
representations allows simple models that represent categories with abstract
prototypes to outperform the more complex memory-based exemplar accounts of
categorization that have dominated in studies using less naturalistic stimuli.
Current Trends in the Use of Eye Tracking in Mathematics Education Research: A PME Survey
Authors: Achim J. Lilienthal, Maike Schindler
Date: 2019-04-26 00:00:00

Eye tracking (ET) is a research method that receives growing interest in mathematics education research (MER). This paper aims to give a literature overview, specifically focusing on the evolution of interest in this technology, ET equipment, and analysis methods used in mathematics education. To capture the current state, we focus on papers published in the proceedings of PME, one of the primary conferences dedicated to MER, of the last ten years. We identify trends in interest, methodology, and methods of analysis that are used in the community, and discuss possible future developments.
Efficient Computation of Expected Hypervolume Improvement Using Box Decomposition Algorithms
Authors: Kaifeng Yang, Michael Emmerich, André Deutz, Thomas Bäck
Date: 2019-04-26 00:00:00
Tasks: global-optimization

In the field of multi-objective optimization algorithms, multi-objective Bayesian Global Optimization (MOBGO) is an important branch, in addition to evolutionary multi-objective optimization algorithms (EMOAs). MOBGO utilizes Gaussian Process models learned from previous objective function evaluations to decide the next evaluation site by maximizing or minimizing an infill criterion. A common criterion in MOBGO is the Expected Hypervolume Improvement (EHVI), which shows a good performance on a wide range of problems, with respect to exploration and exploitation. However, so far it has been a challenge to calculate exact EHVI values efficiently. In this paper, an efficient algorithm for the computation of the exact EHVI for a generic case is proposed. This efficient algorithm is based on partitioning the integration volume into a set of axis-parallel slices. Theoretically, the upper bound time complexities are improved from previously $O (n^2)$ and $O(n^3)$, for two- and three-objective problems respectively, to $\Theta(n\log n)$, which is asymptotically optimal. This article generalizes the scheme in higher dimensional case by utilizing a new hyperbox decomposition technique, which was proposed by D{\"a}chert et al, EJOR, 2017. It also utilizes a generalization of the multilayered integration scheme that scales linearly in the number of hyperboxes of the decomposition. The speed comparison shows that the proposed algorithm in this paper significantly reduces computation time. Finally, this decomposition technique is applied in the calculation of the Probability of Improvement (PoI).
Deep Fitting Degree Scoring Network for Monocular 3D Object Detection
Authors: Lijie Liu, Jiwen Lu, Chunjing Xu, Qi Tian, Jie zhou
Date: 2019-04-26 00:00:00
Proceeding: CVPR 2019 6
Tasks: 3D Object Detection, Monocular 3D Object Detection, Object, object-detection, Object Detection, Vehicle Pose Estimation

In this paper, we propose to learn a deep fitting degree scoring network for monocular 3D object detection, which aims to score fitting degree between proposals and object conclusively. Different from most existing monocular frameworks which use tight constraint to get 3D location, our approach achieves high-precision localization through measuring the visual fitting degree between the projected 3D proposals and the object. We first regress the dimension and orientation of the object using an anchor-based method so that a suitable 3D proposal can be constructed. We propose FQNet, which can infer the 3D IoU between the 3D proposals and the object solely based on 2D cues. Therefore, during the detection process, we sample a large number of candidates in the 3D space and project these 3D bounding boxes on 2D image individually. The best candidate can be picked out by simply exploring the spatial overlap between proposals and the object, in the form of the output 3D IoU score of FQNet. Experiments on the KITTI dataset demonstrate the effectiveness of our framework.
Hierarchical Context enabled Recurrent Neural Network for Recommendation
Authors: Kyungwoo Song, Mingi Ji, Sungrae Park, Il-Chul Moon
Date: 2019-04-26 00:00:00
Tasks: Sequential Recommendation

A long user history inevitably reflects the transitions of personal interests
over time. The analyses on the user history require the robust sequential model
to anticipate the transitions and the decays of user interests. The user
history is often modeled by various RNN structures, but the RNN structures in
the recommendation system still suffer from the long-term dependency and the
interest drifts. To resolve these challenges, we suggest HCRNN with three
hierarchical contexts of the global, the local, and the temporary interests.
This structure is designed to withhold the global long-term interest of users,
to reflect the local sub-sequence interests, and to attend the temporary
interests of each transition. Besides, we propose a hierarchical context-based
gate structure to incorporate our \textit{interest drift assumption}. As we
suggest a new RNN structure, we support HCRNN with a complementary
\textit{bi-channel attention} structure to utilize hierarchical context. We
experimented the suggested structure on the sequential recommendation tasks
with CiteULike, MovieLens, and LastFM, and our model showed the best
performances in the sequential recommendations.
The Neuro-Symbolic Concept Learner: Interpreting Scenes, Words, and Sentences From Natural Supervision
Authors: Jiayuan Mao, Chuang Gan, Pushmeet Kohli, Joshua B. Tenenbaum, Jiajun Wu
Date: 2019-04-26 00:00:00
Proceeding: ICLR 2019 5
Tasks: Image-text Retrieval, Object, Question Answering, Retrieval, Semantic Parsing, Text Retrieval, Visual Question Answering, Visual Question Answering (VQA)

We propose the Neuro-Symbolic Concept Learner (NS-CL), a model that learns
visual concepts, words, and semantic parsing of sentences without explicit
supervision on any of them; instead, our model learns by simply looking at
images and reading paired questions and answers. Our model builds an
object-based scene representation and translates sentences into executable,
symbolic programs. To bridge the learning of two modules, we use a
neuro-symbolic reasoning module that executes these programs on the latent
scene representation. Analogical to human concept learning, the perception
module learns visual concepts based on the language description of the object
being referred to. Meanwhile, the learned visual concepts facilitate learning
new words and parsing new sentences. We use curriculum learning to guide the
searching over the large compositional space of images and language. Extensive
experiments demonstrate the accuracy and efficiency of our model on learning
visual concepts, word representations, and semantic parsing of sentences.
Further, our method allows easy generalization to new object attributes,
compositions, language concepts, scenes and questions, and even new program
domains. It also empowers applications including visual question answering and
bidirectional image-text retrieval.
An efficient branch-and-cut algorithm for approximately submodular function maximization
Authors: Naoya Uematsu, Shunji Umetani, Yoshinobu Kawahara
Date: 2019-04-26 00:00:00

When approaching to problems in computer science, we often encounter
situations where a subset of a finite set maximizing some utility function
needs to be selected. Some of such utility functions are known to be
approximately submodular. For the problem of maximizing an approximately
submodular function (ASFM problem), a greedy algorithm quickly finds good
feasible solutions for many instances while guaranteeing
($1-e^{-\gamma}$)-approximation ratio for a given submodular ratio $\gamma$.
However, we still encounter its applications that ask more accurate or exactly
optimal solutions within a reasonable computation time. In this paper, we
present an efficient branch-and-cut algorithm for the non-decreasing ASFM
problem based on its binary integer programming (BIP) formulation with an
exponential number of constraints. To this end, we first derive a BIP
formulation of the ASFM problem and then, develop an improved constraint
generation algorithm that starts from a reduced BIP problem with a small subset
of constraints and repeats solving the reduced BIP problem while adding a
promising set of constraints at each iteration. Moreover, we incorporate it
into a branch-and-cut algorithm to attain good upper bounds while solving a
smaller number of nodes of a search tree. The computational results for three
types of well-known benchmark instances show that our algorithm performs better
than the conventional exact algorithms.
Self Training Autonomous Driving Agent
Authors: Shashank Kotyan, Danilo Vasconcellos Vargas, Venkanna U
Date: 2019-04-26 00:00:00
Tasks: Autonomous Driving, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL)

Intrinsically, driving is a Markov Decision Process which suits well the
reinforcement learning paradigm. In this paper, we propose a novel agent which
learns to drive a vehicle without any human assistance. We use the concept of
reinforcement learning and evolutionary strategies to train our agent in a 2D
simulation environment. Our model's architecture goes beyond the World Model's
by introducing difference images in the auto encoder. This novel involvement of
difference images in the auto-encoder gives better representation of the latent
space with respect to the motion of vehicle and helps an autonomous agent to
learn more efficiently how to drive a vehicle. Results show that our method
requires fewer (96% less) total agents, (87.5% less) agents per generations,
(70% less) generations and (90% less) rollouts than the original architecture
while achieving the same accuracy of the original.
Using Social Network Service to determine the Initial User Requirements for Small Software Businesses
Authors: Nazakat Ali, Jang-Eui Hong
Date: 2019-04-26 00:00:00

Background/Objectives: Software engineering community has been studied
extensively on large-sized software organizations and has provided suitable and
interesting solutions. However, small software companies that make a large part
of the software industry have been overlooked. Methods/Statistical analysis:
The current requirement engineering practices are not suitable for small
software companies. We propose a social network-based requirement engineering
approach that will complement the traditional requirement engineering
approaches and will make it suitable for small software companies. Findings: We
have applied our SNS-based requirements determination approach to knowing about
its validity. As a result, we concluded that 33.06 % of invited end-users
participated in our approach and figured out 156 distinct user requirements. It
has been seen that it was not necessary for users to have requirements
engineering knowledge to participate in our proposed SNS-based approach that
made maximum users to be involved during requirements elicitation process. By
investigating the ideas and opinions communicated by users, we were able to
figure out a high number of user requirements. It was observed that maximum
user-requirements were determined within a short period of time (7days). Our
experience with SNS-based approach also says that end-users hardly know about
non-functional requirements and express it explicitly.
Improvements/Applications: we believe that researchers will consider SNS other
than Facebook that would allow applying our SNS-based approach for requirements
identification. We have experienced our approach with Facebook but we do not
know how our approach would actually work with other SNSs.