pwc-archive/papers-with-abstracts huggingface dataset:
SCEF: A Support-Confidence-aware Embedding Framework for Knowledge Graph Refinement
Authors: Yu Zhao, Ji Liu
Date: 2019-02-18 00:00:00
Tasks: Translation

Knowledge graph (KG) refinement mainly aims at KG completion and correction (i.e., error detection). However, most conventional KG embedding models only focus on KG completion with an unreasonable assumption that all facts in KG hold without noises, ignoring error detection which also should be significant and essential for KG refinement.In this paper, we propose a novel support-confidence-aware KG embedding framework (SCEF), which implements KG completion and correction simultaneously by learning knowledge representations with both triple support and triple confidence. Specifically, we build model energy function by incorporating conventional translation-based model with support and confidence. To make our triple support-confidence more sufficient and robust, we not only consider the internal structural information in KG, studying the approximate relation entailment as triple confidence constraints, but also the external textual evidence, proposing two kinds of triple supports with entity types and descriptions respectively.Through extensive experiments on real-world datasets, we demonstrate SCEF's effectiveness.
Evolutionary Multitasking for Semantic Web Service Composition
Authors: Chen Wang, Hui Ma, Gang Chen, Sven Hartmann
Date: 2019-02-18 00:00:00
Tasks: Service Composition

Web services are basic functions of a software system to support the concept
of service-oriented architecture. They are often composed together to provide
added values, known as web service composition. Researchers often employ
Evolutionary Computation techniques to efficiently construct composite services
with near-optimized functional quality (i.e., Quality of Semantic Matchmaking)
or non-functional quality (i.e., Quality of Service) or both due to the
complexity of this problem. With a significant increase in service composition
requests, many composition requests have similar input and output requirements
but may vary due to different preferences from different user segments. This
problem is often treated as a multi-objective service composition so as to cope
with different preferences from different user segments simultaneously. Without
taking a multi-objective approach that gives rise to a solution selection
challenge, we perceive multiple similar service composition requests as jointly
forming an evolutionary multi-tasking problem in this work. We propose an
effective permutation-based evolutionary multi-tasking approach that can
simultaneously generate a set of solutions, with one for each service request.
We also introduce a neighborhood structure over multiple tasks to allow newly
evolved solutions to be evaluated on related tasks. Our proposed method can
perform better at the cost of only a fraction of time, compared to one
state-of-art single-tasking EC-based method. We also found that the use of the
proper neighborhood structure can enhance the effectiveness of our approach.
Detecting and Diagnosing Incipient Building Faults Using Uncertainty Information from Deep Neural Networks
Authors: Baihong Jin, Dan Li, Seshadhri Srinivasan, See-Kiong Ng, Kameshwar Poolla, Alberto~Sangiovanni-Vincentelli
Date: 2019-02-18 00:00:00
Tasks: Fault Detection

Early detection of incipient faults is of vital importance to reducing
maintenance costs, saving energy, and enhancing occupant comfort in buildings.
Popular supervised learning models such as deep neural networks are considered
promising due to their ability to directly learn from labeled fault data;
however, it is known that the performance of supervised learning approaches
highly relies on the availability and quality of labeled training data. In
Fault Detection and Diagnosis (FDD) applications, the lack of labeled incipient
fault data has posed a major challenge to applying these supervised learning
techniques to commercial buildings. To overcome this challenge, this paper
proposes using Monte Carlo dropout (MC-dropout) to enhance the supervised
learning pipeline, so that the resulting neural network is able to detect and
diagnose unseen incipient fault examples. We also examine the proposed
MC-dropout method on the RP-1043 dataset to demonstrate its effectiveness in
indicating the most likely incipient fault types.
Automatic Segmentation of Pulmonary Lobes Using a Progressive Dense V-Network
Authors: Abdullah-Al-Zubaer Imran, Ali Hatamizadeh, Shilpa P. Ananth, Xiaowei Ding, Demetri Terzopoulos, Nima Tajbakhsh
Date: 2019-02-18 00:00:00
Tasks: GPU, Segmentation

Reliable and automatic segmentation of lung lobes is important for diagnosis,
assessment, and quantification of pulmonary diseases. The existing techniques
are prohibitively slow, undesirably rely on prior (airway/vessel) segmentation,
and/or require user interactions for optimal results. This work presents a
reliable, fast, and fully automated lung lobe segmentation based on a
progressive dense V-network (PDV-Net). The proposed method can segment lung
lobes in one forward pass of the network, with an average runtime of 2 seconds
using 1 Nvidia Titan XP GPU, eliminating the need for any prior atlases, lung
segmentation or any subsequent user intervention. We evaluated our model using
84 chest CT scans from the LIDC and 154 pathological cases from the LTRC
datasets. Our model achieved a Dice score of $0.939 \pm 0.02$ for the LIDC test
set and $0.950 \pm 0.01$ for the LTRC test set, significantly outperforming a
2D U-net model and a 3D dense V-net. We further evaluated our model against 55
cases from the LOLA11 challenge, obtaining an average Dice score of 0.935---a
performance level competitive to the best performing team with an average score
of 0.938. Our extensive robustness analyses also demonstrate that our model can
reliably segment both healthy and pathological lung lobes in CT scans from
different vendors, and that our model is robust against configurations of CT
scan reconstruction.
A One-Class Support Vector Machine Calibration Method for Time Series Change Point Detection
Authors: Baihong Jin, Yuxin Chen, Dan Li, Kameshwar Poolla, Alberto Sangiovanni-Vincentelli
Date: 2019-02-18 00:00:00
Tasks: Anomaly Detection, Change Point Detection, Heuristic Search, Time Series, Time Series Analysis

It is important to identify the change point of a system's health status,
which usually signifies an incipient fault under development. The One-Class
Support Vector Machine (OC-SVM) is a popular machine learning model for anomaly
detection and hence could be used for identifying change points; however, it is
sometimes difficult to obtain a good OC-SVM model that can be used on sensor
measurement time series to identify the change points in system health status.
In this paper, we propose a novel approach for calibrating OC-SVM models. The
approach uses a heuristic search method to find a good set of input data and
hyperparameters that yield a well-performing model. Our results on the C-MAPSS
dataset demonstrate that OC-SVM can also achieve satisfactory accuracy in
detecting change point in time series with fewer training data, compared to
state-of-the-art deep learning approaches. In our case study, the OC-SVM
calibrated by the proposed model is shown to be useful especially in scenarios
with limited amount of training data.
Distributed Learning for Channel Allocation Over a Shared Spectrum
Authors: S. M. Zafaruddin, Ilai Bistritz, Amir Leshem, Dusit Niyato
Date: 2019-02-17 00:00:00

Channel allocation is the task of assigning channels to users such that some
objective (e.g., sum-rate) is maximized. In centralized networks such as
cellular networks, this task is carried by the base station which gathers the
channel state information (CSI) from the users and computes the optimal
solution. In distributed networks such as ad-hoc and device-to-device (D2D)
networks, no base station exists and conveying global CSI between users is
costly or simply impractical. When the CSI is time varying and unknown to the
users, the users face the challenge of both learning the channel statistics
online and converge to a good channel allocation. This introduces a multi-armed
bandit (MAB) scenario with multiple decision makers. If two users or more
choose the same channel, a collision occurs and they all receive zero reward.
We propose a distributed channel allocation algorithm that each user runs and
converges to the optimal allocation while achieving an order optimal regret of
O\left(\log T\right). The algorithm is based on a carrier sensing multiple
access (CSMA) implementation of the distributed auction algorithm. It does not
require any exchange of information between users. Users need only to observe a
single channel at a time and sense if there is a transmission on that channel,
without decoding the transmissions or identifying the transmitting users. We
demonstrate the performance of our algorithm using simulated LTE and 5G
channels.
Learning to Infer Program Sketches
Authors: Maxwell Nye, Luke Hewitt, Joshua Tenenbaum, Armando Solar-Lezama
Date: 2019-02-17 00:00:00
Tasks: Memorization, Program Synthesis

Our goal is to build systems which write code automatically from the kinds of specifications humans can most easily provide, such as examples and natural language instruction. The key idea of this work is that a flexible combination of pattern recognition and explicit reasoning can be used to solve these complex programming problems. We propose a method for dynamically integrating these types of information. Our novel intermediate representation and training algorithm allow a program synthesis system to learn, without direct supervision, when to rely on pattern recognition and when to perform symbolic search. Our model matches the memorization and generalization performance of neural synthesis and symbolic search, respectively, and achieves state-of-the-art performance on a dataset of simple English description-to-code programming problems.
Accurate Segmentation of Dermoscopic Images based on Local Binary Pattern Clustering
Authors: Pedro M. M. Pereira, Rui Fonseca-Pinto, Rui Pedro Paiva, Luis M. N. Tavora, Pedro A. A. Assuncao, Sergio M. M. de Faria
Date: 2019-02-17 00:00:00
Tasks: Clustering, Segmentation

Segmentation is a key stage in dermoscopic image processing, where the
accuracy of the border line that defines skin lesions is of utmost importance
for subsequent algorithms (e.g., classification) and computer-aided early
diagnosis of serious medical conditions. This paper proposes a novel
segmentation method based on Local Binary Patterns (LBP), where LBP and K-Means
clustering are combined to achieve a detailed delineation in dermoscopic
images. In comparison with usual dermatologist-like segmentation (i.e., the
available ground-truth), the proposed method is capable of finding more
realistic borders of skin lesions, i.e., with much more detail. The results
also exhibit reduced variability amongst different performance measures and
they are consistent across different images. The proposed method can be applied
for cell-based like segmentation adapted to the lesion border growing
specificities. Hence, the method is suitable to follow the growth dynamics
associated with the lesion border geometry in skin melanocytic images.
Limited Lookahead in Imperfect-Information Games
Authors: Christian Kroer, Tuomas Sandholm
Date: 2019-02-17 00:00:00

Limited lookahead has been studied for decades in perfect-information games. We initiate a new direction via two simultaneous deviation points: generalization to imperfect-information games and a game-theoretic approach. We study how one should act when facing an opponent whose lookahead is limited. We study this for opponents that differ based on their lookahead depth, based on whether they, too, have imperfect information, and based on how they break ties. We characterize the hardness of finding a Nash equilibrium or an optimal commitment strategy for either player, showing that in some of these variations the problem can be solved in polynomial time while in others it is PPAD-hard, NP-hard, or inapproximable. We proceed to design algorithms for computing optimal commitment strategies---for when the opponent breaks ties favorably, according to a fixed rule, or adversarially. We then experimentally investigate the impact of limited lookahead. The limited-lookahead player often obtains the value of the game if she knows the expected values of nodes in the game tree for some equilibrium---but we prove this is not sufficient in general. Finally, we study the impact of noise in those estimates and different lookahead depths.
Semantically Interpretable and Controllable Filter Sets
Authors: Mohit Prabhushankar, Gukyeong Kwon, Dogancan Temel, Ghassan AlRegib
Date: 2019-02-17 00:00:00
Tasks: Image Quality Assessment

In this paper, we generate and control semantically interpretable filters
that are directly learned from natural images in an unsupervised fashion. Each
semantic filter learns a visually interpretable local structure in conjunction
with other filters. The significance of learning these interpretable filter
sets is demonstrated on two contrasting applications. The first application is
image recognition under progressive decolorization, in which recognition
algorithms should be color-insensitive to achieve a robust performance. The
second application is image quality assessment where objective methods should
be sensitive to color degradations. In the proposed work, the sensitivity and
lack thereof are controlled by weighing the semantic filters based on the local
structures they represent. To validate the proposed approach, we utilize the
CURE-TSR dataset for image recognition and the TID 2013 dataset for image
quality assessment. We show that the proposed semantic filter set achieves
state-of-the-art performances in both datasets while maintaining its robustness
across progressive distortions.
Quantized Frank-Wolfe: Faster Optimization, Lower Communication, and Projection Free
Authors: Mingrui Zhang, Lin Chen, Aryan Mokhtari, Hamed Hassani, Amin Karbasi
Date: 2019-02-17 00:00:00
Tasks: Distributed Optimization, Quantization, Stochastic Optimization

How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized-Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines.
PIXOR: Real-time 3D Object Detection from Point Clouds
Authors: Bin Yang, Wenjie Luo, Raquel Urtasun
Date: 2019-02-17 00:00:00
Proceeding: CVPR 2018 6
Tasks: 3D Object Detection, Autonomous Driving, Birds Eye View Object Detection, Model Optimization, Object, object-detection, Object Detection, vehicle detection

We address the problem of real-time 3D object detection from point clouds in
the context of autonomous driving. Computation speed is critical as detection
is a necessary component for safety. Existing approaches are, however,
expensive in computation due to high dimensionality of point clouds. We utilize
the 3D data more efficiently by representing the scene from the Bird's Eye View
(BEV), and propose PIXOR, a proposal-free, single-stage detector that outputs
oriented 3D object estimates decoded from pixel-wise neural network
predictions. The input representation, network architecture, and model
optimization are especially designed to balance high accuracy and real-time
efficiency. We validate PIXOR on two datasets: the KITTI BEV object detection
benchmark, and a large-scale 3D vehicle detection benchmark. In both datasets
we show that the proposed detector surpasses other state-of-the-art methods
notably in terms of Average Precision (AP), while still runs at >28 FPS.
Automated Detection of Regions of Interest for Brain Perfusion MR Images
Authors: Svitlana M Alkhimova
Date: 2019-02-17 00:00:00
Tasks: Anatomy, Segmentation

Images with abnormal brain anatomy produce problems for automatic
segmentation techniques, and as a result poor ROI detection affects both
quantitative measurements and visual assessment of perfusion data. This paper
presents a new approach for fully automated and relatively accurate ROI
detection from dynamic susceptibility contrast perfusion magnetic resonance and
can therefore be applied excellently in the perfusion analysis. In the proposed
approach the segmentation output is a binary mask of perfusion ROI that has
zero values for air pixels, pixels that represent non-brain tissues, and
cerebrospinal fluid pixels. The process of binary mask producing starts with
extracting low intensity pixels by thresholding. Optimal low-threshold value is
solved by obtaining intensity pixels information from the approximate
anatomical brain location. Holes filling algorithm and binary region growing
algorithm are used to remove falsely detected regions and produce region of
only brain tissues. Further, CSF pixels extraction is provided by thresholding
of high intensity pixels from region of only brain tissues. Each time-point
image of the perfusion sequence is used for adjustment of CSF pixels location.
The segmentation results were compared with the manual segmentation performed
by experienced radiologists, considered as the reference standard for
evaluation of proposed approach. On average of 120 images the segmentation
results have a good agreement with the reference standard. All detected
perfusion ROIs were deemed by two experienced radiologists as satisfactory
enough for clinical use. The results show that proposed approach is suitable to
be used for perfusion ROI detection from DSC head scans. Segmentation tool
based on the proposed approach can be implemented as a part of any automatic
brain image processing system for clinical use.
Towards Improved Testing For Deep Learning
Authors: Jasmine Sekhon, Cody Fleming
Date: 2019-02-17 00:00:00
Tasks: Deep Learning, software testing

The growing use of deep neural networks in safety-critical applications makes
it necessary to carry out adequate testing to detect and correct any incorrect
behavior for corner case inputs before they can be actually used. Deep neural
networks lack an explicit control-flow structure, making it impossible to apply
to them traditional software testing criteria such as code coverage. In this
paper, we examine existing testing methods for deep neural networks, the
opportunities for improvement and the need for a fast, scalable, generalizable
end-to-end testing method. We also propose a coverage criterion for deep neural
networks that tries to capture all possible parts of the deep neural network's
logic.
ProtoAttend: Attention-Based Prototypical Learning
Authors: Sercan O. Arik, Tomas Pfister
Date: 2019-02-17 00:00:00
Tasks: Decision Making, General Classification, Interpretable Machine Learning

We propose a novel inherently interpretable machine learning method that bases decisions on few relevant examples that we call prototypes. Our method, ProtoAttend, can be integrated into a wide range of neural network architectures including pre-trained models. It utilizes an attention mechanism that relates the encoded representations to samples in order to determine prototypes. The resulting model outperforms state of the art in three high impact problems without sacrificing accuracy of the original model: (1) it enables high-quality interpretability that outputs samples most relevant to the decision-making (i.e. a sample-based interpretability method); (2) it achieves state of the art confidence estimation by quantifying the mismatch across prototype labels; and (3) it obtains state of the art in distribution mismatch detection. All this can be achieved with minimal additional test time and a practically viable training time computational cost.
Neural Network-Based Dynamic Threshold Detection for Non-Volatile Memories
Authors: Zhen Mei, Kui Cai, Xingwei Zhong
Date: 2019-02-17 00:00:00
Tasks: Decoder

The memory physics induced unknown offset of the channel is a critical and
difficult issue to be tackled for many non-volatile memories (NVMs). In this
paper, we first propose novel neural network (NN) detectors by using the
multilayer perceptron (MLP) network and the recurrent neural network (RNN),
which can effectively tackle the unknown offset of the channel. However,
compared with the conventional threshold detector, the NN detectors will incur
a significant delay of the read latency and more power consumption. Therefore,
we further propose a novel dynamic threshold detector (DTD), whose detection
threshold can be derived based on the outputs of the proposed NN detectors. In
this way, the NN-based detection only needs to be invoked when the error
correction code (ECC) decoder fails, or periodically when the system is in the
idle state. Thereafter, the threshold detector will still be adopted by using
the adjusted detection threshold derived base on the outputs of the NN
detector, until a further adjustment of the detection threshold is needed.
Simulation results demonstrate that the proposed DTD based on the RNN detection
can achieve the error performance of the optimum detector, without the prior
knowledge of the channel.
A mixed signal architecture for convolutional neural networks
Authors: Qiuwen Lou, Chenyun Pan, John McGuiness, Andras Horvath, Azad Naeemi, Michael Niemier, X. Sharon Hu
Date: 2018-10-30 00:00:00
Tasks: Edge-computing

Deep neural network (DNN) accelerators with improved energy and delay are desirable for meeting the requirements of hardware targeted for IoT and edge computing systems. Convolutional neural networks (CoNNs) belong to one of the most popular types of DNN architectures. This paper presents the design and evaluation of an accelerator for CoNNs. The system-level architecture is based on mixed-signal, cellular neural networks (CeNNs). Specifically, we present (i) the implementation of different layers, including convolution, ReLU, and pooling, in a CoNN using CeNN, (ii) modified CoNN structures with CeNN-friendly layers to reduce computational overheads typically associated with a CoNN, (iii) a mixed-signal CeNN architecture that performs CoNN computations in the analog and mixed signal domain, and (iv) design space exploration that identifies what CeNN-based algorithm and architectural features fare best compared to existing algorithms and architectures when evaluated over common datasets -- MNIST and CIFAR-10. Notably, the proposed approach can lead to 8.7$\times$ improvements in energy-delay product (EDP) per digit classification for the MNIST dataset at iso-accuracy when compared with the state-of-the-art DNN engine, while our approach could offer 4.3$\times$ improvements in EDP when compared to other network implementations for the CIFAR-10 dataset.