pwc-archive/papers-with-abstracts huggingface dataset:
Automatic Driver Identification from In-Vehicle Network Logs
Authors: Mina Remeli, Szilvia Lestyan, Gergely Acs, Gergely Biczok
Date: 2019-10-25 00:00:00
Tasks: Driver Identification

Data generated by cars is growing at an unprecedented scale. As cars gradually become part of the Internet of Things (IoT) ecosystem, several stakeholders discover the value of in-vehicle network logs containing the measurements of the multitude of sensors deployed within the car. This wealth of data is also expected to be exploitable by third parties for the purpose of profiling drivers in order to provide personalized, valueadded services. Although several prior works have successfully demonstrated the feasibility of driver re-identification using the in-vehicle network data captured on the vehicle's CAN (Controller Area Network) bus, they inferred the identity of the driver only from known sensor signals (such as the vehicle's speed, brake pedal position, steering wheel angle, etc.) extracted from the CAN messages. However, car manufacturers intentionally do not reveal exact signal location and semantics within CAN logs. We show that the inference of driver identity is possible even with off-the-shelf machine learning techniques without reverse-engineering the CAN protocol. We demonstrate our approach on a dataset of 33 drivers and show that a driver can be re-identified and distinguished from other drivers with an accuracy of 75-85%.
Feature Extraction in Augmented Reality
Authors: Jekishan K. Parmar, Ankit Desai
Date: 2019-11-09 00:00:00
Tasks: Information Retrieval, Retrieval

Augmented Reality (AR) is used for various applications associated with the real world. In this paper, first, describe characteristics and essential services of AR. Brief history on Virtual Reality (VR) and AR is also mentioned in the introductory section. Then, AR Technologies along with its workflow is depicted, which includes the complete AR Process consisting of the stages of Image Acquisition, Feature Extraction, Feature Matching, Geometric Verification, and Associated Information Retrieval. Feature extraction is the essence of AR hence its details are furnished in the paper.
The quadratic Wasserstein metric for inverse data matching
Authors: Bjorn Engquist, Kui Ren, Yunan Yang
Date: 2019-11-15 00:00:00

This work characterizes, analytically and numerically, two major effects of the quadratic Wasserstein ($W_2$) distance as the measure of data discrepancy in computational solutions of inverse problems. First, we show, in the infinite-dimensional setup, that the $W_2$ distance has a smoothing effect on the inversion process, making it robust against high-frequency noise in the data but leading to a reduced resolution for the reconstructed objects at a given noise level. Second, we demonstrate that for some finite-dimensional problems, the $W_2$ distance leads to optimization problems that have better convexity than the classical $L^2$ and $H^{-1}$ distances, making it a more preferred distance to use when solving such inverse matching problems.
Feature Extraction Based Machine Learning for Human Burn Diagnosis From Burn Images
Authors: D. P. YADAV, ASHISH SHARMA, MADHUSUDAN SINGH, AND AYUSH GOYAL
Date: 2019-07-18 00:00:00
Proceeding: journal 2019 7
Tasks: BIG-bench Machine Learning

Burn is one of the serious public health problems. Usually, burn diagnoses are based on
expert medical and clinical experience and it is necessary to have a medical or clinical expert to conduct
an examination in restorative clinics or at emergency rooms in hospitals. But sometimes a patient may have
a burn where there is no specialized facility available, and in such a case a computerized automatic burn
assessment tool may aid diagnosis. Burn area, depth, and location are the critical factors in determining
the severity of burns. In this paper, a classication model to diagnose burns is presented using automated
machine learning. The objective of the research is to develop the feature extraction model to classify the
burn. The proposed method based on support vector machine (SVM) is evaluated on a standard data set of
burnsBIP_US database. Training is performed by classifying images into two classes, i.e., those that need
grafts and those that are non-graft. The 74 images of test data set are tested with the proposed SVM based
method and according to the ground truth, the accuracy of 82.43% was achieved for the SVM based model,
which was higher than the 79.73% achieved in past work using the multidimensional scaling analysis (MDS)
approach
Dynamic 3D Avatar Creation from Hand-held Video Input
Authors: Alexandru Eugen Ichim, Sofien Bouaziz, Mark Pauly, EPFL
Date: 2015-01-01 00:00:00
Proceeding: EPFL 2015 1
Tasks: Optical Flow Estimation

We present a complete pipeline for creating fully rigged, personalized 3D facial avatars from hand-held video. Our system faithfully recovers facial expression dynamics of the user by adapting a blendshape template to an image sequence of recorded expressions using an optimization that integrates feature tracking, optical flow, and shape from shading. Fine-scale details such as wrinkles are captured separately in normal maps and ambient occlusion maps. From this user- and expression-specific data, we learn a regressor for on-the-fly detail synthesis during animation to enhance the perceptual realism of the avatars. Our system demonstrates that the use of appropriate reconstruction priors yields compelling face rigs even with a minimalistic acquisition system and limited user assistance. This facilitates a range of new applications in computer animation and consumer-level online communication based on personalized avatars. We present realtime application demos to validate our method
Customized Graph Embedding: Tailoring Embedding Vectors to different Applications
Authors: Bitan Hou, Yujing Wang, Ming Zeng, Shan Jiang, Ole J. Mengshoel, Yunhai Tong, Jing Bai
Date: 2019-11-21 00:00:00
Tasks: Graph Embedding, Graph Mining, Node Classification

Graph is a natural representation of data for a variety of real-word applications, such as knowledge graph mining, social network analysis and biological network comparison. For these applications, graph embedding is crucial as it provides vector representations of the graph. One limitation of existing graph embedding methods is that their embedding optimization procedures are disconnected from the target application. In this paper, we propose a novel approach, namely Customized Graph Embedding (CGE) to tackle this problem. The CGE algorithm learns customized vector representations of graph nodes by differentiating the importance of distinct graph paths automatically for a specific application. Extensive experiments were carried out on a diverse set of node classification datasets, which demonstrate strong performances of CGE and provide deep insights into the model.
Skin Lesion Segmentation using SegNet with Binary Cross-Entropy
Authors: Prashant Brahmbhatt, Siddhi Nath Rajan
Date: 2019-11-15 00:00:00
Proceeding: International Conference On Artificial Intelligence And Speech Technology (AIST 2019) 2019 11
Tasks: Lesion Segmentation, Skin Cancer Segmentation, Skin Lesion Segmentation

In this paper a simple and computationally efficient approach as per the complexity has been presented for Automatic Skin Lesion Segmentation using a Deep Learning architecture called SegNet including some additional specifications for the improvisation of the results. The secondary objective is to keep the pre/post -processing of the images minimal. The presented model is trained on limited images from the PH2 dataset which includes dermoscopic images, manually segmented. It also contains their masks, the clinical diagnosis and the identification of several dermoscopic structures, performed by professional dermatologists. The aim is to achieve a performance threshold Jaccard Index (IOU) 92% after evaluation.
SEDANSPOT: Detecting Anomalies in Edge Streams
Authors: Dhivya Eswaran, Christos Faloutsos
Date: 2018-11-20 00:00:00
Proceeding: ICDM 2018 11
Tasks: Anomaly Detection in Edge Streams

Given a stream of edges from a time-evolving
(un)weighted (un)directed graph, we consider the problem of detecting anomalous edges in near real-time using sublinear memory.
We propose SEDANSPOT, a principled randomized algorithm,
which exploits two tell-tale signs of anomalous edges: they tend
to (i) occur as bursts of activity and (ii) connect parts of the graph
which are sparsely connected. SEDANSPOT has the following desirable properties: (a) Burst resistance: It provably downsamples
edges from bursty periods of network traffic, (b) Holistic scoring:
It takes into account the whole (sampled) graph while scoring
the anomalousness of an edge, giving diminishing importance to
far-away neighbors, (c) Efficiency: It supports fast updates and
scoring and hence can be efficiently maintained over stream;
further, it can detect anomalous edges in sublinear space and
constant time per edge. Through experiments on real-world data,
we demonstrate that SEDANSPOT is 3× faster and 270% more
accurate (in terms of AUC) than the state-of-the-art.
A Scalable Approach for Outlier Detection in Edge Streams Using Sketch-based Approximations
Authors: Stephen Ranshous, Steve Harenberg, Kshitij Sharma, Nagiza F. Samatova
Date: 2016-05-07 00:00:00
Proceeding: SDM 2016 2016 5
Tasks: Anomaly Detection, Anomaly Detection in Edge Streams, Outlier Detection

Dynamic graphs are a powerful way to model an evolving set of objects and their ongoing interactions. A broad spectrum of systems, such as information, communication, and social, are naturally represented by dynamic graphs. Outlier (or anomaly) detection in dynamic graphs can provide unique insights into the relationships of objects and identify novel or emerging relationships. To date, outlier detection in dynamic graphs has been studied in the context of graph streams, focusing on the analysis and comparison of entire graph objects. However, the volume and velocity of data are necessitating a transition from outlier detection in the context of graph streams to outlier detection in the context of edge streams–where the stream consists of individual graph edges instead of entire graph objects.

In this paper, we propose the first approach for outlier detection in edge streams. We first describe a high-level model for outlier detection based on global and local structural properties of a stream. We then propose a novel application of the Count-Min sketch for approximating these properties, and prove probabilistic error bounds on our edge outlier scoring functions. Our sketch-based implementation provides a scalable solution, having constant time updates and constant space requirements. Experiments on synthetic and real-world datasets demonstrate our method's scalability, effectiveness for discovering outliers, and the effects of approximation.
Panoptic-DeepLab: A Simple, Strong, and Fast Baseline for Bottom-Up Panoptic Segmentation
Authors: Bowen Cheng, Maxwell D. Collins, Yukun Zhu, Ting Liu, Thomas S. Huang, Hartwig Adam, Liang-Chieh Chen
Date: 2019-11-22 00:00:00
Proceeding: CVPR 2020 6
Tasks: Decoder, Instance Segmentation, Panoptic Segmentation, Segmentation, Semantic Segmentation

In this work, we introduce Panoptic-DeepLab, a simple, strong, and fast system for panoptic segmentation, aiming to establish a solid baseline for bottom-up methods that can achieve comparable performance of two-stage methods while yielding fast inference speed. In particular, Panoptic-DeepLab adopts the dual-ASPP and dual-decoder structures specific to semantic, and instance segmentation, respectively. The semantic segmentation branch is the same as the typical design of any semantic segmentation model (e.g., DeepLab), while the instance segmentation branch is class-agnostic, involving a simple instance center regression. As a result, our single Panoptic-DeepLab simultaneously ranks first at all three Cityscapes benchmarks, setting the new state-of-art of 84.2% mIoU, 39.0% AP, and 65.5% PQ on test set. Additionally, equipped with MobileNetV3, Panoptic-DeepLab runs nearly in real-time with a single 1025x2049 image (15.8 frames per second), while achieving a competitive performance on Cityscapes (54.1 PQ% on test set). On Mapillary Vistas test set, our ensemble of six models attains 42.7% PQ, outperforming the challenge winner in 2018 by a healthy margin of 1.5%. Finally, our Panoptic-DeepLab also performs on par with several top-down approaches on the challenging COCO dataset. For the first time, we demonstrate a bottom-up approach could deliver state-of-the-art results on panoptic segmentation.
Interactive Text Ranking with Bayesian Optimisation: A Case Study on Community QA and Summarisation
Authors: Edwin Simpson, Yang Gao, Iryna Gurevych
Date: 2019-11-22 00:00:00
Tasks: Bayesian Optimisation, Community Question Answering, Question Answering, Reinforcement Learning, Small Data Image Classification

For many NLP applications, such as question answering and summarisation, the goal is to select the best solution from a large space of candidates to meet a particular user's needs. To address the lack of user-specific training data, we propose an interactive text ranking approach that actively selects pairs of candidates, from which the user selects the best. Unlike previous strategies, which attempt to learn a ranking across the whole candidate space, our method employs Bayesian optimisation to focus the user's labelling effort on high quality candidates and integrates prior knowledge in a Bayesian manner to cope better with small data scenarios. We apply our method to community question answering (cQA) and extractive summarisation, finding that it significantly outperforms existing interactive approaches. We also show that the ranking function learned by our method is an effective reward function for reinforcement learning, which improves the state of the art for interactive summarisation.
Universal adversarial examples in speech command classification
Authors: Jon Vadillo, Roberto Santana
Date: 2019-11-22 00:00:00
Tasks: Classification, domain classification, General Classification

Adversarial examples are inputs intentionally perturbed with the aim of forcing a machine learning model to produce a wrong prediction, while the changes are not easily detectable by a human. Although this topic has been intensively studied in the image domain, classification tasks in the audio domain have received less attention. In this paper we address the existence of universal perturbations for speech command classification. We provide evidence that universal attacks can be generated for speech command classification tasks, which are able to generalize across different models to a significant extent. Additionally, a novel analytical framework is proposed for the evaluation of universal perturbations under different levels of universality, demonstrating that the feasibility of generating effective perturbations decreases as the universality level increases. Finally, we propose a more detailed and rigorous framework to measure the amount of distortion introduced by the perturbations, demonstrating that the methods employed by convention are not realistic in audio-based problems.
Topical Phrase Extraction from Clinical Reports by Incorporating both Local and Global Context
Authors: Gabriele Pergola, Yulan He, David Lowe
Date: 2019-11-22 00:00:00
Tasks: Topic Models, Word Embeddings

Making sense of words often requires to simultaneously examine the surrounding context of a term as well as the global themes characterizing the overall corpus. Several topic models have already exploited word embeddings to recognize local context, however, it has been weakly combined with the global context during the topic inference. This paper proposes to extract topical phrases corroborating the word embedding information with the global context detected by Latent Semantic Analysis, and then combine them by means of the P\'{o}lya urn model. To highlight the effectiveness of this combined approach the model was assessed analyzing clinical reports, a challenging scenario characterized by technical jargon and a limited word statistics available. Results show it outperforms the state-of-the-art approaches in terms of both topic coherence and computational cost.
SparseTrain:Leveraging Dynamic Sparsity in Training DNNs on General-Purpose SIMD Processors
Authors: Zhangxiaowen Gong, Houxiang Ji, Christopher Fletcher, Christopher Hughes, Josep Torrellas
Date: 2019-11-22 00:00:00
Tasks: CPU

Our community has greatly improved the efficiency of deep learning applications, including by exploiting sparsity in inputs. Most of that work, though, is for inference, where weight sparsity is known statically, and/or for specialized hardware. We propose a scheme to leverage dynamic sparsity during training. In particular, we exploit zeros introduced by the ReLU activation function to both feature maps and their gradients. This is challenging because the sparsity degree is moderate and the locations of zeros change over time. We also rely purely on software. We identify zeros in a dense data representation without transforming the data and performs conventional vectorized computation. Variations of the scheme are applicable to all major components of training: forward propagation, backward propagation by inputs, and backward propagation by weights. Our method significantly outperforms a highly-optimized dense direct convolution on several popular deep neural networks. At realistic sparsity, we speed up the training of the non-initial convolutional layers in VGG16, ResNet-34, ResNet-50, and Fixup ResNet-50 by 2.19x, 1.37x, 1.31x, and 1.51x respectively on an Intel Skylake-X CPU.
Privacy-preserving parametric inference: a case for robust statistics
Authors: Marco Avella-Medina
Date: 2019-11-22 00:00:00
Tasks: Privacy Preserving

Differential privacy is a cryptographically-motivated approach to privacy that has become a very active field of research over the last decade in theoretical computer science and machine learning. In this paradigm one assumes there is a trusted curator who holds the data of individuals in a database and the goal of privacy is to simultaneously protect individual data while allowing the release of global characteristics of the database. In this setting we introduce a general framework for parametric inference with differential privacy guarantees. We first obtain differentially private estimators based on bounded influence M-estimators by leveraging their gross-error sensitivity in the calibration of a noise term added to them in order to ensure privacy. We then show how a similar construction can also be applied to construct differentially private test statistics analogous to the Wald, score and likelihood ratio tests. We provide statistical guarantees for all our proposals via an asymptotic analysis. An interesting consequence of our results is to further clarify the connection between differential privacy and robust statistics. In particular, we demonstrate that differential privacy is a weaker stability requirement than infinitesimal robustness, and show that robust M-estimators can be easily randomized in order to guarantee both differential privacy and robustness towards the presence of contaminated data. We illustrate our results both on simulated and real data.
Moral Dilemmas for Artificial Intelligence: a position paper on an application of Compositional Quantum Cognition
Authors: Camilo M. Signorelli, Xerxes D. Arsiwalla
Date: 2019-11-22 00:00:00
Tasks: Position

Traditionally, the way one evaluates the performance of an Artificial Intelligence (AI) system is via a comparison to human performance in specific tasks, treating humans as a reference for high-level cognition. However, these comparisons leave out important features of human intelligence: the capability to transfer knowledge and make complex decisions based on emotional and rational reasoning. These decisions are influenced by current inferences as well as prior experiences, making the decision process strongly subjective and apparently biased. In this context, a definition of compositional intelligence is necessary to incorporate these features in future AI tests. Here, a concrete implementation of this will be suggested, using recent developments in quantum cognition, natural language and compositional meaning of sentences, thanks to categorical compositional models of meaning.
On an Optimal Solution to the Film Scheduling and Showtime Staggering Problem
Authors: Ikjyot Singh Kohli, Katherine Goff Inglis
Date: 2019-11-22 00:00:00
Tasks: Scheduling

The scheduling of films is a major problem for the movie theatre exhibition business. The problem is two-fold: movie exhibitors ideally would like to schedule films to screens in their various locations to maximize attendance and revenue, but would also like to schedule these films such that neighbouring theatre locations play the same films at different times thus giving guests a multitude of showtime options. We refer to this latter problem as the showtime \emph{staggering} problem. We give an exact formulation of this scheduling problem using binary integer linear optimization, and provide a solved example as well. This work further shows that the optimal scheduling of films cannot be done across all theatre locations at once, but rather, must be done for each cluster of neighbouring locations.
PointPainting: Sequential Fusion for 3D Object Detection
Authors: Sourabh Vora, Alex H. Lang, Bassam Helou, Oscar Beijbom
Date: 2019-11-22 00:00:00
Proceeding: CVPR 2020 6
Tasks: 3D Object Detection, Object, object-detection, Object Detection, Segmentation, Self-Driving Cars, Semantic Segmentation, Sensor Fusion

Camera and lidar are important sensor modalities for robotics in general and self-driving cars in particular. The sensors provide complementary information offering an opportunity for tight sensor-fusion. Surprisingly, lidar-only methods outperform fusion methods on the main benchmark datasets, suggesting a gap in the literature. In this work, we propose PointPainting: a sequential fusion method to fill this gap. PointPainting works by projecting lidar points into the output of an image-only semantic segmentation network and appending the class scores to each point. The appended (painted) point cloud can then be fed to any lidar-only method. Experiments show large improvements on three different state-of-the art methods, Point-RCNN, VoxelNet and PointPillars on the KITTI and nuScenes datasets. The painted version of PointRCNN represents a new state of the art on the KITTI leaderboard for the bird's-eye view detection task. In ablation, we study how the effects of Painting depends on the quality and format of the semantic segmentation output, and demonstrate how latency can be minimized through pipelining.