pwc-archive/papers-with-abstracts huggingface dataset:
Retrieving Similar Trajectories from Cellular Data at City Scale
Authors: Zhihao Shen, Wan Du, Xi Zhao, Jianhua Zou
Date: 2019-07-20 00:00:00

Retrieving similar trajectories from a large trajectory dataset is important for a variety of applications, like transportation planning and mobility analysis. Unlike previous works based on fine-grained GPS trajectories, this paper investigates the feasibility of identifying similar trajectories from cellular data observed by mobile infrastructure, which provide more comprehensive coverage. To handle the large localization errors and low sample rates of cellular data, we develop a holistic system, cellSim, which seamlessly integrates map matching and similar trajectory search. A set of map matching techniques are proposed to transform cell tower sequences into moving trajectories on a road map by considering the unique features of cellular data, like the dynamic density of cell towers and bidirectional roads. To further improve the accuracy of similarity search, map matching outputs M trajectory candidates of different confidence, and a new similarity measure scheme is developed to process the map matching results. Meanwhile, M is dynamically adapted to maintain a low false positive rate of the similarity search, and two pruning schemes are proposed to minimize the computation overhead. Extensive experiments on a large-scale dataset and real-world trajectories of 1701 km reveal that cellSim provides high accuracy (precision 62.4% and recall of 89.8%).
Sentiment Analysis of Arabic Tweets Using Semantic Resources
Authors: Lamia Al-Horaibi, Muhammad Badruddin Khan
Date: 2017-01-30 00:00:00
Proceeding: International Journal of Computing & Information Sciences 2017 1
Tasks: Arabic Sentiment Analysis, Classification, General Classification, Opinion Mining, Sentiment Analysis, Sentiment Classification

Sentiment analysis has grown to be one of the most active research areas in natural language processing
and text mining. Many researchers have investigated sentiment analysis and opinion mining from different
classification approaches. However, limited research is conducted on Arabic sentiment analysis as compared to the
English language. In this paper, we have proposed and implemented a technique for Twitter Arabic sentiment
analysis consisting of a semantic approach and Arabic linguistic features. Hence, we introduced a mechanism for
preprocessing Arabic tweets, and for the methodology of sentiment classification we used a semantic approach.
Also, we proposed a technique of classification which uses both Arabic and English sentiment lexicons to classify
the Arabic tweets into three sentiment categories (positive or negative or neutral). Our experiments show that
many issues were encountered when we used the Arabic SentiWordNet facility to classify Arabic tweets directly;
these issues are basically related to Arabic text processing. The Arabic lexicons and Arabic tools must be improved
or built from scratch in order to improve Arabic sentiment analysis using the semantic approach. The improvement
in results, which are due to our contribution in the form of enhanced Arabic lexicons and amended Arabic tools,
demonstrate this need.
Solving Nonlinear and High-Dimensional Partial Differential Equations via Deep Learning
Authors: Ali Al-Aradi, Adolfo Correia, Danilo Naiff, Gabriel Jardim, Yuri Saporito
Date: 2018-11-21 00:00:00
Tasks: Vocal Bursts Intensity Prediction

In this work we apply the Deep Galerkin Method (DGM) described in Sirignano and Spiliopoulos (2018) to solve a number of partial differential equations that arise in quantitative finance applications including option pricing, optimal execution, mean field games, etc. The main idea behind DGM is to represent the unknown function of interest using a deep neural network. A key feature of this approach is the fact that, unlike other commonly used numerical approaches such as finite difference methods, it is mesh-free. As such, it does not suffer (as much as other numerical methods) from the curse of dimensionality associated with highdimensional PDEs and PDE systems. The main goals of this paper are to elucidate the features, capabilities and limitations of DGM by analyzing aspects of its implementation for a number of different PDEs and PDE systems. Additionally, we present: (1) a brief overview of PDEs in quantitative finance along with numerical methods for solving them; (2) a brief overview of deep learning and, in particular, the notion of neural networks; (3) a discussion of the theoretical foundations of DGM with a focus on the justification of why this method is expected to perform well.
A Federated Learning Approach for Mobile Packet Classification
Authors: Evita Bakopoulou, Balint Tillman, Athina Markopoulou
Date: 2019-07-30 00:00:00
Tasks: Classification, feature selection, Federated Learning, General Classification

In order to improve mobile data transparency, a number of network-based approaches have been proposed to inspect packets generated by mobile devices and detect personally identifiable information (PII), ad requests, or other activities. State-of-the-art approaches train classifiers based on features extracted from HTTP packets. So far, these classifiers have only been trained in a centralized way, where mobile users label and upload their packet logs to a central server, which then trains a global classifier and shares it with the users to apply on their devices. However, packet logs used as training data may contain sensitive information that users may not want to share/upload. In this paper, we apply, for the first time, a Federated Learning approach to mobile packet classification, which allows mobile devices to collaborate and train a global model, without sharing raw training data. Methodological challenges we address in this context include: model and feature selection, and tuning the Federated Learning parameters. We apply our framework to two different packet classification tasks (i.e., to predict PII exposure or ad requests in HTTP packets) and we demonstrate its effectiveness in terms of classification performance, communication and computation cost, using three real-world datasets.
Deblurring Face Images using Uncertainty Guided Multi-Stream Semantic Networks
Authors: Rajeev Yasarla, Federico Perazzi, Vishal M. Patel
Date: 2019-07-30 00:00:00
Tasks: Deblurring, Image Deblurring

We propose a novel multi-stream architecture and training methodology that exploits semantic labels for facial image deblurring. The proposed Uncertainty Guided Multi- Stream Semantic Network (UMSN) processes regions belonging to each semantic class independently and learns to combine their outputs into the final deblurred result. Pixel-wise semantic labels are obtained using a segmentation network. A predicted confidence measure is used during training to guide the network towards the challenging regions of the human face such as the eyes and nose. The entire network is trained in an end- to-end fashion. Comprehensive experiments on three different face datasets demonstrate that the proposed method achieves significant improvements over the recent state-of-the-art face deblurring methods. Code is available at: https://github.com/ rajeevyasarla/UMSN-Face-Deblurring
Climate-driven statistical models as effective predictors of local dengue incidence in Costa Rica: A Generalized Additive Model and Random Forest approach
Authors: Paola Vásquez, Antonio Loría, Fabio Sanchez, Luis A. Barboza
Date: 2019-07-30 00:00:00

Climate has been an important factor in shaping the distribution and incidence of dengue cases in tropical and subtropical countries. In Costa Rica, a tropical country with distinctive micro-climates, dengue has been endemic since its introduction in 1993, inflicting substantial economic, social, and public health repercussions. Using the number of dengue reported cases and climate data from 2007-2017, we fitted a prediction model applying a Generalized Additive Model (GAM) and Random Forest (RF) approach, which allowed us to retrospectively predict the relative risk of dengue in five climatological diverse municipalities around the country.
Deformable Filter Convolution for Point Cloud Reasoning
Authors: Yuwen Xiong, Mengye Ren, Renjie Liao, Kelvin Wong, Raquel Urtasun
Date: 2019-07-30 00:00:00
Tasks: LIDAR Semantic Segmentation, object-detection, Object Detection, Semantic Segmentation

Point clouds are the native output of many real-world 3D sensors. To borrow the success of 2D convolutional network architectures, a majority of popular 3D perception models voxelize the points, which can result in a loss of local geometric details that cannot be recovered. In this paper, we propose a novel learnable convolution layer for processing 3D point cloud data directly. Instead of discretizing points into fixed voxels, we deform our learnable 3D filters to match with the point cloud shape. We propose to combine voxelized backbone networks with our deformable filter layer at 1) the network input stream and 2) the output prediction layers to enhance point level reasoning. We obtain state-of-the-art results on LiDAR semantic segmentation and producing a significant gain in performance on LiDAR object detection.
Pay attention to the activations: a modular attention mechanism for fine-grained image recognition
Authors: Pau Rodríguez López, Diego Velazquez Dorta, Guillem Cucurull Preixens, Josep M. Gonfaus, F. Xavier Roca Marva, Jordi Gonzàlez Sabaté
Date: 2019-07-30 00:00:00
Tasks: Fine-Grained Image Recognition, General Classification, Retrieval

Fine-grained image recognition is central to many multimedia tasks such as search, retrieval and captioning. Unfortunately, these tasks are still challenging since the appearance of samples of the same class can be more different than those from different classes. Attention has been typically implemented in neural networks by selecting the most informative regions of the image that improve classification. In contrast, in this paper, attention is not applied at the image level but to the convolutional feature activations. In essence, with our approach, the neural model learns to attend to lower-level feature activations without requiring part annotations and uses those activations to update and rectify the output likelihood distribution. The proposed mechanism is modular, architecture-independent and efficient in terms of both parameters and computation required. Experiments demonstrate that well-known networks such as Wide Residual Networks and ResNeXt, when augmented with our approach, systematically improve their classification accuracy and become more robust to changes in deformation and pose and to the presence of clutter. As a result, our proposal reaches state-of-the-art classification accuracies in CIFAR-10, the Adience gender recognition task, Stanford Dogs, and UEC-Food100 while obtaining competitive performance in ImageNet, CIFAR-100, CUB200 Birds, and Stanford Cars. In addition, we analyze the different components of our model, showing that the proposed attention modules succeed in finding the most discriminative regions of the image. Finally, as a proof of concept, we demonstrate that with only local predictions, an augmented neural network can successfully classify an image before reaching any fully connected layer, thus reducing the computational amount up to 10%.
Predicting assisted ventilation in Amyotrophic Lateral Sclerosis using a mixture of experts and conformal predictors
Authors: Telma Pereira, Sofia Pires, Marta Gromicho, Susana Pinto, Mamede de Carvalho, Sara C. Madeira
Date: 2019-07-30 00:00:00
Tasks: Conformal Prediction, Mixture-of-Experts, Respiratory Failure, Translation

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease characterized by a rapid motor decline, leading to respiratory failure and subsequently to death. In this context, researchers have sought for models to automatically predict disease progression to assisted ventilation in ALS patients. However, the clinical translation of such models is limited by the lack of insight 1) on the risk of error for predictions at patient-level, and 2) on the most adequate time to administer the non-invasive ventilation. To address these issues, we combine Conformal Prediction (a machine learning framework that complements predictions with confidence measures) and a mixture experts into a prognostic model which not only predicts whether an ALS patient will suffer from respiratory insufficiency but also the most likely time window of occurrence, at a given reliability level. Promising results were obtained, with near 80% of predictions being correctly identified.
Iterative Budgeted Exponential Search
Authors: Malte Helmert, Tor Lattimore, Levi H. S. Lelis, Laurent Orseau, Nathan R. Sturtevant
Date: 2019-07-30 00:00:00
Tasks: Heuristic Search

We tackle two long-standing problems related to re-expansions in heuristic search algorithms. For graph search, A* can require $\Omega(2^{n})$ expansions, where $n$ is the number of states within the final $f$ bound. Existing algorithms that address this problem like B and B' improve this bound to $\Omega(n^2)$. For tree search, IDA* can also require $\Omega(n^2)$ expansions. We describe a new algorithmic framework that iteratively controls an expansion budget and solution cost limit, giving rise to new graph and tree search algorithms for which the number of expansions is $O(n \log C)$, where $C$ is the optimal solution cost. Our experiments show that the new algorithms are robust in scenarios where existing algorithms fail. In the case of tree search, our new algorithms have no overhead over IDA* in scenarios to which IDA* is well suited and can therefore be recommended as a general replacement for IDA*.
Screening Mammogram Classification with Prior Exams
Authors: Jungkyu Park, Jason Phang, Yiqiu Shen, Nan Wu, S. Gene Kim, Linda Moy, Kyunghyun Cho, Krzysztof J. Geras
Date: 2019-07-30 00:00:00
Tasks: Classification, General Classification

Radiologists typically compare a patient's most recent breast cancer screening exam to their previous ones in making informed diagnoses. To reflect this practice, we propose new neural network models that compare pairs of screening mammograms from the same patient. We train and evaluate our proposed models on over 665,000 pairs of images (over 166,000 pairs of exams). Our best model achieves an AUC of 0.866 in predicting malignancy in patients who underwent breast cancer screening, reducing the error rate of the corresponding baseline.
Grid Saliency for Context Explanations of Semantic Segmentation
Authors: Lukas Hoyer, Mauricio Munoz, Prateek Katiyar, Anna Khoreva, Volker Fischer
Date: 2019-07-30 00:00:00
Proceeding: NeurIPS 2019 12
Tasks: image-classification, Image Classification, Segmentation, Semantic Segmentation

Recently, there has been a growing interest in developing saliency methods that provide visual explanations of network predictions. Still, the usability of existing methods is limited to image classification models. To overcome this limitation, we extend the existing approaches to generate grid saliencies, which provide spatially coherent visual explanations for (pixel-level) dense prediction networks. As the proposed grid saliency allows to spatially disentangle the object and its context, we specifically explore its potential to produce context explanations for semantic segmentation networks, discovering which context most influences the class predictions inside a target object area. We investigate the effectiveness of grid saliency on a synthetic dataset with an artificially induced bias between objects and their context as well as on the real-world Cityscapes dataset using state-of-the-art segmentation networks. Our results show that grid saliency can be successfully used to provide easily interpretable context explanations and, moreover, can be employed for detecting and localizing contextual biases present in the data.
DeepPlace: Learning to Place Applications in Multi-Tenant Clusters
Authors: Subrata Mitra, Shanka Subhra Mondal, Nikhil Sheoran, Neeraj Dhake, Ravinder Nehra, Ramanuja Simha
Date: 2019-07-30 00:00:00
Tasks: Deep Reinforcement Learning, reinforcement-learning, Reinforcement Learning, Reinforcement Learning (RL), Scheduling

Large multi-tenant production clusters often have to handle a variety of jobs and applications with a variety of complex resource usage characteristics. It is non-trivial and non-optimal to manually create placement rules for scheduling that would decide which applications should co-locate. In this paper, we present DeepPlace, a scheduler that learns to exploits various temporal resource usage patterns of applications using Deep Reinforcement Learning (Deep RL) to reduce resource competition across jobs running in the same machine while at the same time optimizing for overall cluster utilization.
GENESIS: Generative Scene Inference and Sampling with Object-Centric Latent Representations
Authors: Martin Engelcke, Adam R. Kosiorek, Oiwi Parker Jones, Ingmar Posner
Date: 2019-07-30 00:00:00
Proceeding: ICLR 2020 1
Tasks: Image Generation, Object Discovery, Reinforcement Learning, Representation Learning, Scene Generation, Unsupervised Image Segmentation, Unsupervised Object Segmentation

Generative latent-variable models are emerging as promising tools in robotics and reinforcement learning. Yet, even though tasks in these domains typically involve distinct objects, most state-of-the-art generative models do not explicitly capture the compositional nature of visual scenes. Two recent exceptions, MONet and IODINE, decompose scenes into objects in an unsupervised fashion. Their underlying generative processes, however, do not account for component interactions. Hence, neither of them allows for principled sampling of novel scenes. Here we present GENESIS, the first object-centric generative model of 3D visual scenes capable of both decomposing and generating scenes by capturing relationships between scene components. GENESIS parameterises a spatial GMM over images which is decoded from a set of object-centric latent variables that are either inferred sequentially in an amortised fashion or sampled from an autoregressive prior. We train GENESIS on several publicly available datasets and evaluate its performance on scene generation, decomposition, and semi-supervised learning.
Weakly Supervised Body Part Segmentation with Pose based Part Priors
Authors: Zhengyuan Yang, Yuncheng Li, Linjie Yang, Ning Zhang, Jiebo Luo
Date: 2019-07-30 00:00:00
Tasks: Face Parsing, Segmentation, Semantic Segmentation

Human body part segmentation refers to the task of predicting the semantic segmentation mask for each body part. Fully supervised body part segmentation methods achieve good performances but require an enormous amount of effort to annotate part masks for training. In contrast to high annotation costs needed for a limited number of part mask annotations, a large number of weak labels such as poses and full body masks already exist and contain relevant information. Motivated by the possibility of using existing weak labels, we propose the first weakly supervised body part segmentation framework. The core idea is first converting the sparse weak labels such as keypoints to the initial estimate of body part masks, and then iteratively refine the part mask predictions. We name the initial part masks estimated from poses the "part priors." With sufficient extra weak labels, our weakly supervised framework achieves a comparable performance (62.0% mIoU) to the fully supervised method (63.6% mIoU) on the Pascal-Person-Part dataset. Furthermore, in the extended semi-supervised setting, the proposed framework outperforms the state-of-art methods. Moreover, we extend our proposed framework to other keypoint-supervised part segmentation tasks such as face parsing.
Efficient Method for Categorize Animals in the Wild
Authors: Abulikemu Abuduweili, Xin Wu, Xingchen Tao
Date: 2019-07-30 00:00:00
Tasks: Ensemble Learning, General Classification, Image Augmentation

Automatic species classification in camera traps would greatly help the biodiversity monitoring and species analysis in the earth. In order to accelerate the development of automatic species classification task, "Microsoft AI for Earth" have prepared a challenge in FGVC6 workshop at CVPR 2019, which called "iWildCam 2019 competition". In this work, we propose an efficient method for categorizing animals in the wild. We transfer the state-of-the-art ImagaNet pretrained models to the problem. To improve the generalization and robustness of the model, we utilize efficient image augmentation and regularization strategies, like cutout, mixup and label-smoothing. Finally, we use ensemble learning to increase the performance of the model. Thanks to advanced regularization strategies and ensemble learning, we got top 7/336 places in the final leaderboard. Source code of this work is available at https://github.com/Walleclipse/iWildCam_2019_FGVC6
Lung image segmentation by generative adversarial networks
Authors: Jia-xin Cai, Hongfeng Zhu
Date: 2019-07-30 00:00:00
Tasks: Image Segmentation, Segmentation, Semantic Segmentation, Translation

Lung image segmentation plays an important role in computer-aid pulmonary diseases diagnosis and treatment. This paper proposed a lung image segmentation method by generative adversarial networks. We employed a variety of generative adversarial networks and use its capability of image translation to perform image segmentation. The generative adversarial networks was employed to translate the original lung image to the segmented image. The generative adversarial networks based segmentation method was test on real lung image data set. Experimental results shows that the proposed method is effective and outperform state-of-the art method.
SkeleMotion: A New Representation of Skeleton Joint Sequences Based on Motion Information for 3D Action Recognition
Authors: Carlos Caetano, Jessica Sena, François Brémond, Jefersson A. dos Santos, William Robson Schwartz
Date: 2019-07-30 00:00:00
Tasks: 3D Action Recognition, Action Recognition, Skeleton Based Action Recognition, Temporal Action Localization

Due to the availability of large-scale skeleton datasets, 3D human action recognition has recently called the attention of computer vision community. Many works have focused on encoding skeleton data as skeleton image representations based on spatial structure of the skeleton joints, in which the temporal dynamics of the sequence is encoded as variations in columns and the spatial structure of each frame is represented as rows of a matrix. To further improve such representations, we introduce a novel skeleton image representation to be used as input of Convolutional Neural Networks (CNNs), named SkeleMotion. The proposed approach encodes the temporal dynamics by explicitly computing the magnitude and orientation values of the skeleton joints. Different temporal scales are employed to compute motion values to aggregate more temporal dynamics to the representation making it able to capture longrange joint interactions involved in actions as well as filtering noisy motion values. Experimental results demonstrate the effectiveness of the proposed representation on 3D action recognition outperforming the state-of-the-art on NTU RGB+D 120 dataset.