pwc-archive/papers-with-abstracts huggingface dataset:
Automatic non-invasive Cough Detection based on Accelerometer and Audio Signals
Authors: Madhurananda Pahar, Igor Miranda, Andreas Diacon, Thomas Niesler
Date: 2021-08-31 00:00:00

We present an automatic non-invasive way of detecting cough events based on both accelerometer and audio signals. The acceleration signals are captured by a smartphone firmly attached to the patient's bed, using its integrated accelerometer. The audio signals are captured simultaneously by the same smartphone using an external microphone. We have compiled a manually-annotated dataset containing such simultaneously-captured acceleration and audio signals for approximately 6000 cough and 68000 non-cough events from 14 adult male patients in a tuberculosis clinic. LR, SVM and MLP are evaluated as baseline classifiers and compared with deep architectures such as CNN, LSTM, and Resnet50 using a leave-one-out cross-validation scheme. We find that the studied classifiers can use either acceleration or audio signals to distinguish between coughing and other activities including sneezing, throat-clearing, and movement on the bed with high accuracy. However, in all cases, the deep neural networks outperform the shallow classifiers by a clear margin and the Resnet50 offers the best performance by achieving an AUC exceeding 0.98 and 0.99 for acceleration and audio signals respectively. While audio-based classification consistently offers a better performance than acceleration-based classification, we observe that the difference is very small for the best systems. Since the acceleration signal requires less processing power, and since the need to record audio is sidestepped and thus privacy is inherently secured, and since the recording device is attached to the bed and not worn, an accelerometer-based highly accurate non-invasive cough detector may represent a more convenient and readily accepted method in long-term cough monitoring.
Proceedings of KDD 2021 Workshop on Data-driven Humanitarian Mapping: Harnessing Human-Machine Intelligence for High-Stake Public Policy and Resilience Planning
Authors: Snehalkumar, S. Gaikwad, Shankar Iyer, Dalton Lunga, Elizabeth Bondi
Date: 2021-08-31 00:00:00
Tasks: Ethics, Fairness, Humanitarian

Humanitarian challenges, including natural disasters, food insecurity, climate change, racial and gender violence, environmental crises, the COVID-19 coronavirus pandemic, human rights violations, and forced displacements, disproportionately impact vulnerable communities worldwide. According to UN OCHA, 235 million people will require humanitarian assistance in 2021. Despite these growing perils, there remains a notable paucity of data science research to scientifically inform equitable public policy decisions for improving the livelihood of at-risk populations. Scattered data science efforts exist to address these challenges, but they remain isolated from practice and prone to algorithmic harms concerning lack of privacy, fairness, interpretability, accountability, transparency, and ethics. Biases in data-driven methods carry the risk of amplifying inequalities in high-stakes policy decisions that impact the livelihood of millions of people. Consequently, proclaimed benefits of data-driven innovations remain inaccessible to policymakers, practitioners, and marginalized communities at the core of humanitarian actions and global development. To help fill this gap, we propose the Data-driven Humanitarian Mapping Research Program, which focuses on developing novel data science methodologies that harness human-machine intelligence for high-stakes public policy and resilience planning. The proceedings of the 2nd Data-driven Humanitarian Mapping workshop at the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining. August 15th, 2021
Informing Autonomous Deception Systems with Cyber Expert Performance Data
Authors: Maxine Major, Brian Souza, Joseph DiVita, Kimberly Ferguson-Walter
Date: 2021-08-31 00:00:00
Tasks: reinforcement-learning, Reinforcement Learning (RL)

The performance of artificial intelligence (AI) algorithms in practice depends on the realism and correctness of the data, models, and feedback (labels or rewards) provided to the algorithm. This paper discusses methods for improving the realism and ecological validity of AI used for autonomous cyber defense by exploring the potential to use Inverse Reinforcement Learning (IRL) to gain insight into attacker actions, utilities of those actions, and ultimately decision points which cyber deception could thwart. The Tularosa study, as one example, provides experimental data of real-world techniques and tools commonly used by attackers, from which core data vectors can be leveraged to inform an autonomous cyber defense system.
Deep DNA Storage: Scalable and Robust DNA Storage via Coding Theory and Deep Learning
Authors: Daniella Bar-Lev, Itai Orr, Omer Sabary, Tuvi Etzion, Eitan Yaakobi
Date: 2021-08-31 00:00:00
Tasks: Clustering, Information Retrieval, Retrieval

DNA-based storage is an emerging technology that enables digital information to be archived in DNA molecules. This method enjoys major advantages over magnetic and optical storage solutions such as exceptional information density, enhanced data durability, and negligible power consumption to maintain data integrity. To access the data, an information retrieval process is employed, where some of the main bottlenecks are the scalability and accuracy, which have a natural tradeoff between the two. Here we show a modular and holistic approach that combines Deep Neural Networks (DNN) trained on simulated data, Tensor-Product (TP) based Error-Correcting Codes (ECC), and a safety margin mechanism into a single coherent pipeline. We demonstrated our solution on 3.1MB of information using two different sequencing technologies. Our work improves upon the current leading solutions by up to x3200 increase in speed, 40% improvement in accuracy, and offers a code rate of 1.6 bits per base in a high noise regime. In a broader sense, our work shows a viable path to commercial DNA storage solutions hindered by current information retrieval processes.
Working Memory Connections for LSTM
Authors: Federico Landi, Lorenzo Baraldi, Marcella Cornia, Rita Cucchiara
Date: 2021-08-31 00:00:00

Recurrent Neural Networks with Long Short-Term Memory (LSTM) make use of gating mechanisms to mitigate exploding and vanishing gradients when learning long-term dependencies. For this reason, LSTMs and other gated RNNs are widely adopted, being the standard de facto for many sequence modeling tasks. Although the memory cell inside the LSTM contains essential information, it is not allowed to influence the gating mechanism directly. In this work, we improve the gate potential by including information coming from the internal cell state. The proposed modification, named Working Memory Connection, consists in adding a learnable nonlinear projection of the cell content into the network gates. This modification can fit into the classical LSTM gates without any assumption on the underlying task, being particularly effective when dealing with longer sequences. Previous research effort in this direction, which goes back to the early 2000s, could not bring a consistent improvement over vanilla LSTM. As part of this paper, we identify a key issue tied to previous connections that heavily limits their effectiveness, hence preventing a successful integration of the knowledge coming from the internal cell state. We show through extensive experimental evaluation that Working Memory Connections constantly improve the performance of LSTMs on a variety of tasks. Numerical results suggest that the cell state contains useful information that is worth including in the gate structure.
Common Objects in 3D: Large-Scale Learning and Evaluation of Real-life 3D Category Reconstruction
Authors: Jeremy Reizenstein, Roman Shapovalov, Philipp Henzler, Luca Sbordone, Patrick Labatut, David Novotny
Date: 2021-09-01 00:00:00
Proceeding: ICCV 2021 10
Tasks: 3D Reconstruction, Neural Rendering, Object

Traditional approaches for learning 3D object categories have been predominantly trained and evaluated on synthetic datasets due to the unavailability of real 3D-annotated category-centric data. Our main goal is to facilitate advances in this field by collecting real-world data in a magnitude similar to the existing synthetic counterparts. The principal contribution of this work is thus a large-scale dataset, called Common Objects in 3D, with real multi-view images of object categories annotated with camera poses and ground truth 3D point clouds. The dataset contains a total of 1.5 million frames from nearly 19,000 videos capturing objects from 50 MS-COCO categories and, as such, it is significantly larger than alternatives both in terms of the number of categories and objects. We exploit this new dataset to conduct one of the first large-scale "in-the-wild" evaluations of several new-view-synthesis and category-centric 3D reconstruction methods. Finally, we contribute NerFormer - a novel neural rendering method that leverages the powerful Transformer to reconstruct an object given a small number of its views. The CO3D dataset is available at https://github.com/facebookresearch/co3d .
Looking at the whole picture: constrained unsupervised anomaly segmentation
Authors: Julio Silva-Rodríguez, Valery Naranjo, Jose Dolz
Date: 2021-09-01 00:00:00
Tasks: Anomaly Localization, Anomaly Segmentation, Lesion Segmentation

Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. However, a main limitation of nearly all prior literature is the need of employing anomalous images to set a class-specific threshold to locate the anomalies. This limits their usability in realistic scenarios, where only normal data is typically accessible. Despite this major drawback, only a handful of works have addressed this limitation, by integrating supervision on attention maps during training. In this work, we propose a novel formulation that does not require accessing images with abnormalities to define the threshold. Furthermore, and in contrast to very recent work, the proposed constraint is formulated in a more principled manner, leveraging well-known knowledge in constrained optimization. In particular, the equality constraint on the attention maps in prior work is replaced by an inequality constraint, which allows more flexibility. In addition, to address the limitations of penalty-based functions we employ an extension of the popular log-barrier methods to handle the constraint. Comprehensive experiments on the popular BRATS'19 dataset demonstrate that the proposed approach substantially outperforms relevant literature, establishing new state-of-the-art results for unsupervised lesion segmentation.
Towards Learning a Vocabulary of Visual Concepts and Operators using Deep Neural Networks
Authors: Sunil Kumar Vengalil, Neelam Sinha
Date: 2021-09-01 00:00:00
Tasks: Video Recognition

Deep neural networks have become the default choice for many applications like image and video recognition, segmentation and other image and video related tasks.However, a critical challenge with these models is the lack of explainability.This requirement of generating explainable predictions has motivated the research community to perform various analysis on trained models.In this study, we analyze the learned feature maps of trained models using MNIST images for achieving more explainable predictions.Our study is focused on deriving a set of primitive elements, here called visual concepts, that can be used to generate any arbitrary sample from the data generating distribution.We derive the primitive elements from the feature maps learned by the model.We illustrate the idea by generating visual concepts from a Variational Autoencoder trained using MNIST images.We augment the training data of MNIST dataset by adding about 60,000 new images generated with visual concepts chosen at random.With this we were able to reduce the reconstruction loss (mean square error) from an initial value of 120 without augmentation to 60 with augmentation.Our approach is a first step towards the final goal of achieving trained deep neural network models whose predictions, features in hidden layers and the learned filters can be well explained.Such a model when deployed in production can easily be modified to adapt to new data, whereas existing deep learning models need a re training or fine tuning. This process again needs a huge number of data samples that are not easy to generate unless the model has good explainability.
Sparse to Dense Motion Transfer for Face Image Animation
Authors: Ruiqi Zhao, Tianyi Wu, Guodong Guo
Date: 2021-09-01 00:00:00
Tasks: Image Animation, Motion Estimation, Translation

Face image animation from a single image has achieved remarkable progress. However, it remains challenging when only sparse landmarks are available as the driving signal. Given a source face image and a sequence of sparse face landmarks, our goal is to generate a video of the face imitating the motion of landmarks. We develop an efficient and effective method for motion transfer from sparse landmarks to the face image. We then combine global and local motion estimation in a unified model to faithfully transfer the motion. The model can learn to segment the moving foreground from the background and generate not only global motion, such as rotation and translation of the face, but also subtle local motion such as the gaze change. We further improve face landmark detection on videos. With temporally better aligned landmark sequences for training, our method can generate temporally coherent videos with higher visual quality. Experiments suggest we achieve results comparable to the state-of-the-art image driven method on the same identity testing and better results on cross identity testing.
Weakly-Supervised Surface Crack Segmentation by Generating Pseudo-Labels using Localization with a Classifier and Thresholding
Authors: Jacob König, Mark Jenkins, Mike Mannion, Peter Barrie, Gordon Morison
Date: 2021-09-01 00:00:00
Tasks: Crack Segmentation, Segmentation

Surface cracks are a common sight on public infrastructure nowadays. Recent work has been addressing this problem by supporting structural maintenance measures using machine learning methods. Those methods are used to segment surface cracks from their background, making them easier to localize. However, a common issue is that to create a well-functioning algorithm, the training data needs to have detailed annotations of pixels that belong to cracks. Our work proposes a weakly supervised approach that leverages a CNN classifier in a novel way to create surface crack pseudo labels. First, we use the classifier to create a rough crack localization map by using its class activation maps and a patch based classification approach and fuse this with a thresholding based approach to segment the mostly darker crack pixels. The classifier assists in suppressing noise from the background regions, which commonly are incorrectly highlighted as cracks by standard thresholding methods. Then, the pseudo labels can be used in an end-to-end approach when training a standard CNN for surface crack segmentation. Our method is shown to yield sufficiently accurate pseudo labels. Those labels, incorporated into segmentation CNN training using multiple recent crack segmentation architectures, achieve comparable performance to fully supervised methods on four popular crack segmentation datasets.
Self-supervised Point Cloud Representation Learning via Separating Mixed Shapes
Authors: Chao Sun, Zhedong Zheng, Xiaohan Wang, Mingliang Xu, Yi Yang
Date: 2021-09-01 00:00:00
Tasks: 3D Part Segmentation, 3D Point Cloud Classification, Decoder, Point Cloud Classification, Point Cloud Pre-training, Representation Learning, Self-Supervised Learning, Semantic Segmentation

The manual annotation for large-scale point clouds costs a lot of time and is usually unavailable in harsh real-world scenarios. Inspired by the great success of the pre-training and fine-tuning paradigm in both vision and language tasks, we argue that pre-training is one potential solution for obtaining a scalable model to 3D point cloud downstream tasks as well. In this paper, we, therefore, explore a new self-supervised learning method, called Mixing and Disentangling (MD), for 3D point cloud representation learning. As the name implies, we mix two input shapes and demand the model learning to separate the inputs from the mixed shape. We leverage this reconstruction task as the pretext optimization objective for self-supervised learning. There are two primary advantages: 1) Compared to prevailing image datasets, eg, ImageNet, point cloud datasets are de facto small. The mixing process can provide a much larger online training sample pool. 2) On the other hand, the disentangling process motivates the model to mine the geometric prior knowledge, eg, key points. To verify the effectiveness of the proposed pretext task, we build one baseline network, which is composed of one encoder and one decoder. During pre-training, we mix two original shapes and obtain the geometry-aware embedding from the encoder, then an instance-adaptive decoder is applied to recover the original shapes from the embedding. Albeit simple, the pre-trained encoder can capture the key points of an unseen point cloud and surpasses the encoder trained from scratch on downstream tasks. The proposed method has improved the empirical performance on both ModelNet-40 and ShapeNet-Part datasets in terms of point cloud classification and segmentation tasks. We further conduct ablation studies to explore the effect of each component and verify the generalization of our proposed strategy by harnessing different backbones.
EVReflex: Dense Time-to-Impact Prediction for Event-based Obstacle Avoidance
Authors: Celyn Walters, Simon Hadfield
Date: 2021-09-01 00:00:00

The broad scope of obstacle avoidance has led to many kinds of computer vision-based approaches. Despite its popularity, it is not a solved problem. Traditional computer vision techniques using cameras and depth sensors often focus on static scenes, or rely on priors about the obstacles. Recent developments in bio-inspired sensors present event cameras as a compelling choice for dynamic scenes. Although these sensors have many advantages over their frame-based counterparts, such as high dynamic range and temporal resolution, event-based perception has largely remained in 2D. This often leads to solutions reliant on heuristics and specific to a particular task. We show that the fusion of events and depth overcomes the failure cases of each individual modality when performing obstacle avoidance. Our proposed approach unifies event camera and lidar streams to estimate metric time-to-impact without prior knowledge of the scene geometry or obstacles. In addition, we release an extensive event-based dataset with six visual streams spanning over 700 scanned scenes.
An Integrated Framework for the Heterogeneous Spatio-Spectral-Temporal Fusion of Remote Sensing Images
Authors: Menghui Jiang, Huanfeng Shen, Jie Li, Liangpei Zhang
Date: 2021-09-01 00:00:00

Image fusion technology is widely used to fuse the complementary information between multi-source remote sensing images. Inspired by the frontier of deep learning, this paper first proposes a heterogeneous-integrated framework based on a novel deep residual cycle GAN. The proposed network consists of a forward fusion part and a backward degeneration feedback part. The forward part generates the desired fusion result from the various observations; the backward degeneration feedback part considers the imaging degradation process and regenerates the observations inversely from the fusion result. The proposed network can effectively fuse not only the homogeneous but also the heterogeneous information. In addition, for the first time, a heterogeneous-integrated fusion framework is proposed to simultaneously merge the complementary heterogeneous spatial, spectral and temporal information of multi-source heterogeneous observations. The proposed heterogeneous-integrated framework also provides a uniform mode that can complete various fusion tasks, including heterogeneous spatio-spectral fusion, spatio-temporal fusion, and heterogeneous spatio-spectral-temporal fusion. Experiments are conducted for two challenging scenarios of land cover changes and thick cloud coverage. Images from many remote sensing satellites, including MODIS, Landsat-8, Sentinel-1, and Sentinel-2, are utilized in the experiments. Both qualitative and quantitative evaluations confirm the effectiveness of the proposed method.
ImageTBAD: A 3D Computed Tomography Angiography Image Dataset for Automatic Segmentation of Type-B Aortic Dissection
Authors: Zeyang Yao, Jiawei Zhang, Hailong Qiu, Tianchen Wang, Yiyu Shi, Jian Zhuang, Yuhao Dong, Meiping Huang, Xiaowei Xu
Date: 2021-09-01 00:00:00
Tasks: Prognosis, Segmentation

Type-B Aortic Dissection (TBAD) is one of the most serious cardiovascular events characterized by a growing yearly incidence,and the severity of disease prognosis. Currently, computed tomography angiography (CTA) has been widely adopted for the diagnosis and prognosis of TBAD. Accurate segmentation of true lumen (TL), false lumen (FL), and false lumen thrombus (FLT) in CTA are crucial for the precise quantification of anatomical features. However, existing works only focus on only TL and FL without considering FLT. In this paper, we propose ImageTBAD, the first 3D computed tomography angiography (CTA) image dataset of TBAD with annotation of TL, FL, and FLT. The proposed dataset contains 100 TBAD CTA images, which is of decent size compared with existing medical imaging datasets. As FLT can appear almost anywhere along the aorta with irregular shapes, segmentation of FLT presents a wide class of segmentation problems where targets exist in a variety of positions with irregular shapes. We further propose a baseline method for automatic segmentation of TBAD. Results show that the baseline method can achieve comparable results with existing works on aorta and TL segmentation. However, the segmentation accuracy of FLT is only 52%, which leaves large room for improvement and also shows the challenge of our dataset. To facilitate further research on this challenging problem, our dataset and codes are released to the public.
Memory Based Video Scene Parsing
Authors: Zhenchao Jin, Dongdong Yu, Kai Su, Zehuan Yuan, Changhu Wang
Date: 2021-09-01 00:00:00
Tasks: Scene Parsing, Semantic Segmentation

Video scene parsing is a long-standing challenging task in computer vision, aiming to assign pre-defined semantic labels to pixels of all frames in a given video. Compared with image semantic segmentation, this task pays more attention on studying how to adopt the temporal information to obtain higher predictive accuracy. In this report, we introduce our solution for the 1st Video Scene Parsing in the Wild Challenge, which achieves a mIoU of 57.44 and obtained the 2nd place (our team name is CharlesBLWX).