pwc-archive/papers-with-abstracts huggingface dataset:
Constraint-Based Software Diversification for Efficient Mitigation of Code-Reuse Attacks
Authors: Rodothea Myrsini Tsoupidi, Roberto Castañeda Lozano, Benoit Baudry
Date: 2020-07-17 00:00:00
Tasks: Diversity

Modern software deployment process produces software that is uniform, and hence vulnerable to large-scale code-reuse attacks. Compiler-based diversification improves the resilience and security of software systems by automatically generating different assembly code versions of a given program. Existing techniques are efficient but do not have a precise control over the quality of the generated code variants. This paper introduces Diversity by Construction (DivCon), a constraint-based compiler approach to software diversification. Unlike previous approaches, DivCon allows users to control and adjust the conflicting goals of diversity and code quality. A key enabler is the use of Large Neighborhood Search (LNS) to generate highly diverse assembly code efficiently. Experiments using two popular compiler benchmark suites confirm that there is a trade-off between quality of each assembly code version and diversity of the entire pool of versions. Our results show that DivCon allows users to trade between these two properties by generating diverse assembly code for a range of quality bounds. In particular, the experiments show that DivCon is able to mitigate code-reuse attacks effectively while delivering near-optimal code (< 10% optimality gap). For constraint programming researchers and practitioners, this paper demonstrates that LNS is a valuable technique for finding diverse solutions. For security researchers and software engineers, DivCon extends the scope of compiler-based diversification to performance-critical and resource-constrained applications.
SummPip: Unsupervised Multi-Document Summarization with Sentence Graph Compression
Authors: Jinming Zhao, Ming Liu, Longxiang Gao, Yuan Jin, Lan Du, He Zhao, He Zhang, Gholamreza Haffari
Date: 2020-07-17 00:00:00
Tasks: Clustering, Document Summarization, Multi-Document Summarization, Sentence

Obtaining training data for multi-document summarization (MDS) is time consuming and resource-intensive, so recent neural models can only be trained for limited domains. In this paper, we propose SummPip: an unsupervised method for multi-document summarization, in which we convert the original documents to a sentence graph, taking both linguistic and deep representation into account, then apply spectral clustering to obtain multiple clusters of sentences, and finally compress each cluster to generate the final summary. Experiments on Multi-News and DUC-2004 datasets show that our method is competitive to previous unsupervised methods and is even comparable to the neural supervised approaches. In addition, human evaluation shows our system produces consistent and complete summaries compared to human written ones.
Always-On 674uW @ 4GOP/s Error Resilient Binary Neural Networks with Aggressive SRAM Voltage Scaling on a 22nm IoT End-Node
Authors: Alfio Di Mauro, Francesco Conti, Pasquale Davide Schiavone, Davide Rossi, Luca Benini
Date: 2020-07-17 00:00:00
Tasks: PICO

Binary Neural Networks (BNNs) have been shown to be robust to random bit-level noise, making aggressive voltage scaling attractive as a power-saving technique for both logic and SRAMs. In this work, we introduce the first fully programmable IoT end-node system-on-chip (SoC) capable of executing software-defined, hardware-accelerated BNNs at ultra-low voltage. Our SoC exploits a hybrid memory scheme where error-vulnerable SRAMs are complemented by reliable standard-cell memories to safely store critical data under aggressive voltage scaling. On a prototype in 22nm FDX technology, we demonstrate that both the logic and SRAM voltage can be dropped to 0.5Vwithout any accuracy penalty on a BNN trained for the CIFAR-10 dataset, improving energy efficiency by 2.2X w.r.t. nominal conditions. Furthermore, we show that the supply voltage can be dropped to 0.42V (50% of nominal) while keeping more than99% of the nominal accuracy (with a bit error rate ~1/1000). In this operating point, our prototype performs 4Gop/s (15.4Inference/s on the CIFAR-10 dataset) by computing up to 13binary ops per pJ, achieving 22.8 Inference/s/mW while keeping within a peak power envelope of 674uW - low enough to enable always-on operation in ultra-low power smart cameras, long-lifetime environmental sensors, and insect-sized pico-drones.
Probabilistic Active Meta-Learning
Authors: Jean Kaddour, Steindór Sæmundsson, Marc Peter Deisenroth
Date: 2020-07-17 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Meta-Learning

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics due to the wear and tear. To address this problem, meta-learning algorithms use prior experience about tasks to learn new, related tasks efficiently. Typically, a set of training tasks is assumed given or randomly chosen. However, this setting does not take into account the sequential nature that naturally arises when training a model from scratch in real-life: how do we collect a set of training tasks in a data-efficient manner? In this work, we introduce task selection based on prior experience into a meta-learning algorithm by conceptualizing the learner and the active meta-learning setting using a probabilistic latent variable model. We provide empirical evidence that our approach improves data-efficiency when compared to strong baselines on simulated robotic experiments.
HDNet: Human Depth Estimation for Multi-Person Camera-Space Localization
Authors: Jiahao Lin, Gim Hee Lee
Date: 2020-07-17 00:00:00
Proceeding: ECCV 2020 8
Tasks: 3D Multi-Person Pose Estimation (absolute), 3D Multi-Person Pose Estimation (root-relative), 3D Pose Estimation, Depth Estimation, Graph Neural Network, Pose Estimation, Root Joint Localization

Current works on multi-person 3D pose estimation mainly focus on the estimation of the 3D joint locations relative to the root joint and ignore the absolute locations of each pose. In this paper, we propose the Human Depth Estimation Network (HDNet), an end-to-end framework for absolute root joint localization in the camera coordinate space. Our HDNet first estimates the 2D human pose with heatmaps of the joints. These estimated heatmaps serve as attention masks for pooling features from image regions corresponding to the target person. A skeleton-based Graph Neural Network (GNN) is utilized to propagate features among joints. We formulate the target depth regression as a bin index estimation problem, which can be transformed with a soft-argmax operation from the classification output of our HDNet. We evaluate our HDNet on the root joint localization and root-relative 3D pose estimation tasks with two benchmark datasets, i.e., Human3.6M and MuPoTS-3D. The experimental results show that we outperform the previous state-of-the-art consistently under multiple evaluation metrics. Our source code is available at: https://github.com/jiahaoLjh/HumanDepth.
Geometric Correspondence Fields: Learned Differentiable Rendering for 3D Pose Refinement in the Wild
Authors: Alexander Grabner, Yaming Wang, Peizhao Zhang, Peihong Guo, Tong Xiao, Peter Vajda, Peter M. Roth, Vincent Lepetit
Date: 2020-07-17 00:00:00
Proceeding: ECCV 2020 8

We present a novel 3D pose refinement approach based on differentiable rendering for objects of arbitrary categories in the wild. In contrast to previous methods, we make two main contributions: First, instead of comparing real-world images and synthetic renderings in the RGB or mask space, we compare them in a feature space optimized for 3D pose refinement. Second, we introduce a novel differentiable renderer that learns to approximate the rasterization backward pass from data instead of relying on a hand-crafted algorithm. For this purpose, we predict deep cross-domain correspondences between RGB images and 3D model renderings in the form of what we call geometric correspondence fields. These correspondence fields serve as pixel-level gradients which are analytically propagated backward through the rendering pipeline to perform a gradient-based optimization directly on the 3D pose. In this way, we precisely align 3D models to objects in RGB images which results in significantly improved 3D pose estimates. We evaluate our approach on the challenging Pix3D dataset and achieve up to 55% relative improvement compared to state-of-the-art refinement methods in multiple metrics.
Provably Consistent Partial-Label Learning
Authors: Lei Feng, Jiaqi Lv, Bo Han, Miao Xu, Gang Niu, Xin Geng, Bo An, Masashi Sugiyama
Date: 2020-07-17 00:00:00
Proceeding: NeurIPS 2020 12
Tasks: Multi-class Classification, Partial Label Learning

Partial-label learning (PLL) is a multi-class classification problem, where each training example is associated with a set of candidate labels. Even though many practical PLL methods have been proposed in the last two decades, there lacks a theoretical understanding of the consistency of those methods-none of the PLL methods hitherto possesses a generation process of candidate label sets, and then it is still unclear why such a method works on a specific dataset and when it may fail given a different dataset. In this paper, we propose the first generation model of candidate label sets, and develop two novel PLL methods that are guaranteed to be provably consistent, i.e., one is risk-consistent and the other is classifier-consistent. Our methods are advantageous, since they are compatible with any deep network or stochastic optimizer. Furthermore, thanks to the generation model, we would be able to answer the two questions above by testing if the generation model matches given candidate label sets. Experiments on benchmark and real-world datasets validate the effectiveness of the proposed generation model and two PLL methods.
Training with reduced precision of a support vector machine model for text classification
Authors: Dominik Żurek, Marcin Pietroń
Date: 2020-07-17 00:00:00
Tasks: CPU, General Classification, GPU, Multi Class Text Classification, Quantization, text-classification, Text Classification

This paper presents the impact of using quantization on the efficiency of multi-class text classification in the training process of a support vector machine (SVM). This work is focused on comparing the efficiency of SVM model trained using reduced precision with its original form. The main advantage of using quantization is decrease in computation time and in memory footprint on the dedicated hardware platform which supports low precision computation like GPU (16-bit) or FPGA (any bit-width). The paper presents the impact of a precision reduction of the SVM training process on text classification accuracy. The implementation of the CPU was performed using the OpenMP library. Additionally, the results of the implementation of the GPU using double, single and half precision are presented.
Can Learned Frame-Prediction Compete with Block-Motion Compensation for Video Coding?
Authors: Serkan Sulun, A. Murat Tekalp
Date: 2020-07-17 00:00:00
Tasks: Motion Compensation, Prediction, Video Prediction

Given recent advances in learned video prediction, we investigate whether a simple video codec using a pre-trained deep model for next frame prediction based on previously encoded/decoded frames without sending any motion side information can compete with standard video codecs based on block-motion compensation. Frame differences given learned frame predictions are encoded by a standard still-image (intra) codec. Experimental results show that the rate-distortion performance of the simple codec with symmetric complexity is on average better than that of x264 codec on 10 MPEG test videos, but does not yet reach the level of x265 codec. This result demonstrates the power of learned frame prediction (LFP), since unlike motion compensation, LFP does not use information from the current picture. The implications of training with L1, L2, or combined L2 and adversarial loss on prediction performance and compression efficiency are analyzed.
Boundary-preserving Mask R-CNN
Authors: Tianheng Cheng, Xinggang Wang, Lichao Huang, Wenyu Liu
Date: 2020-07-17 00:00:00
Proceeding: ECCV 2020 8
Tasks: Instance Segmentation, Object, Semantic Segmentation

Tremendous efforts have been made to improve mask localization accuracy in instance segmentation. Modern instance segmentation methods relying on fully convolutional networks perform pixel-wise classification, which ignores object boundaries and shapes, leading coarse and indistinct mask prediction results and imprecise localization. To remedy these problems, we propose a conceptually simple yet effective Boundary-preserving Mask R-CNN (BMask R-CNN) to leverage object boundary information to improve mask localization accuracy. BMask R-CNN contains a boundary-preserving mask head in which object boundary and mask are mutually learned via feature fusion blocks. As a result, the predicted masks are better aligned with object boundaries. Without bells and whistles, BMask R-CNN outperforms Mask R-CNN by a considerable margin on the COCO dataset; in the Cityscapes dataset, there are more accurate boundary groundtruths available, so that BMask R-CNN obtains remarkable improvements over Mask R-CNN. Besides, it is not surprising to observe that BMask R-CNN obtains more obvious improvement when the evaluation criterion requires better localization (e.g., AP$_{75}$) as shown in Fig.1. Code and models are available at \url{https://github.com/hustvl/BMaskR-CNN}.
Vision-based Estimation of MDS-UPDRS Gait Scores for Assessing Parkinson's Disease Motor Severity
Authors: Mandy Lu, Kathleen Poston, Adolf Pfefferbaum, Edith V. Sullivan, Li Fei-Fei, Kilian M. Pohl, Juan Carlos Niebles, Ehsan Adeli
Date: 2020-07-17 00:00:00

Parkinson's disease (PD) is a progressive neurological disorder primarily affecting motor function resulting in tremor at rest, rigidity, bradykinesia, and postural instability. The physical severity of PD impairments can be quantified through the Movement Disorder Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS), a widely used clinical rating scale. Accurate and quantitative assessment of disease progression is critical to developing a treatment that slows or stops further advancement of the disease. Prior work has mainly focused on dopamine transport neuroimaging for diagnosis or costly and intrusive wearables evaluating motor impairments. For the first time, we propose a computer vision-based model that observes non-intrusive video recordings of individuals, extracts their 3D body skeletons, tracks them through time, and classifies the movements according to the MDS-UPDRS gait scores. Experimental results show that our proposed method performs significantly better than chance and competing methods with an F1-score of 0.83 and a balanced accuracy of 81%. This is the first benchmark for classifying PD patients based on MDS-UPDRS gait severity and could be an objective biomarker for disease severity. Our work demonstrates how computer-assisted technologies can be used to non-intrusively monitor patients and their motor impairments. The code is available at https://github.com/mlu355/PD-Motor-Severity-Estimation.
Edge-Preserving Guided Semantic Segmentation for VIPriors Challenge
Authors: Chih-Chung Hsu, Hsin-Ti Ma
Date: 2020-07-17 00:00:00
Tasks: Segmentation, Semantic Segmentation

Semantic segmentation is one of the most attractive research fields in computer vision. In the VIPriors challenge, only very limited numbers of training samples are allowed, leading to that the current state-of-the-art and deep learning-based semantic segmentation techniques are hard to train well. To overcome this shortcoming, therefore, we propose edge-preserving guidance to obtain the extra prior information, to avoid the overfitting under small-scale training dataset. First, a two-channeled convolutional layer is concatenated to the last layer of the conventional semantic segmentation network. Then, an edge map is calculated from the ground truth by Sobel operation and followed by concatenating a hard-thresholding operation to indicate whether the pixel is the edge or not. Then, the two-dimensional cross-entropy loss is adopted to calculate the loss between the predicted edge map and its ground truth, termed as an edge-preserving loss. In this way, the continuity of boundaries between different instances can be forced by the proposed edge-preserving loss. Experiments demonstrate that the proposed method can achieve excellent performance under small-scale training set, compared to state-of-the-art semantic segmentation techniques.
Technologies for Trustworthy Machine Learning: A Survey in a Socio-Technical Context
Authors: Ehsan Toreini, Mhairi Aitken, Kovila P. L. Coopamootoo, Karen Elliott, Vladimiro Gonzalez Zelaya, Paolo Missier, Magdalene Ng, Aad van Moorsel
Date: 2020-07-17 00:00:00
Tasks: BIG-bench Machine Learning, Fairness

Concerns about the societal impact of AI-based services and systems has encouraged governments and other organisations around the world to propose AI policy frameworks to address fairness, accountability, transparency and related topics. To achieve the objectives of these frameworks, the data and software engineers who build machine-learning systems require knowledge about a variety of relevant supporting tools and techniques. In this paper we provide an overview of technologies that support building trustworthy machine learning systems, i.e., systems whose properties justify that people place trust in them. We argue that four categories of system properties are instrumental in achieving the policy objectives, namely fairness, explainability, auditability and safety & security (FEAS). We discuss how these properties need to be considered across all stages of the machine learning life cycle, from data collection through run-time model inference. As a consequence, we survey in this paper the main technologies with respect to all four of the FEAS properties, for data-centric as well as model-centric stages of the machine learning system life cycle. We conclude with an identification of open research problems, with a particular focus on the connection between trustworthy machine learning technologies and their implications for individuals and society.
Identification of Tree Species in Japanese Forests based on Aerial Photography and Deep Learning
Authors: Sarah Kentsch, Savvas Karatsiolis, Andreas Kamilaris, Luca Tomhave, Maximo Larry Lopez Caceres
Date: 2020-07-17 00:00:00
Tasks: Management

Natural forests are complex ecosystems whose tree species distribution and their ecosystem functions are still not well understood. Sustainable management of these forests is of high importance because of their significant role in climate regulation, biodiversity, soil erosion and disaster prevention among many other ecosystem services they provide. In Japan particularly, natural forests are mainly located in steep mountains, hence the use of aerial imagery in combination with computer vision are important modern tools that can be applied to forest research. Thus, this study constitutes a preliminary research in this field, aiming at classifying tree species in Japanese mixed forests using UAV images and deep learning in two different mixed forest types: a black pine (Pinus thunbergii)-black locust (Robinia pseudoacacia) and a larch (Larix kaempferi)-oak (Quercus mongolica) mixed forest. Our results indicate that it is possible to identify black locust trees with 62.6 % True Positives (TP) and 98.1% True Negatives (TN), while lower precision was reached for larch trees (37.4% TP and 97.7% TN).
Attraction-Repulsion Spectrum in Neighbor Embeddings
Authors: Jan Niklas Böhm, Philipp Berens, Dmitry Kobak
Date: 2020-07-17 00:00:00

Neighbor embeddings are a family of methods for visualizing complex high-dimensional datasets using $k$NN graphs. To find the low-dimensional embedding, these algorithms combine an attractive force between neighboring pairs of points with a repulsive force between all points. One of the most popular examples of such algorithms is t-SNE. Here we empirically show that changing the balance between the attractive and the repulsive forces in t-SNE using the exaggeration parameter yields a spectrum of embeddings, which is characterized by a simple trade-off: stronger attraction can better represent continuous manifold structures, while stronger repulsion can better represent discrete cluster structures and yields higher $k$NN recall. We find that UMAP embeddings correspond to t-SNE with increased attraction; mathematical analysis shows that this is because the negative sampling optimisation strategy employed by UMAP strongly lowers the effective repulsion. Likewise, ForceAtlas2, commonly used for visualizing developmental single-cell transcriptomic data, yields embeddings corresponding to t-SNE with the attraction increased even more. At the extreme of this spectrum lie Laplacian Eigenmaps. Our results demonstrate that many prominent neighbor embedding algorithms can be placed onto the attraction-repulsion spectrum, and highlight the inherent trade-offs between them.
Superpixel-Guided Label Softening for Medical Image Segmentation
Authors: Hang Li, Dong Wei, Shilei Cao, Kai Ma, Liansheng Wang, Yefeng Zheng
Date: 2020-07-17 00:00:00
Tasks: Image Segmentation, Medical Image Analysis, Medical Image Segmentation, Segmentation, Semantic Segmentation

Segmentation of objects of interest is one of the central tasks in medical image analysis, which is indispensable for quantitative analysis. When developing machine-learning based methods for automated segmentation, manual annotations are usually used as the ground truth toward which the models learn to mimic. While the bulky parts of the segmentation targets are relatively easy to label, the peripheral areas are often difficult to handle due to ambiguous boundaries and the partial volume effect, etc., and are likely to be labeled with uncertainty. This uncertainty in labeling may, in turn, result in unsatisfactory performance of the trained models. In this paper, we propose superpixel-based label softening to tackle the above issue. Generated by unsupervised over-segmentation, each superpixel is expected to represent a locally homogeneous area. If a superpixel intersects with the annotation boundary, we consider a high probability of uncertain labeling within this area. Driven by this intuition, we soften labels in this area based on signed distances to the annotation boundary and assign probability values within [0, 1] to them, in comparison with the original "hard", binary labels of either 0 or 1. The softened labels are then used to train the segmentation models together with the hard labels. Experimental results on a brain MRI dataset and an optical coherence tomography dataset demonstrate that this conceptually simple and implementation-wise easy method achieves overall superior segmentation performances to baseline and comparison methods for both 3D and 2D medical images.