OpenRaiser/Intern-Atlas huggingface dataset:
WLCG Networks: Update on Monitoring and Analytics
Venue: arXiv cs.NI 2020
Year: 2020

WLCG relies on the network as a critical part of its infrastructure and therefore needs to guarantee effective network usage and prompt detection and resolution of any network issues including connection failures, congestion and traffic routing. The OSG Networking Area, in partnership with WLCG, is focused on being the primary source of networking information for its partners and constituents. It was established to ensure sites and experiments can better understand and fix networking issues, while providing an analytics platform that aggregates network monitoring data with higher level workload and data trans-fer services. This has been facilitated by the global network of the perfSONAR instances that have been commissioned and are operated in collaboration with WLCG Network Throughput Working Group. An additional important updateis the inclusion of the newly funded NSF project SAND (Service Analytics and Network Diagnosis) which is focusing on network analytics. This paper describes the current state of the network measurement and analytics platform and summarizes the activities taken by the working group and our collaborators. This includes the progress being made in providing higher level analytics,alerting and alarming from the rich set of network metrics we are gathering.
Minimal witnesses for probabilistic timed automata
Venue: arXiv cs.LO 2020
Year: 2020

Witnessing subsystems have proven to be a useful concept in the analysis of probabilistic systems, for example as diagnostic information on why a given property holds or as input to refinement algorithms. This paper introduces witnessing subsystems for reachability problems in probabilistic timed automata (PTA). Using a new operation on difference bounds matrices, it is shown how Farkas certificates of finite-state bisimulation quotients of a PTA can be translated into witnessing subsystems. We present algorithms for the computation of minimal witnessing subsystems under three notions of minimality, which capture the timed behavior from different perspectives, and discuss their complexity.
Revisiting Comparative Performance of DNS Resolvers in the IPv6 and ECS Era
Venue: arXiv cs.NI 2020
Year: 2020

This paper revisits the issue of the performance of DNS resolution services available to Internet users. While several prior studies addressed this important issue, significant developments, namely, the IPv6 finally getting traction and the adoption of the ECS extension to DNS by major DNS resolution services, warrant a reassessment under these new realities. We find that DNS resolution services differ drastically -- by an order of magnitude in some locations -- in their query response time. We also find established resolvers (Google DNS and OpenDNS) to lag far behind relative newcomers (Cloudflair and Quad9) in terms of DNS latency, and trace the cause to drastically lower cache hit rates, which we further trace to less cache sharing within the resolver platform. In addition, we find that public resolvers have largely closed the gap with ISP resolvers in the quality of CDNs' client-to-edge-server mappings as measured by latency. Finally, in most locations, we observe IPv6 penalty in the latency of client-to-CDN-edge-server mappings produced by the resolvers. Moreover, this penalty, while often significant, still does not rise above typical thresholds employed by the Happy Eyeballs algorithm for preferring IPv4 communication. resolvers. Thus, dual-stacked clients in these locations may experience suboptimal performance.
Constructing Basis Path Set by Eliminating Path Dependency
Venue: arXiv cs.DS 2020
Year: 2020

The way the basis path set works in neural network remains mysterious, and the generalization of newly appeared G-SGD algorithm to more practical network is hindered. The Basis Path Set Searching problem is formulated from the perspective of graph theory, to find the basis path set in a regular complicated neural network. Our paper aims to discover the underlying cause of path dependency between two independent substructures. Algorithm DEAH is designed to solve the Basis Path Set Searching problem by eliminating such path dependency. The path subdivision chain is proposed to effectively eliminate the path dependency inside the chain and between chains. The theoretical proofs and analysis of polynomial time complexity are presented. The paper therefore provides one methodology to find the basis path set in a more general neural network, which offers theoretical and algorithmic support for the application of G-SGD algorithm in more practical scenarios.
Response by the Montreal AI Ethics Institute to the Santa Clara Principles on Transparency and Accountability in Online Content Moderation
Venue: arXiv cs.CY 2020
Year: 2020

In April 2020, the Electronic Frontier Foundation (EFF) publicly called for comments on expanding and improving the Santa Clara Principles on Transparency and Accountability (SCP), originally published in May 2018. The Montreal AI Ethics Institute (MAIEI) responded to this call by drafting a set of recommendations based on insights and analysis by the MAIEI staff and supplemented by workshop contributions from the AI Ethics community convened during two online public consultation workshops. In its submission, MAIEI provides 12 overarching recommendations for the SCP, these include: 1) ensure there is more diversity in the content moderation process; 2) increase transparency into how platforms guide content-ranking; 3) disclose anonymized data on the training and/or cultural background of the content moderators for a platform; 4) tailor content moderation tools for specific issues; 5) draft specific guidelines for messaging applications with regards to data protection in content moderation; 6) take into account cultural differences relevant to what constitutes acceptable behavior online; 7) ensure platforms are transparent in regards to political advertising; 8) ensure greater transparency into the user-generated flagging/reporting systems deployed by a platform; 9) clarify if user content is flagged or reported through an automated system; 10) provide more data on the types of content removed from platforms; 11) provide clear guidelines on the appeal process, as well as data on prior appeals; 12) create a system for periodically revisiting the SCP so it reflects various technological advancements, modifications in law and policy, as well as changing trends or movements in content moderation.
Computing Conceptual Distances between Breast Cancer Screening Guidelines: An Implementation of a Near-Peer Epistemic Model of Medical Disagreement
Venue: arXiv cs.IR 2020
Year: 2020

Using natural language processing tools, we investigate the differences of recommendations in medical guidelines for the same decision problem -- breast cancer screening. We show that these differences arise from knowledge brought to the problem by different medical societies, as reflected in the conceptual vocabularies used by the different groups of authors.The computational models we build and analyze agree with the near-peer epistemic model of expert disagreement proposed by Garbayo. Even though the article is a case study focused on one set of guidelines, the proposed methodology is broadly applicable. In addition to proposing a novel graph-based similarity model for comparing collections of documents, we perform an extensive analysis of the model performance. In a series of a few dozen experiments, in three broad categories, we show, at a very high statistical significance level of 3-4 standard deviations for our best models, that the high similarity between expert annotated model and our concept based, automatically created, computational models is not accidental. Our best model achieves roughly 70% similarity. We also describe possible extensions of this work.
Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images
Venue: arXiv cs.CV 2020
Year: 2020

Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging. Weakly supervised semantic segmentation helps to overcome these issues and also provides explainable deep learning models. In this paper, we propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision. We improve the disease localization accuracy by combining three approaches as consecutive steps. First, we generate pseudo segmentation labels of abnormal regions in the training images through a supervised classification model enhanced with a regularization procedure. The obtained activation maps are then post-processed and propagated into a second classification model-Inter-pixel Relation Network, which improves the boundaries between different object classes. Finally, the resulting pseudo-labels are used to train a proposed fully supervised segmentation model. We analyze the robustness of the presented method and test its performance on two distinct datasets: PASCAL VOC 2012 and SIIM-ACR Pneumothorax. We achieve significant results in the segmentation on both datasets using only image-level annotations. We show that this approach is applicable to chest X-rays for detecting an anomalous volume of air in the pleural space between the lung and the chest wall. Our code has been made publicly available.
Opportunities and Challenges in Deep Learning Adversarial Robustness: A Survey
Venue: arXiv cs.LG 2020
Year: 2020

As we seek to deploy machine learning models beyond virtual and controlled domains, it is critical to analyze not only the accuracy or the fact that it works most of the time, but if such a model is truly robust and reliable. This paper studies strategies to implement adversary robustly trained algorithms towards guaranteeing safety in machine learning algorithms. We provide a taxonomy to classify adversarial attacks and defenses, formulate the Robust Optimization problem in a min-max setting and divide it into 3 subcategories, namely: Adversarial (re)Training, Regularization Approach, and Certified Defenses. We survey the most recent and important results in adversarial example generation, defense mechanisms with adversarial (re)Training as their main defense against perturbations. We also survey mothods that add regularization terms that change the behavior of the gradient, making it harder for attackers to achieve their objective. Alternatively, we've surveyed methods which formally derive certificates of robustness by exactly solving the optimization problem or by approximations using upper or lower bounds. In addition, we discuss the challenges faced by most of the recent algorithms presenting future research perspectives.
Threshold Logic with Current-Driven Magnetic Domain Walls
Venue: arXiv cs.ET 2020
Year: 2020

The recent demonstration of current-driven magnetic domain wall logic [Z. Luo et al., Nature 579:214] was based on a three-input logic gate that was identified as a reconfigurable NAND/NOR function. We reinterpret this logic gate as a minority gate within the context of threshold logic, enabling a domain wall threshold logic paradigm in which the device count can be reduced by 80%. Furthermore, by extending the logic gate to more than three inputs of non-equal weight, an 87% reduction in device count can be achieved.
Deep Learning Methods for Universal MISO Beamforming
Venue: arXiv cs.IT 2020
Year: 2020

This letter studies deep learning (DL) approaches to optimize beamforming vectors in downlink multi-user multi-antenna systems that can be universally applied to arbitrarily given transmit power limitation at a base station. We exploit the sum power budget as side information so that deep neural networks (DNNs) can effectively learn the impact of the power constraint in the beamforming optimization. Consequently, a single training process is sufficient for the proposed universal DL approach, whereas conventional methods need to train multiple DNNs for all possible power budget levels. Numerical results demonstrate the effectiveness of the proposed DL methods over existing schemes.
Surface Denoising based on Normal Filtering in a Robust Statistics Framework
Venue: arXiv cs.GR 2020
Year: 2020

During a surface acquisition process using 3D scanners, noise is inevitable and an important step in geometry processing is to remove these noise components from these surfaces (given as points-set or triangulated mesh). The noise-removal process (denoising) can be performed by filtering the surface normals first and by adjusting the vertex positions according to filtered normals afterwards. Therefore, in many available denoising algorithms, the computation of noise-free normals is a key factor. A variety of filters have been introduced for noise-removal from normals, with different focus points like robustness against outliers or large amplitude of noise. Although these filters are performing well in different aspects, a unified framework is missing to establish the relation between them and to provide a theoretical analysis beyond the performance of each method. In this paper, we introduce such a framework to establish relations between a number of widely-used nonlinear filters for face normals in mesh denoising and vertex normals in point set denoising. We cover robust statistical estimation with M-smoothers and their application to linear and non-linear normal filtering. Although these methods originate in different mathematical theories - which include diffusion-, bilateral-, and directional curvature-based algorithms - we demonstrate that all of them can be cast into a unified framework of robust statistics using robust error norms and their corresponding influence functions. This unification contributes to a better understanding of the individual methods and their relations with each other. Furthermore, the presented framework provides a platform for new techniques to combine the advantages of known filters and to compare them with available methods.
Compressed Sensing via Measurement-Conditional Generative Models
Venue: arXiv cs.LG 2020
Year: 2020

A pre-trained generator has been frequently adopted in compressed sensing (CS) due to its ability to effectively estimate signals with the prior of NNs. In order to further refine the NN-based prior, we propose a framework that allows the generator to utilize additional information from a given measurement for prior learning, thereby yielding more accurate prediction for signals. As our framework has a simple form, it is easily applied to existing CS methods using pre-trained generators. We demonstrate through extensive experiments that our framework exhibits uniformly superior performances by large margin and can reduce the reconstruction error up to an order of magnitude for some applications. We also explain the experimental success in theory by showing that our framework can slightly relax the stringent signal presence condition, which is required to guarantee the success of signal recovery.
Drone swarms in fire suppression activities
Venue: arXiv cs.RO 2020
Year: 2020

Recent huge technological development of Unmanned Aerial Vehicles (UAVs) can provide breakthrough means of fighting wildland fires. We propose an innovative forest firefighting system based on the use of a swarm of hundreds of UAVs able to generate a continuous flow of extinguishing liquid on the fire front, simulating rain effect. Automatic battery replacement and refilling of the extinguishing liquid ensure the continuity of the action, and fire-resistant materials protect drones exposed to possible high temperatures. We demonstrate the validity of the approach in Mediterranean scrub first computing the critical water flow rate according to the main factors involved in the evolution of a fire, then estimating the number of linear meters of active fire front that can be extinguished depending on the number of drones available and the amount of extinguishing fluid carried. A fire propagation cellular automata model is also employed to study the evolution of the fire. Simulation results suggest that the proposed system can successfully integrate, or in case of low-intensity and limited extent fires completely replace, current forest firefighting techniques.
Decentralized Blockchain for Privacy-Preserving Large-Scale Contact Tracing
Venue: arXiv cs.CR 2020
Year: 2020

Activity-tracking applications and location-based services using short-range communication (SRC) techniques have been abruptly demanded in the COVID-19 pandemic, especially for automated contact tracing. The attention from both public and policy keeps raising on related practical problems, including \textit{1) how to protect data security and location privacy? 2) how to efficiently and dynamically deploy SRC Internet of Thing (IoT) witnesses to monitor large areas?} To answer these questions, in this paper, we propose a decentralized and permissionless blockchain protocol, named \textit{Bychain}. Specifically, 1) a privacy-preserving SRC protocol for activity-tracking and corresponding generalized block structure is developed, by connecting an interactive zero-knowledge proof protocol and the key escrow mechanism. As a result, connections between personal identity and the ownership of on-chain location information are decoupled. Meanwhile, the owner of the on-chain location data can still claim its ownership without revealing the private key to anyone else. 2) An artificial potential field-based incentive allocation mechanism is proposed to incentivize IoT witnesses to pursue the maximum monitoring coverage deployment. We implemented and evaluated the proposed blockchain protocol in the real-world using the Bluetooth 5.0. The storage, CPU utilization, power consumption, time delay, and security of each procedure and performance of activities are analyzed. The experiment and security analysis is shown to provide a real-world performance evaluation.
The Impact of Explanations on AI Competency Prediction in VQA
Venue: arXiv cs.CV 2020
Year: 2020

Explainability is one of the key elements for building trust in AI systems. Among numerous attempts to make AI explainable, quantifying the effect of explanations remains a challenge in conducting human-AI collaborative tasks. Aside from the ability to predict the overall behavior of AI, in many applications, users need to understand an AI agent's competency in different aspects of the task domain. In this paper, we evaluate the impact of explanations on the user's mental model of AI agent competency within the task of visual question answering (VQA). We quantify users' understanding of competency, based on the correlation between the actual system performance and user rankings. We introduce an explainable VQA system that uses spatial and object features and is powered by the BERT language model. Each group of users sees only one kind of explanation to rank the competencies of the VQA model. The proposed model is evaluated through between-subject experiments to probe explanations' impact on the user's perception of competency. The comparison between two VQA models shows BERT based explanations and the use of object features improve the user's prediction of the model's competencies.
Fact-based Text Editing
Venue: arXiv cs.CL 2020
Year: 2020

We propose a novel text editing task, referred to as \textit{fact-based text editing}, in which the goal is to revise a given document to better describe the facts in a knowledge base (e.g., several triples). The task is important in practice because reflecting the truth is a common requirement in text editing. First, we propose a method for automatically generating a dataset for research on fact-based text editing, where each instance consists of a draft text, a revised text, and several facts represented in triples. We apply the method into two public table-to-text datasets, obtaining two new datasets consisting of 233k and 37k instances, respectively. Next, we propose a new neural network architecture for fact-based text editing, called \textsc{FactEditor}, which edits a draft text by referring to given facts using a buffer, a stream, and a memory. A straightforward approach to address the problem would be to employ an encoder-decoder model. Our experimental results on the two datasets show that \textsc{FactEditor} outperforms the encoder-decoder approach in terms of fidelity and fluency. The results also show that \textsc{FactEditor} conducts inference faster than the encoder-decoder approach.
Project PIAF: Building a Native French Question-Answering Dataset
Venue: arXiv cs.CL 2020
Year: 2020

Motivated by the lack of data for non-English languages, in particular for the evaluation of downstream tasks such as Question Answering, we present a participatory effort to collect a native French Question Answering Dataset. Furthermore, we describe and publicly release the annotation tool developed for our collection effort, along with the data obtained and preliminary baselines.
ADD: Analytically Differentiable Dynamics for Multi-Body Systems with Frictional Contact
Venue: arXiv cs.GR 2020
Year: 2020

We present a differentiable dynamics solver that is able to handle frictional contact for rigid and deformable objects within a unified framework. Through a principled mollification of normal and tangential contact forces, our method circumvents the main difficulties inherent to the non-smooth nature of frictional contact. We combine this new contact model with fully-implicit time integration to obtain a robust and efficient dynamics solver that is analytically differentiable. In conjunction with adjoint sensitivity analysis, our formulation enables gradient-based optimization with adaptive trade-offs between simulation accuracy and smoothness of objective function landscapes. We thoroughly analyse our approach on a set of simulation examples involving rigid bodies, visco-elastic materials, and coupled multi-body systems. We furthermore showcase applications of our differentiable simulator to parameter estimation for deformable objects, motion planning for robotic manipulation, trajectory optimization for compliant walking robots, as well as efficient self-supervised learning of control policies.
Deep Learning Defenses Against Adversarial Examples for Dynamic Risk Assessment
Venue: arXiv cs.LG 2020
Year: 2020

Deep Neural Networks were first developed decades ago, but it was not until recently that they started being extensively used, due to their computing power requirements. Since then, they are increasingly being applied to many fields and have undergone far-reaching advancements. More importantly, they have been utilized for critical matters, such as making decisions in healthcare procedures or autonomous driving, where risk management is crucial. Any mistakes in the diagnostics or decision-making in these fields could entail grave accidents, and even death. This is preoccupying, because it has been repeatedly reported that it is straightforward to attack this type of models. Thus, these attacks must be studied to be able to assess their risk, and defenses need to be developed to make models more robust. For this work, the most widely known attack was selected (adversarial attack) and several defenses were implemented against it (i.e. adversarial training, dimensionality reduc tion and prediction similarity). The obtained outcomes make the model more robust while keeping a similar accuracy. The idea was developed using a breast cancer dataset and a VGG16 and dense neural network model, but the solutions could be applied to datasets from other areas and different convolutional and dense deep neural network models.