OpenRaiser/Intern-Atlas huggingface dataset:
Recursive Multi-model Complementary Deep Fusion forRobust Salient Object Detection via Parallel Sub Networks
Venue: arXiv cs.CV 2020
Year: 2020

Fully convolutional networks have shown outstanding performance in the salient object detection (SOD) field. The state-of-the-art (SOTA) methods have a tendency to become deeper and more complex, which easily homogenize their learned deep features, resulting in a clear performance bottleneck. In sharp contrast to the conventional ``deeper'' schemes, this paper proposes a ``wider'' network architecture which consists of parallel sub networks with totally different network architectures. In this way, those deep features obtained via these two sub networks will exhibit large diversity, which will have large potential to be able to complement with each other. However, a large diversity may easily lead to the feature conflictions, thus we use the dense short-connections to enable a recursively interaction between the parallel sub networks, pursuing an optimal complementary status between multi-model deep features. Finally, all these complementary multi-model deep features will be selectively fused to make high-performance salient object detections. Extensive experiments on several famous benchmarks clearly demonstrate the superior performance, good generalization, and powerful learning ability of the proposed wider framework.
Connected Components in Undirected Set--Based Graphs. Applications in Object--Oriented Model Manipulation
Venue: arXiv cs.DS 2020
Year: 2020

This work introduces a novel algorithm for finding the connected components of a graph where the vertices and edges are grouped into sets defining a Set--Based Graph. The algorithm, under certain restrictions on those sets, has the remarkable property of achieving constant computational costs with the number of vertices and edges. The mentioned restrictions are related to the possibility of representing the sets of vertices by intension and the sets of edges using some particular type of maps. While these restrictions can result strong in a general context, they are usually satisfied in the problem of transforming connections into equations in object oriented models, which is the main application of the proposed algorithm. Besides describing the new algorithm and studying its computational cost, the work describes its prototype implementation and shows its application in different examples.
On a recipe for quantum graphical languages
Venue: arXiv cs.LO 2020
Year: 2020

Different graphical calculi have been proposed to represent quantum computation. First the ZX- calculus [4], followed by the ZW-calculus [12] and then the ZH-calculus [1]. We can wonder if new Z*-calculi will continue to be proposed forever. This article answers negatively. All those language share a common core structure we call Z*-algebras. We classify Z*-algebras up to isomorphism in two dimensional Hilbert spaces and show that they are all variations of the aforementioned calculi. We do the same for linear relations and show that the calculus of [2] is essentially the unique one.
FireBERT: Hardening BERT-based classifiers against adversarial attack
Venue: arXiv cs.CL 2020
Year: 2020

We present FireBERT, a set of three proof-of-concept NLP classifiers hardened against TextFooler-style word-perturbation by producing diverse alternatives to original samples. In one approach, we co-tune BERT against the training data and synthetic adversarial samples. In a second approach, we generate the synthetic samples at evaluation time through substitution of words and perturbation of embedding vectors. The diversified evaluation results are then combined by voting. A third approach replaces evaluation-time word substitution with perturbation of embedding vectors. We evaluate FireBERT for MNLI and IMDB Movie Review datasets, in the original and on adversarial examples generated by TextFooler. We also test whether TextFooler is less successful in creating new adversarial samples when manipulating FireBERT, compared to working on unhardened classifiers. We show that it is possible to improve the accuracy of BERT-based models in the face of adversarial attacks without significantly reducing the accuracy for regular benchmark samples. We present co-tuning with a synthetic data generator as a highly effective method to protect against 95% of pre-manufactured adversarial samples while maintaining 98% of original benchmark performance. We also demonstrate evaluation-time perturbation as a promising direction for further research, restoring accuracy up to 75% of benchmark performance for pre-made adversarials, and up to 65% (from a baseline of 75% orig. / 12% attack) under active attack by TextFooler.
Deep learning for photoacoustic imaging: a survey
Venue: arXiv cs.CV 2020
Year: 2020

Machine learning has been developed dramatically and witnessed a lot of applications in various fields over the past few years. This boom originated in 2009, when a new model emerged, that is, the deep artificial neural network, which began to surpass other established mature models on some important benchmarks. Later, it was widely used in academia and industry. Ranging from image analysis to natural language processing, it fully exerted its magic and now become the state-of-the-art machine learning models. Deep neural networks have great potential in medical imaging technology, medical data analysis, medical diagnosis and other healthcare issues, and is promoted in both pre-clinical and even clinical stages. In this review, we performed an overview of some new developments and challenges in the application of machine learning to medical image analysis, with a special focus on deep learning in photoacoustic imaging. The aim of this review is threefold: (i) introducing deep learning with some important basics, (ii) reviewing recent works that apply deep learning in the entire ecological chain of photoacoustic imaging, from image reconstruction to disease diagnosis, (iii) providing some open source materials and other resources for researchers interested in applying deep learning to photoacoustic imaging.
Communication Efficient Self-Stabilizing Leader Election (Full Version)
Venue: arXiv cs.DC 2020
Year: 2020

This paper presents a randomized self-stabilizing algorithm that elects a leader $r$ in a general $n$-node undirected graph and constructs a spanning tree $T$ rooted at $r$. The algorithm works under the synchronous message passing network model, assuming that the nodes know a linear upper bound on $n$ and that each edge has a unique ID known to both its endpoints (or, alternatively, assuming the $KT_{1}$ model). The highlight of this algorithm is its superior communication efficiency: It is guaranteed to send a total of $\tilde{O} (n)$ messages, each of constant size, till stabilization, while stabilizing in $\tilde{O} (n)$ rounds, in expectation and with high probability. After stabilization, the algorithm sends at most one constant size message per round while communicating only over the ($n - 1$) edges of $T$. In all these aspects, the communication overhead of the new algorithm is far smaller than that of the existing (mostly deterministic) self-stabilizing leader election algorithms. The algorithm is relatively simple and relies mostly on known modules that are common in the fault free leader election literature; these modules are enhanced in various subtle ways in order to assemble them into a communication efficient self-stabilizing algorithm.
Lie PCA: Density estimation for symmetric manifolds
Venue: arXiv cs.LG 2020
Year: 2020

We introduce an extension to local principal component analysis for learning symmetric manifolds. In particular, we use a spectral method to approximate the Lie algebra corresponding to the symmetry group of the underlying manifold. We derive the sample complexity of our method for a variety of manifolds before applying it to various data sets for improved density estimation.
COVID19 Tracking: An Interactive Tracking, Visualizing and Analyzing Platform
Venue: arXiv cs.CY 2020
Year: 2020

The Coronavirus Disease 2019 (COVID-19) has now become a pandemic, inflicting millions of people and causing tens of thousands of deaths. To better understand the dynamics of COVID-19, we present a comprehensive COVID-19 tracking and visualization platform that pinpoints the dynamics of the COVID-19 worldwide. Four essential components are implemented: 1) presenting the visualization map of COVID-19 confirmed cases and total counts all over the world; 2) showing the worldwide trends of COVID-19 at multi-grained levels; 3) provide multi-view comparisons, including confirmed cases per million people, mortality rate and accumulative cure rate; 4) integrating a multi-grained view of the disease spreading dynamics in China and showing how the epidemic is taken under control in China.
An Efficient Sliding Mesh Interface Method for High-Order Discontinuous Galerkin Schemes
Venue: arXiv cs.CE 2020
Year: 2020

Sliding meshes are a powerful method to treat deformed domains in computational fluid dynamics, where different parts of the domain are in relative motion. In this paper, we present an efficient implementation of a sliding mesh method into a discontinuous Galerkin compressible Navier-Stokes solver and its application to a large eddy simulation of a 1-1/2 stage turbine. The method is based on the mortar method and is high-order accurate. It can handle three-dimensional sliding mesh interfaces with various interface shapes. For plane interfaces, which are the most common case, conservativity and free-stream preservation are ensured. We put an emphasis on efficient parallel implementation. Our implementation generates little computational and storage overhead. Inter-node communication via MPI in a dynamically changing mesh topology is reduced to a bare minimum by ensuring a priori information about communication partners and data sorting. We provide performance and scaling results showing the capability of the implementation strategy. Apart from analytical validation computations and convergence results, we present a wall-resolved implicit LES of the 1-1/2 stage Aachen turbine test case as a large scale practical application example.
Deep Detail Enhancement for Any Garment
Venue: arXiv cs.GR 2020
Year: 2020

Creating fine garment details requires significant efforts and huge computational resources. In contrast, a coarse shape may be easy to acquire in many scenarios (e.g., via low-resolution physically-based simulation, linear blend skinning driven by skeletal motion, portable scanners). In this paper, we show how to enhance, in a data-driven manner, rich yet plausible details starting from a coarse garment geometry. Once the parameterization of the garment is given, we formulate the task as a style transfer problem over the space of associated normal maps. In order to facilitate generalization across garment types and character motions, we introduce a patch-based formulation, that produces high-resolution details by matching a Gram matrix based style loss, to hallucinate geometric details (i.e., wrinkle density and shape). We extensively evaluate our method on a variety of production scenarios and show that our method is simple, light-weight, efficient, and generalizes across underlying garment types, sewing patterns, and body motion.
The Polynomial Learning With Errors Problem and the Smearing Condition
Venue: arXiv cs.IT 2020
Year: 2020

As quantum computing advances rapidly, guaranteeing the security of cryptographic protocols resistant to quantum attacks is paramount. Some leading candidate cryptosystems use the Learning with Errors (LWE) problem, attractive for its simplicity and hardness guaranteed by reductions from hard computational lattice problems. Its algebraic variants, Ring-Learning with Errors (RLWE) and Polynomial Learning with Errors (PLWE), gain in efficiency over standard LWE, but their security remains to be thoroughly investigated. In this work, we consider the "smearing" condition, a condition for attacks on PLWE and RLWE introduced in [6]. We expand upon some questions about smearing posed by Elias et al. in [6] and show how smearing is related to the Coupon Collector's Problem Furthermore, we develop some practical algorithms for calculating probabilities related to smearing. Finally, we present a smearing-based attack on PLWE, and demonstrate its effectiveness.
Key-Nets: Optical Transformation Convolutional Networks for Privacy Preserving Vision Sensors
Venue: arXiv cs.CV 2020
Year: 2020

Modern cameras are not designed with computer vision or machine learning as the target application. There is a need for a new class of vision sensors that are privacy preserving by design, that do not leak private information and collect only the information necessary for a target machine learning task. In this paper, we introduce key-nets, which are convolutional networks paired with a custom vision sensor which applies an optical/analog transform such that the key-net can perform exact encrypted inference on this transformed image, but the image is not interpretable by a human or any other key-net. We provide five sufficient conditions for an optical transformation suitable for a key-net, and show that generalized stochastic matrices (e.g. scale, bias and fractional pixel shuffling) satisfy these conditions. We motivate the key-net by showing that without it there is a utility/privacy tradeoff for a network fine-tuned directly on optically transformed images for face identification and object detection. Finally, we show that a key-net is equivalent to homomorphic encryption using a Hill cipher, with an upper bound on memory and runtime that scales quadratically with a user specified privacy parameter. Therefore, the key-net is the first practical, efficient and privacy preserving vision sensor based on optical homomorphic encryption.
RegionTrack: A Trace-based Sound and Complete Checker to Debug Transactional Atomicity Violations and Non-Serializable Traces
Venue: arXiv cs.SE 2020
Year: 2020

Atomicity is a correctness criterion to reason about isolated code regions in a multithreaded program when they are executed concurrently. However, dynamic instances of these code regions, called transactions, may fail to behave atomically, resulting in transactional atomicity violations. Existing dynamic online atomicity checkers incur either false positives or false negatives in detecting transactions experiencing transactional atomicity violations. This paper proposes RegionTrack. RegionTrack tracks cross-thread dependences at the event, dynamic subregion, and transaction levels. It maintains both dynamic subregions within selected transactions and transactional happens-before relations through its novel timestamp propagation approach. We prove that RegionTrack is sound and complete in detecting both transactional atomicity violations and non-serializable traces. To the best of our knowledge, it is the first online technique that precisely captures the transitively closed set of happens-before relations over all conflicting events with respect to every running transaction for the above two kinds of issues. We have evaluated RegionTrack on 19 subjects of the DaCapo and the Java Grande Forum benchmarks. The empirical results confirm that RegionTrack precisely detected all those transactions which experienced transactional atomicity violations and identified all non-serializable traces. The overall results also show that RegionTrack incurred 1.10x and 1.08x lower memory and runtime overheads than Velodrome and 2.10x and 1.21x lower than Aerodrome, respectively. Moreover, it incurred 2.89x lower memory overhead than DoubleChecker. On average, Velodrome detected about 55% fewer violations than RegionTrack, which in turn reported about 3%-70% fewer violations than DoubleChecker.
Fingerprinting the Fingerprinters: Learning to Detect Browser Fingerprinting Behaviors
Venue: arXiv cs.CR 2020
Year: 2020

Browser fingerprinting is an invasive and opaque stateless tracking technique. Browser vendors, academics, and standards bodies have long struggled to provide meaningful protections against browser fingerprinting that are both accurate and do not degrade user experience. We propose FP-Inspector, a machine learning based syntactic-semantic approach to accurately detect browser fingerprinting. We show that FP-Inspector performs well, allowing us to detect 26% more fingerprinting scripts than the state-of-the-art. We show that an API-level fingerprinting countermeasure, built upon FP-Inspector, helps reduce website breakage by a factor of 2. We use FP-Inspector to perform a measurement study of browser fingerprinting on top-100K websites. We find that browser fingerprinting is now present on more than 10% of the top-100K websites and over a quarter of the top-10K websites. We also discover previously unreported uses of JavaScript APIs by fingerprinting scripts suggesting that they are looking to exploit APIs in new and unexpected ways.
Federated Learning via Synthetic Data
Venue: arXiv cs.LG 2020
Year: 2020

Federated learning allows for the training of a model using data on multiple clients without the clients transmitting that raw data. However the standard method is to transmit model parameters (or updates), which for modern neural networks can be on the scale of millions of parameters, inflicting significant computational costs on the clients. We propose a method for federated learning where instead of transmitting a gradient update back to the server, we instead transmit a small amount of synthetic `data'. We describe the procedure and show some experimental results suggesting this procedure has potential, providing more than an order of magnitude reduction in communication costs with minimal model degradation.
Content Format and Quality of Experience in Virtual Reality
Venue: arXiv cs.MM 2020
Year: 2020

In this paper, we investigate three forms of virtual reality content production and consumption. Namely, 360 stereoscopic video, the combination of a 3D environment with a video billboard for dynamic elements, and a full 3D rendered scene. On one hand, video based techniques facilitate the acquisition of content, but they can limit the experience of the user since the content is captured from a fixed point of view. On the other hand, 3D content allows for point of view translation, but real-time photorealistic rendering is not trivial and comes at high production and processing costs. We also compare the two extremes with an approach that combines dynamic video elements with a 3D virtual environment. We discuss the advantages and disadvantages of these systems, and present the result of a user study with 24 participants. In the study, we evaluated the quality of experience, including presence, simulation sickness and participants' assessment of content quality, of three versions of a cinematic segment with two actors. We found that, in this context, mixing video and 3D content produced the best experience.
Report prepared by the Montreal AI Ethics Institute In Response to Mila's Proposal for a Contact Tracing App
Venue: arXiv cs.CY 2020
Year: 2020

Contact tracing has grown in popularity as a promising solution to the COVID-19 pandemic. The benefits of automated contact tracing are two-fold. Contact tracing promises to reduce the number of infections by being able to: 1) systematically identify all of those that have been in contact with someone who has had COVID; and, 2) ensure those that have been exposed to the virus do not unknowingly infect others. "COVI" is the name of a recent contact tracing app developed by Mila and was proposed to help combat COVID-19 in Canada. The app was designed to inform each individual of their relative risk of being infected with the virus, which Mila claimed would empower citizens to make informed decisions about their movement and allow for a data-driven approach to public health policy; all the while ensuring data is safeguarded from governments, companies, and individuals. This article will provide a critical response to Mila's COVI White Paper. Specifically, this article will discuss: the extent to which diversity has been considered in the design of the app, assumptions surrounding users' interaction with the app and the app's utility, as well as unanswered questions surrounding transparency, accountability, and security. We see this as an opportunity to supplement the excellent risk analysis done by the COVI team to surface insights that can be applied to other contact- and proximity-tracing apps that are being developed and deployed across the world. Our hope is that, through a meaningful dialogue, we can ultimately help organizations develop better solutions that respect the fundamental rights and values of the communities these solutions are meant to serve.
Pen-based Interaction with Spreadsheets in Mobile Virtual Reality
Venue: arXiv cs.HC 2020
Year: 2020

Virtual Reality (VR) can enhance the display and interaction of mobile knowledge work and in particular, spreadsheet applications. While spreadsheets are widely used yet are challenging to interact with, especially on mobile devices, using them in VR has not been explored in depth. A special uniqueness of the domain is the contrast between the immersive and large display space afforded by VR, contrasted by the very limited interaction space that may be afforded for the information worker on the go, such as an airplane seat or a small work-space. To close this gap, we present a tool-set for enhancing spreadsheet interaction on tablets using immersive VR headsets and pen-based input. This combination opens up many possibilities for enhancing the productivity for spreadsheet interaction. We propose to use the space around and in front of the tablet for enhanced visualization of spreadsheet data and meta-data. For example, extending sheet display beyond the bounds of the physical screen, or easier debugging by uncovering hidden dependencies between sheet's cells. Combining the precise on-screen input of a pen with spatial sensing around the tablet, we propose tools for the efficient creation and editing of spreadsheets functions such as off-the-screen layered menus, visualization of sheets dependencies, and gaze-and-touch-based switching between spreadsheet tabs. We study the feasibility of the proposed tool-set using a video-based online survey and an expert-based assessment of indicative human performance potential.
Breaking the Screen: Interaction Across Touchscreen Boundaries in Virtual Reality for Mobile Knowledge Workers
Venue: arXiv cs.HC 2020
Year: 2020

Virtual Reality (VR) has the potential to transform knowledge work. One advantage of VR knowledge work is that it allows extending 2D displays into the third dimension, enabling new operations, such as selecting overlapping objects or displaying additional layers of information. On the other hand, mobile knowledge workers often work on established mobile devices, such as tablets, limiting interaction with those devices to a small input space. This challenge of a constrained input space is intensified in situations when VR knowledge work is situated in cramped environments, such as airplanes and touchdown spaces. In this paper, we investigate the feasibility of interacting jointly between an immersive VR head-mounted display and a tablet within the context of knowledge work. Specifically, we 1) design, implement and study how to interact with information that reaches beyond a single physical touchscreen in VR; 2) design and evaluate a set of interaction concepts; and 3) build example applications and gather user feedback on those applications.