OpenRaiser/Intern-Atlas huggingface dataset:
MQT-TZ: Secure MQTT Broker for Biomedical Signal Processing on the Edge
Venue: arXiv cs.CR 2020
Year: 2020

Physical health records belong to healthcare providers, but the information contained within belongs to each patient. In an increasing manner, more health-related data is being acquired by wearables and other IoT devices following the ever-increasing trend of the "Quantified Self". Even though data protection regulations (e.g., GDPR) encourage the usage of privacy-preserving processing techniques, most of the current IoT infrastructure was not originally conceived for such purposes. One of the most used communication protocols, MQTT, is a lightweight publish-subscribe protocol commonly used in the Edge and IoT applications. In MQTT, the broker must process data on clear text, hence exposing a large attack surface for a malicious agent to steal/tamper with this health-related data. In this paper, we introduce MQT-TZ, a secure MQTT broker leveraging Arm TrustZone, a popular Trusted Execution Environment (TEE). We define a mutual TLS-based handshake and a two-layer encryption for end-to-end security using the TEE as a trusted proxy. We provide quantitative evaluation of our open-source PoC on streaming ECGs in real time and highlight the trade-offs.
GRANDPA: a Byzantine Finality Gadget
Venue: arXiv cs.DC 2020
Year: 2020

Classic Byzantine fault-tolerant consensus protocols forfeit liveness in the face of asynchrony in order to preserve safety, whereas most deployed blockchain protocols forfeit safety in order to remain live. In this work, we achieve the best of both worlds by proposing a novel abstractions called the finality gadget. A finality gadget allows for transactions to always optimistically commit but informs the clients that these transactions might be unsafe. As a result, a blockchain can execute transactions optimistically and only commit them after they have been sufficiently and provably audited. In this work, we formally model the finality gadget abstraction, prove that it is impossible to solve it deterministically in full asynchrony (even though it is stronger than consensus) and provide a partially synchronous protocol which is currently securing a major blockchain. This way we show that the protocol designer can decouple safety and liveness in order to speed up recovery from failures. We believe that there can be other types of finality gadgets that provide weaker safety (e.g., probabilistic) in order to gain more efficiency and this can depend on the probability that the network is not in synchrony.
Identification and Remediation of Self-Admitted Technical Debt in Issue Trackers
Venue: arXiv cs.SE 2020
Year: 2020

Technical debt refers to taking shortcuts to achieve short-term goals, which might negatively influence software maintenance in the long-term. There is increasing attention on technical debt that is admitted by developers in source code comments (termed as self-admitted technical debt or SATD). But SATD in issue trackers is relatively unexplored. We performed a case study, where we manually examined 500 issues from two open source projects (i.e. Hadoop and Camel), which contained 152 SATD items. We found that: 1) eight types of technical debt are identified in issues, namely architecture, build, code, defect, design, documentation, requirement, and test debt; 2) developers identify technical debt in issues in three different points in time, and a small part is identified by its creators; 3) the majority of technical debt is paid off, 4) mostly by those who identified it or created it; 5) the median time and average time to repay technical debt are 872.3 and 25.0 hours respectively.
Team voyTECH: User Activity Modeling with Boosting Trees
Venue: arXiv cs.LG 2020
Year: 2020

This paper describes our winning solution for the ECML-PKDD ChAT Discovery Challenge 2020. We show that whether or not a Twitch user has subscribed to a channel can be well predicted by modeling user activity with boosting trees. We introduce the connection between target-encodings and boosting trees in the context of high cardinality categoricals and find that modeling user activity is more powerful then direct modeling of content when encoded properly and combined with a suitable optimization approach.
Few-Shot Microscopy Image Cell Segmentation
Venue: arXiv cs.CV 2020
Year: 2020

Automatic cell segmentation in microscopy images works well with the support of deep neural networks trained with full supervision. Collecting and annotating images, though, is not a sustainable solution for every new microscopy database and cell type. Instead, we assume that we can access a plethora of annotated image data sets from different domains (sources) and a limited number of annotated image data sets from the domain of interest (target), where each domain denotes not only different image appearance but also a different type of cell segmentation problem. We pose this problem as meta-learning where the goal is to learn a generic and adaptable few-shot learning model from the available source domain data sets and cell segmentation tasks. The model can be afterwards fine-tuned on the few annotated images of the target domain that contains different image appearance and different cell type. In our meta-learning training, we propose the combination of three objective functions to segment the cells, move the segmentation results away from the classification boundary using cross-domain tasks, and learn an invariant representation between tasks of the source domains. Our experiments on five public databases show promising results from 1- to 10-shot meta-learning using standard segmentation neural network architectures.
Online publication of court records: circumventing the privacy-transparency trade-off
Venue: arXiv cs.CR 2020
Year: 2020

The open data movement is leading to the massive publishing of court records online, increasing transparency and accessibility of justice, and to the design of legal technologies building on the wealth of legal data available. However, the sensitive nature of legal decisions also raises important privacy issues. Current practices solve the resulting privacy versus transparency trade-off by combining access control with (manual or semi-manual) text redaction. In this work, we claim that current practices are insufficient for coping with massive access to legal data (restrictive access control policies is detrimental to openness and to utility while text redaction is unable to provide sound privacy protection) and advocate for a in-tegrative approach that could benefit from the latest developments of the privacy-preserving data publishing domain. We present a thorough analysis of the problem and of the current approaches, and propose a straw man multimodal architecture paving the way to a full-fledged privacy-preserving legal data publishing system.
Mapping Datasets to Object Storage System
Venue: arXiv cs.DC 2020
Year: 2020

Access libraries such as ROOT and HDF5 allow users to interact with datasets using high level abstractions, like coordinate systems and associated slicing operations. Unfortunately, the implementations of access libraries are based on outdated assumptions about storage systems interfaces and are generally unable to fully benefit from modern fast storage devices. The situation is getting worse with rapidly evolving storage devices such as non-volatile memory and ever larger datasets. This project explores distributed dataset mapping infrastructures that can integrate and scale out existing access libraries using Ceph's extensible object model, avoiding re-implementation or even modifications of these access libraries as much as possible. These programmable storage extensions coupled with our distributed dataset mapping techniques enable: 1) access library operations to be offloaded to storage system servers, 2) the independent evolution of access libraries and storage systems and 3) fully leveraging of the existing load balancing, elasticity, and failure management of distributed storage systems like Ceph. They also create more opportunities to conduct storage server-local optimizations specific to storage servers. For example, storage servers might include local key/value stores combined with chunk stores that require different optimizations than a local file system. As storage servers evolve to support new storage devices like non-volatile memory, these server-local optimizations can be implemented while minimizing disruptions to applications. We will report progress on the means by which distributed dataset mapping can be abstracted over particular access libraries, including access libraries for ROOT data, and how we address some of the challenges revolving around data partitioning and composability of access operations.
Transfer Function Models for Cylindrical MC Channels with Diffusion and Laminar Flow
Venue: arXiv cs.ET 2020
Year: 2020

The analysis and design of advection-diffusion based molecular communication (MC) systems in cylindrical environments is of particular interest for applications such as micro-fluidics and targeted drug delivery in blood vessels. Therefore, the accurate modeling of the corresponding MC channel is of high importance. The propagation of particles in these systems is caused by a combination of diffusion and flow with a parabolic velocity profile, i.e., laminar flow. The propagation characteristics of the particles can be categorized into three different regimes: The flow dominant regime where the influence of diffusion on the particle transport is negligible, the dispersive regime where diffusion has a much stronger impact than flow, and the mixed regime where both effects are important. For the limiting regimes, i.e., the flow dominant and dispersive regimes, there are well-known solutions and approximations for particle transport. In contrast, there is no general analytical solution for the mixed regime, and instead, approximations, numerical techniques, and particle based simulations have been employed. In this paper, we develop a general model for the advection-diffusion problem in cylindrical environments which provides an analytical solution applicable in all regimes. The modeling procedure is based on a transfer function approach and the main focus lies on the incorporation of laminar flow into the analytical model. The properties of the proposed model are analyzed by numerical evaluation for different scenarios including the uniform and point release of particles. We provide a comparison with particle based simulations and the well-known solutions for the limiting regimes to demonstrate the validity of the proposed analytical model.
WordPress on AWS: a Communication Framework
Venue: arXiv cs.CY 2020
Year: 2020

Every organization needs to communicate with its audience, and social media is an attractive and inexpensive way to maintain dialogic communication. About 1/3 of the Internet web pages are powered by WordPress, and about a million companies have moved their IT infrastructure to the AWS cloud. Together, AWS and WordPress offer an attractive, effective and inexpensive way for companies, both large and small, to maintain their presence on the web.
Deep Learning Models for Visual Inspection on Automotive Assembling Line
Venue: arXiv cs.CV 2020
Year: 2020

Automotive manufacturing assembly tasks are built upon visual inspections such as scratch identification on machined surfaces, part identification and selection, etc, which guarantee product and process quality. These tasks can be related to more than one type of vehicle that is produced within the same manufacturing line. Visual inspection was essentially human-led but has recently been supplemented by the artificial perception provided by computer vision systems (CVSs). Despite their relevance, the accuracy of CVSs varies accordingly to environmental settings such as lighting, enclosure and quality of image acquisition. These issues entail costly solutions and override part of the benefits introduced by computer vision systems, mainly when it interferes with the operating cycle time of the factory. In this sense, this paper proposes the use of deep learning-based methodologies to assist in visual inspection tasks while leaving very little footprints in the manufacturing environment and exploring it as an end-to-end tool to ease CVSs setup. The proposed approach is illustrated by four proofs of concept in a real automotive assembly line based on models for object detection, semantic segmentation, and anomaly detection.
Improved flat-back 3D gadgets in origami extrusions completely downward compatible with the conventional pyramid-supported 3D gadgets
Venue: arXiv cs.CG 2020
Year: 2020

An origami extrusion is a folding of a 3D object in the middle of a flat piece of paper, using 3D gadgets which create faces with solid angles. In this paper we focus on 3D gadgets which create a top face parallel to the ambient paper and two side faces sharing a ridge, with two outgoing simple pleats, where a simple pleat is a pair of a mountain fold and a valley fold. There are two such types of 3D gadgets. One is the conventional type of 3D gadgets with a triangular pyramid supporting the two side faces from inside. The other is the newer type of 3D gadgets presented in our previous paper, which improve the conventional ones in several respects: They have flat back sides above the ambient paper and no gap between the side faces; they are less interfering with adjacent gadgets so that we can make the extrusion higher at one time; they are downward compatible with conventional ones if constructible; they have a modified flat-back gadget used for repetition which does not interfere with adjacent gadgets; the angles of their outgoing pleats can be changed under certain conditions. However, there are cases where we can apply the conventional gadgets while we cannot our previous ones. The purpose of this paper is to improve our previous 3D gadgets to be completely downward compatible with the conventional ones, in the sense that any conventional gadget can be replaced by our improved one with the same outgoing pleats, but the converse is not always possible. To be more precise, we prove that for any given conventional 3D gadget there are an infinite number of improved 3D gadgets which are compatible with it, and the conventional 3D gadget can be replaced with any of these 3D gadgets without affecting any other conventional 3D gadget. Also, we see that our improved 3D gadget keep all of the above advantages over the conventional ones.
Meta-SAC: Auto-tune the Entropy Temperature of Soft Actor-Critic via Metagradient
Venue: arXiv cs.LG 2020
Year: 2020

Exploration-exploitation dilemma has long been a crucial issue in reinforcement learning. In this paper, we propose a new approach to automatically balance between these two. Our method is built upon the Soft Actor-Critic (SAC) algorithm, which uses an "entropy temperature" that balances the original task reward and the policy entropy, and hence controls the trade-off between exploitation and exploration. It is empirically shown that SAC is very sensitive to this hyperparameter, and the follow-up work (SAC-v2), which uses constrained optimization for automatic adjustment, has some limitations. The core of our method, namely Meta-SAC, is to use metagradient along with a novel meta objective to automatically tune the entropy temperature in SAC. We show that Meta-SAC achieves promising performances on several of the Mujoco benchmarking tasks, and outperforms SAC-v2 over 10% in one of the most challenging tasks, humanoid-v2.
FracBits: Mixed Precision Quantization via Fractional Bit-Widths
Venue: arXiv cs.CV 2020
Year: 2020

Model quantization helps to reduce model size and latency of deep neural networks. Mixed precision quantization is favorable with customized hardwares supporting arithmetic operations at multiple bit-widths to achieve maximum efficiency. We propose a novel learning-based algorithm to derive mixed precision models end-to-end under target computation constraints and model sizes. During the optimization, the bit-width of each layer / kernel in the model is at a fractional status of two consecutive bit-widths which can be adjusted gradually. With a differentiable regularization term, the resource constraints can be met during the quantization-aware training which results in an optimized mixed precision model. Further, our method can be naturally combined with channel pruning for better computation cost allocation. Our final models achieve comparable or better performance than previous quantization methods with mixed precision on MobilenetV1/V2, ResNet18 under different resource constraints on ImageNet dataset.
DRDr: Automatic Masking of Exudates and Microaneurysms Caused By Diabetic Retinopathy Using Mask R-CNN and Transfer Learning
Venue: arXiv cs.CV 2020
Year: 2020

This paper addresses the problem of identifying two main types of lesions - Exudates and Microaneurysms - caused by Diabetic Retinopathy (DR) in the eyes of diabetic patients. We make use of Convolutional Neural Networks (CNNs) and Transfer Learning to locate and generate high-quality segmentation mask for each instance of the lesion that can be found in the patients' fundus images. We create our normalized database out of e-ophtha EX and e-ophtha MA and tweak Mask R-CNN to detect small lesions. Moreover, we employ data augmentation and the pre-trained weights of ResNet101 to compensate for our small dataset. Our model achieves promising test mAP of 0.45, altogether showing that it can aid clinicians and ophthalmologist in the process of detecting and treating the infamous DR.
Self-Calibration Supported Robust Projective Structure-from-Motion
Venue: arXiv cs.CV 2020
Year: 2020

Typical Structure-from-Motion (SfM) pipelines rely on finding correspondences across images, recovering the projective structure of the observed scene and upgrading it to a metric frame using camera self-calibration constraints. Solving each problem is mainly carried out independently from the others. For instance, camera self-calibration generally assumes correct matches and a good projective reconstruction have been obtained. In this paper, we propose a unified SfM method, in which the matching process is supported by self-calibration constraints. We use the idea that good matches should yield a valid calibration. In this process, we make use of the Dual Image of Absolute Quadric projection equations within a multiview correspondence framework, in order to obtain robust matching from a set of putative correspondences. The matching process classifies points as inliers or outliers, which is learned in an unsupervised manner using a deep neural network. Together with theoretical reasoning why the self-calibration constraints are necessary, we show experimental results demonstrating robust multiview matching and accurate camera calibration by exploiting these constraints.
Customized Handling of Unintended Interface Operation in Assistive Robots
Venue: arXiv cs.RO 2020
Year: 2020

We present an assistance system that reasons about a human's intended actions during robot teleoperation in order to provide appropriate corrections for unintended behavior. We model the human's physical interaction with a control interface during robot teleoperation and distinguish between intended and measured physical actions explicitly. By reasoning over the unobserved intentions using model-based inference techniques, our assistive system provides customized corrections on a user's issued commands. We validate our algorithm with a 10-person human subject study in which we evaluate the performance of the proposed assistance paradigms. Our results show that the assistance paradigms helped to significantly reduce task completion time, number of mode switches, cognitive workload, and user frustration and improve overall user satisfaction.
Automatic Target Recognition on Synthetic Aperture Radar Imagery: A Survey
Venue: arXiv cs.CV 2020
Year: 2020

Automatic Target Recognition (ATR) for military applications is one of the core processes towards enhancing intelligencer and autonomously operating military platforms. Spurred by this and given that Synthetic Aperture Radar (SAR) presents several advantages over its counterpart data domains, this paper surveys and assesses current SAR ATR architectures that employ the most popular dataset for the SAR domain, namely the Moving and Stationary Target Acquisition and Recognition (MSTAR) dataset. Based on the current methodology trends, we propose a taxonomy for the SAR ATR architectures, along with a direct comparison of the strengths and weaknesses of each method under both standard and extended operational conditions. Additionally, despite MSTAR being the standard SAR ATR benchmarking dataset we also highlight its weaknesses and suggest future research directions.
Off-Policy Exploitability-Evaluation in Two-Player Zero-Sum Markov Games
Venue: arXiv cs.LG 2020
Year: 2020

Off-policy evaluation (OPE) is the problem of evaluating new policies using historical data obtained from a different policy. In the recent OPE context, most studies have focused on single-player cases, and not on multi-player cases. In this study, we propose OPE estimators constructed by the doubly robust and double reinforcement learning estimators in two-player zero-sum Markov games. The proposed estimators project exploitability that is often used as a metric for determining how close a policy profile (i.e., a tuple of policies) is to a Nash equilibrium in two-player zero-sum games. We prove the exploitability estimation error bounds for the proposed estimators. We then propose the methods to find the best candidate policy profile by selecting the policy profile that minimizes the estimated exploitability from a given policy profile class. We prove the regret bounds of the policy profiles selected by our methods. Finally, we demonstrate the effectiveness and performance of the proposed estimators through experiments.
On Class Orderings for Incremental Learning
Venue: arXiv cs.CV 2020
Year: 2020

The influence of class orderings in the evaluation of incremental learning has received very little attention. In this paper, we investigate the impact of class orderings for incrementally learned classifiers. We propose a method to compute various orderings for a dataset. The orderings are derived by simulated annealing optimization from the confusion matrix and reflect different incremental learning scenarios, including maximally and minimally confusing tasks. We evaluate a wide range of state-of-the-art incremental learning methods on the proposed orderings. Results show that orderings can have a significant impact on performance and the ranking of the methods.
Static Detection of Uninitialized Stack Variables in Binary Code
Venue: arXiv cs.CR 2020
Year: 2020

More than two decades after the first stack smashing attacks, memory corruption vulnerabilities utilizing stack anomalies are still prevalent and play an important role in practice. Among such vulnerabilities, uninitialized variables play an exceptional role due to their unpleasant property of unpredictability: as compilers are tailored to operate fast, costly interprocedural analysis procedures are not used in practice to detect such vulnerabilities. As a result, complex relationships that expose uninitialized memory reads remain undiscovered in binary code. Recent vulnerability reports show the versatility on how uninitialized memory reads are utilized in practice, especially for memory disclosure and code execution. Research in recent years proposed detection and prevention techniques tailored to source code. To date, however, there has not been much attention for these types of software bugs within binary executables. In this paper, we present a static analysis framework to find uninitialized variables in binary executables. We developed methods to lift the binaries into a knowledge representation which builds the base for specifically crafted algorithms to detect uninitialized reads. Our prototype implementation is capable of detecting uninitialized memory errors in complex binaries such as web browsers and OS kernels, and we detected 7 novel bugs.