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The early detection of glaucoma is essential in preventing visual impairment. Artificial intelligence (AI) can be used to analyze color fundus photographs (CFPs) in a cost-effective manner, making glaucoma screening more accessible. While AI models for glaucoma screening from CFPs have shown promising results in laboratory settings, their performance decreases significantly in real-world scenarios due to the presence of out-of-distribution and low-quality images. To address this issue, we propose the Artificial Intelligence for Robust Glaucoma Screening (AIROGS) challenge. This challenge includes a large dataset of around 113,000 images from about 60,000 patients and 500 different screening centers, and encourages the development of algorithms that are robust to ungradable and unexpected input data. We evaluated solutions from 14 teams in this paper, and found that the best teams performed similarly to a set of 20 expert ophthalmologists and optometrists. The highest-scoring team achieved an area under the receiver operating characteristic curve of 0.99 (95% CI: 0.98-0.99) for detecting ungradable images on-the-fly. Additionally, many of the algorithms showed robust performance when tested on three other publicly available datasets. These results demonstrate the feasibility of robust AI-enabled glaucoma screening., Comment: 19 pages, 8 figures, 3 tables

The Endoscopy Computer Vision Challenge (EndoCV) is a crowd-sourcing initiative to address eminent problems in developing reliable computer aided detection and diagnosis endoscopy systems and suggest a pathway for clinical translation of technologies. Whilst endoscopy is a widely used diagnostic and treatment tool for hollow-organs, there are several core challenges often faced by endoscopists, mainly: 1) presence of multi-class artefacts that hinder their visual interpretation, and 2) difficulty in identifying subtle precancerous precursors and cancer abnormalities. Artefacts often affect the robustness of deep learning methods applied to the gastrointestinal tract organs as they can be confused with tissue of interest. EndoCV2020 challenges are designed to address research questions in these remits. In this paper, we present a summary of methods developed by the top 17 teams and provide an objective comparison of state-of-the-art methods and methods designed by the participants for two sub-challenges: i) artefact detection and segmentation (EAD2020), and ii) disease detection and segmentation (EDD2020). Multi-center, multi-organ, multi-class, and multi-modal clinical endoscopy datasets were compiled for both EAD2020 and EDD2020 sub-challenges. The out-of-sample generalization ability of detection algorithms was also evaluated. Whilst most teams focused on accuracy improvements, only a few methods hold credibility for clinical usability. The best performing teams provided solutions to tackle class imbalance, and variabilities in size, origin, modality and occurrences by exploring data augmentation, data fusion, and optimal class thresholding techniques., Comment: 32 pages

As an increasing number of deep-learning-based malware scanners have been proposed, the existing evasion techniques, including code obfuscation and polymorphic malware, are found to be less effective. In this work, we propose a reinforcement learning-based semantics-preserving (i.e.functionality-preserving) attack against black-box GNNs (GraphNeural Networks) for malware detection. The key factor of adversarial malware generation via semantic Nops insertion is to select the appropriate semanticNopsand their corresponding basic blocks. The proposed attack uses reinforcement learning to automatically make these "how to select" decisions. To evaluate the attack, we have trained two kinds of GNNs with five types(i.e., Backdoor, Trojan-Downloader, Trojan-Ransom, Adware, and Worm) of Windows malware samples and various benign Windows programs. The evaluation results have shown that the proposed attack can achieve a significantly higher evasion rate than three baseline attacks, namely the semantics-preserving random instruction insertion attack, the semantics-preserving accumulative instruction insertion attack, and the semantics-preserving gradient-based instruction insertion attack.

Although using machine learning techniques to solve computer security challenges is not a new idea, the rapidly emerging Deep Learning technology has recently triggered a substantial amount of interests in the computer security community. This paper seeks to provide a dedicated review of the very recent research works on using Deep Learning techniques to solve computer security challenges. In particular, the review covers eight computer security problems being solved by applications of Deep Learning: security-oriented program analysis, defending return-oriented programming (ROP) attacks, achieving control-flow integrity (CFI), defending network attacks, malware classification, system-event-based anomaly detection, memory forensics, and fuzzing for software security., Comment: 43 pages with 7 figures and two tables

There is a strong devotion in the automotive industry to be part of a wider progression towards the Fifth Generation (5G) era. In-vehicle integration costs between cellular and vehicle-to-vehicle networks using Dedicated Short Range Communication could be avoided by adopting Cellular Vehicle-to-Everything (C-V2X) technology with the possibility to re-use the existing mobile network infrastructure. More and more, with the emergence of Software Defined Networks, the flexibility and the programmability of the network have not only impacted the design of new vehicular network architectures but also the implementation of V2X services in future intelligent transportation systems. In this paper, we define the concepts that help evaluate software-defined-based vehicular network systems in the literature based on their modeling and implementation schemes. We first overview the current studies available in the literature on C-V2X technology in support of V2X applications. We then present the different architectures and their underlying system models for LTE-V2X communications. We later describe the key ideas of software-defined networks and their concepts for V2X services. Lastly, we provide a comparative analysis of existing SDN-based vehicular network system grouped according to their modeling and simulation concepts. We provide a discussion and highlight vehicular ad-hoc networks' challenges handled by SDN-based vehicular networks., Comment: 14 pages, 3 figures, Sensors 2019

Flat plumbing basket surfaces of links were introduced to study the geometry of the complement of the links. These flat plumbing basket surface can be presented by a sequential presentation known as flat plumbing basket code first found by Furihata, Hirasawa and Kobayashi. The minimum number of flat plumbings to obtain a flat plumbing basket surfaces of a link is defined to be the flat plumbing basket number of the given link. In present article, we use these sequential presentations to find the complete classification theorem of prime knots whose flat plumbing basket number $6$ or less. As applications, this result improves the work of Hirose and Nakashima which finds the flat plumbing basket number of prime knots up to 9 crossings., Comment: 12 pages, 6 figures and 1 table. arXiv admin note: text overlap with arXiv:1108.1455

In this paper, we propose a generalized successive approximation method (SAM), called invariantly admissible policy iteration (PI), for finding the solution to a class of input-affine nonlinear optimal control problems by iterations. Unlike the existing SAM, the proposed method updates the domain of the next policy and value function for admissibility (and invariance). In the existing SAM, the admissibility of the generated policies are guaranteed under the two implicit assumptions regarding Lyapunov's theorem and invariance, both of which are presented and discussed in this paper and are generally not true. On the contrary, the proposed invariantly admissible PI guarantees the admissibility in a more refined manner, without such assumptions. The admissibility and invariance of the updated region, with respect to the corresponding policies, are mathematically prove under the specific invariant admissible update rule. We also provide monotonic decreasing and uniform convergence properties of the sequence of value functions under certain conditions. Finally, numerical simulations are presented to illustrate the proposed PI method and its effectiveness., Comment: This is a preprint of a paper submitted to Systems & Control Letters. Paper submitted April-2014. The paper has 25 pages and 3 figures

Efficiency droop is a major obstacle facing high-power application of InGaN/GaN quantum-well (QW) light-emitting diodes. In this letter, we report the suppression of efficiency droop induced by density-activated defect recombination in nanorod structure of a-plane InGaN/GaN QWs. In the high carrier density regime, the retained emission efficiency in a dry-etched nanorod sample is observed to be over two times higher than that in its parent QW sample. We further argue that the improvement is a combined effect of the amendment contributed by lateral carrier confinement and the deterioration made by surface trapping., Comment: 18 Pages,5 figures

We report on the observation of carrier-diffusion-induced defect emission at high excitation density in a-plane InGaN single quantum wells. When increasing excitation density in a relatively high regime, we observed the emergence of defect-related emission together with a significant reduction in bandedge emission efficiency. The experimental results can be well explained with the density-activated carrier diffusion from localized states to defect states. Such a scenario of density-activated defect recombination, as confirmed by the dependences of photoluminescence on the excitation photon energy and temperature, is a plausible origin of efficiency droop in a-plane InGaN quantum-well light-emitting diodes., Comment: 15 pages, 4 figures