OpenRaiser/Intern-Atlas huggingface dataset:
Evaluation of Static Analysis Tools for Finding Vulnerabilities in Java and C/C++ Source Code
Venue: arXiv cs.SE 2018
Year: 2018

It is quite common for security testing to be delayed until after the software has been developed, but vulnerabilities may get noticed throughout the implementation phase and the earlier they are discovered, the easier and cheaper it will be to fix them. Software development processes such as the secure software development lifecycle incorporates security at every stage of the design and development process. Static code scanning tools find vulnerabilities in code by highlighting potential security flaws and offer examples on how to resolve them, and some may even modify the code to remove the susceptibility. This paper compares static analysis tools for Java and C/C++ source code, and explores their pros and cons.
Grounding the Semantics of Part-of-Day Nouns Worldwide using Twitter
Venue: arXiv cs.CL 2018
Year: 2018

The usage of part-of-day nouns, such as 'night', and their time-specific greetings ('good night'), varies across languages and cultures. We show the possibilities that Twitter offers for studying the semantics of these terms and its variability between countries. We mine a worldwide sample of multilingual tweets with temporal greetings, and study how their frequencies vary in relation with local time. The results provide insights into the semantics of these temporal expressions and the cultural and sociological factors influencing their usage.
Maize Haploid Identification via LSTM-CNN and Hyperspectral Imaging Technology
Venue: arXiv cs.CV 2018
Year: 2018

Accurate and fast identification of seed cultivars is crucial to plant breeding, with accelerating breeding of new products and increasing its quality. In our study, the first attempt to design a high-accurate identification model of maize haploid seeds from diploid ones based on optimum waveband selection of the LSTM-CNN algorithm is realized via deep learning and hyperspectral imaging technology, with accuracy reaching 97% in the determining optimum waveband of 1367.6-1526.4nm. The verification of testing another cultivar achieved an accuracy of 93% in the same waveband. The model collected images of 256 wavebands of seeds in the spectral region of 862.9-1704.2nm. The high-noise waveband intervals were found and deleted by the LSTM. The optimum-data waveband intervals were determined by CNN's waveband-based detection. The optimum sample set for network training only accounted for 1/5 of total sample data. The accuracy was significantly higher than the full-waveband modeling or modeling of any other wavebands. Our study demonstrates that the proposed model has outstanding effect on maize haploid identification and it could be generalized to some extent.
Segmentation of Liver Lesions with Reduced Complexity Deep Models
Venue: arXiv cs.CV 2018
Year: 2018

We propose a computationally efficient architecture that learns to segment lesions from CT images of the liver. The proposed architecture uses bilinear interpolation with sub-pixel convolution at the last layer to upscale the course feature in bottle neck architecture. Since bilinear interpolation and sub-pixel convolution do not have any learnable parameter, our overall model is faster and occupies less memory footprint than the traditional U-net. We evaluate our proposed architecture on the highly competitive dataset of 2017 Liver Tumor Segmentation (LiTS) Challenge. Our method achieves competitive results while reducing the number of learnable parameters roughly by a factor of 13.8 compared to the original UNet model.
Cleaning up the neighborhood: A full classification for adversarial partial monitoring
Venue: arXiv cs.LG 2018
Year: 2018

Partial monitoring is a generalization of the well-known multi-armed bandit framework where the loss is not directly observed by the learner. We complete the classification of finite adversarial partial monitoring to include all games, solving an open problem posed by Bartok et al. [2014]. Along the way we simplify and improve existing algorithms and correct errors in previous analyses. Our second contribution is a new algorithm for the class of games studied by Bartok [2013] where we prove upper and lower regret bounds that shed more light on the dependence of the regret on the game structure.
Time-accurate Middleware for the Virtualization of Communication Protocols
Venue: arXiv cs.NI 2018
Year: 2018

Communication between devices in avionics systems must be predictable and deterministic, and data must be delivered reliably. To help the system architects comply with these requirements, network protocol standards like ARINC 429 and AFDX were created. Even though the behaviour of each component in a network is well defined, it is still necessary to test extensively every applications before deployment. But physical test benches used in the aircraft development process are complex and expensive platforms. In order to limit the need for physical tests, we propose a time-accurate middleware for virtualizing communication protocols that can be used to replace physical tests with simulations. We specified three formal models of AFDX networks that take into account temporal constraints with different levels of precision. We also developed a prototype for a network virtualization middleware based on the AFDX protocol specification that provides an easy-to-setup environment for testing network configurations. Finally, we used formal models together with virtualization in order to define runtime monitors for checking whether the behavior of the middleware is time-accurate with respect to a real system.
Self-Organizing Relay Selection in UAV Communication Networks: A Matching Game Perspective
Venue: arXiv cs.NI 2018
Year: 2018

For large unmanned aerial vehicle (UAV) networks, the timely communication is needed to accomplish a series of missions accurately and effectively. The relay technology will play an important role in UAV networks by helping drones communicating with long-distance drones, which solves the problem of the limited transmission power of drones. In this paper, the relay selection is seen as the entry point to improve the performance of self-organizing network with multiple optimizing factors. Different from the ground relay models, the relay selection in UAV communication networks presents new challenges, including heterogeneous, dynamic, dense and limited information characteristics. More effective schemes with distributed, fast, robust and scalable features are required to solve the optimizing problem. After discussing the challenges and requirements, we find that the matching game is suitable to model the complex relay model. The advantages of the matching game in self-organizing UAV communications are discussed. Moreover, we provide extensive applications of matching markets, and then propose a novel classification of matching game which focuses on the competitive relationship between players. Specifically, basic preliminary models are presented and some future research directions of matching game in UAV relay models are discussed.
Learning Illuminant Estimation from Object Recognition
Venue: arXiv cs.CV 2018
Year: 2018

In this paper we present a deep learning method to estimate the illuminant of an image. Our model is not trained with illuminant annotations, but with the objective of improving performance on an auxiliary task such as object recognition. To the best of our knowledge, this is the first example of a deep learning architecture for illuminant estimation that is trained without ground truth illuminants. We evaluate our solution on standard datasets for color constancy, and compare it with state of the art methods. Our proposal is shown to outperform most deep learning methods in a cross-dataset evaluation setup, and to present competitive results in a comparison with parametric solutions.
Collective Online Learning of Gaussian Processes in Massive Multi-Agent Systems
Venue: arXiv cs.LG 2018
Year: 2018

Distributed machine learning (ML) is a modern computation paradigm that divides its workload into independent tasks that can be simultaneously achieved by multiple machines (i.e., agents) for better scalability. However, a typical distributed system is usually implemented with a central server that collects data statistics from multiple independent machines operating on different subsets of data to build a global analytic model. This centralized communication architecture however exposes a single choke point for operational failure and places severe bottlenecks on the server's communication and computation capacities as it has to process a growing volume of communication from a crowd of learning agents. To mitigate these bottlenecks, this paper introduces a novel Collective Online Learning Gaussian Process framework for massive distributed systems that allows each agent to build its local model, which can be exchanged and combined efficiently with others via peer-to-peer communication to converge on a global model of higher quality. Finally, our empirical results consistently demonstrate the efficiency of our framework on both synthetic and real-world datasets.
Classifying cooking object's state using a tuned VGG convolutional neural network
Venue: arXiv cs.CV 2018
Year: 2018

In robotics, knowing the object states and recognizing the desired states are very important. Objects at different states would require different grasping. To achieve different states, different manipulations would be required, as well as different grasping. To analyze the objects at different states, a dataset of cooking objects was created. Cooking consists of various cutting techniques needed for different dishes (e.g. diced, julienne etc.). Identifying each of this state of cooking objects by the human can be difficult sometimes too. In this paper, we have analyzed seven different cooking object states by tuning a convolutional neural network (CNN). For this task, images were downloaded and annotated by students and they are divided into training and a completely different test set. By tuning the vgg-16 CNN 77% accuracy was obtained. The work presented in this paper focuses on classification between various object states rather than task recognition or recipe prediction. This framework can be easily adapted in any other object state classification activity.
You Only Look Twice: Rapid Multi-Scale Object Detection In Satellite Imagery
Venue: arXiv cs.CV 2018
Year: 2018

Detection of small objects in large swaths of imagery is one of the primary problems in satellite imagery analytics. While object detection in ground-based imagery has benefited from research into new deep learning approaches, transitioning such technology to overhead imagery is nontrivial. Among the challenges is the sheer number of pixels and geographic extent per image: a single DigitalGlobe satellite image encompasses >64 km2 and over 250 million pixels. Another challenge is that objects of interest are minuscule (often only ~10 pixels in extent), which complicates traditional computer vision techniques. To address these issues, we propose a pipeline (You Only Look Twice, or YOLT) that evaluates satellite images of arbitrary size at a rate of >0.5 km2/s. The proposed approach can rapidly detect objects of vastly different scales with relatively little training data over multiple sensors. We evaluate large test images at native resolution, and yield scores of F1 > 0.8 for vehicle localization. We further explore resolution and object size requirements by systematically testing the pipeline at decreasing resolution, and conclude that objects only ~5 pixels in size can still be localized with high confidence. Code is available at https://github.com/CosmiQ/yolt.
A sequent calculus with dependent types for classical arithmetic
Venue: arXiv cs.LO 2018
Year: 2018

In a recent paper, Herbelin developed a calculus dPA$^ω$ in which constructive proofs for the axioms of countable and dependent choices could be derived via the encoding of a proof of countable universal quantification as a stream of it components. However, the property of normalization (and therefore the one of soundness) was only conjectured. The difficulty for the proof of normalization is due to the simultaneous presence of dependent dependent types (for the constructive part of the choice), of control operators (for classical logic), of coinductive objects (to encode functions of type $N\rightarrow A$ into streams $(a_0,a_1,\ldots)$) and of lazy evaluation with sharing (for these coinductive objects).Building on previous works, we introduce in this paper a variant of dPA$^ω$ presented as a sequent calculus. On the one hand, we take advantage of a variant of Krivine classical realizability we developed to prove the normalization of classical call-by-need. On the other hand, we benefit of dL, a classical sequent calculus with dependent types in which type safety is ensured using delimited continuations together with a syntactic restriction. By combining the techniques developed in these papers, we manage to define a realizability interpretation {à} la Krivine of our calculus that allows us to prove normalization and soundness.
Optimal noise functions for location privacy on continuous regions
Venue: arXiv cs.CR 2018
Year: 2018

Users of location-based services (LBSs) are highly vulnerable to privacy risks since they need to disclose, at least partially, their locations to benefit from these services. One possibility to limit these risks is to obfuscate the location of a user by adding random noise drawn from a noise function. In this paper, we require the noise functions to satisfy a generic location privacy notion called $\ell$-privacy, which makes the position of the user in a given region $\mathcal{X}$ relatively indistinguishable from other points in $\mathcal{X}$. We also aim at minimizing the loss in the service utility due to such obfuscation. While existing optimization frameworks regard the region $\mathcal{X}$ restrictively as a finite set of points, we consider the more realistic case in which the region is rather continuous with a non-zero area. In this situation, we demonstrate that circular noise functions are enough to satisfy $\ell$-privacy on $\mathcal{X}$ and equivalently on the entire space without any penalty in the utility. Afterwards, we describe a large parametric space of noise functions that satisfy $\ell$-privacy on $\mathcal{X}$, and show that this space has always an optimal member, regardless of $\ell$ and $\mathcal{X}$. We also investigate the recent notion of $ε$-geo-indistinguishability as an instance of $\ell$-privacy, and prove in this case that with respect to any increasing loss function, the planar Laplace noise function is optimal for any region having a nonzero area.
Residual Networks as Geodesic Flows of Diffeomorphisms
Venue: arXiv cs.CV 2018
Year: 2018

This paper addresses the understanding and characterization of residual networks (ResNet), which are among the state-of-the-art deep learning architectures for a variety of supervised learning problems. We focus on the mapping component of ResNets, which map the embedding space towards a new unknown space where the prediction or classification can be stated according to linear criteria. We show that this mapping component can be regarded as the numerical implementation of continuous flows of diffeomorphisms governed by ordinary differential equations. Especially, ResNets with shared weights are fully characterized as numerical approximation of exponential diffeomorphic operators. We stress both theoretically and numerically the relevance of the enforcement of diffeormorphic properties and the importance of numerical issues to make consistent the continuous formulation and the discretized ResNet implementation. We further discuss the resulting theoretical and computational insights on ResNet architectures.
Native Language Cognate Effects on Second Language Lexical Choice
Venue: arXiv cs.CL 2018
Year: 2018

We present a computational analysis of cognate effects on the spontaneous linguistic productions of advanced non-native speakers. Introducing a large corpus of highly competent non-native English speakers, and using a set of carefully selected lexical items, we show that the lexical choices of non-natives are affected by cognates in their native language. This effect is so powerful that we are able to reconstruct the phylogenetic language tree of the Indo-European language family solely from the frequencies of specific lexical items in the English of authors with various native languages. We quantitatively analyze non-native lexical choice, highlighting cognate facilitation as one of the important phenomena shaping the language of non-native speakers.
SEVered: Subverting AMD's Virtual Machine Encryption
Venue: arXiv cs.CR 2018
Year: 2018

AMD SEV is a hardware feature designed for the secure encryption of virtual machines. SEV aims to protect virtual machine memory not only from other malicious guests and physical attackers, but also from a possibly malicious hypervisor. This relieves cloud and virtual server customers from fully trusting their server providers and the hypervisors they are using. We present the design and implementation of SEVered, an attack from a malicious hypervisor capable of extracting the full contents of main memory in plaintext from SEV-encrypted virtual machines. SEVered neither requires physical access nor colluding virtual machines, but only relies on a remote communication service, such as a web server, running in the targeted virtual machine. We verify the effectiveness of SEVered on a recent AMD SEV-enabled server platform running different services, such as web or SSH servers, in encrypted virtual machines. With these examples, we demonstrate that SEVered reliably and efficiently extracts all memory contents even in scenarios where the targeted virtual machine is under high load.
Eternal dominating sets on digraphs and orientations of graphs
Venue: arXiv cs.DM 2018
Year: 2018

We study the eternal dominating number and the m-eternal dominating number on digraphs. We generalize known results on graphs to digraphs. We also consider the problem "oriented (m-)eternal domination", consisting in finding an orientation of a graph that minimizes its eternal dominating number. We prove that computing the oriented eternal dominating number is NP-hard and characterize the graphs for which the oriented m-eternal dominating number is 2. We also study these two parameters on trees, cycles, complete graphs, complete bipartite graphs, trivially perfect graphs and different kinds of grids and products of graphs.
Forming IDEAS Interactive Data Exploration & Analysis System
Venue: arXiv cs.CR 2018
Year: 2018

Modern cyber security operations collect an enormous amount of logging and alerting data. While analysts have the ability to query and compute simple statistics and plots from their data, current analytical tools are too simple to admit deep understanding. To detect advanced and novel attacks, analysts turn to manual investigations. While commonplace, current investigations are time-consuming, intuition-based, and proving insufficient. Our hypothesis is that arming the analyst with easy-to-use data science tools will increase their work efficiency, provide them with the ability to resolve hypotheses with scientific inquiry of their data, and support their decisions with evidence over intuition. To this end, we present our work to build IDEAS (Interactive Data Exploration and Analysis System). We present three real-world use-cases that drive the system design from the algorithmic capabilities to the user interface. Finally, a modular and scalable software architecture is discussed along with plans for our pilot deployment with a security operation command.
A Bug Bounty Perspective on the Disclosure of Web Vulnerabilities
Venue: arXiv cs.CR 2018
Year: 2018

Bug bounties have become increasingly popular in recent years. This paper discusses bug bounties by framing these theoretically against so-called platform economy. Empirically the interest is on the disclosure of web vulnerabilities through the Open Bug Bounty (OBB) platform between 2015 and late 2017. According to the empirical results based on a dataset covering nearly 160 thousand web vulnerabilities, (i) OBB has been successful as a community-based platform for the dissemination of web vulnerabilities. The platform has also attracted many productive hackers, (ii) but there exists a large productivity gap, which likely relates to (iii) a knowledge gap and the use of automated tools for web vulnerability discovery. While the platform (iv) has been exceptionally fast to evaluate new vulnerability submissions, (v) the patching times of the web vulnerabilities disseminated have been long. With these empirical results and the accompanying theoretical discussion, the paper contributes to the small but rapidly growing amount of research on bug bounties. In addition, the paper makes a practical contribution by discussing the business models behind bug bounties from the viewpoints of platforms, ecosystems, and vulnerability markets.
An experimental comparison of label selection methods for hierarchical document clusters
Venue: arXiv cs.IR 2018
Year: 2018

The focus of this paper is on the evaluation of sixteen labeling methods for hierarchical document clusters over five datasets. All of the methods are independent from clustering algorithms, applied subsequently to the dendrogram construction and based on probabilistic dependence relations among labels and clusters. To reach a fair comparison as well as a standard benchmark, we rewrote and presented the labeling methods in a similar notation. The experimental results were analyzed through a proposed evaluation methodology based on: (i) data standardization before applying the cluster labeling methods and over the labeling results; (ii) a particular information retrieval process, using the obtained labels and their hierarchical relations to construct the search queries; (iii) evaluation of the retrieval process through precision, recall and F measure; (iv) variance analysis of the retrieval results to better understanding the differences among the labeling methods; and, (v) the emulation of a human judgment through the analysis of a topic observed coherence measure - normalized Pointwise Mutual Information (PMI). Applying the methodology, we are able to highlight the advantages of certain methods: to capture specific information; for a better document hierarchy comprehension at different levels of granularity; and, to capture the most coherent labels through the label selections. Finally, the experimental results demonstrated that the label selection methods which hardly consider hierarchical relations had the best results.
Finite Time Robust Control of the Sit-to-Stand Movement for Powered Lower Limb Orthoses
Venue: arXiv cs.SY 2018
Year: 2018

This study presents a technique to safely control the Sit-to-Stand movement of powered lower limb orthoses in the presence of parameter uncertainty. The weight matrices used to calculate the finite time horizon linear-quadratic regulator (LQR) gain in the feedback loop are chosen from a pool of candidates as to minimize a robust performance metric involving induced gains that measure the deviation of variables of interest in a linear time-varying (LTV) system, at specific times within a finite horizon, caused by a perturbation signal modeling the variation of the parameters. Two relevant Sit-to-Stand movements are simulated for drawing comparisons with the results documented in a previous work.